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'''simple docstring'''
def _lowerCAmelCase ( lowercase ) -> str:
__lowerCAmelCase = len(lowercase )
for i in range(length - 1 ):
__lowerCAmelCase = i
for k in range(i + 1 , lowercase ):
if collection[k] < collection[least]:
__lowerCAmelCase = k
if least != i:
__lowerCAmelCase , __lowerCAmelCase = (collection[i], collection[least])
return collection
if __name__ == "__main__":
_a : Optional[int] = input("""Enter numbers separated by a comma:\n""").strip()
_a : List[Any] = [int(item) for item in user_input.split(""",""")]
print(selection_sort(unsorted))
| 689 |
'''simple docstring'''
import argparse
import json
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
VideoMAEConfig,
VideoMAEForPreTraining,
VideoMAEForVideoClassification,
VideoMAEImageProcessor,
)
def _lowerCAmelCase ( lowercase ) -> List[Any]:
__lowerCAmelCase = VideoMAEConfig()
set_architecture_configs(lowercase , lowercase )
if "finetuned" not in model_name:
__lowerCAmelCase = False
if "finetuned" in model_name:
__lowerCAmelCase = """huggingface/label-files"""
if "kinetics" in model_name:
__lowerCAmelCase = 400
__lowerCAmelCase = """kinetics400-id2label.json"""
elif "ssv2" in model_name:
__lowerCAmelCase = 174
__lowerCAmelCase = """something-something-v2-id2label.json"""
else:
raise ValueError("""Model name should either contain 'kinetics' or 'ssv2' in case it's fine-tuned.""" )
__lowerCAmelCase = json.load(open(hf_hub_download(lowercase , lowercase , repo_type="""dataset""" ) , """r""" ) )
__lowerCAmelCase = {int(lowercase ): v for k, v in idalabel.items()}
__lowerCAmelCase = idalabel
__lowerCAmelCase = {v: k for k, v in idalabel.items()}
return config
def _lowerCAmelCase ( lowercase , lowercase ) -> Any:
if "small" in model_name:
__lowerCAmelCase = 384
__lowerCAmelCase = 1536
__lowerCAmelCase = 12
__lowerCAmelCase = 16
__lowerCAmelCase = 12
__lowerCAmelCase = 3
__lowerCAmelCase = 192
__lowerCAmelCase = 768
elif "large" in model_name:
__lowerCAmelCase = 1024
__lowerCAmelCase = 4096
__lowerCAmelCase = 24
__lowerCAmelCase = 16
__lowerCAmelCase = 12
__lowerCAmelCase = 8
__lowerCAmelCase = 512
__lowerCAmelCase = 2048
elif "huge" in model_name:
__lowerCAmelCase = 1280
__lowerCAmelCase = 5120
__lowerCAmelCase = 32
__lowerCAmelCase = 16
__lowerCAmelCase = 12
__lowerCAmelCase = 8
__lowerCAmelCase = 640
__lowerCAmelCase = 2560
elif "base" not in model_name:
raise ValueError("""Model name should include either \"small\", \"base\", \"large\", or \"huge\"""" )
def _lowerCAmelCase ( lowercase ) -> List[str]:
if "encoder." in name:
__lowerCAmelCase = name.replace("""encoder.""" , """""" )
if "cls_token" in name:
__lowerCAmelCase = name.replace("""cls_token""" , """videomae.embeddings.cls_token""" )
if "decoder_pos_embed" in name:
__lowerCAmelCase = name.replace("""decoder_pos_embed""" , """decoder.decoder_pos_embed""" )
if "pos_embed" in name and "decoder" not in name:
__lowerCAmelCase = name.replace("""pos_embed""" , """videomae.embeddings.position_embeddings""" )
if "patch_embed.proj" in name:
__lowerCAmelCase = name.replace("""patch_embed.proj""" , """videomae.embeddings.patch_embeddings.projection""" )
if "patch_embed.norm" in name:
__lowerCAmelCase = name.replace("""patch_embed.norm""" , """videomae.embeddings.norm""" )
if "decoder.blocks" in name:
__lowerCAmelCase = name.replace("""decoder.blocks""" , """decoder.decoder_layers""" )
if "blocks" in name:
__lowerCAmelCase = name.replace("""blocks""" , """videomae.encoder.layer""" )
if "attn.proj" in name:
__lowerCAmelCase = name.replace("""attn.proj""" , """attention.output.dense""" )
if "attn" in name and "bias" not in name:
__lowerCAmelCase = name.replace("""attn""" , """attention.self""" )
if "attn" in name:
__lowerCAmelCase = name.replace("""attn""" , """attention.attention""" )
if "norm1" in name:
__lowerCAmelCase = name.replace("""norm1""" , """layernorm_before""" )
if "norm2" in name:
__lowerCAmelCase = name.replace("""norm2""" , """layernorm_after""" )
if "mlp.fc1" in name:
__lowerCAmelCase = name.replace("""mlp.fc1""" , """intermediate.dense""" )
if "mlp.fc2" in name:
__lowerCAmelCase = name.replace("""mlp.fc2""" , """output.dense""" )
if "decoder_embed" in name:
__lowerCAmelCase = name.replace("""decoder_embed""" , """decoder.decoder_embed""" )
if "decoder_norm" in name:
__lowerCAmelCase = name.replace("""decoder_norm""" , """decoder.decoder_norm""" )
if "decoder_pred" in name:
__lowerCAmelCase = name.replace("""decoder_pred""" , """decoder.decoder_pred""" )
if "norm.weight" in name and "decoder" not in name and "fc" not in name:
__lowerCAmelCase = name.replace("""norm.weight""" , """videomae.layernorm.weight""" )
if "norm.bias" in name and "decoder" not in name and "fc" not in name:
__lowerCAmelCase = name.replace("""norm.bias""" , """videomae.layernorm.bias""" )
if "head" in name and "decoder" not in name:
__lowerCAmelCase = name.replace("""head""" , """classifier""" )
return name
def _lowerCAmelCase ( lowercase , lowercase ) -> List[Any]:
for key in orig_state_dict.copy().keys():
__lowerCAmelCase = orig_state_dict.pop(lowercase )
if key.startswith("""encoder.""" ):
__lowerCAmelCase = key.replace("""encoder.""" , """""" )
if "qkv" in key:
__lowerCAmelCase = key.split(""".""" )
if key.startswith("""decoder.blocks""" ):
__lowerCAmelCase = config.decoder_hidden_size
__lowerCAmelCase = int(key_split[2] )
__lowerCAmelCase = """decoder.decoder_layers."""
if "weight" in key:
__lowerCAmelCase = val[:dim, :]
__lowerCAmelCase = val[dim : dim * 2, :]
__lowerCAmelCase = val[-dim:, :]
else:
__lowerCAmelCase = config.hidden_size
__lowerCAmelCase = int(key_split[1] )
__lowerCAmelCase = """videomae.encoder.layer."""
if "weight" in key:
__lowerCAmelCase = val[:dim, :]
__lowerCAmelCase = val[dim : dim * 2, :]
__lowerCAmelCase = val[-dim:, :]
else:
__lowerCAmelCase = val
return orig_state_dict
def _lowerCAmelCase ( ) -> str:
__lowerCAmelCase = hf_hub_download(
repo_id="""hf-internal-testing/spaghetti-video""" , filename="""eating_spaghetti.npy""" , repo_type="""dataset""" )
__lowerCAmelCase = np.load(lowercase )
return list(lowercase )
def _lowerCAmelCase ( lowercase , lowercase , lowercase , lowercase ) -> Optional[Any]:
__lowerCAmelCase = get_videomae_config(lowercase )
if "finetuned" in model_name:
__lowerCAmelCase = VideoMAEForVideoClassification(lowercase )
else:
__lowerCAmelCase = VideoMAEForPreTraining(lowercase )
# download original checkpoint, hosted on Google Drive
__lowerCAmelCase = """pytorch_model.bin"""
gdown.cached_download(lowercase , lowercase , quiet=lowercase )
__lowerCAmelCase = torch.load(lowercase , map_location="""cpu""" )
if "model" in files:
__lowerCAmelCase = files["""model"""]
else:
__lowerCAmelCase = files["""module"""]
__lowerCAmelCase = convert_state_dict(lowercase , lowercase )
model.load_state_dict(lowercase )
model.eval()
# verify model on basic input
__lowerCAmelCase = VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5] , image_std=[0.5, 0.5, 0.5] )
__lowerCAmelCase = prepare_video()
__lowerCAmelCase = image_processor(lowercase , return_tensors="""pt""" )
if "finetuned" not in model_name:
__lowerCAmelCase = hf_hub_download(repo_id="""hf-internal-testing/bool-masked-pos""" , filename="""bool_masked_pos.pt""" )
__lowerCAmelCase = torch.load(lowercase )
__lowerCAmelCase = model(**lowercase )
__lowerCAmelCase = outputs.logits
__lowerCAmelCase = [
"""videomae-small-finetuned-kinetics""",
"""videomae-small-finetuned-ssv2""",
# Kinetics-400 checkpoints (short = pretrained only for 800 epochs instead of 1600)
"""videomae-base-short""",
"""videomae-base-short-finetuned-kinetics""",
"""videomae-base""",
"""videomae-base-finetuned-kinetics""",
"""videomae-large""",
"""videomae-large-finetuned-kinetics""",
"""videomae-huge-finetuned-kinetics""",
# Something-Something-v2 checkpoints (short = pretrained only for 800 epochs instead of 2400)
"""videomae-base-short-ssv2""",
"""videomae-base-short-finetuned-ssv2""",
"""videomae-base-ssv2""",
"""videomae-base-finetuned-ssv2""",
]
# NOTE: logits were tested with image_mean and image_std equal to [0.5, 0.5, 0.5] and [0.5, 0.5, 0.5]
if model_name == "videomae-small-finetuned-kinetics":
__lowerCAmelCase = torch.Size([1, 400] )
__lowerCAmelCase = torch.tensor([-0.92_91, -0.40_61, -0.93_07] )
elif model_name == "videomae-small-finetuned-ssv2":
__lowerCAmelCase = torch.Size([1, 174] )
__lowerCAmelCase = torch.tensor([0.26_71, -0.46_89, -0.82_35] )
elif model_name == "videomae-base":
__lowerCAmelCase = torch.Size([1, 1408, 1536] )
__lowerCAmelCase = torch.tensor([[0.77_39, 0.79_68, 0.70_89], [0.67_01, 0.74_87, 0.62_09], [0.42_87, 0.51_58, 0.47_73]] )
elif model_name == "videomae-base-short":
__lowerCAmelCase = torch.Size([1, 1408, 1536] )
__lowerCAmelCase = torch.tensor([[0.79_94, 0.96_12, 0.85_08], [0.74_01, 0.89_58, 0.83_02], [0.58_62, 0.74_68, 0.73_25]] )
# we verified the loss both for normalized and unnormalized targets for this one
__lowerCAmelCase = torch.tensor([0.51_42] ) if config.norm_pix_loss else torch.tensor([0.64_69] )
elif model_name == "videomae-large":
__lowerCAmelCase = torch.Size([1, 1408, 1536] )
__lowerCAmelCase = torch.tensor([[0.71_49, 0.79_97, 0.69_66], [0.67_68, 0.78_69, 0.69_48], [0.51_39, 0.62_21, 0.56_05]] )
elif model_name == "videomae-large-finetuned-kinetics":
__lowerCAmelCase = torch.Size([1, 400] )
__lowerCAmelCase = torch.tensor([0.07_71, 0.00_11, -0.36_25] )
elif model_name == "videomae-huge-finetuned-kinetics":
__lowerCAmelCase = torch.Size([1, 400] )
__lowerCAmelCase = torch.tensor([0.24_33, 0.16_32, -0.48_94] )
elif model_name == "videomae-base-short-finetuned-kinetics":
__lowerCAmelCase = torch.Size([1, 400] )
__lowerCAmelCase = torch.tensor([0.65_88, 0.09_90, -0.24_93] )
elif model_name == "videomae-base-finetuned-kinetics":
__lowerCAmelCase = torch.Size([1, 400] )
__lowerCAmelCase = torch.tensor([0.36_69, -0.06_88, -0.24_21] )
elif model_name == "videomae-base-short-ssv2":
__lowerCAmelCase = torch.Size([1, 1408, 1536] )
__lowerCAmelCase = torch.tensor([[0.47_12, 0.52_96, 0.57_86], [0.22_78, 0.27_29, 0.40_26], [0.03_52, 0.07_30, 0.25_06]] )
elif model_name == "videomae-base-short-finetuned-ssv2":
__lowerCAmelCase = torch.Size([1, 174] )
__lowerCAmelCase = torch.tensor([-0.05_37, -0.15_39, -0.32_66] )
elif model_name == "videomae-base-ssv2":
__lowerCAmelCase = torch.Size([1, 1408, 1536] )
__lowerCAmelCase = torch.tensor([[0.81_31, 0.87_27, 0.85_46], [0.73_66, 0.93_77, 0.88_70], [0.59_35, 0.88_74, 0.85_64]] )
elif model_name == "videomae-base-finetuned-ssv2":
__lowerCAmelCase = torch.Size([1, 174] )
__lowerCAmelCase = torch.tensor([0.19_61, -0.83_37, -0.63_89] )
else:
raise ValueError(f'Model name not supported. Should be one of {model_names}' )
# verify logits
assert logits.shape == expected_shape
if "finetuned" in model_name:
assert torch.allclose(logits[0, :3] , lowercase , atol=1e-4 )
else:
print("""Logits:""" , logits[0, :3, :3] )
assert torch.allclose(logits[0, :3, :3] , lowercase , atol=1e-4 )
print("""Logits ok!""" )
# verify loss, if applicable
if model_name == "videomae-base-short":
__lowerCAmelCase = outputs.loss
assert torch.allclose(lowercase , lowercase , atol=1e-4 )
print("""Loss ok!""" )
if pytorch_dump_folder_path is not None:
print(f'Saving model and image processor to {pytorch_dump_folder_path}' )
image_processor.save_pretrained(lowercase )
model.save_pretrained(lowercase )
if push_to_hub:
print("""Pushing to the hub...""" )
model.push_to_hub(lowercase , organization="""nielsr""" )
if __name__ == "__main__":
_a : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--checkpoint_url""",
default="""https://drive.google.com/u/1/uc?id=1tEhLyskjb755TJ65ptsrafUG2llSwQE1&export=download&confirm=t&uuid=aa3276eb-fb7e-482a-adec-dc7171df14c4""",
type=str,
help=(
"""URL of the original PyTorch checkpoint (on Google Drive) you'd like to convert. Should be a direct"""
""" download link."""
),
)
parser.add_argument(
"""--pytorch_dump_folder_path""",
default="""/Users/nielsrogge/Documents/VideoMAE/Test""",
type=str,
help="""Path to the output PyTorch model directory.""",
)
parser.add_argument("""--model_name""", default="""videomae-base""", type=str, help="""Name of the model.""")
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
)
_a : int = parser.parse_args()
convert_videomae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 689 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_a : Union[str, Any] = {
"""configuration_bigbird_pegasus""": [
"""BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""BigBirdPegasusConfig""",
"""BigBirdPegasusOnnxConfig""",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_a : Tuple = [
"""BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""BigBirdPegasusForCausalLM""",
"""BigBirdPegasusForConditionalGeneration""",
"""BigBirdPegasusForQuestionAnswering""",
"""BigBirdPegasusForSequenceClassification""",
"""BigBirdPegasusModel""",
"""BigBirdPegasusPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_bigbird_pegasus import (
BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
BigBirdPegasusConfig,
BigBirdPegasusOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_bigbird_pegasus import (
BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
BigBirdPegasusForCausalLM,
BigBirdPegasusForConditionalGeneration,
BigBirdPegasusForQuestionAnswering,
BigBirdPegasusForSequenceClassification,
BigBirdPegasusModel,
BigBirdPegasusPreTrainedModel,
)
else:
import sys
_a : str = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 689 |
'''simple docstring'''
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
_a : Tuple = """\
"""
_a : Tuple = """
Perplexity (PPL) is one of the most common metrics for evaluating language models.
It is defined as the exponentiated average negative log-likelihood of a sequence.
For more information, see https://huggingface.co/docs/transformers/perplexity
"""
_a : Optional[Any] = """
Args:
model_id (str): model used for calculating Perplexity
NOTE: Perplexity can only be calculated for causal language models.
This includes models such as gpt2, causal variations of bert,
causal versions of t5, and more (the full list can be found
in the AutoModelForCausalLM documentation here:
https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )
input_texts (list of str): input text, each separate text snippet
is one list entry.
batch_size (int): the batch size to run texts through the model. Defaults to 16.
add_start_token (bool): whether to add the start token to the texts,
so the perplexity can include the probability of the first word. Defaults to True.
device (str): device to run on, defaults to 'cuda' when available
Returns:
perplexity: dictionary containing the perplexity scores for the texts
in the input list, as well as the mean perplexity. If one of the input texts is
longer than the max input length of the model, then it is truncated to the
max length for the perplexity computation.
Examples:
Example 1:
>>> perplexity = datasets.load_metric(\"perplexity\")
>>> input_texts = [\"lorem ipsum\", \"Happy Birthday!\", \"Bienvenue\"]
>>> results = perplexity.compute(model_id='gpt2',
... add_start_token=False,
... input_texts=input_texts) # doctest:+ELLIPSIS
>>> print(list(results.keys()))
['perplexities', 'mean_perplexity']
>>> print(round(results[\"mean_perplexity\"], 2))
78.22
>>> print(round(results[\"perplexities\"][0], 2))
11.11
Example 2:
>>> perplexity = datasets.load_metric(\"perplexity\")
>>> input_texts = datasets.load_dataset(\"wikitext\",
... \"wikitext-2-raw-v1\",
... split=\"test\")[\"text\"][:50] # doctest:+ELLIPSIS
[...]
>>> input_texts = [s for s in input_texts if s!='']
>>> results = perplexity.compute(model_id='gpt2',
... input_texts=input_texts) # doctest:+ELLIPSIS
>>> print(list(results.keys()))
['perplexities', 'mean_perplexity']
>>> print(round(results[\"mean_perplexity\"], 2))
60.35
>>> print(round(results[\"perplexities\"][0], 2))
81.12
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _UpperCAmelCase ( datasets.Metric ):
def lowerCamelCase__ ( self ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION,citation=_CITATION,inputs_description=_KWARGS_DESCRIPTION,features=datasets.Features(
{
"""input_texts""": datasets.Value("""string""" ),
} ),reference_urls=["""https://huggingface.co/docs/transformers/perplexity"""],)
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE = 16,__SCREAMING_SNAKE_CASE = True,__SCREAMING_SNAKE_CASE=None ):
'''simple docstring'''
if device is not None:
assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
if device == "gpu":
__lowerCAmelCase = """cuda"""
else:
__lowerCAmelCase = """cuda""" if torch.cuda.is_available() else """cpu"""
__lowerCAmelCase = AutoModelForCausalLM.from_pretrained(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = model.to(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE )
# if batch_size > 1 (which generally leads to padding being required), and
# if there is not an already assigned pad_token, assign an existing
# special token to also be the padding token
if tokenizer.pad_token is None and batch_size > 1:
__lowerCAmelCase = list(tokenizer.special_tokens_map_extended.values() )
# check that the model already has at least one special token defined
assert (
len(__SCREAMING_SNAKE_CASE ) > 0
), "If batch_size > 1, model must have at least one special token to use for padding. Please use a different model or set batch_size=1."
# assign one of the special tokens to also be the pad token
tokenizer.add_special_tokens({"""pad_token""": existing_special_tokens[0]} )
if add_start_token:
# leave room for <BOS> token to be added:
assert (
tokenizer.bos_token is not None
), "Input model must already have a BOS token if using add_start_token=True. Please use a different model, or set add_start_token=False"
__lowerCAmelCase = model.config.max_length - 1
else:
__lowerCAmelCase = model.config.max_length
__lowerCAmelCase = tokenizer(
__SCREAMING_SNAKE_CASE,add_special_tokens=__SCREAMING_SNAKE_CASE,padding=__SCREAMING_SNAKE_CASE,truncation=__SCREAMING_SNAKE_CASE,max_length=__SCREAMING_SNAKE_CASE,return_tensors="""pt""",return_attention_mask=__SCREAMING_SNAKE_CASE,).to(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = encodings["""input_ids"""]
__lowerCAmelCase = encodings["""attention_mask"""]
# check that each input is long enough:
if add_start_token:
assert torch.all(torch.ge(attn_masks.sum(1 ),1 ) ), "Each input text must be at least one token long."
else:
assert torch.all(
torch.ge(attn_masks.sum(1 ),2 ) ), "When add_start_token=False, each input text must be at least two tokens long. Run with add_start_token=True if inputting strings of only one token, and remove all empty input strings."
__lowerCAmelCase = []
__lowerCAmelCase = CrossEntropyLoss(reduction="""none""" )
for start_index in logging.tqdm(range(0,len(__SCREAMING_SNAKE_CASE ),__SCREAMING_SNAKE_CASE ) ):
__lowerCAmelCase = min(start_index + batch_size,len(__SCREAMING_SNAKE_CASE ) )
__lowerCAmelCase = encoded_texts[start_index:end_index]
__lowerCAmelCase = attn_masks[start_index:end_index]
if add_start_token:
__lowerCAmelCase = torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0 ) ).to(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = torch.cat([bos_tokens_tensor, encoded_batch],dim=1 )
__lowerCAmelCase = torch.cat(
[torch.ones(bos_tokens_tensor.size(),dtype=torch.intaa ).to(__SCREAMING_SNAKE_CASE ), attn_mask],dim=1 )
__lowerCAmelCase = encoded_batch
with torch.no_grad():
__lowerCAmelCase = model(__SCREAMING_SNAKE_CASE,attention_mask=__SCREAMING_SNAKE_CASE ).logits
__lowerCAmelCase = out_logits[..., :-1, :].contiguous()
__lowerCAmelCase = labels[..., 1:].contiguous()
__lowerCAmelCase = attn_mask[..., 1:].contiguous()
__lowerCAmelCase = torch.expa(
(loss_fct(shift_logits.transpose(1,2 ),__SCREAMING_SNAKE_CASE ) * shift_attention_mask_batch).sum(1 )
/ shift_attention_mask_batch.sum(1 ) )
ppls += perplexity_batch.tolist()
return {"perplexities": ppls, "mean_perplexity": np.mean(__SCREAMING_SNAKE_CASE )}
| 689 | 1 |
'''simple docstring'''
import argparse
import os
# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils import find_executable_batch_size
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
_a : List[str] = 1_6
_a : int = 3_2
def _lowerCAmelCase ( lowercase , lowercase = 16 ) -> List[Any]:
__lowerCAmelCase = AutoTokenizer.from_pretrained("""bert-base-cased""" )
__lowerCAmelCase = load_dataset("""glue""" , """mrpc""" )
def tokenize_function(lowercase ):
# max_length=None => use the model max length (it's actually the default)
__lowerCAmelCase = tokenizer(examples["""sentence1"""] , examples["""sentence2"""] , truncation=lowercase , max_length=lowercase )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
__lowerCAmelCase = datasets.map(
lowercase , batched=lowercase , remove_columns=["""idx""", """sentence1""", """sentence2"""] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
__lowerCAmelCase = tokenized_datasets.rename_column("""label""" , """labels""" )
def collate_fn(lowercase ):
# On TPU it's best to pad everything to the same length or training will be very slow.
__lowerCAmelCase = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
__lowerCAmelCase = 16
elif accelerator.mixed_precision != "no":
__lowerCAmelCase = 8
else:
__lowerCAmelCase = None
return tokenizer.pad(
lowercase , padding="""longest""" , max_length=lowercase , pad_to_multiple_of=lowercase , return_tensors="""pt""" , )
# Instantiate dataloaders.
__lowerCAmelCase = DataLoader(
tokenized_datasets["""train"""] , shuffle=lowercase , collate_fn=lowercase , batch_size=lowercase )
__lowerCAmelCase = DataLoader(
tokenized_datasets["""validation"""] , shuffle=lowercase , collate_fn=lowercase , batch_size=lowercase )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
_a : Any = mocked_dataloaders # noqa: F811
def _lowerCAmelCase ( lowercase , lowercase ) -> Tuple:
# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""" , lowercase ) == "1":
__lowerCAmelCase = 2
# Initialize accelerator
__lowerCAmelCase = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
__lowerCAmelCase = config["""lr"""]
__lowerCAmelCase = int(config["""num_epochs"""] )
__lowerCAmelCase = int(config["""seed"""] )
__lowerCAmelCase = int(config["""batch_size"""] )
__lowerCAmelCase = evaluate.load("""glue""" , """mrpc""" )
# New Code #
# We now can define an inner training loop function. It should take a batch size as the only parameter,
# and build the dataloaders in there.
# It also gets our decorator
@find_executable_batch_size(starting_batch_size=lowercase )
def inner_training_loop(lowercase ):
# And now just move everything below under this function
# We need to bring in the Accelerator object from earlier
nonlocal accelerator
# And reset all of its attributes that could hold onto any memory:
accelerator.free_memory()
# Then we can declare the model, optimizer, and everything else:
set_seed(lowercase )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
__lowerCAmelCase = AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""" , return_dict=lowercase )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
__lowerCAmelCase = model.to(accelerator.device )
# Instantiate optimizer
__lowerCAmelCase = AdamW(params=model.parameters() , lr=lowercase )
__lowerCAmelCase , __lowerCAmelCase = get_dataloaders(lowercase , lowercase )
# Instantiate scheduler
__lowerCAmelCase = get_linear_schedule_with_warmup(
optimizer=lowercase , num_warmup_steps=100 , num_training_steps=(len(lowercase ) * num_epochs) , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = accelerator.prepare(
lowercase , lowercase , lowercase , lowercase , lowercase )
# Now we train the model
for epoch in range(lowercase ):
model.train()
for step, batch in enumerate(lowercase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
__lowerCAmelCase = model(**lowercase )
__lowerCAmelCase = outputs.loss
accelerator.backward(lowercase )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(lowercase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
__lowerCAmelCase = model(**lowercase )
__lowerCAmelCase = outputs.logits.argmax(dim=-1 )
__lowerCAmelCase , __lowerCAmelCase = accelerator.gather_for_metrics((predictions, batch["""labels"""]) )
metric.add_batch(
predictions=lowercase , references=lowercase , )
__lowerCAmelCase = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f'epoch {epoch}:' , lowercase )
# New Code #
# And call it at the end with no arguments
# Note: You could also refactor this outside of your training loop function
inner_training_loop()
def _lowerCAmelCase ( ) -> int:
__lowerCAmelCase = argparse.ArgumentParser(description="""Simple example of training script.""" )
parser.add_argument(
"""--mixed_precision""" , type=lowercase , default=lowercase , choices=["""no""", """fp16""", """bf16""", """fp8"""] , help="""Whether to use mixed precision. Choose"""
"""between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
"""and an Nvidia Ampere GPU.""" , )
parser.add_argument("""--cpu""" , action="""store_true""" , help="""If passed, will train on the CPU.""" )
__lowerCAmelCase = parser.parse_args()
__lowerCAmelCase = {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
training_function(lowercase , lowercase )
if __name__ == "__main__":
main()
| 689 |
'''simple docstring'''
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
import torch
if is_tf_available():
import tensorflow as tf
class _UpperCAmelCase ( lowerCAmelCase_ ):
a : Union[str, Any] =["""image_processor"""]
a : Dict ="""SamImageProcessor"""
def __init__( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
super().__init__(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = self.image_processor
__lowerCAmelCase = -10
__lowerCAmelCase = self.image_processor.size["""longest_edge"""]
def __call__( self,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE = None,**__SCREAMING_SNAKE_CASE,):
'''simple docstring'''
__lowerCAmelCase = self.image_processor(
__SCREAMING_SNAKE_CASE,return_tensors=__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE,)
# pop arguments that are not used in the foward but used nevertheless
__lowerCAmelCase = encoding_image_processor["""original_sizes"""]
if hasattr(__SCREAMING_SNAKE_CASE,"""numpy""" ): # Checks if Torch or TF tensor
__lowerCAmelCase = original_sizes.numpy()
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = self._check_and_preprocess_points(
input_points=__SCREAMING_SNAKE_CASE,input_labels=__SCREAMING_SNAKE_CASE,input_boxes=__SCREAMING_SNAKE_CASE,)
__lowerCAmelCase = self._normalize_and_convert(
__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,input_points=__SCREAMING_SNAKE_CASE,input_labels=__SCREAMING_SNAKE_CASE,input_boxes=__SCREAMING_SNAKE_CASE,return_tensors=__SCREAMING_SNAKE_CASE,)
return encoding_image_processor
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE="pt",):
'''simple docstring'''
if input_points is not None:
if len(__SCREAMING_SNAKE_CASE ) != len(__SCREAMING_SNAKE_CASE ):
__lowerCAmelCase = [
self._normalize_coordinates(self.target_size,__SCREAMING_SNAKE_CASE,original_sizes[0] ) for point in input_points
]
else:
__lowerCAmelCase = [
self._normalize_coordinates(self.target_size,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
for point, original_size in zip(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
]
# check that all arrays have the same shape
if not all(point.shape == input_points[0].shape for point in input_points ):
if input_labels is not None:
__lowerCAmelCase , __lowerCAmelCase = self._pad_points_and_labels(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = np.array(__SCREAMING_SNAKE_CASE )
if input_labels is not None:
__lowerCAmelCase = np.array(__SCREAMING_SNAKE_CASE )
if input_boxes is not None:
if len(__SCREAMING_SNAKE_CASE ) != len(__SCREAMING_SNAKE_CASE ):
__lowerCAmelCase = [
self._normalize_coordinates(self.target_size,__SCREAMING_SNAKE_CASE,original_sizes[0],is_bounding_box=__SCREAMING_SNAKE_CASE )
for box in input_boxes
]
else:
__lowerCAmelCase = [
self._normalize_coordinates(self.target_size,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,is_bounding_box=__SCREAMING_SNAKE_CASE )
for box, original_size in zip(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
]
__lowerCAmelCase = np.array(__SCREAMING_SNAKE_CASE )
if input_boxes is not None:
if return_tensors == "pt":
__lowerCAmelCase = torch.from_numpy(__SCREAMING_SNAKE_CASE )
# boxes batch size of 1 by default
__lowerCAmelCase = input_boxes.unsqueeze(1 ) if len(input_boxes.shape ) != 3 else input_boxes
elif return_tensors == "tf":
__lowerCAmelCase = tf.convert_to_tensor(__SCREAMING_SNAKE_CASE )
# boxes batch size of 1 by default
__lowerCAmelCase = tf.expand_dims(__SCREAMING_SNAKE_CASE,1 ) if len(input_boxes.shape ) != 3 else input_boxes
encoding_image_processor.update({"""input_boxes""": input_boxes} )
if input_points is not None:
if return_tensors == "pt":
__lowerCAmelCase = torch.from_numpy(__SCREAMING_SNAKE_CASE )
# point batch size of 1 by default
__lowerCAmelCase = input_points.unsqueeze(1 ) if len(input_points.shape ) != 4 else input_points
elif return_tensors == "tf":
__lowerCAmelCase = tf.convert_to_tensor(__SCREAMING_SNAKE_CASE )
# point batch size of 1 by default
__lowerCAmelCase = tf.expand_dims(__SCREAMING_SNAKE_CASE,1 ) if len(input_points.shape ) != 4 else input_points
encoding_image_processor.update({"""input_points""": input_points} )
if input_labels is not None:
if return_tensors == "pt":
__lowerCAmelCase = torch.from_numpy(__SCREAMING_SNAKE_CASE )
# point batch size of 1 by default
__lowerCAmelCase = input_labels.unsqueeze(1 ) if len(input_labels.shape ) != 3 else input_labels
elif return_tensors == "tf":
__lowerCAmelCase = tf.convert_to_tensor(__SCREAMING_SNAKE_CASE )
# point batch size of 1 by default
__lowerCAmelCase = tf.expand_dims(__SCREAMING_SNAKE_CASE,1 ) if len(input_labels.shape ) != 3 else input_labels
encoding_image_processor.update({"""input_labels""": input_labels} )
return encoding_image_processor
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase = max([point.shape[0] for point in input_points] )
__lowerCAmelCase = []
for i, point in enumerate(__SCREAMING_SNAKE_CASE ):
if point.shape[0] != expected_nb_points:
__lowerCAmelCase = np.concatenate(
[point, np.zeros((expected_nb_points - point.shape[0], 2) ) + self.point_pad_value],axis=0 )
__lowerCAmelCase = np.append(input_labels[i],[self.point_pad_value] )
processed_input_points.append(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = processed_input_points
return input_points, input_labels
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=False ):
'''simple docstring'''
__lowerCAmelCase , __lowerCAmelCase = original_size
__lowerCAmelCase , __lowerCAmelCase = self.image_processor._get_preprocess_shape(__SCREAMING_SNAKE_CASE,longest_edge=__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = deepcopy(__SCREAMING_SNAKE_CASE ).astype(__SCREAMING_SNAKE_CASE )
if is_bounding_box:
__lowerCAmelCase = coords.reshape(-1,2,2 )
__lowerCAmelCase = coords[..., 0] * (new_w / old_w)
__lowerCAmelCase = coords[..., 1] * (new_h / old_h)
if is_bounding_box:
__lowerCAmelCase = coords.reshape(-1,4 )
return coords
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=None,):
'''simple docstring'''
if input_points is not None:
if hasattr(__SCREAMING_SNAKE_CASE,"""numpy""" ): # Checks for TF or Torch tensor
__lowerCAmelCase = input_points.numpy().tolist()
if not isinstance(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE ) or not isinstance(input_points[0],__SCREAMING_SNAKE_CASE ):
raise ValueError("""Input points must be a list of list of floating points.""" )
__lowerCAmelCase = [np.array(__SCREAMING_SNAKE_CASE ) for input_point in input_points]
else:
__lowerCAmelCase = None
if input_labels is not None:
if hasattr(__SCREAMING_SNAKE_CASE,"""numpy""" ):
__lowerCAmelCase = input_labels.numpy().tolist()
if not isinstance(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE ) or not isinstance(input_labels[0],__SCREAMING_SNAKE_CASE ):
raise ValueError("""Input labels must be a list of list integers.""" )
__lowerCAmelCase = [np.array(__SCREAMING_SNAKE_CASE ) for label in input_labels]
else:
__lowerCAmelCase = None
if input_boxes is not None:
if hasattr(__SCREAMING_SNAKE_CASE,"""numpy""" ):
__lowerCAmelCase = input_boxes.numpy().tolist()
if (
not isinstance(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
or not isinstance(input_boxes[0],__SCREAMING_SNAKE_CASE )
or not isinstance(input_boxes[0][0],__SCREAMING_SNAKE_CASE )
):
raise ValueError("""Input boxes must be a list of list of list of floating points.""" )
__lowerCAmelCase = [np.array(__SCREAMING_SNAKE_CASE ).astype(np.floataa ) for box in input_boxes]
else:
__lowerCAmelCase = None
return input_points, input_labels, input_boxes
@property
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = self.image_processor.model_input_names
return list(dict.fromkeys(__SCREAMING_SNAKE_CASE ) )
def lowerCamelCase__ ( self,*__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
return self.image_processor.post_process_masks(*__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE )
| 689 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
class _UpperCAmelCase :
def __init__( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase = size
# approximate the overall size of segment tree with given value
__lowerCAmelCase = [0 for i in range(0,4 * size )]
# create array to store lazy update
__lowerCAmelCase = [0 for i in range(0,4 * size )]
__lowerCAmelCase = [0 for i in range(0,4 * size )] # flag for lazy update
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
return idx * 2
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
return idx * 2 + 1
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
if left_element == right_element:
__lowerCAmelCase = a[left_element - 1]
else:
__lowerCAmelCase = (left_element + right_element) // 2
self.build(self.left(__SCREAMING_SNAKE_CASE ),__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
self.build(self.right(__SCREAMING_SNAKE_CASE ),mid + 1,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = max(
self.segment_tree[self.left(__SCREAMING_SNAKE_CASE )],self.segment_tree[self.right(__SCREAMING_SNAKE_CASE )] )
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
if self.flag[idx] is True:
__lowerCAmelCase = self.lazy[idx]
__lowerCAmelCase = False
if left_element != right_element:
__lowerCAmelCase = self.lazy[idx]
__lowerCAmelCase = self.lazy[idx]
__lowerCAmelCase = True
__lowerCAmelCase = True
if right_element < a or left_element > b:
return True
if left_element >= a and right_element <= b:
__lowerCAmelCase = val
if left_element != right_element:
__lowerCAmelCase = val
__lowerCAmelCase = val
__lowerCAmelCase = True
__lowerCAmelCase = True
return True
__lowerCAmelCase = (left_element + right_element) // 2
self.update(self.left(__SCREAMING_SNAKE_CASE ),__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
self.update(self.right(__SCREAMING_SNAKE_CASE ),mid + 1,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = max(
self.segment_tree[self.left(__SCREAMING_SNAKE_CASE )],self.segment_tree[self.right(__SCREAMING_SNAKE_CASE )] )
return True
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
if self.flag[idx] is True:
__lowerCAmelCase = self.lazy[idx]
__lowerCAmelCase = False
if left_element != right_element:
__lowerCAmelCase = self.lazy[idx]
__lowerCAmelCase = self.lazy[idx]
__lowerCAmelCase = True
__lowerCAmelCase = True
if right_element < a or left_element > b:
return -math.inf
if left_element >= a and right_element <= b:
return self.segment_tree[idx]
__lowerCAmelCase = (left_element + right_element) // 2
__lowerCAmelCase = self.query(self.left(__SCREAMING_SNAKE_CASE ),__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = self.query(self.right(__SCREAMING_SNAKE_CASE ),mid + 1,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
return max(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
def __str__( self ):
'''simple docstring'''
return str([self.query(1,1,self.size,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE ) for i in range(1,self.size + 1 )] )
if __name__ == "__main__":
_a : Optional[int] = [1, 2, -4, 7, 3, -5, 6, 1_1, -2_0, 9, 1_4, 1_5, 5, 2, -8]
_a : Dict = 1_5
_a : Optional[Any] = SegmentTree(size)
segt.build(1, 1, size, A)
print(segt.query(1, 1, size, 4, 6))
print(segt.query(1, 1, size, 7, 1_1))
print(segt.query(1, 1, size, 7, 1_2))
segt.update(1, 1, size, 1, 3, 1_1_1)
print(segt.query(1, 1, size, 1, 1_5))
segt.update(1, 1, size, 7, 8, 2_3_5)
print(segt)
| 689 |
'''simple docstring'''
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequenceClassification,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
TapexTokenizer,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version
from transformers.utils.versions import require_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("""4.17.0.dev0""")
require_version("""datasets>=1.8.0""", """To fix: pip install -r examples/pytorch/text-classification/requirements.txt""")
_a : int = logging.getLogger(__name__)
@dataclass
class _UpperCAmelCase :
a : Optional[str] =field(
default="""tab_fact""" , metadata={"""help""": """The name of the dataset to use (via the datasets library)."""} )
a : Optional[str] =field(
default="""tab_fact""" , metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""} , )
a : int =field(
default=10_24 , metadata={
"""help""": (
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
)
} , )
a : bool =field(
default=lowerCAmelCase_ , metadata={"""help""": """Overwrite the cached preprocessed datasets or not."""} )
a : bool =field(
default=lowerCAmelCase_ , metadata={
"""help""": (
"""Whether to pad all samples to `max_seq_length`. """
"""If False, will pad the samples dynamically when batching to the maximum length in the batch."""
)
} , )
a : Optional[int] =field(
default=lowerCAmelCase_ , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of training examples to this """
"""value if set."""
)
} , )
a : Optional[int] =field(
default=lowerCAmelCase_ , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of evaluation examples to this """
"""value if set."""
)
} , )
a : Optional[int] =field(
default=lowerCAmelCase_ , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of prediction examples to this """
"""value if set."""
)
} , )
a : Optional[str] =field(
default=lowerCAmelCase_ , metadata={"""help""": """A csv or a json file containing the training data."""} )
a : Optional[str] =field(
default=lowerCAmelCase_ , metadata={"""help""": """A csv or a json file containing the validation data."""} )
a : Optional[str] =field(default=lowerCAmelCase_ , metadata={"""help""": """A csv or a json file containing the test data."""} )
def lowerCamelCase__ ( self ):
'''simple docstring'''
if self.dataset_name is not None:
pass
elif self.train_file is None or self.validation_file is None:
raise ValueError("""Need either a GLUE task, a training/validation file or a dataset name.""" )
else:
__lowerCAmelCase = self.train_file.split(""".""" )[-1]
assert train_extension in ["csv", "json"], "`train_file` should be a csv or a json file."
__lowerCAmelCase = self.validation_file.split(""".""" )[-1]
assert (
validation_extension == train_extension
), "`validation_file` should have the same extension (csv or json) as `train_file`."
@dataclass
class _UpperCAmelCase :
a : str =field(
default=lowerCAmelCase_ , metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""} )
a : Optional[str] =field(
default=lowerCAmelCase_ , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""} )
a : Optional[str] =field(
default=lowerCAmelCase_ , metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""} )
a : Optional[str] =field(
default=lowerCAmelCase_ , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} , )
a : bool =field(
default=lowerCAmelCase_ , metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""} , )
a : str =field(
default="""main""" , metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""} , )
a : bool =field(
default=lowerCAmelCase_ , metadata={
"""help""": (
"""Will use the token generated when running `huggingface-cli login` (necessary to use this script """
"""with private models)."""
)
} , )
def _lowerCAmelCase ( ) -> Optional[Any]:
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
__lowerCAmelCase = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith(""".json""" ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = parser.parse_args_into_dataclasses()
# Setup logging
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , handlers=[logging.StreamHandler(sys.stdout )] , )
__lowerCAmelCase = training_args.get_process_log_level()
logger.setLevel(lowercase )
datasets.utils.logging.set_verbosity(lowercase )
transformers.utils.logging.set_verbosity(lowercase )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
f'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
+ f'distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}' )
logger.info(f'Training/evaluation parameters {training_args}' )
# Detecting last checkpoint.
__lowerCAmelCase = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
__lowerCAmelCase = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
f'Output directory ({training_args.output_dir}) already exists and is not empty. '
"""Use --overwrite_output_dir to overcome.""" )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
f'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
"""the `--output_dir` or add `--overwrite_output_dir` to train from scratch.""" )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
# or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub).
#
# For JSON files, this script will use the `question` column for the input question and `table` column for the corresponding table.
#
# If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this
# single column. You can easily tweak this behavior (see below)
#
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
# download the dataset.
if data_args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
__lowerCAmelCase = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , cache_dir=model_args.cache_dir )
else:
# Loading a dataset from your local files.
# CSV/JSON training and evaluation files are needed.
__lowerCAmelCase = {"""train""": data_args.train_file, """validation""": data_args.validation_file}
# Get the test dataset: you can provide your own CSV/JSON test file (see below)
# when you use `do_predict` without specifying a GLUE benchmark task.
if training_args.do_predict:
if data_args.test_file is not None:
__lowerCAmelCase = data_args.train_file.split(""".""" )[-1]
__lowerCAmelCase = data_args.test_file.split(""".""" )[-1]
assert (
test_extension == train_extension
), "`test_file` should have the same extension (csv or json) as `train_file`."
__lowerCAmelCase = data_args.test_file
else:
raise ValueError("""Need either a GLUE task or a test file for `do_predict`.""" )
for key in data_files.keys():
logger.info(f'load a local file for {key}: {data_files[key]}' )
if data_args.train_file.endswith(""".csv""" ):
# Loading a dataset from local csv files
__lowerCAmelCase = load_dataset("""csv""" , data_files=lowercase , cache_dir=model_args.cache_dir )
else:
# Loading a dataset from local json files
__lowerCAmelCase = load_dataset("""json""" , data_files=lowercase , cache_dir=model_args.cache_dir )
# See more about loading any type of standard or custom dataset at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Labels
__lowerCAmelCase = raw_datasets["""train"""].features["""label"""].names
__lowerCAmelCase = len(lowercase )
# Load pretrained model and tokenizer
#
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
__lowerCAmelCase = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=lowercase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# load tapex tokenizer
__lowerCAmelCase = TapexTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , add_prefix_space=lowercase , )
__lowerCAmelCase = BartForSequenceClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool(""".ckpt""" in model_args.model_name_or_path ) , config=lowercase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# Padding strategy
if data_args.pad_to_max_length:
__lowerCAmelCase = """max_length"""
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
__lowerCAmelCase = False
# Some models have set the order of the labels to use, so let's make sure we do use it.
__lowerCAmelCase = {"""Refused""": 0, """Entailed""": 1}
__lowerCAmelCase = {0: """Refused""", 1: """Entailed"""}
if data_args.max_seq_length > tokenizer.model_max_length:
logger.warning(
f'The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the'
f'model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.' )
__lowerCAmelCase = min(data_args.max_seq_length , tokenizer.model_max_length )
def preprocess_tabfact_function(lowercase ):
# Tokenize the texts
def _convert_table_text_to_pandas(lowercase ):
__lowerCAmelCase = [_table_row.split("""#""" ) for _table_row in _table_text.strip("""\n""" ).split("""\n""" )]
__lowerCAmelCase = pd.DataFrame.from_records(_table_content[1:] , columns=_table_content[0] )
return _table_pd
__lowerCAmelCase = examples["""statement"""]
__lowerCAmelCase = list(map(_convert_table_text_to_pandas , examples["""table_text"""] ) )
__lowerCAmelCase = tokenizer(lowercase , lowercase , padding=lowercase , max_length=lowercase , truncation=lowercase )
__lowerCAmelCase = examples["""label"""]
return result
with training_args.main_process_first(desc="""dataset map pre-processing""" ):
__lowerCAmelCase = raw_datasets.map(
lowercase , batched=lowercase , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on dataset""" , )
if training_args.do_train:
if "train" not in raw_datasets:
raise ValueError("""--do_train requires a train dataset""" )
__lowerCAmelCase = raw_datasets["""train"""]
if data_args.max_train_samples is not None:
__lowerCAmelCase = train_dataset.select(range(data_args.max_train_samples ) )
if training_args.do_eval:
if "validation" not in raw_datasets and "validation_matched" not in raw_datasets:
raise ValueError("""--do_eval requires a validation dataset""" )
__lowerCAmelCase = raw_datasets["""validation"""]
if data_args.max_eval_samples is not None:
__lowerCAmelCase = eval_dataset.select(range(data_args.max_eval_samples ) )
if training_args.do_predict or data_args.test_file is not None:
if "test" not in raw_datasets and "test_matched" not in raw_datasets:
raise ValueError("""--do_predict requires a test dataset""" )
__lowerCAmelCase = raw_datasets["""test"""]
if data_args.max_predict_samples is not None:
__lowerCAmelCase = predict_dataset.select(range(data_args.max_predict_samples ) )
# Log a few random samples from the training set:
if training_args.do_train:
for index in random.sample(range(len(lowercase ) ) , 3 ):
logger.info(f'Sample {index} of the training set: {train_dataset[index]}.' )
# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(lowercase ):
__lowerCAmelCase = p.predictions[0] if isinstance(p.predictions , lowercase ) else p.predictions
__lowerCAmelCase = np.argmax(lowercase , axis=1 )
return {"accuracy": (preds == p.label_ids).astype(np.floataa ).mean().item()}
# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
if data_args.pad_to_max_length:
__lowerCAmelCase = default_data_collator
elif training_args.fpaa:
__lowerCAmelCase = DataCollatorWithPadding(lowercase , pad_to_multiple_of=8 )
else:
__lowerCAmelCase = None
# Initialize our Trainer
__lowerCAmelCase = Trainer(
model=lowercase , args=lowercase , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , compute_metrics=lowercase , tokenizer=lowercase , data_collator=lowercase , )
# Training
if training_args.do_train:
__lowerCAmelCase = None
if training_args.resume_from_checkpoint is not None:
__lowerCAmelCase = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
__lowerCAmelCase = last_checkpoint
__lowerCAmelCase = trainer.train(resume_from_checkpoint=lowercase )
__lowerCAmelCase = train_result.metrics
__lowerCAmelCase = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(lowercase )
)
__lowerCAmelCase = min(lowercase , len(lowercase ) )
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics("""train""" , lowercase )
trainer.save_metrics("""train""" , lowercase )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info("""*** Evaluate ***""" )
__lowerCAmelCase = trainer.evaluate(eval_dataset=lowercase )
__lowerCAmelCase = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(lowercase )
__lowerCAmelCase = min(lowercase , len(lowercase ) )
trainer.log_metrics("""eval""" , lowercase )
trainer.save_metrics("""eval""" , lowercase )
if training_args.do_predict:
logger.info("""*** Predict ***""" )
# Removing the `label` columns because it contains -1 and Trainer won't like that.
__lowerCAmelCase = predict_dataset.remove_columns("""label""" )
__lowerCAmelCase = trainer.predict(lowercase , metric_key_prefix="""predict""" ).predictions
__lowerCAmelCase = np.argmax(lowercase , axis=1 )
__lowerCAmelCase = os.path.join(training_args.output_dir , """predict_results_tabfact.txt""" )
if trainer.is_world_process_zero():
with open(lowercase , """w""" ) as writer:
logger.info("""***** Predict Results *****""" )
writer.write("""index\tprediction\n""" )
for index, item in enumerate(lowercase ):
__lowerCAmelCase = label_list[item]
writer.write(f'{index}\t{item}\n' )
__lowerCAmelCase = {"""finetuned_from""": model_args.model_name_or_path, """tasks""": """text-classification"""}
if training_args.push_to_hub:
trainer.push_to_hub(**lowercase )
else:
trainer.create_model_card(**lowercase )
def _lowerCAmelCase ( lowercase ) -> str:
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 689 | 1 |
'''simple docstring'''
import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def _lowerCAmelCase ( lowercase , lowercase , lowercase , lowercase , lowercase , lowercase ) -> np.ndarray:
# prepare kernel
# the kernel size have to be odd
if (ksize % 2) == 0:
__lowerCAmelCase = ksize + 1
__lowerCAmelCase = np.zeros((ksize, ksize) , dtype=np.floataa )
# each value
for y in range(lowercase ):
for x in range(lowercase ):
# distance from center
__lowerCAmelCase = x - ksize // 2
__lowerCAmelCase = y - ksize // 2
# degree to radiant
__lowerCAmelCase = theta / 180 * np.pi
__lowerCAmelCase = np.cos(_theta )
__lowerCAmelCase = np.sin(_theta )
# get kernel x
__lowerCAmelCase = cos_theta * px + sin_theta * py
# get kernel y
__lowerCAmelCase = -sin_theta * px + cos_theta * py
# fill kernel
__lowerCAmelCase = np.exp(
-(_x**2 + gamma**2 * _y**2) / (2 * sigma**2) ) * np.cos(2 * np.pi * _x / lambd + psi )
return gabor
if __name__ == "__main__":
import doctest
doctest.testmod()
# read original image
_a : List[str] = imread("""../image_data/lena.jpg""")
# turn image in gray scale value
_a : Any = cvtColor(img, COLOR_BGR2GRAY)
# Apply multiple Kernel to detect edges
_a : str = np.zeros(gray.shape[:2])
for theta in [0, 3_0, 6_0, 9_0, 1_2_0, 1_5_0]:
_a : Optional[Any] = gabor_filter_kernel(1_0, 8, theta, 1_0, 0, 0)
out += filteraD(gray, CV_8UC3, kernel_aa)
_a : Union[str, Any] = out / out.max() * 2_5_5
_a : int = out.astype(np.uinta)
imshow("""Original""", gray)
imshow("""Gabor filter with 20x20 mask and 6 directions""", out)
waitKey(0)
| 689 |
'''simple docstring'''
import os
import sys
import unittest
_a : List[Any] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init # noqa: E402
# Align TRANSFORMERS_PATH in check_dummies with the current path
_a : Union[str, Any] = os.path.join(git_repo_path, """src""", """diffusers""")
class _UpperCAmelCase ( unittest.TestCase ):
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = find_backend(""" if not is_torch_available():""" )
self.assertEqual(__SCREAMING_SNAKE_CASE,"""torch""" )
# backend_with_underscore = find_backend(" if not is_tensorflow_text_available():")
# self.assertEqual(backend_with_underscore, "tensorflow_text")
__lowerCAmelCase = find_backend(""" if not (is_torch_available() and is_transformers_available()):""" )
self.assertEqual(__SCREAMING_SNAKE_CASE,"""torch_and_transformers""" )
# double_backend_with_underscore = find_backend(
# " if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
# )
# self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")
__lowerCAmelCase = find_backend(
""" if not (is_torch_available() and is_transformers_available() and is_onnx_available()):""" )
self.assertEqual(__SCREAMING_SNAKE_CASE,"""torch_and_transformers_and_onnx""" )
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = read_init()
# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
self.assertIn("""torch""",__SCREAMING_SNAKE_CASE )
self.assertIn("""torch_and_transformers""",__SCREAMING_SNAKE_CASE )
self.assertIn("""flax_and_transformers""",__SCREAMING_SNAKE_CASE )
self.assertIn("""torch_and_transformers_and_onnx""",__SCREAMING_SNAKE_CASE )
# Likewise, we can't assert on the exact content of a key
self.assertIn("""UNet2DModel""",objects["""torch"""] )
self.assertIn("""FlaxUNet2DConditionModel""",objects["""flax"""] )
self.assertIn("""StableDiffusionPipeline""",objects["""torch_and_transformers"""] )
self.assertIn("""FlaxStableDiffusionPipeline""",objects["""flax_and_transformers"""] )
self.assertIn("""LMSDiscreteScheduler""",objects["""torch_and_scipy"""] )
self.assertIn("""OnnxStableDiffusionPipeline""",objects["""torch_and_transformers_and_onnx"""] )
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = create_dummy_object("""CONSTANT""","""'torch'""" )
self.assertEqual(__SCREAMING_SNAKE_CASE,"""\nCONSTANT = None\n""" )
__lowerCAmelCase = create_dummy_object("""function""","""'torch'""" )
self.assertEqual(
__SCREAMING_SNAKE_CASE,"""\ndef function(*args, **kwargs):\n requires_backends(function, 'torch')\n""" )
__lowerCAmelCase = """
class FakeClass(metaclass=DummyObject):
_backends = 'torch'
def __init__(self, *args, **kwargs):
requires_backends(self, 'torch')
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, 'torch')
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, 'torch')
"""
__lowerCAmelCase = create_dummy_object("""FakeClass""","""'torch'""" )
self.assertEqual(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = """# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends
CONSTANT = None
def function(*args, **kwargs):
requires_backends(function, [\"torch\"])
class FakeClass(metaclass=DummyObject):
_backends = [\"torch\"]
def __init__(self, *args, **kwargs):
requires_backends(self, [\"torch\"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, [\"torch\"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, [\"torch\"])
"""
__lowerCAmelCase = create_dummy_files({"""torch""": ["""CONSTANT""", """function""", """FakeClass"""]} )
self.assertEqual(dummy_files["""torch"""],__SCREAMING_SNAKE_CASE )
| 689 | 1 |
'''simple docstring'''
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
from ...utils.dataclasses import (
ComputeEnvironment,
DistributedType,
DynamoBackend,
PrecisionType,
SageMakerDistributedType,
)
from ..menu import BulletMenu
_a : Any = [
"""EAGER""",
"""AOT_EAGER""",
"""INDUCTOR""",
"""NVFUSER""",
"""AOT_NVFUSER""",
"""AOT_CUDAGRAPHS""",
"""OFI""",
"""FX2TRT""",
"""ONNXRT""",
"""IPEX""",
]
def _lowerCAmelCase ( lowercase , lowercase=None , lowercase=None , lowercase=None ) -> List[str]:
__lowerCAmelCase = True
while ask_again:
__lowerCAmelCase = input(lowercase )
try:
if default is not None and len(lowercase ) == 0:
return default
return convert_value(lowercase ) if convert_value is not None else result
except Exception:
if error_message is not None:
print(lowercase )
def _lowerCAmelCase ( lowercase , lowercase=[] , lowercase=None , lowercase=0 ) -> str:
__lowerCAmelCase = BulletMenu(lowercase , lowercase )
__lowerCAmelCase = menu.run(default_choice=lowercase )
return convert_value(lowercase ) if convert_value is not None else result
def _lowerCAmelCase ( lowercase ) -> List[str]:
__lowerCAmelCase = int(lowercase )
return ComputeEnvironment(["""LOCAL_MACHINE""", """AMAZON_SAGEMAKER"""][value] )
def _lowerCAmelCase ( lowercase ) -> List[str]:
__lowerCAmelCase = int(lowercase )
return DistributedType(["""NO""", """MULTI_CPU""", """MULTI_XPU""", """MULTI_GPU""", """MULTI_NPU""", """TPU"""][value] )
def _lowerCAmelCase ( lowercase ) -> Dict:
__lowerCAmelCase = int(lowercase )
return DynamoBackend(DYNAMO_BACKENDS[value] ).value
def _lowerCAmelCase ( lowercase ) -> List[Any]:
__lowerCAmelCase = int(lowercase )
return PrecisionType(["""no""", """fp16""", """bf16""", """fp8"""][value] )
def _lowerCAmelCase ( lowercase ) -> Any:
__lowerCAmelCase = int(lowercase )
return SageMakerDistributedType(["""NO""", """DATA_PARALLEL""", """MODEL_PARALLEL"""][value] )
def _lowerCAmelCase ( lowercase ) -> Optional[int]:
return {"yes": True, "no": False}[value.lower()]
class _UpperCAmelCase ( argparse.RawDescriptionHelpFormatter ):
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase = super()._format_usage(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = usage.replace("""<command> [<args>] ""","""""" )
return usage
| 689 |
'''simple docstring'''
def _lowerCAmelCase ( lowercase ) -> tuple[int, int]:
try:
__lowerCAmelCase = float(lowercase )
except ValueError:
raise ValueError("""Please enter a valid number""" )
__lowerCAmelCase = decimal - int(lowercase )
if fractional_part == 0:
return int(lowercase ), 1
else:
__lowerCAmelCase = len(str(lowercase ).split(""".""" )[1] )
__lowerCAmelCase = int(decimal * (10**number_of_frac_digits) )
__lowerCAmelCase = 10**number_of_frac_digits
__lowerCAmelCase , __lowerCAmelCase = denominator, numerator
while True:
__lowerCAmelCase = dividend % divisor
if remainder == 0:
break
__lowerCAmelCase , __lowerCAmelCase = divisor, remainder
__lowerCAmelCase , __lowerCAmelCase = numerator / divisor, denominator / divisor
return int(lowercase ), int(lowercase )
if __name__ == "__main__":
print(f'{decimal_to_fraction(2) = }')
print(f'{decimal_to_fraction(89.0) = }')
print(f'{decimal_to_fraction("67") = }')
print(f'{decimal_to_fraction("45.0") = }')
print(f'{decimal_to_fraction(1.5) = }')
print(f'{decimal_to_fraction("6.25") = }')
print(f'{decimal_to_fraction("78td") = }')
| 689 | 1 |
'''simple docstring'''
import datasets
from .evaluate import evaluate
_a : str = """\
@article{hendrycks2021cuad,
title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},
author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},
journal={arXiv preprint arXiv:2103.06268},
year={2021}
}
"""
_a : Dict = """
This metric wrap the official scoring script for version 1 of the Contract
Understanding Atticus Dataset (CUAD).
Contract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510
commercial legal contracts that have been manually labeled to identify 41 categories of important
clauses that lawyers look for when reviewing contracts in connection with corporate transactions.
"""
_a : int = """
Computes CUAD scores (EM, F1, AUPR, Precision@80%Recall, and Precision@90%Recall).
Args:
predictions: List of question-answers dictionaries with the following key-values:
- 'id': id of the question-answer pair as given in the references (see below)
- 'prediction_text': list of possible texts for the answer, as a list of strings
depending on a threshold on the confidence probability of each prediction.
references: List of question-answers dictionaries with the following key-values:
- 'id': id of the question-answer pair (see above),
- 'answers': a Dict in the CUAD dataset format
{
'text': list of possible texts for the answer, as a list of strings
'answer_start': list of start positions for the answer, as a list of ints
}
Note that answer_start values are not taken into account to compute the metric.
Returns:
'exact_match': Exact match (the normalized answer exactly match the gold answer)
'f1': The F-score of predicted tokens versus the gold answer
'aupr': Area Under the Precision-Recall curve
'prec_at_80_recall': Precision at 80% recall
'prec_at_90_recall': Precision at 90% recall
Examples:
>>> predictions = [{'prediction_text': ['The seller:', 'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.'], 'id': 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties'}]
>>> references = [{'answers': {'answer_start': [143, 49], 'text': ['The seller:', 'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.']}, 'id': 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties'}]
>>> cuad_metric = datasets.load_metric(\"cuad\")
>>> results = cuad_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'exact_match': 100.0, 'f1': 100.0, 'aupr': 0.0, 'prec_at_80_recall': 1.0, 'prec_at_90_recall': 1.0}
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _UpperCAmelCase ( datasets.Metric ):
def lowerCamelCase__ ( self ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION,citation=_CITATION,inputs_description=_KWARGS_DESCRIPTION,features=datasets.Features(
{
"""predictions""": {
"""id""": datasets.Value("""string""" ),
"""prediction_text""": datasets.features.Sequence(datasets.Value("""string""" ) ),
},
"""references""": {
"""id""": datasets.Value("""string""" ),
"""answers""": datasets.features.Sequence(
{
"""text""": datasets.Value("""string""" ),
"""answer_start""": datasets.Value("""int32""" ),
} ),
},
} ),codebase_urls=["""https://www.atticusprojectai.org/cuad"""],reference_urls=["""https://www.atticusprojectai.org/cuad"""],)
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase = {prediction["""id"""]: prediction["""prediction_text"""] for prediction in predictions}
__lowerCAmelCase = [
{
"""paragraphs""": [
{
"""qas""": [
{
"""answers""": [{"""text""": answer_text} for answer_text in ref["""answers"""]["""text"""]],
"""id""": ref["""id"""],
}
for ref in references
]
}
]
}
]
__lowerCAmelCase = evaluate(dataset=__SCREAMING_SNAKE_CASE,predictions=__SCREAMING_SNAKE_CASE )
return score
| 689 |
'''simple docstring'''
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
_a : Dict = _symbol_database.Default()
_a : Union[str, Any] = _descriptor_pool.Default().AddSerializedFile(
b"""\n\x19sentencepiece_model.proto\x12\rsentencepiece\"\x80\x0c\n\x0bTrainerSpec\x12\r\n\x05input\x18\x01 \x03(\t\x12\x14\n\x0cinput_format\x18\x07 \x01(\t\x12\x14\n\x0cmodel_prefix\x18\x02 \x01(\t\x12\x41\n\nmodel_type\x18\x03 \x01(\x0e\x32$.sentencepiece.TrainerSpec.ModelType:\x07UNIGRAM\x12\x18\n\nvocab_size\x18\x04 \x01(\x05:\x04\x38\x30\x30\x30\x12\x17\n\x0f\x61\x63\x63\x65pt_language\x18\x05 \x03(\t\x12 \n\x15self_test_sample_size\x18\x06 \x01(\x05:\x01\x30\x12*\n\x1b\x65nable_differential_privacy\x18\x32 \x01(\x08:\x05\x66\x61lse\x12+\n differential_privacy_noise_level\x18\x33 \x01(\x02:\x01\x30\x12\x32\n\'differential_privacy_clipping_threshold\x18\x34 \x01(\x04:\x01\x30\x12\"\n\x12\x63haracter_coverage\x18\n \x01(\x02:\x06\x30.9995\x12\x1e\n\x13input_sentence_size\x18\x0b \x01(\x04:\x01\x30\x12$\n\x16shuffle_input_sentence\x18\x13 \x01(\x08:\x04true\x12 \n\x14mining_sentence_size\x18\x0c \x01(\x05\x42\x02\x18\x01\x12\"\n\x16training_sentence_size\x18\r \x01(\x05\x42\x02\x18\x01\x12(\n\x17seed_sentencepiece_size\x18\x0e \x01(\x05:\x07\x31\x30\x30\x30\x30\x30\x30\x12\x1e\n\x10shrinking_factor\x18\x0f \x01(\x02:\x04\x30.75\x12!\n\x13max_sentence_length\x18\x12 \x01(\x05:\x04\x34\x31\x39\x32\x12\x17\n\x0bnum_threads\x18\x10 \x01(\x05:\x02\x31\x36\x12\x1d\n\x12num_sub_iterations\x18\x11 \x01(\x05:\x01\x32\x12$\n\x18max_sentencepiece_length\x18\x14 \x01(\x05:\x02\x31\x36\x12%\n\x17split_by_unicode_script\x18\x15 \x01(\x08:\x04true\x12\x1d\n\x0fsplit_by_number\x18\x17 \x01(\x08:\x04true\x12!\n\x13split_by_whitespace\x18\x16 \x01(\x08:\x04true\x12)\n\x1atreat_whitespace_as_suffix\x18\x18 \x01(\x08:\x05\x66\x61lse\x12+\n\x1c\x61llow_whitespace_only_pieces\x18\x1a \x01(\x08:\x05\x66\x61lse\x12\x1b\n\x0csplit_digits\x18\x19 \x01(\x08:\x05\x66\x61lse\x12#\n\x19pretokenization_delimiter\x18\x35 \x01(\t:\x00\x12\x17\n\x0f\x63ontrol_symbols\x18\x1e \x03(\t\x12\x1c\n\x14user_defined_symbols\x18\x1f \x03(\t\x12\x16\n\x0erequired_chars\x18$ \x01(\t\x12\x1c\n\rbyte_fallback\x18# \x01(\x08:\x05\x66\x61lse\x12+\n\x1dvocabulary_output_piece_score\x18 \x01(\x08:\x04true\x12\x1e\n\x10hard_vocab_limit\x18! \x01(\x08:\x04true\x12\x1c\n\ruse_all_vocab\x18\" \x01(\x08:\x05\x66\x61lse\x12\x11\n\x06unk_id\x18( \x01(\x05:\x01\x30\x12\x11\n\x06\x62os_id\x18) \x01(\x05:\x01\x31\x12\x11\n\x06\x65os_id\x18* \x01(\x05:\x01\x32\x12\x12\n\x06pad_id\x18+ \x01(\x05:\x02-1\x12\x18\n\tunk_piece\x18- \x01(\t:\x05<unk>\x12\x16\n\tbos_piece\x18. \x01(\t:\x03<s>\x12\x17\n\teos_piece\x18/ \x01(\t:\x04</s>\x12\x18\n\tpad_piece\x18\x30 \x01(\t:\x05<pad>\x12\x1a\n\x0bunk_surface\x18, \x01(\t:\x05 \xe2\x81\x87 \x12+\n\x1ctrain_extremely_large_corpus\x18\x31 \x01(\x08:\x05\x66\x61lse\"5\n\tModelType\x12\x0b\n\x07UNIGRAM\x10\x01\x12\x07\n\x03\x42PE\x10\x02\x12\x08\n\x04WORD\x10\x03\x12\x08\n\x04\x43HAR\x10\x04*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"\xd1\x01\n\x0eNormalizerSpec\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x1c\n\x14precompiled_charsmap\x18\x02 \x01(\x0c\x12\x1e\n\x10\x61\x64\x64_dummy_prefix\x18\x03 \x01(\x08:\x04true\x12&\n\x18remove_extra_whitespaces\x18\x04 \x01(\x08:\x04true\x12 \n\x12\x65scape_whitespaces\x18\x05 \x01(\x08:\x04true\x12\x1e\n\x16normalization_rule_tsv\x18\x06 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"y\n\x0cSelfTestData\x12\x33\n\x07samples\x18\x01 \x03(\x0b\x32\".sentencepiece.SelfTestData.Sample\x1a)\n\x06Sample\x12\r\n\x05input\x18\x01 \x01(\t\x12\x10\n\x08\x65xpected\x18\x02 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"\xfe\x03\n\nModelProto\x12\x37\n\x06pieces\x18\x01 \x03(\x0b\x32\'.sentencepiece.ModelProto.SentencePiece\x12\x30\n\x0ctrainer_spec\x18\x02 \x01(\x0b\x32\x1a.sentencepiece.TrainerSpec\x12\x36\n\x0fnormalizer_spec\x18\x03 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x12\x33\n\x0eself_test_data\x18\x04 \x01(\x0b\x32\x1b.sentencepiece.SelfTestData\x12\x38\n\x11\x64\x65normalizer_spec\x18\x05 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x1a\xd2\x01\n\rSentencePiece\x12\r\n\x05piece\x18\x01 \x01(\t\x12\r\n\x05score\x18\x02 \x01(\x02\x12\x42\n\x04type\x18\x03 \x01(\x0e\x32,.sentencepiece.ModelProto.SentencePiece.Type:\x06NORMAL\"T\n\x04Type\x12\n\n\x06NORMAL\x10\x01\x12\x0b\n\x07UNKNOWN\x10\x02\x12\x0b\n\x07\x43ONTROL\x10\x03\x12\x10\n\x0cUSER_DEFINED\x10\x04\x12\x08\n\x04\x42YTE\x10\x06\x12\n\n\x06UNUSED\x10\x05*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\x42\x02H\x03"""
)
_a : str = globals()
_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, """sentencepiece_model_pb2""", _globals)
if _descriptor._USE_C_DESCRIPTORS is False:
_a : str = None
_a : Union[str, Any] = b"""H\003"""
# (generated by protobuf compiler, but `_TRAINERSPEC` is not defined)
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._serialized_options = b"\030\001"
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._serialized_options = b"\030\001"
_a : Optional[int] = 4_5
_a : List[Any] = 1_5_8_1
_a : str = 1_5_1_7
_a : Optional[Any] = 1_5_7_0
_a : List[str] = 1_5_8_4
_a : List[Any] = 1_7_9_3
_a : Union[str, Any] = 1_7_9_5
_a : Tuple = 1_9_1_6
_a : List[Any] = 1_8_6_4
_a : Any = 1_9_0_5
_a : Optional[Any] = 1_9_1_9
_a : Optional[int] = 2_4_2_9
_a : Tuple = 2_2_0_8
_a : Optional[Any] = 2_4_1_8
_a : List[Any] = 2_3_2_3
_a : str = 2_4_0_7
# @@protoc_insertion_point(module_scope)
| 689 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class _UpperCAmelCase :
a : int
a : TreeNode | None =None
a : TreeNode | None =None
_a : List[str] = namedtuple("""CoinsDistribResult""", """moves excess""")
def _lowerCAmelCase ( lowercase ) -> int:
if root is None:
return 0
# Validation
def count_nodes(lowercase ) -> int:
if node is None:
return 0
return count_nodes(node.left ) + count_nodes(node.right ) + 1
def count_coins(lowercase ) -> int:
if node is None:
return 0
return count_coins(node.left ) + count_coins(node.right ) + node.data
if count_nodes(lowercase ) != count_coins(lowercase ):
raise ValueError("""The nodes number should be same as the number of coins""" )
# Main calculation
def get_distrib(lowercase ) -> CoinsDistribResult:
if node is None:
return CoinsDistribResult(0 , 1 )
__lowerCAmelCase , __lowerCAmelCase = get_distrib(node.left )
__lowerCAmelCase , __lowerCAmelCase = get_distrib(node.right )
__lowerCAmelCase = 1 - left_distrib_excess
__lowerCAmelCase = 1 - right_distrib_excess
__lowerCAmelCase = (
left_distrib_moves
+ right_distrib_moves
+ abs(lowercase )
+ abs(lowercase )
)
__lowerCAmelCase = node.data - coins_to_left - coins_to_right
return CoinsDistribResult(lowercase , lowercase )
return get_distrib(lowercase )[0]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 689 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block
@dataclass
class _UpperCAmelCase ( lowerCAmelCase_ ):
a : torch.FloatTensor
class _UpperCAmelCase ( nn.Module ):
def __init__( self,__SCREAMING_SNAKE_CASE=3,__SCREAMING_SNAKE_CASE=3,__SCREAMING_SNAKE_CASE=("DownEncoderBlock2D",),__SCREAMING_SNAKE_CASE=(64,),__SCREAMING_SNAKE_CASE=2,__SCREAMING_SNAKE_CASE=32,__SCREAMING_SNAKE_CASE="silu",__SCREAMING_SNAKE_CASE=True,):
'''simple docstring'''
super().__init__()
__lowerCAmelCase = layers_per_block
__lowerCAmelCase = torch.nn.Convad(
__SCREAMING_SNAKE_CASE,block_out_channels[0],kernel_size=3,stride=1,padding=1,)
__lowerCAmelCase = None
__lowerCAmelCase = nn.ModuleList([] )
# down
__lowerCAmelCase = block_out_channels[0]
for i, down_block_type in enumerate(__SCREAMING_SNAKE_CASE ):
__lowerCAmelCase = output_channel
__lowerCAmelCase = block_out_channels[i]
__lowerCAmelCase = i == len(__SCREAMING_SNAKE_CASE ) - 1
__lowerCAmelCase = get_down_block(
__SCREAMING_SNAKE_CASE,num_layers=self.layers_per_block,in_channels=__SCREAMING_SNAKE_CASE,out_channels=__SCREAMING_SNAKE_CASE,add_downsample=not is_final_block,resnet_eps=1e-6,downsample_padding=0,resnet_act_fn=__SCREAMING_SNAKE_CASE,resnet_groups=__SCREAMING_SNAKE_CASE,attention_head_dim=__SCREAMING_SNAKE_CASE,temb_channels=__SCREAMING_SNAKE_CASE,)
self.down_blocks.append(__SCREAMING_SNAKE_CASE )
# mid
__lowerCAmelCase = UNetMidBlockaD(
in_channels=block_out_channels[-1],resnet_eps=1e-6,resnet_act_fn=__SCREAMING_SNAKE_CASE,output_scale_factor=1,resnet_time_scale_shift="""default""",attention_head_dim=block_out_channels[-1],resnet_groups=__SCREAMING_SNAKE_CASE,temb_channels=__SCREAMING_SNAKE_CASE,)
# out
__lowerCAmelCase = nn.GroupNorm(num_channels=block_out_channels[-1],num_groups=__SCREAMING_SNAKE_CASE,eps=1e-6 )
__lowerCAmelCase = nn.SiLU()
__lowerCAmelCase = 2 * out_channels if double_z else out_channels
__lowerCAmelCase = nn.Convad(block_out_channels[-1],__SCREAMING_SNAKE_CASE,3,padding=1 )
__lowerCAmelCase = False
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase = x
__lowerCAmelCase = self.conv_in(__SCREAMING_SNAKE_CASE )
if self.training and self.gradient_checkpointing:
def create_custom_forward(__SCREAMING_SNAKE_CASE ):
def custom_forward(*__SCREAMING_SNAKE_CASE ):
return module(*__SCREAMING_SNAKE_CASE )
return custom_forward
# down
if is_torch_version(""">=""","""1.11.0""" ):
for down_block in self.down_blocks:
__lowerCAmelCase = torch.utils.checkpoint.checkpoint(
create_custom_forward(__SCREAMING_SNAKE_CASE ),__SCREAMING_SNAKE_CASE,use_reentrant=__SCREAMING_SNAKE_CASE )
# middle
__lowerCAmelCase = torch.utils.checkpoint.checkpoint(
create_custom_forward(self.mid_block ),__SCREAMING_SNAKE_CASE,use_reentrant=__SCREAMING_SNAKE_CASE )
else:
for down_block in self.down_blocks:
__lowerCAmelCase = torch.utils.checkpoint.checkpoint(create_custom_forward(__SCREAMING_SNAKE_CASE ),__SCREAMING_SNAKE_CASE )
# middle
__lowerCAmelCase = torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block ),__SCREAMING_SNAKE_CASE )
else:
# down
for down_block in self.down_blocks:
__lowerCAmelCase = down_block(__SCREAMING_SNAKE_CASE )
# middle
__lowerCAmelCase = self.mid_block(__SCREAMING_SNAKE_CASE )
# post-process
__lowerCAmelCase = self.conv_norm_out(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = self.conv_act(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = self.conv_out(__SCREAMING_SNAKE_CASE )
return sample
class _UpperCAmelCase ( nn.Module ):
def __init__( self,__SCREAMING_SNAKE_CASE=3,__SCREAMING_SNAKE_CASE=3,__SCREAMING_SNAKE_CASE=("UpDecoderBlock2D",),__SCREAMING_SNAKE_CASE=(64,),__SCREAMING_SNAKE_CASE=2,__SCREAMING_SNAKE_CASE=32,__SCREAMING_SNAKE_CASE="silu",__SCREAMING_SNAKE_CASE="group",):
'''simple docstring'''
super().__init__()
__lowerCAmelCase = layers_per_block
__lowerCAmelCase = nn.Convad(
__SCREAMING_SNAKE_CASE,block_out_channels[-1],kernel_size=3,stride=1,padding=1,)
__lowerCAmelCase = None
__lowerCAmelCase = nn.ModuleList([] )
__lowerCAmelCase = in_channels if norm_type == """spatial""" else None
# mid
__lowerCAmelCase = UNetMidBlockaD(
in_channels=block_out_channels[-1],resnet_eps=1e-6,resnet_act_fn=__SCREAMING_SNAKE_CASE,output_scale_factor=1,resnet_time_scale_shift="""default""" if norm_type == """group""" else norm_type,attention_head_dim=block_out_channels[-1],resnet_groups=__SCREAMING_SNAKE_CASE,temb_channels=__SCREAMING_SNAKE_CASE,)
# up
__lowerCAmelCase = list(reversed(__SCREAMING_SNAKE_CASE ) )
__lowerCAmelCase = reversed_block_out_channels[0]
for i, up_block_type in enumerate(__SCREAMING_SNAKE_CASE ):
__lowerCAmelCase = output_channel
__lowerCAmelCase = reversed_block_out_channels[i]
__lowerCAmelCase = i == len(__SCREAMING_SNAKE_CASE ) - 1
__lowerCAmelCase = get_up_block(
__SCREAMING_SNAKE_CASE,num_layers=self.layers_per_block + 1,in_channels=__SCREAMING_SNAKE_CASE,out_channels=__SCREAMING_SNAKE_CASE,prev_output_channel=__SCREAMING_SNAKE_CASE,add_upsample=not is_final_block,resnet_eps=1e-6,resnet_act_fn=__SCREAMING_SNAKE_CASE,resnet_groups=__SCREAMING_SNAKE_CASE,attention_head_dim=__SCREAMING_SNAKE_CASE,temb_channels=__SCREAMING_SNAKE_CASE,resnet_time_scale_shift=__SCREAMING_SNAKE_CASE,)
self.up_blocks.append(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = output_channel
# out
if norm_type == "spatial":
__lowerCAmelCase = SpatialNorm(block_out_channels[0],__SCREAMING_SNAKE_CASE )
else:
__lowerCAmelCase = nn.GroupNorm(num_channels=block_out_channels[0],num_groups=__SCREAMING_SNAKE_CASE,eps=1e-6 )
__lowerCAmelCase = nn.SiLU()
__lowerCAmelCase = nn.Convad(block_out_channels[0],__SCREAMING_SNAKE_CASE,3,padding=1 )
__lowerCAmelCase = False
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=None ):
'''simple docstring'''
__lowerCAmelCase = z
__lowerCAmelCase = self.conv_in(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = next(iter(self.up_blocks.parameters() ) ).dtype
if self.training and self.gradient_checkpointing:
def create_custom_forward(__SCREAMING_SNAKE_CASE ):
def custom_forward(*__SCREAMING_SNAKE_CASE ):
return module(*__SCREAMING_SNAKE_CASE )
return custom_forward
if is_torch_version(""">=""","""1.11.0""" ):
# middle
__lowerCAmelCase = torch.utils.checkpoint.checkpoint(
create_custom_forward(self.mid_block ),__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,use_reentrant=__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = sample.to(__SCREAMING_SNAKE_CASE )
# up
for up_block in self.up_blocks:
__lowerCAmelCase = torch.utils.checkpoint.checkpoint(
create_custom_forward(__SCREAMING_SNAKE_CASE ),__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,use_reentrant=__SCREAMING_SNAKE_CASE )
else:
# middle
__lowerCAmelCase = torch.utils.checkpoint.checkpoint(
create_custom_forward(self.mid_block ),__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = sample.to(__SCREAMING_SNAKE_CASE )
# up
for up_block in self.up_blocks:
__lowerCAmelCase = torch.utils.checkpoint.checkpoint(create_custom_forward(__SCREAMING_SNAKE_CASE ),__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
else:
# middle
__lowerCAmelCase = self.mid_block(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = sample.to(__SCREAMING_SNAKE_CASE )
# up
for up_block in self.up_blocks:
__lowerCAmelCase = up_block(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
# post-process
if latent_embeds is None:
__lowerCAmelCase = self.conv_norm_out(__SCREAMING_SNAKE_CASE )
else:
__lowerCAmelCase = self.conv_norm_out(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = self.conv_act(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = self.conv_out(__SCREAMING_SNAKE_CASE )
return sample
class _UpperCAmelCase ( nn.Module ):
def __init__( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE="random",__SCREAMING_SNAKE_CASE=False,__SCREAMING_SNAKE_CASE=True ):
'''simple docstring'''
super().__init__()
__lowerCAmelCase = n_e
__lowerCAmelCase = vq_embed_dim
__lowerCAmelCase = beta
__lowerCAmelCase = legacy
__lowerCAmelCase = nn.Embedding(self.n_e,self.vq_embed_dim )
self.embedding.weight.data.uniform_(-1.0 / self.n_e,1.0 / self.n_e )
__lowerCAmelCase = remap
if self.remap is not None:
self.register_buffer("""used""",torch.tensor(np.load(self.remap ) ) )
__lowerCAmelCase = self.used.shape[0]
__lowerCAmelCase = unknown_index # "random" or "extra" or integer
if self.unknown_index == "extra":
__lowerCAmelCase = self.re_embed
__lowerCAmelCase = self.re_embed + 1
print(
f'Remapping {self.n_e} indices to {self.re_embed} indices. '
f'Using {self.unknown_index} for unknown indices.' )
else:
__lowerCAmelCase = n_e
__lowerCAmelCase = sane_index_shape
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase = inds.shape
assert len(__SCREAMING_SNAKE_CASE ) > 1
__lowerCAmelCase = inds.reshape(ishape[0],-1 )
__lowerCAmelCase = self.used.to(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = (inds[:, :, None] == used[None, None, ...]).long()
__lowerCAmelCase = match.argmax(-1 )
__lowerCAmelCase = match.sum(2 ) < 1
if self.unknown_index == "random":
__lowerCAmelCase = torch.randint(0,self.re_embed,size=new[unknown].shape ).to(device=new.device )
else:
__lowerCAmelCase = self.unknown_index
return new.reshape(__SCREAMING_SNAKE_CASE )
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase = inds.shape
assert len(__SCREAMING_SNAKE_CASE ) > 1
__lowerCAmelCase = inds.reshape(ishape[0],-1 )
__lowerCAmelCase = self.used.to(__SCREAMING_SNAKE_CASE )
if self.re_embed > self.used.shape[0]: # extra token
__lowerCAmelCase = 0 # simply set to zero
__lowerCAmelCase = torch.gather(used[None, :][inds.shape[0] * [0], :],1,__SCREAMING_SNAKE_CASE )
return back.reshape(__SCREAMING_SNAKE_CASE )
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase = z.permute(0,2,3,1 ).contiguous()
__lowerCAmelCase = z.view(-1,self.vq_embed_dim )
# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
__lowerCAmelCase = torch.argmin(torch.cdist(__SCREAMING_SNAKE_CASE,self.embedding.weight ),dim=1 )
__lowerCAmelCase = self.embedding(__SCREAMING_SNAKE_CASE ).view(z.shape )
__lowerCAmelCase = None
__lowerCAmelCase = None
# compute loss for embedding
if not self.legacy:
__lowerCAmelCase = self.beta * torch.mean((z_q.detach() - z) ** 2 ) + torch.mean((z_q - z.detach()) ** 2 )
else:
__lowerCAmelCase = torch.mean((z_q.detach() - z) ** 2 ) + self.beta * torch.mean((z_q - z.detach()) ** 2 )
# preserve gradients
__lowerCAmelCase = z + (z_q - z).detach()
# reshape back to match original input shape
__lowerCAmelCase = z_q.permute(0,3,1,2 ).contiguous()
if self.remap is not None:
__lowerCAmelCase = min_encoding_indices.reshape(z.shape[0],-1 ) # add batch axis
__lowerCAmelCase = self.remap_to_used(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = min_encoding_indices.reshape(-1,1 ) # flatten
if self.sane_index_shape:
__lowerCAmelCase = min_encoding_indices.reshape(z_q.shape[0],z_q.shape[2],z_q.shape[3] )
return z_q, loss, (perplexity, min_encodings, min_encoding_indices)
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
if self.remap is not None:
__lowerCAmelCase = indices.reshape(shape[0],-1 ) # add batch axis
__lowerCAmelCase = self.unmap_to_all(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = indices.reshape(-1 ) # flatten again
# get quantized latent vectors
__lowerCAmelCase = self.embedding(__SCREAMING_SNAKE_CASE )
if shape is not None:
__lowerCAmelCase = z_q.view(__SCREAMING_SNAKE_CASE )
# reshape back to match original input shape
__lowerCAmelCase = z_q.permute(0,3,1,2 ).contiguous()
return z_q
class _UpperCAmelCase ( lowerCAmelCase_ ):
def __init__( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=False ):
'''simple docstring'''
__lowerCAmelCase = parameters
__lowerCAmelCase , __lowerCAmelCase = torch.chunk(__SCREAMING_SNAKE_CASE,2,dim=1 )
__lowerCAmelCase = torch.clamp(self.logvar,-30.0,20.0 )
__lowerCAmelCase = deterministic
__lowerCAmelCase = torch.exp(0.5 * self.logvar )
__lowerCAmelCase = torch.exp(self.logvar )
if self.deterministic:
__lowerCAmelCase = __lowerCAmelCase = torch.zeros_like(
self.mean,device=self.parameters.device,dtype=self.parameters.dtype )
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE = None ):
'''simple docstring'''
__lowerCAmelCase = randn_tensor(
self.mean.shape,generator=__SCREAMING_SNAKE_CASE,device=self.parameters.device,dtype=self.parameters.dtype )
__lowerCAmelCase = self.mean + self.std * sample
return x
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE=None ):
'''simple docstring'''
if self.deterministic:
return torch.Tensor([0.0] )
else:
if other is None:
return 0.5 * torch.sum(torch.pow(self.mean,2 ) + self.var - 1.0 - self.logvar,dim=[1, 2, 3] )
else:
return 0.5 * torch.sum(
torch.pow(self.mean - other.mean,2 ) / other.var
+ self.var / other.var
- 1.0
- self.logvar
+ other.logvar,dim=[1, 2, 3],)
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=[1, 2, 3] ):
'''simple docstring'''
if self.deterministic:
return torch.Tensor([0.0] )
__lowerCAmelCase = np.log(2.0 * np.pi )
return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean,2 ) / self.var,dim=__SCREAMING_SNAKE_CASE )
def lowerCamelCase__ ( self ):
'''simple docstring'''
return self.mean
| 689 | 1 |
'''simple docstring'''
def _lowerCAmelCase ( lowercase , lowercase ) -> float:
return price * (1 + tax_rate)
if __name__ == "__main__":
print(f'{price_plus_tax(1_0_0, 0.25) = }')
print(f'{price_plus_tax(125.50, 0.05) = }')
| 689 |
'''simple docstring'''
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
_a : Optional[int] = logging.get_logger(__name__)
_a : int = list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
_a : str = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class _UpperCAmelCase :
a : str =field(
default=lowerCAmelCase_ , metadata={"""help""": """Model type selected in the list: """ + """, """.join(lowerCAmelCase_ )} )
a : str =field(
default=lowerCAmelCase_ , metadata={"""help""": """The input data dir. Should contain the .json files for the SQuAD task."""} )
a : int =field(
default=1_28 , metadata={
"""help""": (
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
)
} , )
a : int =field(
default=1_28 , metadata={"""help""": """When splitting up a long document into chunks, how much stride to take between chunks."""} , )
a : int =field(
default=64 , metadata={
"""help""": (
"""The maximum number of tokens for the question. Questions longer than this will """
"""be truncated to this length."""
)
} , )
a : int =field(
default=30 , metadata={
"""help""": (
"""The maximum length of an answer that can be generated. This is needed because the start """
"""and end predictions are not conditioned on one another."""
)
} , )
a : bool =field(
default=lowerCAmelCase_ , metadata={"""help""": """Overwrite the cached training and evaluation sets"""} )
a : bool =field(
default=lowerCAmelCase_ , metadata={"""help""": """If true, the SQuAD examples contain some that do not have an answer."""} )
a : float =field(
default=0.0 , metadata={"""help""": """If null_score - best_non_null is greater than the threshold predict null."""} )
a : int =field(
default=20 , metadata={"""help""": """If null_score - best_non_null is greater than the threshold predict null."""} )
a : int =field(
default=0 , metadata={
"""help""": (
"""language id of input for language-specific xlm models (see"""
""" tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)"""
)
} , )
a : int =field(default=1 , metadata={"""help""": """multiple threads for converting example to features"""} )
class _UpperCAmelCase ( lowerCAmelCase_ ):
a : Optional[Any] ="""train"""
a : Optional[int] ="""dev"""
class _UpperCAmelCase ( lowerCAmelCase_ ):
a : SquadDataTrainingArguments
a : List[SquadFeatures]
a : Split
a : bool
def __init__( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = Split.train,__SCREAMING_SNAKE_CASE = False,__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = "pt",):
'''simple docstring'''
__lowerCAmelCase = args
__lowerCAmelCase = is_language_sensitive
__lowerCAmelCase = SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
if isinstance(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE ):
try:
__lowerCAmelCase = Split[mode]
except KeyError:
raise KeyError("""mode is not a valid split name""" )
__lowerCAmelCase = mode
# Load data features from cache or dataset file
__lowerCAmelCase = """v2""" if args.version_2_with_negative else """v1"""
__lowerCAmelCase = os.path.join(
cache_dir if cache_dir is not None else args.data_dir,f'cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{version_tag}',)
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
__lowerCAmelCase = cached_features_file + """.lock"""
with FileLock(__SCREAMING_SNAKE_CASE ):
if os.path.exists(__SCREAMING_SNAKE_CASE ) and not args.overwrite_cache:
__lowerCAmelCase = time.time()
__lowerCAmelCase = torch.load(__SCREAMING_SNAKE_CASE )
# Legacy cache files have only features, while new cache files
# will have dataset and examples also.
__lowerCAmelCase = self.old_features["""features"""]
__lowerCAmelCase = self.old_features.get("""dataset""",__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = self.old_features.get("""examples""",__SCREAMING_SNAKE_CASE )
logger.info(
f'Loading features from cached file {cached_features_file} [took %.3f s]',time.time() - start )
if self.dataset is None or self.examples is None:
logger.warning(
f'Deleting cached file {cached_features_file} will allow dataset and examples to be cached in'
""" future run""" )
else:
if mode == Split.dev:
__lowerCAmelCase = self.processor.get_dev_examples(args.data_dir )
else:
__lowerCAmelCase = self.processor.get_train_examples(args.data_dir )
__lowerCAmelCase , __lowerCAmelCase = squad_convert_examples_to_features(
examples=self.examples,tokenizer=__SCREAMING_SNAKE_CASE,max_seq_length=args.max_seq_length,doc_stride=args.doc_stride,max_query_length=args.max_query_length,is_training=mode == Split.train,threads=args.threads,return_dataset=__SCREAMING_SNAKE_CASE,)
__lowerCAmelCase = time.time()
torch.save(
{"""features""": self.features, """dataset""": self.dataset, """examples""": self.examples},__SCREAMING_SNAKE_CASE,)
# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
logger.info(
f'Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]' )
def __len__( self ):
'''simple docstring'''
return len(self.features )
def __getitem__( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase = self.features[i]
__lowerCAmelCase = torch.tensor(feature.input_ids,dtype=torch.long )
__lowerCAmelCase = torch.tensor(feature.attention_mask,dtype=torch.long )
__lowerCAmelCase = torch.tensor(feature.token_type_ids,dtype=torch.long )
__lowerCAmelCase = torch.tensor(feature.cls_index,dtype=torch.long )
__lowerCAmelCase = torch.tensor(feature.p_mask,dtype=torch.float )
__lowerCAmelCase = torch.tensor(feature.is_impossible,dtype=torch.float )
__lowerCAmelCase = {
"""input_ids""": input_ids,
"""attention_mask""": attention_mask,
"""token_type_ids""": token_type_ids,
}
if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
del inputs["token_type_ids"]
if self.args.model_type in ["xlnet", "xlm"]:
inputs.update({"""cls_index""": cls_index, """p_mask""": p_mask} )
if self.args.version_2_with_negative:
inputs.update({"""is_impossible""": is_impossible} )
if self.is_language_sensitive:
inputs.update({"""langs""": (torch.ones(input_ids.shape,dtype=torch.intaa ) * self.args.lang_id)} )
if self.mode == Split.train:
__lowerCAmelCase = torch.tensor(feature.start_position,dtype=torch.long )
__lowerCAmelCase = torch.tensor(feature.end_position,dtype=torch.long )
inputs.update({"""start_positions""": start_positions, """end_positions""": end_positions} )
return inputs
| 689 | 1 |
'''simple docstring'''
from math import isclose, sqrt
def _lowerCAmelCase ( lowercase , lowercase , lowercase ) -> tuple[float, float, float]:
__lowerCAmelCase = point_y / 4 / point_x
__lowerCAmelCase = 2 * normal_gradient / (1 + normal_gradient * normal_gradient)
__lowerCAmelCase = (1 - normal_gradient * normal_gradient) / (
1 + normal_gradient * normal_gradient
)
__lowerCAmelCase = (sa - ca * incoming_gradient) / (ca + sa * incoming_gradient)
# to find the next point, solve the simultaeneous equations:
# y^2 + 4x^2 = 100
# y - b = m * (x - a)
# ==> A x^2 + B x + C = 0
__lowerCAmelCase = outgoing_gradient**2 + 4
__lowerCAmelCase = 2 * outgoing_gradient * (point_y - outgoing_gradient * point_x)
__lowerCAmelCase = (point_y - outgoing_gradient * point_x) ** 2 - 100
__lowerCAmelCase = (
-linear_term - sqrt(linear_term**2 - 4 * quadratic_term * constant_term )
) / (2 * quadratic_term)
__lowerCAmelCase = (
-linear_term + sqrt(linear_term**2 - 4 * quadratic_term * constant_term )
) / (2 * quadratic_term)
# two solutions, one of which is our input point
__lowerCAmelCase = x_minus if isclose(lowercase , lowercase ) else x_plus
__lowerCAmelCase = point_y + outgoing_gradient * (next_x - point_x)
return next_x, next_y, outgoing_gradient
def _lowerCAmelCase ( lowercase = 1.4 , lowercase = -9.6 ) -> int:
__lowerCAmelCase = 0
__lowerCAmelCase = first_x_coord
__lowerCAmelCase = first_y_coord
__lowerCAmelCase = (10.1 - point_y) / (0.0 - point_x)
while not (-0.01 <= point_x <= 0.01 and point_y > 0):
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = next_point(lowercase , lowercase , lowercase )
num_reflections += 1
return num_reflections
if __name__ == "__main__":
print(f'{solution() = }')
| 689 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def _lowerCAmelCase ( lowercase ) -> Optional[Any]:
# vision encoder
if "img_encoder.pos_embed" in name:
__lowerCAmelCase = name.replace("""img_encoder.pos_embed""" , """vision_model.embeddings.position_embeddings""" )
if "img_encoder.patch_embed.proj" in name:
__lowerCAmelCase = name.replace("""img_encoder.patch_embed.proj""" , """vision_model.embeddings.patch_embeddings.projection""" )
if "img_encoder.patch_embed.norm" in name:
__lowerCAmelCase = name.replace("""img_encoder.patch_embed.norm""" , """vision_model.embeddings.layernorm""" )
if "img_encoder.layers" in name:
__lowerCAmelCase = name.replace("""img_encoder.layers""" , """vision_model.encoder.stages""" )
if "blocks" in name and "res" not in name:
__lowerCAmelCase = name.replace("""blocks""" , """layers""" )
if "attn" in name and "pre_assign" not in name:
__lowerCAmelCase = name.replace("""attn""" , """self_attn""" )
if "proj" in name and "self_attn" in name and "text" not in name:
__lowerCAmelCase = name.replace("""proj""" , """out_proj""" )
if "pre_assign_attn.attn.proj" in name:
__lowerCAmelCase = name.replace("""pre_assign_attn.attn.proj""" , """pre_assign_attn.attn.out_proj""" )
if "norm1" in name:
__lowerCAmelCase = name.replace("""norm1""" , """layer_norm1""" )
if "norm2" in name and "pre_assign" not in name:
__lowerCAmelCase = name.replace("""norm2""" , """layer_norm2""" )
if "img_encoder.norm" in name:
__lowerCAmelCase = name.replace("""img_encoder.norm""" , """vision_model.layernorm""" )
# text encoder
if "text_encoder.token_embedding" in name:
__lowerCAmelCase = name.replace("""text_encoder.token_embedding""" , """text_model.embeddings.token_embedding""" )
if "text_encoder.positional_embedding" in name:
__lowerCAmelCase = name.replace("""text_encoder.positional_embedding""" , """text_model.embeddings.position_embedding.weight""" )
if "text_encoder.transformer.resblocks." in name:
__lowerCAmelCase = name.replace("""text_encoder.transformer.resblocks.""" , """text_model.encoder.layers.""" )
if "ln_1" in name:
__lowerCAmelCase = name.replace("""ln_1""" , """layer_norm1""" )
if "ln_2" in name:
__lowerCAmelCase = name.replace("""ln_2""" , """layer_norm2""" )
if "c_fc" in name:
__lowerCAmelCase = name.replace("""c_fc""" , """fc1""" )
if "c_proj" in name:
__lowerCAmelCase = name.replace("""c_proj""" , """fc2""" )
if "text_encoder" in name:
__lowerCAmelCase = name.replace("""text_encoder""" , """text_model""" )
if "ln_final" in name:
__lowerCAmelCase = name.replace("""ln_final""" , """final_layer_norm""" )
# projection layers
if "img_projector.linear_hidden." in name:
__lowerCAmelCase = name.replace("""img_projector.linear_hidden.""" , """visual_projection.""" )
if "img_projector.linear_out." in name:
__lowerCAmelCase = name.replace("""img_projector.linear_out.""" , """visual_projection.3.""" )
if "text_projector.linear_hidden" in name:
__lowerCAmelCase = name.replace("""text_projector.linear_hidden""" , """text_projection""" )
if "text_projector.linear_out" in name:
__lowerCAmelCase = name.replace("""text_projector.linear_out""" , """text_projection.3""" )
return name
def _lowerCAmelCase ( lowercase , lowercase ) -> Dict:
for key in orig_state_dict.copy().keys():
__lowerCAmelCase = orig_state_dict.pop(lowercase )
if "qkv" in key:
# weights and biases of the key, value and query projections of vision encoder's attention layers require special treatment:
# we need to split them up into separate matrices/vectors
__lowerCAmelCase = key.split(""".""" )
__lowerCAmelCase , __lowerCAmelCase = int(key_split[2] ), int(key_split[4] )
__lowerCAmelCase = config.vision_config.hidden_size
if "weight" in key:
__lowerCAmelCase = val[:dim, :]
__lowerCAmelCase = val[dim : dim * 2, :]
__lowerCAmelCase = val[-dim:, :]
else:
__lowerCAmelCase = val[:dim]
__lowerCAmelCase = val[dim : dim * 2]
__lowerCAmelCase = val[-dim:]
elif "in_proj" in key:
# weights and biases of the key, value and query projections of text encoder's attention layers require special treatment:
# we need to split them up into separate matrices/vectors
__lowerCAmelCase = key.split(""".""" )
__lowerCAmelCase = int(key_split[3] )
__lowerCAmelCase = config.text_config.hidden_size
if "weight" in key:
__lowerCAmelCase = val[:dim, :]
__lowerCAmelCase = val[
dim : dim * 2, :
]
__lowerCAmelCase = val[-dim:, :]
else:
__lowerCAmelCase = val[:dim]
__lowerCAmelCase = val[dim : dim * 2]
__lowerCAmelCase = val[-dim:]
else:
__lowerCAmelCase = rename_key(lowercase )
# squeeze if necessary
if (
"text_projection.0" in new_name
or "text_projection.3" in new_name
or "visual_projection.0" in new_name
or "visual_projection.3" in new_name
):
__lowerCAmelCase = val.squeeze_()
else:
__lowerCAmelCase = val
return orig_state_dict
def _lowerCAmelCase ( ) -> str:
__lowerCAmelCase = """http://images.cocodataset.org/val2017/000000039769.jpg"""
__lowerCAmelCase = Image.open(requests.get(lowercase , stream=lowercase ).raw )
return im
@torch.no_grad()
def _lowerCAmelCase ( lowercase , lowercase , lowercase="groupvit-gcc-yfcc" , lowercase=False ) -> List[Any]:
__lowerCAmelCase = GroupViTConfig()
__lowerCAmelCase = GroupViTModel(lowercase ).eval()
__lowerCAmelCase = torch.load(lowercase , map_location="""cpu""" )["""model"""]
__lowerCAmelCase = convert_state_dict(lowercase , lowercase )
__lowerCAmelCase , __lowerCAmelCase = model.load_state_dict(lowercase , strict=lowercase )
assert missing_keys == ["text_model.embeddings.position_ids"]
assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(lowercase ) == 0)
# verify result
__lowerCAmelCase = CLIPProcessor.from_pretrained("""openai/clip-vit-base-patch32""" )
__lowerCAmelCase = prepare_img()
__lowerCAmelCase = processor(text=["""a photo of a cat""", """a photo of a dog"""] , images=lowercase , padding=lowercase , return_tensors="""pt""" )
with torch.no_grad():
__lowerCAmelCase = model(**lowercase )
if model_name == "groupvit-gcc-yfcc":
__lowerCAmelCase = torch.tensor([[13.35_23, 6.36_29]] )
elif model_name == "groupvit-gcc-redcaps":
__lowerCAmelCase = torch.tensor([[16.18_73, 8.62_30]] )
else:
raise ValueError(f'Model name {model_name} not supported.' )
assert torch.allclose(outputs.logits_per_image , lowercase , atol=1e-3 )
processor.save_pretrained(lowercase )
model.save_pretrained(lowercase )
print("""Successfully saved processor and model to""" , lowercase )
if push_to_hub:
print("""Pushing to the hub...""" )
processor.push_to_hub(lowercase , organization="""nielsr""" )
model.push_to_hub(lowercase , organization="""nielsr""" )
if __name__ == "__main__":
_a : int = argparse.ArgumentParser()
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to dump the processor and PyTorch model."""
)
parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to GroupViT checkpoint""")
parser.add_argument(
"""--model_name""",
default="""groupvit-gccy-fcc""",
type=str,
help="""Name of the model. Expecting either 'groupvit-gcc-yfcc' or 'groupvit-gcc-redcaps'""",
)
parser.add_argument(
"""--push_to_hub""",
action="""store_true""",
help="""Whether or not to push the converted model and processor to the 🤗 hub using the provided `model_name`.""",
)
_a : List[str] = parser.parse_args()
convert_groupvit_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 689 | 1 |
'''simple docstring'''
def _lowerCAmelCase ( lowercase ) -> int:
__lowerCAmelCase = 1
for i in range(1 , num + 1 ):
fact *= i
return fact
def _lowerCAmelCase ( lowercase ) -> int:
__lowerCAmelCase = 0
while number > 0:
__lowerCAmelCase = number % 10
sum_of_digits += last_digit
__lowerCAmelCase = number // 10 # Removing the last_digit from the given number
return sum_of_digits
def _lowerCAmelCase ( lowercase = 100 ) -> int:
__lowerCAmelCase = factorial(lowercase )
__lowerCAmelCase = split_and_add(lowercase )
return result
if __name__ == "__main__":
print(solution(int(input("""Enter the Number: """).strip())))
| 689 |
'''simple docstring'''
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGeneration,
MusicgenProcessor,
TaEncoderModel,
)
from transformers.models.musicgen.modeling_musicgen import MusicgenForCausalLM
from transformers.utils import logging
logging.set_verbosity_info()
_a : Tuple = logging.get_logger(__name__)
_a : Optional[int] = ["""model.decoder.embed_positions.weights"""]
def _lowerCAmelCase ( lowercase ) -> Optional[Any]:
if "emb" in name:
__lowerCAmelCase = name.replace("""emb""" , """model.decoder.embed_tokens""" )
if "transformer" in name:
__lowerCAmelCase = name.replace("""transformer""" , """model.decoder""" )
if "cross_attention" in name:
__lowerCAmelCase = name.replace("""cross_attention""" , """encoder_attn""" )
if "linear1" in name:
__lowerCAmelCase = name.replace("""linear1""" , """fc1""" )
if "linear2" in name:
__lowerCAmelCase = name.replace("""linear2""" , """fc2""" )
if "norm1" in name:
__lowerCAmelCase = name.replace("""norm1""" , """self_attn_layer_norm""" )
if "norm_cross" in name:
__lowerCAmelCase = name.replace("""norm_cross""" , """encoder_attn_layer_norm""" )
if "norm2" in name:
__lowerCAmelCase = name.replace("""norm2""" , """final_layer_norm""" )
if "out_norm" in name:
__lowerCAmelCase = name.replace("""out_norm""" , """model.decoder.layer_norm""" )
if "linears" in name:
__lowerCAmelCase = name.replace("""linears""" , """lm_heads""" )
if "condition_provider.conditioners.description.output_proj" in name:
__lowerCAmelCase = name.replace("""condition_provider.conditioners.description.output_proj""" , """enc_to_dec_proj""" )
return name
def _lowerCAmelCase ( lowercase , lowercase ) -> Tuple[Dict, Dict]:
__lowerCAmelCase = list(state_dict.keys() )
__lowerCAmelCase = {}
for key in keys:
__lowerCAmelCase = state_dict.pop(lowercase )
__lowerCAmelCase = rename_keys(lowercase )
if "in_proj_weight" in key:
# split fused qkv proj
__lowerCAmelCase = val[:hidden_size, :]
__lowerCAmelCase = val[hidden_size : 2 * hidden_size, :]
__lowerCAmelCase = val[-hidden_size:, :]
elif "enc_to_dec_proj" in key:
__lowerCAmelCase = val
else:
__lowerCAmelCase = val
return state_dict, enc_dec_proj_state_dict
def _lowerCAmelCase ( lowercase ) -> MusicgenDecoderConfig:
if checkpoint == "small":
# default config values
__lowerCAmelCase = 1024
__lowerCAmelCase = 24
__lowerCAmelCase = 16
elif checkpoint == "medium":
__lowerCAmelCase = 1536
__lowerCAmelCase = 48
__lowerCAmelCase = 24
elif checkpoint == "large":
__lowerCAmelCase = 2048
__lowerCAmelCase = 48
__lowerCAmelCase = 32
else:
raise ValueError(f'Checkpoint should be one of `[\'small\', \'medium\', \'large\']`, got {checkpoint}.' )
__lowerCAmelCase = MusicgenDecoderConfig(
hidden_size=lowercase , ffn_dim=hidden_size * 4 , num_hidden_layers=lowercase , num_attention_heads=lowercase , )
return config
@torch.no_grad()
def _lowerCAmelCase ( lowercase , lowercase=None , lowercase=None , lowercase="cpu" ) -> Optional[Any]:
__lowerCAmelCase = MusicGen.get_pretrained(lowercase , device=lowercase )
__lowerCAmelCase = decoder_config_from_checkpoint(lowercase )
__lowerCAmelCase = fairseq_model.lm.state_dict()
__lowerCAmelCase , __lowerCAmelCase = rename_state_dict(
lowercase , hidden_size=decoder_config.hidden_size )
__lowerCAmelCase = TaEncoderModel.from_pretrained("""t5-base""" )
__lowerCAmelCase = EncodecModel.from_pretrained("""facebook/encodec_32khz""" )
__lowerCAmelCase = MusicgenForCausalLM(lowercase ).eval()
# load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
__lowerCAmelCase , __lowerCAmelCase = decoder.load_state_dict(lowercase , strict=lowercase )
for key in missing_keys.copy():
if key.startswith(("""text_encoder""", """audio_encoder""") ) or key in EXPECTED_MISSING_KEYS:
missing_keys.remove(lowercase )
if len(lowercase ) > 0:
raise ValueError(f'Missing key(s) in state_dict: {missing_keys}' )
if len(lowercase ) > 0:
raise ValueError(f'Unexpected key(s) in state_dict: {unexpected_keys}' )
# init the composite model
__lowerCAmelCase = MusicgenForConditionalGeneration(text_encoder=lowercase , audio_encoder=lowercase , decoder=lowercase )
# load the pre-trained enc-dec projection (from the decoder state dict)
model.enc_to_dec_proj.load_state_dict(lowercase )
# check we can do a forward pass
__lowerCAmelCase = torch.arange(0 , 8 , dtype=torch.long ).reshape(2 , -1 )
__lowerCAmelCase = input_ids.reshape(2 * 4 , -1 )
with torch.no_grad():
__lowerCAmelCase = model(input_ids=lowercase , decoder_input_ids=lowercase ).logits
if logits.shape != (8, 1, 2048):
raise ValueError("""Incorrect shape for logits""" )
# now construct the processor
__lowerCAmelCase = AutoTokenizer.from_pretrained("""t5-base""" )
__lowerCAmelCase = AutoFeatureExtractor.from_pretrained("""facebook/encodec_32khz""" , padding_side="""left""" )
__lowerCAmelCase = MusicgenProcessor(feature_extractor=lowercase , tokenizer=lowercase )
# set the appropriate bos/pad token ids
__lowerCAmelCase = 2048
__lowerCAmelCase = 2048
# set other default generation config params
__lowerCAmelCase = int(30 * audio_encoder.config.frame_rate )
__lowerCAmelCase = True
__lowerCAmelCase = 3.0
if pytorch_dump_folder is not None:
Path(lowercase ).mkdir(exist_ok=lowercase )
logger.info(f'Saving model {checkpoint} to {pytorch_dump_folder}' )
model.save_pretrained(lowercase )
processor.save_pretrained(lowercase )
if repo_id:
logger.info(f'Pushing model {checkpoint} to {repo_id}' )
model.push_to_hub(lowercase )
processor.push_to_hub(lowercase )
if __name__ == "__main__":
_a : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--checkpoint""",
default="""small""",
type=str,
help="""Checkpoint size of the MusicGen model you'd like to convert. Can be one of: `['small', 'medium', 'large']`.""",
)
parser.add_argument(
"""--pytorch_dump_folder""",
required=True,
default=None,
type=str,
help="""Path to the output PyTorch model directory.""",
)
parser.add_argument(
"""--push_to_hub""", default=None, type=str, help="""Where to upload the converted model on the 🤗 hub."""
)
parser.add_argument(
"""--device""", default="""cpu""", type=str, help="""Torch device to run the conversion, either cpu or cuda."""
)
_a : List[Any] = parser.parse_args()
convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)
| 689 | 1 |
'''simple docstring'''
import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF
import datasets
_a : Tuple = """\
@inproceedings{popovic-2015-chrf,
title = \"chr{F}: character n-gram {F}-score for automatic {MT} evaluation\",
author = \"Popovi{\'c}, Maja\",
booktitle = \"Proceedings of the Tenth Workshop on Statistical Machine Translation\",
month = sep,
year = \"2015\",
address = \"Lisbon, Portugal\",
publisher = \"Association for Computational Linguistics\",
url = \"https://aclanthology.org/W15-3049\",
doi = \"10.18653/v1/W15-3049\",
pages = \"392--395\",
}
@inproceedings{popovic-2017-chrf,
title = \"chr{F}++: words helping character n-grams\",
author = \"Popovi{\'c}, Maja\",
booktitle = \"Proceedings of the Second Conference on Machine Translation\",
month = sep,
year = \"2017\",
address = \"Copenhagen, Denmark\",
publisher = \"Association for Computational Linguistics\",
url = \"https://aclanthology.org/W17-4770\",
doi = \"10.18653/v1/W17-4770\",
pages = \"612--618\",
}
@inproceedings{post-2018-call,
title = \"A Call for Clarity in Reporting {BLEU} Scores\",
author = \"Post, Matt\",
booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",
month = oct,
year = \"2018\",
address = \"Belgium, Brussels\",
publisher = \"Association for Computational Linguistics\",
url = \"https://www.aclweb.org/anthology/W18-6319\",
pages = \"186--191\",
}
"""
_a : str = """\
ChrF and ChrF++ are two MT evaluation metrics. They both use the F-score statistic for character n-gram matches,
and ChrF++ adds word n-grams as well which correlates more strongly with direct assessment. We use the implementation
that is already present in sacrebleu.
The implementation here is slightly different from sacrebleu in terms of the required input format. The length of
the references and hypotheses lists need to be the same, so you may need to transpose your references compared to
sacrebleu's required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534
See the README.md file at https://github.com/mjpost/sacreBLEU#chrf--chrf for more information.
"""
_a : Optional[int] = """
Produces ChrF(++) scores for hypotheses given reference translations.
Args:
predictions (list of str): The predicted sentences.
references (list of list of str): The references. There should be one reference sub-list for each prediction sentence.
char_order (int): Character n-gram order. Defaults to `6`.
word_order (int): Word n-gram order. If equals to `2`, the metric is referred to as chrF++. Defaults to `0`.
beta (int): Determine the importance of recall w.r.t precision. Defaults to `2`.
lowercase (bool): if `True`, enables case-insensitivity. Defaults to `False`.
whitespace (bool): If `True`, include whitespaces when extracting character n-grams.
eps_smoothing (bool): If `True`, applies epsilon smoothing similar
to reference chrF++.py, NLTK and Moses implementations. If `False`,
it takes into account effective match order similar to sacreBLEU < 2.0.0. Defaults to `False`.
Returns:
'score' (float): The chrF (chrF++) score,
'char_order' (int): The character n-gram order,
'word_order' (int): The word n-gram order. If equals to 2, the metric is referred to as chrF++,
'beta' (int): Determine the importance of recall w.r.t precision
Examples:
Example 1--a simple example of calculating chrF:
>>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]
>>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]
>>> chrf = datasets.load_metric(\"chrf\")
>>> results = chrf.compute(predictions=prediction, references=reference)
>>> print(results)
{'score': 84.64214891738334, 'char_order': 6, 'word_order': 0, 'beta': 2}
Example 2--the same example, but with the argument word_order=2, to calculate chrF++ instead of chrF:
>>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]
>>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]
>>> chrf = datasets.load_metric(\"chrf\")
>>> results = chrf.compute(predictions=prediction,
... references=reference,
... word_order=2)
>>> print(results)
{'score': 82.87263732906315, 'char_order': 6, 'word_order': 2, 'beta': 2}
Example 3--the same chrF++ example as above, but with `lowercase=True` to normalize all case:
>>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]
>>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]
>>> chrf = datasets.load_metric(\"chrf\")
>>> results = chrf.compute(predictions=prediction,
... references=reference,
... word_order=2,
... lowercase=True)
>>> print(results)
{'score': 92.12853119829202, 'char_order': 6, 'word_order': 2, 'beta': 2}
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _UpperCAmelCase ( datasets.Metric ):
def lowerCamelCase__ ( self ):
'''simple docstring'''
if version.parse(scb.__version__ ) < version.parse("""1.4.12""" ):
raise ImportWarning(
"""To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn't match this condition.\n"""
"""You can install it with `pip install \"sacrebleu>=1.4.12\"`.""" )
return datasets.MetricInfo(
description=_DESCRIPTION,citation=_CITATION,homepage="""https://github.com/mjpost/sacreBLEU#chrf--chrf""",inputs_description=_KWARGS_DESCRIPTION,features=datasets.Features(
{
"""predictions""": datasets.Value("""string""",id="""sequence""" ),
"""references""": datasets.Sequence(datasets.Value("""string""",id="""sequence""" ),id="""references""" ),
} ),codebase_urls=["""https://github.com/mjpost/sacreBLEU#chrf--chrf"""],reference_urls=[
"""https://github.com/m-popovic/chrF""",
],)
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE = CHRF.CHAR_ORDER,__SCREAMING_SNAKE_CASE = CHRF.WORD_ORDER,__SCREAMING_SNAKE_CASE = CHRF.BETA,__SCREAMING_SNAKE_CASE = False,__SCREAMING_SNAKE_CASE = False,__SCREAMING_SNAKE_CASE = False,):
'''simple docstring'''
__lowerCAmelCase = len(references[0] )
if any(len(__SCREAMING_SNAKE_CASE ) != references_per_prediction for refs in references ):
raise ValueError("""Sacrebleu requires the same number of references for each prediction""" )
__lowerCAmelCase = [[refs[i] for refs in references] for i in range(__SCREAMING_SNAKE_CASE )]
__lowerCAmelCase = CHRF(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = sb_chrf.corpus_score(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
return {
"score": output.score,
"char_order": output.char_order,
"word_order": output.word_order,
"beta": output.beta,
}
| 689 |
'''simple docstring'''
from collections import deque
def _lowerCAmelCase ( lowercase ) -> Dict:
__lowerCAmelCase = len(lowercase )
__lowerCAmelCase = deque()
__lowerCAmelCase = [False for _ in range(lowercase )]
__lowerCAmelCase = [-1 for _ in range(lowercase )]
__lowerCAmelCase = index_of[:]
def strong_connect(lowercase , lowercase , lowercase ):
__lowerCAmelCase = index # the number when this node is seen
__lowerCAmelCase = index # lowest rank node reachable from here
index += 1
stack.append(lowercase )
__lowerCAmelCase = True
for w in g[v]:
if index_of[w] == -1:
__lowerCAmelCase = strong_connect(lowercase , lowercase , lowercase )
__lowerCAmelCase = (
lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
)
elif on_stack[w]:
__lowerCAmelCase = (
lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
)
if lowlink_of[v] == index_of[v]:
__lowerCAmelCase = []
__lowerCAmelCase = stack.pop()
__lowerCAmelCase = False
component.append(lowercase )
while w != v:
__lowerCAmelCase = stack.pop()
__lowerCAmelCase = False
component.append(lowercase )
components.append(lowercase )
return index
__lowerCAmelCase = []
for v in range(lowercase ):
if index_of[v] == -1:
strong_connect(lowercase , 0 , lowercase )
return components
def _lowerCAmelCase ( lowercase , lowercase ) -> str:
__lowerCAmelCase = [[] for _ in range(lowercase )]
for u, v in edges:
g[u].append(lowercase )
return g
if __name__ == "__main__":
# Test
_a : Any = 7
_a : Tuple = [0, 0, 1, 2, 3, 3, 4, 4, 6]
_a : Optional[int] = [1, 3, 2, 0, 1, 4, 5, 6, 5]
_a : Optional[Any] = [(u, v) for u, v in zip(source, target)]
_a : Optional[int] = create_graph(n_vertices, edges)
assert [[5], [6], [4], [3, 2, 1, 0]] == tarjan(g)
| 689 | 1 |
'''simple docstring'''
from math import isqrt, loga
def _lowerCAmelCase ( lowercase ) -> list[int]:
__lowerCAmelCase = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , lowercase , lowercase ):
__lowerCAmelCase = False
return [i for i in range(2 , lowercase ) if is_prime[i]]
def _lowerCAmelCase ( lowercase = 80_0800 , lowercase = 80_0800 ) -> int:
__lowerCAmelCase = degree * loga(lowercase )
__lowerCAmelCase = int(lowercase )
__lowerCAmelCase = calculate_prime_numbers(lowercase )
__lowerCAmelCase = 0
__lowerCAmelCase = 0
__lowerCAmelCase = len(lowercase ) - 1
while left < right:
while (
prime_numbers[right] * loga(prime_numbers[left] )
+ prime_numbers[left] * loga(prime_numbers[right] )
> upper_bound
):
right -= 1
hybrid_integers_count += right - left
left += 1
return hybrid_integers_count
if __name__ == "__main__":
print(f'{solution() = }')
| 689 |
'''simple docstring'''
from argparse import ArgumentParser
from .env import EnvironmentCommand
def _lowerCAmelCase ( ) -> Union[str, Any]:
__lowerCAmelCase = ArgumentParser("""Diffusers CLI tool""" , usage="""diffusers-cli <command> [<args>]""" )
__lowerCAmelCase = parser.add_subparsers(help="""diffusers-cli command helpers""" )
# Register commands
EnvironmentCommand.register_subcommand(lowercase )
# Let's go
__lowerCAmelCase = parser.parse_args()
if not hasattr(lowercase , """func""" ):
parser.print_help()
exit(1 )
# Run
__lowerCAmelCase = args.func(lowercase )
service.run()
if __name__ == "__main__":
main()
| 689 | 1 |
'''simple docstring'''
def _lowerCAmelCase ( lowercase ) -> list[int]:
__lowerCAmelCase = len(lowercase )
for i in range(lowercase ):
for j in range(i + 1 , lowercase ):
if numbers[j] < numbers[i]:
__lowerCAmelCase , __lowerCAmelCase = numbers[j], numbers[i]
return numbers
if __name__ == "__main__":
_a : Union[str, Any] = input("""Enter numbers separated by a comma:\n""").strip()
_a : Dict = [int(item) for item in user_input.split(""",""")]
print(exchange_sort(unsorted))
| 689 |
'''simple docstring'''
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
_a : List[Any] = logging.get_logger(__name__)
_a : int = {
"""post_extract_proj""": """feature_projection.projection""",
"""encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
"""self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
"""self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
"""self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
"""self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
"""self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
"""fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
"""fc2""": """encoder.layers.*.feed_forward.output_dense""",
"""final_layer_norm""": """encoder.layers.*.final_layer_norm""",
"""encoder.layer_norm""": """encoder.layer_norm""",
"""encoder.layer_norm_for_extract""": """layer_norm_for_extract""",
"""w2v_model.layer_norm""": """feature_projection.layer_norm""",
"""quantizer.weight_proj""": """quantizer.weight_proj""",
"""quantizer.vars""": """quantizer.codevectors""",
"""project_q""": """project_q""",
"""final_proj""": """project_hid""",
"""w2v_encoder.proj""": """lm_head""",
"""label_embs_concat""": """label_embeddings_concat""",
"""mask_emb""": """masked_spec_embed""",
"""spk_proj""": """speaker_proj""",
}
_a : Any = [
"""lm_head""",
"""quantizer.weight_proj""",
"""quantizer.codevectors""",
"""project_q""",
"""project_hid""",
"""label_embeddings_concat""",
"""speaker_proj""",
"""layer_norm_for_extract""",
]
def _lowerCAmelCase ( lowercase , lowercase , lowercase , lowercase , lowercase ) -> str:
for attribute in key.split(""".""" ):
__lowerCAmelCase = getattr(lowercase , lowercase )
if weight_type is not None:
__lowerCAmelCase = getattr(lowercase , lowercase ).shape
else:
__lowerCAmelCase = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
f'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
f' {value.shape} for {full_name}' )
if weight_type == "weight":
__lowerCAmelCase = value
elif weight_type == "weight_g":
__lowerCAmelCase = value
elif weight_type == "weight_v":
__lowerCAmelCase = value
elif weight_type == "bias":
__lowerCAmelCase = value
else:
__lowerCAmelCase = value
logger.info(f'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.' )
def _lowerCAmelCase ( lowercase , lowercase ) -> List[Any]:
__lowerCAmelCase = []
__lowerCAmelCase = fairseq_model.state_dict()
__lowerCAmelCase = hf_model.unispeech_sat.feature_extractor
for name, value in fairseq_dict.items():
__lowerCAmelCase = False
if "conv_layers" in name:
load_conv_layer(
lowercase , lowercase , lowercase , lowercase , hf_model.config.feat_extract_norm == """group""" , )
__lowerCAmelCase = True
else:
for key, mapped_key in MAPPING.items():
__lowerCAmelCase = """unispeech_sat.""" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split("""w2v_model.""" )[-1] == name.split(""".""" )[0]:
if "layer_norm_for_extract" in name and (".".join(name.split(""".""" )[:-1] ) != key):
# special case since naming is very similar
continue
__lowerCAmelCase = True
if "*" in mapped_key:
__lowerCAmelCase = name.split(lowercase )[0].split(""".""" )[-2]
__lowerCAmelCase = mapped_key.replace("""*""" , lowercase )
if "weight_g" in name:
__lowerCAmelCase = """weight_g"""
elif "weight_v" in name:
__lowerCAmelCase = """weight_v"""
elif "bias" in name:
__lowerCAmelCase = """bias"""
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
__lowerCAmelCase = """weight"""
else:
__lowerCAmelCase = None
set_recursively(lowercase , lowercase , lowercase , lowercase , lowercase )
continue
if not is_used:
unused_weights.append(lowercase )
logger.warning(f'Unused weights: {unused_weights}' )
def _lowerCAmelCase ( lowercase , lowercase , lowercase , lowercase , lowercase ) -> Optional[Any]:
__lowerCAmelCase = full_name.split("""conv_layers.""" )[-1]
__lowerCAmelCase = name.split(""".""" )
__lowerCAmelCase = int(items[0] )
__lowerCAmelCase = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.' )
__lowerCAmelCase = value
logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.' )
__lowerCAmelCase = value
logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.' )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor[layer_id].layer_norm.bias.data.shape} was found.' )
__lowerCAmelCase = value
logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.' )
__lowerCAmelCase = value
logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
else:
unused_weights.append(lowercase )
@torch.no_grad()
def _lowerCAmelCase ( lowercase , lowercase , lowercase=None , lowercase=None , lowercase=True ) -> Dict:
if config_path is not None:
__lowerCAmelCase = UniSpeechSatConfig.from_pretrained(lowercase )
else:
__lowerCAmelCase = UniSpeechSatConfig()
__lowerCAmelCase = """"""
if is_finetuned:
__lowerCAmelCase = UniSpeechSatForCTC(lowercase )
else:
__lowerCAmelCase = UniSpeechSatForPreTraining(lowercase )
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={"""data""": """/""".join(dict_path.split("""/""" )[:-1] )} )
__lowerCAmelCase = model[0].eval()
recursively_load_weights(lowercase , lowercase )
hf_wavavec.save_pretrained(lowercase )
if __name__ == "__main__":
_a : List[str] = argparse.ArgumentParser()
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
parser.add_argument(
"""--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
)
_a : Union[str, Any] = parser.parse_args()
convert_unispeech_sat_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 689 | 1 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
_a : str = logging.get_logger(__name__)
_a : List[str] = """▁"""
_a : List[Any] = {"""vocab_file""": """sentencepiece.bpe.model"""}
_a : List[str] = {
"""vocab_file""": {
"""facebook/mbart-large-en-ro""": (
"""https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model"""
),
"""facebook/mbart-large-cc25""": (
"""https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model"""
),
}
}
_a : Dict = {
"""facebook/mbart-large-en-ro""": 1_0_2_4,
"""facebook/mbart-large-cc25""": 1_0_2_4,
}
# fmt: off
_a : Optional[Any] = ["""ar_AR""", """cs_CZ""", """de_DE""", """en_XX""", """es_XX""", """et_EE""", """fi_FI""", """fr_XX""", """gu_IN""", """hi_IN""", """it_IT""", """ja_XX""", """kk_KZ""", """ko_KR""", """lt_LT""", """lv_LV""", """my_MM""", """ne_NP""", """nl_XX""", """ro_RO""", """ru_RU""", """si_LK""", """tr_TR""", """vi_VN""", """zh_CN"""]
class _UpperCAmelCase ( lowerCAmelCase_ ):
a : List[Any] =VOCAB_FILES_NAMES
a : List[Any] =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a : Union[str, Any] =PRETRAINED_VOCAB_FILES_MAP
a : Dict =["""input_ids""", """attention_mask"""]
a : List[int] =[]
a : List[int] =[]
def __init__( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE="<s>",__SCREAMING_SNAKE_CASE="</s>",__SCREAMING_SNAKE_CASE="</s>",__SCREAMING_SNAKE_CASE="<s>",__SCREAMING_SNAKE_CASE="<unk>",__SCREAMING_SNAKE_CASE="<pad>",__SCREAMING_SNAKE_CASE="<mask>",__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE=None,**__SCREAMING_SNAKE_CASE,):
'''simple docstring'''
__lowerCAmelCase = AddedToken(__SCREAMING_SNAKE_CASE,lstrip=__SCREAMING_SNAKE_CASE,rstrip=__SCREAMING_SNAKE_CASE ) if isinstance(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE ) else mask_token
__lowerCAmelCase = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=__SCREAMING_SNAKE_CASE,eos_token=__SCREAMING_SNAKE_CASE,unk_token=__SCREAMING_SNAKE_CASE,sep_token=__SCREAMING_SNAKE_CASE,cls_token=__SCREAMING_SNAKE_CASE,pad_token=__SCREAMING_SNAKE_CASE,mask_token=__SCREAMING_SNAKE_CASE,tokenizer_file=__SCREAMING_SNAKE_CASE,src_lang=__SCREAMING_SNAKE_CASE,tgt_lang=__SCREAMING_SNAKE_CASE,additional_special_tokens=__SCREAMING_SNAKE_CASE,sp_model_kwargs=self.sp_model_kwargs,**__SCREAMING_SNAKE_CASE,)
__lowerCAmelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(__SCREAMING_SNAKE_CASE ) )
__lowerCAmelCase = vocab_file
# Original fairseq vocab and spm vocab must be "aligned":
# Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
# fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's' | '▁de' | '-'
# spm | '<unk>' | '<s>' | '</s>' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a'
# Mimic fairseq token-to-id alignment for the first 4 token
__lowerCAmelCase = {"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}
# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
__lowerCAmelCase = 1
__lowerCAmelCase = len(self.sp_model )
__lowerCAmelCase = {
code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(__SCREAMING_SNAKE_CASE )
}
__lowerCAmelCase = {v: k for k, v in self.lang_code_to_id.items()}
__lowerCAmelCase = len(self.sp_model ) + len(self.lang_code_to_id ) + self.fairseq_offset
self.fairseq_tokens_to_ids.update(self.lang_code_to_id )
__lowerCAmelCase = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
__lowerCAmelCase = list(self.lang_code_to_id.keys() )
if additional_special_tokens is not None:
# Only add those special tokens if they are not already there.
self._additional_special_tokens.extend(
[t for t in additional_special_tokens if t not in self._additional_special_tokens] )
__lowerCAmelCase = src_lang if src_lang is not None else """en_XX"""
__lowerCAmelCase = self.lang_code_to_id[self._src_lang]
__lowerCAmelCase = tgt_lang
self.set_src_lang_special_tokens(self._src_lang )
def __getstate__( self ):
'''simple docstring'''
__lowerCAmelCase = self.__dict__.copy()
__lowerCAmelCase = None
__lowerCAmelCase = self.sp_model.serialized_model_proto()
return state
def __setstate__( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase = d
# for backward compatibility
if not hasattr(self,"""sp_model_kwargs""" ):
__lowerCAmelCase = {}
__lowerCAmelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.LoadFromSerializedProto(self.sp_model_proto )
@property
def lowerCamelCase__ ( self ):
'''simple docstring'''
return len(self.sp_model ) + len(self.lang_code_to_id ) + self.fairseq_offset + 1 # Plus 1 for the mask token
@property
def lowerCamelCase__ ( self ):
'''simple docstring'''
return self._src_lang
@src_lang.setter
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = False ):
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__SCREAMING_SNAKE_CASE,token_ids_a=__SCREAMING_SNAKE_CASE,already_has_special_tokens=__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = [1] * len(self.prefix_tokens )
__lowerCAmelCase = [1] * len(self.suffix_tokens )
if token_ids_a is None:
return prefix_ones + ([0] * len(__SCREAMING_SNAKE_CASE )) + suffix_ones
return prefix_ones + ([0] * len(__SCREAMING_SNAKE_CASE )) + ([0] * len(__SCREAMING_SNAKE_CASE )) + suffix_ones
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE = None ):
'''simple docstring'''
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE = None ):
'''simple docstring'''
__lowerCAmelCase = [self.sep_token_id]
__lowerCAmelCase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
if src_lang is None or tgt_lang is None:
raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model""" )
__lowerCAmelCase = src_lang
__lowerCAmelCase = self(__SCREAMING_SNAKE_CASE,add_special_tokens=__SCREAMING_SNAKE_CASE,return_tensors=__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = self.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = tgt_lang_id
return inputs
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = {self.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
return self.sp_model.encode(__SCREAMING_SNAKE_CASE,out_type=__SCREAMING_SNAKE_CASE )
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
__lowerCAmelCase = self.sp_model.PieceToId(__SCREAMING_SNAKE_CASE )
# Need to return unknown token if the SP model returned 0
return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset )
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase = """""".join(__SCREAMING_SNAKE_CASE ).replace(__SCREAMING_SNAKE_CASE,""" """ ).strip()
return out_string
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE = None ):
'''simple docstring'''
if not os.path.isdir(__SCREAMING_SNAKE_CASE ):
logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
return
__lowerCAmelCase = os.path.join(
__SCREAMING_SNAKE_CASE,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__SCREAMING_SNAKE_CASE ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file,__SCREAMING_SNAKE_CASE )
elif not os.path.isfile(self.vocab_file ):
with open(__SCREAMING_SNAKE_CASE,"""wb""" ) as fi:
__lowerCAmelCase = self.sp_model.serialized_model_proto()
fi.write(__SCREAMING_SNAKE_CASE )
return (out_vocab_file,)
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE = "en_XX",__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = "ro_RO",**__SCREAMING_SNAKE_CASE,):
'''simple docstring'''
__lowerCAmelCase = src_lang
__lowerCAmelCase = tgt_lang
return super().prepare_seqaseq_batch(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE )
def lowerCamelCase__ ( self ):
'''simple docstring'''
return self.set_src_lang_special_tokens(self.src_lang )
def lowerCamelCase__ ( self ):
'''simple docstring'''
return self.set_tgt_lang_special_tokens(self.tgt_lang )
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase = self.lang_code_to_id[src_lang]
__lowerCAmelCase = []
__lowerCAmelCase = [self.eos_token_id, self.cur_lang_code]
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase = self.lang_code_to_id[lang]
__lowerCAmelCase = []
__lowerCAmelCase = [self.eos_token_id, self.cur_lang_code]
| 689 |
'''simple docstring'''
from scipy.stats import spearmanr
import datasets
_a : str = """
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlations imply that as data in dataset x increases, so
does data in dataset y. Negative correlations imply that as x increases,
y decreases. Correlations of -1 or +1 imply an exact monotonic relationship.
Unlike the Pearson correlation, the Spearman correlation does not
assume that both datasets are normally distributed.
The p-value roughly indicates the probability of an uncorrelated system
producing datasets that have a Spearman correlation at least as extreme
as the one computed from these datasets. The p-values are not entirely
reliable but are probably reasonable for datasets larger than 500 or so.
"""
_a : Dict = """
Args:
predictions (`List[float]`): Predicted labels, as returned by a model.
references (`List[float]`): Ground truth labels.
return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns
only the spearmanr score. Defaults to `False`.
Returns:
spearmanr (`float`): Spearman correlation coefficient.
p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.
Examples:
Example 1:
>>> spearmanr_metric = datasets.load_metric(\"spearmanr\")
>>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])
>>> print(results)
{'spearmanr': -0.7}
Example 2:
>>> spearmanr_metric = datasets.load_metric(\"spearmanr\")
>>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],
... predictions=[10, 9, 2.5, 6, 4],
... return_pvalue=True)
>>> print(results['spearmanr'])
-0.7
>>> print(round(results['spearmanr_pvalue'], 2))
0.19
"""
_a : List[str] = r"""\
@book{kokoska2000crc,
title={CRC standard probability and statistics tables and formulae},
author={Kokoska, Stephen and Zwillinger, Daniel},
year={2000},
publisher={Crc Press}
}
@article{2020SciPy-NMeth,
author = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
Haberland, Matt and Reddy, Tyler and Cournapeau, David and
Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
Kern, Robert and Larson, Eric and Carey, C J and
Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and
{VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
Harris, Charles R. and Archibald, Anne M. and
Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and
{van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
title = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
Computing in Python}},
journal = {Nature Methods},
year = {2020},
volume = {17},
pages = {261--272},
adsurl = {https://rdcu.be/b08Wh},
doi = {10.1038/s41592-019-0686-2},
}
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _UpperCAmelCase ( datasets.Metric ):
def lowerCamelCase__ ( self ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION,citation=_CITATION,inputs_description=_KWARGS_DESCRIPTION,features=datasets.Features(
{
"""predictions""": datasets.Value("""float""" ),
"""references""": datasets.Value("""float""" ),
} ),reference_urls=["""https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.spearmanr.html"""],)
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=False ):
'''simple docstring'''
__lowerCAmelCase = spearmanr(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
if return_pvalue:
return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
else:
return {"spearmanr": results[0]}
| 689 | 1 |
'''simple docstring'''
def _lowerCAmelCase ( lowercase = 10**12 ) -> int:
__lowerCAmelCase = 1
__lowerCAmelCase = 0
__lowerCAmelCase = 1
__lowerCAmelCase = 1
while numerator <= 2 * min_total - 1:
prev_numerator += 2 * numerator
numerator += 2 * prev_numerator
prev_denominator += 2 * denominator
denominator += 2 * prev_denominator
return (denominator + 1) // 2
if __name__ == "__main__":
print(f'{solution() = }')
| 689 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _UpperCAmelCase ( metaclass=lowerCAmelCase_ ):
a : List[str] =["""onnx"""]
def __init__( self,*__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
requires_backends(self,["""onnx"""] )
@classmethod
def lowerCamelCase__ ( cls,*__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
requires_backends(cls,["""onnx"""] )
@classmethod
def lowerCamelCase__ ( cls,*__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
requires_backends(cls,["""onnx"""] )
| 689 | 1 |
'''simple docstring'''
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from argparse import ArgumentParser
from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command_parser
def _lowerCAmelCase ( ) -> Optional[Any]:
__lowerCAmelCase = ArgumentParser("""Accelerate CLI tool""" , usage="""accelerate <command> [<args>]""" , allow_abbrev=lowercase )
__lowerCAmelCase = parser.add_subparsers(help="""accelerate command helpers""" )
# Register commands
get_config_parser(subparsers=lowercase )
env_command_parser(subparsers=lowercase )
launch_command_parser(subparsers=lowercase )
tpu_command_parser(subparsers=lowercase )
test_command_parser(subparsers=lowercase )
# Let's go
__lowerCAmelCase = parser.parse_args()
if not hasattr(lowercase , """func""" ):
parser.print_help()
exit(1 )
# Run
args.func(lowercase )
if __name__ == "__main__":
main()
| 689 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
_a : int = logging.get_logger(__name__)
_a : Optional[int] = {
"""EleutherAI/gpt-j-6B""": """https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json""",
# See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}
class _UpperCAmelCase ( lowerCAmelCase_ ):
a : List[str] ="""gptj"""
a : Optional[int] ={
"""max_position_embeddings""": """n_positions""",
"""hidden_size""": """n_embd""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self,__SCREAMING_SNAKE_CASE=5_04_00,__SCREAMING_SNAKE_CASE=20_48,__SCREAMING_SNAKE_CASE=40_96,__SCREAMING_SNAKE_CASE=28,__SCREAMING_SNAKE_CASE=16,__SCREAMING_SNAKE_CASE=64,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE="gelu_new",__SCREAMING_SNAKE_CASE=0.0,__SCREAMING_SNAKE_CASE=0.0,__SCREAMING_SNAKE_CASE=0.0,__SCREAMING_SNAKE_CASE=1e-5,__SCREAMING_SNAKE_CASE=0.02,__SCREAMING_SNAKE_CASE=True,__SCREAMING_SNAKE_CASE=5_02_56,__SCREAMING_SNAKE_CASE=5_02_56,__SCREAMING_SNAKE_CASE=False,**__SCREAMING_SNAKE_CASE,):
'''simple docstring'''
__lowerCAmelCase = vocab_size
__lowerCAmelCase = n_positions
__lowerCAmelCase = n_embd
__lowerCAmelCase = n_layer
__lowerCAmelCase = n_head
__lowerCAmelCase = n_inner
__lowerCAmelCase = rotary_dim
__lowerCAmelCase = activation_function
__lowerCAmelCase = resid_pdrop
__lowerCAmelCase = embd_pdrop
__lowerCAmelCase = attn_pdrop
__lowerCAmelCase = layer_norm_epsilon
__lowerCAmelCase = initializer_range
__lowerCAmelCase = use_cache
__lowerCAmelCase = bos_token_id
__lowerCAmelCase = eos_token_id
super().__init__(
bos_token_id=__SCREAMING_SNAKE_CASE,eos_token_id=__SCREAMING_SNAKE_CASE,tie_word_embeddings=__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE )
class _UpperCAmelCase ( lowerCAmelCase_ ):
def __init__( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE = "default",__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = False,):
'''simple docstring'''
super().__init__(__SCREAMING_SNAKE_CASE,task=__SCREAMING_SNAKE_CASE,patching_specs=__SCREAMING_SNAKE_CASE,use_past=__SCREAMING_SNAKE_CASE )
if not getattr(self._config,"""pad_token_id""",__SCREAMING_SNAKE_CASE ):
# TODO: how to do that better?
__lowerCAmelCase = 0
@property
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}} )
if self.use_past:
self.fill_with_past_key_values_(__SCREAMING_SNAKE_CASE,direction="""inputs""" )
__lowerCAmelCase = {0: """batch""", 1: """past_sequence + sequence"""}
else:
__lowerCAmelCase = {0: """batch""", 1: """sequence"""}
return common_inputs
@property
def lowerCamelCase__ ( self ):
'''simple docstring'''
return self._config.n_layer
@property
def lowerCamelCase__ ( self ):
'''simple docstring'''
return self._config.n_head
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE = -1,__SCREAMING_SNAKE_CASE = -1,__SCREAMING_SNAKE_CASE = False,__SCREAMING_SNAKE_CASE = None,):
'''simple docstring'''
__lowerCAmelCase = super(__SCREAMING_SNAKE_CASE,self ).generate_dummy_inputs(
__SCREAMING_SNAKE_CASE,batch_size=__SCREAMING_SNAKE_CASE,seq_length=__SCREAMING_SNAKE_CASE,is_pair=__SCREAMING_SNAKE_CASE,framework=__SCREAMING_SNAKE_CASE )
# We need to order the input in the way they appears in the forward()
__lowerCAmelCase = OrderedDict({"""input_ids""": common_inputs["""input_ids"""]} )
# Need to add the past_keys
if self.use_past:
if not is_torch_available():
raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed.""" )
else:
import torch
__lowerCAmelCase , __lowerCAmelCase = common_inputs["""input_ids"""].shape
# Not using the same length for past_key_values
__lowerCAmelCase = seqlen + 2
__lowerCAmelCase = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
__lowerCAmelCase = [
(torch.zeros(__SCREAMING_SNAKE_CASE ), torch.zeros(__SCREAMING_SNAKE_CASE )) for _ in range(self.num_layers )
]
__lowerCAmelCase = common_inputs["""attention_mask"""]
if self.use_past:
__lowerCAmelCase = ordered_inputs["""attention_mask"""].dtype
__lowerCAmelCase = torch.cat(
[ordered_inputs["""attention_mask"""], torch.ones(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,dtype=__SCREAMING_SNAKE_CASE )],dim=1 )
return ordered_inputs
@property
def lowerCamelCase__ ( self ):
'''simple docstring'''
return 13
| 689 | 1 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
_a : List[Any] = logging.get_logger(__name__)
@dataclass
class _UpperCAmelCase ( lowerCAmelCase_ ):
a : Optional[int] =[
"""no_inference""",
"""no_cuda""",
"""no_tpu""",
"""no_speed""",
"""no_memory""",
"""no_env_print""",
"""no_multi_process""",
]
def __init__( self,**__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
for deprecated_arg in self.deprecated_args:
if deprecated_arg in kwargs:
__lowerCAmelCase = deprecated_arg[3:]
__lowerCAmelCase = not kwargs.pop(__SCREAMING_SNAKE_CASE )
logger.warning(
f'{deprecated_arg} is depreciated. Please use --no-{positive_arg} or'
f' {positive_arg}={kwargs[positive_arg]}' )
__lowerCAmelCase = kwargs.pop("""tpu_name""",self.tpu_name )
__lowerCAmelCase = kwargs.pop("""device_idx""",self.device_idx )
__lowerCAmelCase = kwargs.pop("""eager_mode""",self.eager_mode )
__lowerCAmelCase = kwargs.pop("""use_xla""",self.use_xla )
super().__init__(**__SCREAMING_SNAKE_CASE )
a : str =field(
default=lowerCAmelCase_ , metadata={"""help""": """Name of TPU"""} , )
a : int =field(
default=0 , metadata={"""help""": """CPU / GPU device index. Defaults to 0."""} , )
a : bool =field(default=lowerCAmelCase_ , metadata={"""help""": """Benchmark models in eager model."""} )
a : bool =field(
default=lowerCAmelCase_ , metadata={
"""help""": """Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`."""
} , )
@cached_property
def lowerCamelCase__ ( self ):
'''simple docstring'''
requires_backends(self,["""tf"""] )
__lowerCAmelCase = None
if self.tpu:
try:
if self.tpu_name:
__lowerCAmelCase = tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name )
else:
__lowerCAmelCase = tf.distribute.cluster_resolver.TPUClusterResolver()
except ValueError:
__lowerCAmelCase = None
return tpu
@cached_property
def lowerCamelCase__ ( self ):
'''simple docstring'''
requires_backends(self,["""tf"""] )
if self.is_tpu:
tf.config.experimental_connect_to_cluster(self._setup_tpu )
tf.tpu.experimental.initialize_tpu_system(self._setup_tpu )
__lowerCAmelCase = tf.distribute.TPUStrategy(self._setup_tpu )
else:
# currently no multi gpu is allowed
if self.is_gpu:
# TODO: Currently only single GPU is supported
tf.config.set_visible_devices(self.gpu_list[self.device_idx],"""GPU""" )
__lowerCAmelCase = tf.distribute.OneDeviceStrategy(device=f'/gpu:{self.device_idx}' )
else:
tf.config.set_visible_devices([],"""GPU""" ) # disable GPU
__lowerCAmelCase = tf.distribute.OneDeviceStrategy(device=f'/cpu:{self.device_idx}' )
return strategy
@property
def lowerCamelCase__ ( self ):
'''simple docstring'''
requires_backends(self,["""tf"""] )
return self._setup_tpu is not None
@property
def lowerCamelCase__ ( self ):
'''simple docstring'''
requires_backends(self,["""tf"""] )
return self._setup_strategy
@property
def lowerCamelCase__ ( self ):
'''simple docstring'''
requires_backends(self,["""tf"""] )
return tf.config.list_physical_devices("""GPU""" )
@property
def lowerCamelCase__ ( self ):
'''simple docstring'''
requires_backends(self,["""tf"""] )
if self.cuda:
return len(self.gpu_list )
return 0
@property
def lowerCamelCase__ ( self ):
'''simple docstring'''
return self.n_gpu > 0
| 689 |
'''simple docstring'''
def _lowerCAmelCase ( lowercase = 5000_0000 ) -> int:
__lowerCAmelCase = set()
__lowerCAmelCase = int((limit - 24) ** (1 / 2) )
__lowerCAmelCase = set(range(3 , prime_square_limit + 1 , 2 ) )
primes.add(2 )
for p in range(3 , prime_square_limit + 1 , 2 ):
if p not in primes:
continue
primes.difference_update(set(range(p * p , prime_square_limit + 1 , lowercase ) ) )
for primea in primes:
__lowerCAmelCase = primea * primea
for primea in primes:
__lowerCAmelCase = primea * primea * primea
if square + cube >= limit - 16:
break
for primea in primes:
__lowerCAmelCase = primea * primea * primea * primea
__lowerCAmelCase = square + cube + tetr
if total >= limit:
break
ret.add(lowercase )
return len(lowercase )
if __name__ == "__main__":
print(f'{solution() = }')
| 689 | 1 |
'''simple docstring'''
import numpy as np
import torch
from imwatermark import WatermarkEncoder
# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
_a : List[Any] = 0B10_11_00_11_11_10_11_00_10_01_00_00_01_11_10_11_10_11_00_01_10_01_11_10
# bin(x)[2:] gives bits of x as str, use int to convert them to 0/1
_a : str = [int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]
class _UpperCAmelCase :
def __init__( self ):
'''simple docstring'''
__lowerCAmelCase = WATERMARK_BITS
__lowerCAmelCase = WatermarkEncoder()
self.encoder.set_watermark("""bits""",self.watermark )
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
if images.shape[-1] < 2_56:
return images
__lowerCAmelCase = (2_55 * (images / 2 + 0.5)).cpu().permute(0,2,3,1 ).float().numpy()
__lowerCAmelCase = [self.encoder.encode(__SCREAMING_SNAKE_CASE,"""dwtDct""" ) for image in images]
__lowerCAmelCase = torch.from_numpy(np.array(__SCREAMING_SNAKE_CASE ) ).permute(0,3,1,2 )
__lowerCAmelCase = torch.clamp(2 * (images / 2_55 - 0.5),min=-1.0,max=1.0 )
return images
| 689 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, load_numpy, skip_mps, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
@skip_mps
class _UpperCAmelCase ( lowerCAmelCase_ , unittest.TestCase ):
a : Optional[int] =TextToVideoSDPipeline
a : Optional[int] =TEXT_TO_IMAGE_PARAMS
a : Any =TEXT_TO_IMAGE_BATCH_PARAMS
# No `output_type`.
a : Union[str, Any] =frozenset(
[
"""num_inference_steps""",
"""generator""",
"""latents""",
"""return_dict""",
"""callback""",
"""callback_steps""",
] )
def lowerCamelCase__ ( self ):
'''simple docstring'''
torch.manual_seed(0 )
__lowerCAmelCase = UNetaDConditionModel(
block_out_channels=(32, 64, 64, 64),layers_per_block=2,sample_size=32,in_channels=4,out_channels=4,down_block_types=("""CrossAttnDownBlock3D""", """CrossAttnDownBlock3D""", """CrossAttnDownBlock3D""", """DownBlock3D"""),up_block_types=("""UpBlock3D""", """CrossAttnUpBlock3D""", """CrossAttnUpBlock3D""", """CrossAttnUpBlock3D"""),cross_attention_dim=32,attention_head_dim=4,)
__lowerCAmelCase = DDIMScheduler(
beta_start=0.0_0085,beta_end=0.012,beta_schedule="""scaled_linear""",clip_sample=__SCREAMING_SNAKE_CASE,set_alpha_to_one=__SCREAMING_SNAKE_CASE,)
torch.manual_seed(0 )
__lowerCAmelCase = AutoencoderKL(
block_out_channels=[32, 64],in_channels=3,out_channels=3,down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""],up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""],latent_channels=4,sample_size=1_28,)
torch.manual_seed(0 )
__lowerCAmelCase = CLIPTextConfig(
bos_token_id=0,eos_token_id=2,hidden_size=32,intermediate_size=37,layer_norm_eps=1e-05,num_attention_heads=4,num_hidden_layers=5,pad_token_id=1,vocab_size=10_00,hidden_act="""gelu""",projection_dim=5_12,)
__lowerCAmelCase = CLIPTextModel(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
__lowerCAmelCase = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
}
return components
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=0 ):
'''simple docstring'''
if str(__SCREAMING_SNAKE_CASE ).startswith("""mps""" ):
__lowerCAmelCase = torch.manual_seed(__SCREAMING_SNAKE_CASE )
else:
__lowerCAmelCase = torch.Generator(device=__SCREAMING_SNAKE_CASE ).manual_seed(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 6.0,
"""output_type""": """pt""",
}
return inputs
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = """cpu""" # ensure determinism for the device-dependent torch.Generator
__lowerCAmelCase = self.get_dummy_components()
__lowerCAmelCase = TextToVideoSDPipeline(**__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = sd_pipe.to(__SCREAMING_SNAKE_CASE )
sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = self.get_dummy_inputs(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = """np"""
__lowerCAmelCase = sd_pipe(**__SCREAMING_SNAKE_CASE ).frames
__lowerCAmelCase = frames[0][-3:, -3:, -1]
assert frames[0].shape == (64, 64, 3)
__lowerCAmelCase = np.array([158.0, 160.0, 153.0, 125.0, 100.0, 121.0, 111.0, 93.0, 113.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def lowerCamelCase__ ( self ):
'''simple docstring'''
self._test_attention_slicing_forward_pass(test_mean_pixel_difference=__SCREAMING_SNAKE_CASE,expected_max_diff=3e-3 )
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available(),reason="""XFormers attention is only available with CUDA and `xformers` installed""",)
def lowerCamelCase__ ( self ):
'''simple docstring'''
self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=__SCREAMING_SNAKE_CASE,expected_max_diff=1e-2 )
@unittest.skip(reason="""Batching needs to be properly figured out first for this pipeline.""" )
def lowerCamelCase__ ( self ):
'''simple docstring'''
pass
@unittest.skip(reason="""Batching needs to be properly figured out first for this pipeline.""" )
def lowerCamelCase__ ( self ):
'''simple docstring'''
pass
@unittest.skip(reason="""`num_images_per_prompt` argument is not supported for this pipeline.""" )
def lowerCamelCase__ ( self ):
'''simple docstring'''
pass
def lowerCamelCase__ ( self ):
'''simple docstring'''
return super().test_progress_bar()
@slow
@skip_mps
class _UpperCAmelCase ( unittest.TestCase ):
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video.npy""" )
__lowerCAmelCase = TextToVideoSDPipeline.from_pretrained("""damo-vilab/text-to-video-ms-1.7b""" )
__lowerCAmelCase = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
__lowerCAmelCase = pipe.to("""cuda""" )
__lowerCAmelCase = """Spiderman is surfing"""
__lowerCAmelCase = torch.Generator(device="""cpu""" ).manual_seed(0 )
__lowerCAmelCase = pipe(__SCREAMING_SNAKE_CASE,generator=__SCREAMING_SNAKE_CASE,num_inference_steps=25,output_type="""pt""" ).frames
__lowerCAmelCase = video_frames.cpu().numpy()
assert np.abs(expected_video - video ).mean() < 5e-2
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video_2step.npy""" )
__lowerCAmelCase = TextToVideoSDPipeline.from_pretrained("""damo-vilab/text-to-video-ms-1.7b""" )
__lowerCAmelCase = pipe.to("""cuda""" )
__lowerCAmelCase = """Spiderman is surfing"""
__lowerCAmelCase = torch.Generator(device="""cpu""" ).manual_seed(0 )
__lowerCAmelCase = pipe(__SCREAMING_SNAKE_CASE,generator=__SCREAMING_SNAKE_CASE,num_inference_steps=2,output_type="""pt""" ).frames
__lowerCAmelCase = video_frames.cpu().numpy()
assert np.abs(expected_video - video ).mean() < 5e-2
| 689 | 1 |
'''simple docstring'''
def _lowerCAmelCase ( lowercase , lowercase ) -> float:
if mass < 0:
raise ValueError("""The mass of a body cannot be negative""" )
return 0.5 * mass * abs(lowercase ) * abs(lowercase )
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
| 689 |
'''simple docstring'''
from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def _lowerCAmelCase ( lowercase ) -> Optional[int]:
if not is_accelerate_available():
return method
__lowerCAmelCase = version.parse(accelerate.__version__ ).base_version
if version.parse(lowercase ) < version.parse("""0.17.0""" ):
return method
def wrapper(self , *lowercase , **lowercase ):
if hasattr(self , """_hf_hook""" ) and hasattr(self._hf_hook , """pre_forward""" ):
self._hf_hook.pre_forward(self )
return method(self , *lowercase , **lowercase )
return wrapper
| 689 | 1 |
'''simple docstring'''
import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple
import numpy as np
from .import_utils import is_flax_available, is_tf_available, is_torch_available, is_torch_fx_proxy
if is_flax_available():
import jax.numpy as jnp
class _UpperCAmelCase ( lowerCAmelCase_ ):
def __get__( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=None ):
'''simple docstring'''
if obj is None:
return self
if self.fget is None:
raise AttributeError("""unreadable attribute""" )
__lowerCAmelCase = """__cached_""" + self.fget.__name__
__lowerCAmelCase = getattr(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
if cached is None:
__lowerCAmelCase = self.fget(__SCREAMING_SNAKE_CASE )
setattr(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
return cached
def _lowerCAmelCase ( lowercase ) -> List[Any]:
__lowerCAmelCase = val.lower()
if val in {"y", "yes", "t", "true", "on", "1"}:
return 1
if val in {"n", "no", "f", "false", "off", "0"}:
return 0
raise ValueError(f'invalid truth value {val!r}' )
def _lowerCAmelCase ( lowercase ) -> Optional[Any]:
if is_torch_fx_proxy(lowercase ):
return True
if is_torch_available():
import torch
if isinstance(lowercase , torch.Tensor ):
return True
if is_tf_available():
import tensorflow as tf
if isinstance(lowercase , tf.Tensor ):
return True
if is_flax_available():
import jax.numpy as jnp
from jax.core import Tracer
if isinstance(lowercase , (jnp.ndarray, Tracer) ):
return True
return isinstance(lowercase , np.ndarray )
def _lowerCAmelCase ( lowercase ) -> Union[str, Any]:
return isinstance(lowercase , np.ndarray )
def _lowerCAmelCase ( lowercase ) -> Any:
return _is_numpy(lowercase )
def _lowerCAmelCase ( lowercase ) -> Any:
import torch
return isinstance(lowercase , torch.Tensor )
def _lowerCAmelCase ( lowercase ) -> Optional[int]:
return False if not is_torch_available() else _is_torch(lowercase )
def _lowerCAmelCase ( lowercase ) -> int:
import torch
return isinstance(lowercase , torch.device )
def _lowerCAmelCase ( lowercase ) -> Tuple:
return False if not is_torch_available() else _is_torch_device(lowercase )
def _lowerCAmelCase ( lowercase ) -> int:
import torch
if isinstance(lowercase , lowercase ):
if hasattr(lowercase , lowercase ):
__lowerCAmelCase = getattr(lowercase , lowercase )
else:
return False
return isinstance(lowercase , torch.dtype )
def _lowerCAmelCase ( lowercase ) -> Optional[int]:
return False if not is_torch_available() else _is_torch_dtype(lowercase )
def _lowerCAmelCase ( lowercase ) -> Union[str, Any]:
import tensorflow as tf
return isinstance(lowercase , tf.Tensor )
def _lowerCAmelCase ( lowercase ) -> Optional[int]:
return False if not is_tf_available() else _is_tensorflow(lowercase )
def _lowerCAmelCase ( lowercase ) -> Optional[int]:
import tensorflow as tf
# the `is_symbolic_tensor` predicate is only available starting with TF 2.14
if hasattr(lowercase , """is_symbolic_tensor""" ):
return tf.is_symbolic_tensor(lowercase )
return type(lowercase ) == tf.Tensor
def _lowerCAmelCase ( lowercase ) -> Tuple:
return False if not is_tf_available() else _is_tf_symbolic_tensor(lowercase )
def _lowerCAmelCase ( lowercase ) -> Dict:
import jax.numpy as jnp # noqa: F811
return isinstance(lowercase , jnp.ndarray )
def _lowerCAmelCase ( lowercase ) -> List[Any]:
return False if not is_flax_available() else _is_jax(lowercase )
def _lowerCAmelCase ( lowercase ) -> Union[str, Any]:
if isinstance(lowercase , (dict, UserDict) ):
return {k: to_py_obj(lowercase ) for k, v in obj.items()}
elif isinstance(lowercase , (list, tuple) ):
return [to_py_obj(lowercase ) for o in obj]
elif is_tf_tensor(lowercase ):
return obj.numpy().tolist()
elif is_torch_tensor(lowercase ):
return obj.detach().cpu().tolist()
elif is_jax_tensor(lowercase ):
return np.asarray(lowercase ).tolist()
elif isinstance(lowercase , (np.ndarray, np.number) ): # tolist also works on 0d np arrays
return obj.tolist()
else:
return obj
def _lowerCAmelCase ( lowercase ) -> str:
if isinstance(lowercase , (dict, UserDict) ):
return {k: to_numpy(lowercase ) for k, v in obj.items()}
elif isinstance(lowercase , (list, tuple) ):
return np.array(lowercase )
elif is_tf_tensor(lowercase ):
return obj.numpy()
elif is_torch_tensor(lowercase ):
return obj.detach().cpu().numpy()
elif is_jax_tensor(lowercase ):
return np.asarray(lowercase )
else:
return obj
class _UpperCAmelCase ( lowerCAmelCase_ ):
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = fields(self )
# Safety and consistency checks
if not len(__SCREAMING_SNAKE_CASE ):
raise ValueError(f'{self.__class__.__name__} has no fields.' )
if not all(field.default is None for field in class_fields[1:] ):
raise ValueError(f'{self.__class__.__name__} should not have more than one required field.' )
__lowerCAmelCase = getattr(self,class_fields[0].name )
__lowerCAmelCase = all(getattr(self,field.name ) is None for field in class_fields[1:] )
if other_fields_are_none and not is_tensor(__SCREAMING_SNAKE_CASE ):
if isinstance(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE ):
__lowerCAmelCase = first_field.items()
__lowerCAmelCase = True
else:
try:
__lowerCAmelCase = iter(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = True
except TypeError:
__lowerCAmelCase = False
# if we provided an iterator as first field and the iterator is a (key, value) iterator
# set the associated fields
if first_field_iterator:
for idx, element in enumerate(__SCREAMING_SNAKE_CASE ):
if (
not isinstance(__SCREAMING_SNAKE_CASE,(list, tuple) )
or not len(__SCREAMING_SNAKE_CASE ) == 2
or not isinstance(element[0],__SCREAMING_SNAKE_CASE )
):
if idx == 0:
# If we do not have an iterator of key/values, set it as attribute
__lowerCAmelCase = first_field
else:
# If we have a mixed iterator, raise an error
raise ValueError(
f'Cannot set key/value for {element}. It needs to be a tuple (key, value).' )
break
setattr(self,element[0],element[1] )
if element[1] is not None:
__lowerCAmelCase = element[1]
elif first_field is not None:
__lowerCAmelCase = first_field
else:
for field in class_fields:
__lowerCAmelCase = getattr(self,field.name )
if v is not None:
__lowerCAmelCase = v
def __delitem__( self,*__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
raise Exception(f'You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.' )
def lowerCamelCase__ ( self,*__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
raise Exception(f'You cannot use ``setdefault`` on a {self.__class__.__name__} instance.' )
def lowerCamelCase__ ( self,*__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
raise Exception(f'You cannot use ``pop`` on a {self.__class__.__name__} instance.' )
def lowerCamelCase__ ( self,*__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
raise Exception(f'You cannot use ``update`` on a {self.__class__.__name__} instance.' )
def __getitem__( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
if isinstance(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE ):
__lowerCAmelCase = dict(self.items() )
return inner_dict[k]
else:
return self.to_tuple()[k]
def __setattr__( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
if name in self.keys() and value is not None:
# Don't call self.__setitem__ to avoid recursion errors
super().__setitem__(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
super().__setattr__(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
def __setitem__( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
super().__setitem__(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
# Don't call self.__setattr__ to avoid recursion errors
super().__setattr__(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
def lowerCamelCase__ ( self ):
'''simple docstring'''
return tuple(self[k] for k in self.keys() )
class _UpperCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ ):
@classmethod
def lowerCamelCase__ ( cls,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
raise ValueError(
f'{value} is not a valid {cls.__name__}, please select one of {list(cls._valueamember_map_.keys() )}' )
class _UpperCAmelCase ( lowerCAmelCase_ ):
a : Tuple ="""longest"""
a : Union[str, Any] ="""max_length"""
a : Any ="""do_not_pad"""
class _UpperCAmelCase ( lowerCAmelCase_ ):
a : Tuple ="""pt"""
a : Tuple ="""tf"""
a : Union[str, Any] ="""np"""
a : Any ="""jax"""
class _UpperCAmelCase :
def __init__( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase = context_managers
__lowerCAmelCase = ExitStack()
def __enter__( self ):
'''simple docstring'''
for context_manager in self.context_managers:
self.stack.enter_context(__SCREAMING_SNAKE_CASE )
def __exit__( self,*__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
self.stack.__exit__(*__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE )
def _lowerCAmelCase ( lowercase ) -> List[Any]:
__lowerCAmelCase = infer_framework(lowercase )
if framework == "tf":
__lowerCAmelCase = inspect.signature(model_class.call ) # TensorFlow models
elif framework == "pt":
__lowerCAmelCase = inspect.signature(model_class.forward ) # PyTorch models
else:
__lowerCAmelCase = inspect.signature(model_class.__call__ ) # Flax models
for p in signature.parameters:
if p == "return_loss" and signature.parameters[p].default is True:
return True
return False
def _lowerCAmelCase ( lowercase ) -> int:
__lowerCAmelCase = model_class.__name__
__lowerCAmelCase = infer_framework(lowercase )
if framework == "tf":
__lowerCAmelCase = inspect.signature(model_class.call ) # TensorFlow models
elif framework == "pt":
__lowerCAmelCase = inspect.signature(model_class.forward ) # PyTorch models
else:
__lowerCAmelCase = inspect.signature(model_class.__call__ ) # Flax models
if "QuestionAnswering" in model_name:
return [p for p in signature.parameters if "label" in p or p in ("start_positions", "end_positions")]
else:
return [p for p in signature.parameters if "label" in p]
def _lowerCAmelCase ( lowercase , lowercase = "" , lowercase = "." ) -> str:
def _flatten_dict(lowercase , lowercase="" , lowercase="." ):
for k, v in d.items():
__lowerCAmelCase = str(lowercase ) + delimiter + str(lowercase ) if parent_key else k
if v and isinstance(lowercase , lowercase ):
yield from flatten_dict(lowercase , lowercase , delimiter=lowercase ).items()
else:
yield key, v
return dict(_flatten_dict(lowercase , lowercase , lowercase ) )
@contextmanager
def _lowerCAmelCase ( lowercase , lowercase = False ) -> List[Any]:
if use_temp_dir:
with tempfile.TemporaryDirectory() as tmp_dir:
yield tmp_dir
else:
yield working_dir
def _lowerCAmelCase ( lowercase , lowercase=None ) -> List[str]:
if is_numpy_array(lowercase ):
return np.transpose(lowercase , axes=lowercase )
elif is_torch_tensor(lowercase ):
return array.T if axes is None else array.permute(*lowercase )
elif is_tf_tensor(lowercase ):
import tensorflow as tf
return tf.transpose(lowercase , perm=lowercase )
elif is_jax_tensor(lowercase ):
return jnp.transpose(lowercase , axes=lowercase )
else:
raise ValueError(f'Type not supported for transpose: {type(lowercase )}.' )
def _lowerCAmelCase ( lowercase , lowercase ) -> Union[str, Any]:
if is_numpy_array(lowercase ):
return np.reshape(lowercase , lowercase )
elif is_torch_tensor(lowercase ):
return array.reshape(*lowercase )
elif is_tf_tensor(lowercase ):
import tensorflow as tf
return tf.reshape(lowercase , lowercase )
elif is_jax_tensor(lowercase ):
return jnp.reshape(lowercase , lowercase )
else:
raise ValueError(f'Type not supported for reshape: {type(lowercase )}.' )
def _lowerCAmelCase ( lowercase , lowercase=None ) -> str:
if is_numpy_array(lowercase ):
return np.squeeze(lowercase , axis=lowercase )
elif is_torch_tensor(lowercase ):
return array.squeeze() if axis is None else array.squeeze(dim=lowercase )
elif is_tf_tensor(lowercase ):
import tensorflow as tf
return tf.squeeze(lowercase , axis=lowercase )
elif is_jax_tensor(lowercase ):
return jnp.squeeze(lowercase , axis=lowercase )
else:
raise ValueError(f'Type not supported for squeeze: {type(lowercase )}.' )
def _lowerCAmelCase ( lowercase , lowercase ) -> List[Any]:
if is_numpy_array(lowercase ):
return np.expand_dims(lowercase , lowercase )
elif is_torch_tensor(lowercase ):
return array.unsqueeze(dim=lowercase )
elif is_tf_tensor(lowercase ):
import tensorflow as tf
return tf.expand_dims(lowercase , axis=lowercase )
elif is_jax_tensor(lowercase ):
return jnp.expand_dims(lowercase , axis=lowercase )
else:
raise ValueError(f'Type not supported for expand_dims: {type(lowercase )}.' )
def _lowerCAmelCase ( lowercase ) -> Optional[int]:
if is_numpy_array(lowercase ):
return np.size(lowercase )
elif is_torch_tensor(lowercase ):
return array.numel()
elif is_tf_tensor(lowercase ):
import tensorflow as tf
return tf.size(lowercase )
elif is_jax_tensor(lowercase ):
return array.size
else:
raise ValueError(f'Type not supported for expand_dims: {type(lowercase )}.' )
def _lowerCAmelCase ( lowercase , lowercase ) -> str:
for key, value in auto_map.items():
if isinstance(lowercase , (tuple, list) ):
__lowerCAmelCase = [f'{repo_id}--{v}' if (v is not None and """--""" not in v) else v for v in value]
elif value is not None and "--" not in value:
__lowerCAmelCase = f'{repo_id}--{value}'
return auto_map
def _lowerCAmelCase ( lowercase ) -> List[str]:
for base_class in inspect.getmro(lowercase ):
__lowerCAmelCase = base_class.__module__
__lowerCAmelCase = base_class.__name__
if module.startswith("""tensorflow""" ) or module.startswith("""keras""" ) or name == "TFPreTrainedModel":
return "tf"
elif module.startswith("""torch""" ) or name == "PreTrainedModel":
return "pt"
elif module.startswith("""flax""" ) or module.startswith("""jax""" ) or name == "FlaxPreTrainedModel":
return "flax"
else:
raise TypeError(f'Could not infer framework from class {model_class}.' )
| 689 |
'''simple docstring'''
import argparse
import torch
from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline
def _lowerCAmelCase ( lowercase , lowercase , lowercase , lowercase , lowercase ) -> Optional[int]:
# load base model
__lowerCAmelCase = StableDiffusionPipeline.from_pretrained(lowercase , torch_dtype=torch.floataa )
# load LoRA weight from .safetensors
__lowerCAmelCase = load_file(lowercase )
__lowerCAmelCase = []
# directly update weight in diffusers model
for key in state_dict:
# it is suggested to print out the key, it usually will be something like below
# "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"
# as we have set the alpha beforehand, so just skip
if ".alpha" in key or key in visited:
continue
if "text" in key:
__lowerCAmelCase = key.split(""".""" )[0].split(LORA_PREFIX_TEXT_ENCODER + """_""" )[-1].split("""_""" )
__lowerCAmelCase = pipeline.text_encoder
else:
__lowerCAmelCase = key.split(""".""" )[0].split(LORA_PREFIX_UNET + """_""" )[-1].split("""_""" )
__lowerCAmelCase = pipeline.unet
# find the target layer
__lowerCAmelCase = layer_infos.pop(0 )
while len(lowercase ) > -1:
try:
__lowerCAmelCase = curr_layer.__getattr__(lowercase )
if len(lowercase ) > 0:
__lowerCAmelCase = layer_infos.pop(0 )
elif len(lowercase ) == 0:
break
except Exception:
if len(lowercase ) > 0:
temp_name += "_" + layer_infos.pop(0 )
else:
__lowerCAmelCase = layer_infos.pop(0 )
__lowerCAmelCase = []
if "lora_down" in key:
pair_keys.append(key.replace("""lora_down""" , """lora_up""" ) )
pair_keys.append(lowercase )
else:
pair_keys.append(lowercase )
pair_keys.append(key.replace("""lora_up""" , """lora_down""" ) )
# update weight
if len(state_dict[pair_keys[0]].shape ) == 4:
__lowerCAmelCase = state_dict[pair_keys[0]].squeeze(3 ).squeeze(2 ).to(torch.floataa )
__lowerCAmelCase = state_dict[pair_keys[1]].squeeze(3 ).squeeze(2 ).to(torch.floataa )
curr_layer.weight.data += alpha * torch.mm(lowercase , lowercase ).unsqueeze(2 ).unsqueeze(3 )
else:
__lowerCAmelCase = state_dict[pair_keys[0]].to(torch.floataa )
__lowerCAmelCase = state_dict[pair_keys[1]].to(torch.floataa )
curr_layer.weight.data += alpha * torch.mm(lowercase , lowercase )
# update visited list
for item in pair_keys:
visited.append(lowercase )
return pipeline
if __name__ == "__main__":
_a : Union[str, Any] = argparse.ArgumentParser()
parser.add_argument(
"""--base_model_path""", default=None, type=str, required=True, help="""Path to the base model in diffusers format."""
)
parser.add_argument(
"""--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
)
parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")
parser.add_argument(
"""--lora_prefix_unet""", default="""lora_unet""", type=str, help="""The prefix of UNet weight in safetensors"""
)
parser.add_argument(
"""--lora_prefix_text_encoder""",
default="""lora_te""",
type=str,
help="""The prefix of text encoder weight in safetensors""",
)
parser.add_argument("""--alpha""", default=0.75, type=float, help="""The merging ratio in W = W0 + alpha * deltaW""")
parser.add_argument(
"""--to_safetensors""", action="""store_true""", help="""Whether to store pipeline in safetensors format or not."""
)
parser.add_argument("""--device""", type=str, help="""Device to use (e.g. cpu, cuda:0, cuda:1, etc.)""")
_a : Optional[int] = parser.parse_args()
_a : Dict = args.base_model_path
_a : Optional[Any] = args.checkpoint_path
_a : Union[str, Any] = args.dump_path
_a : Optional[int] = args.lora_prefix_unet
_a : int = args.lora_prefix_text_encoder
_a : str = args.alpha
_a : Any = convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)
_a : Tuple = pipe.to(args.device)
pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
| 689 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_a : List[str] = {"""configuration_fnet""": ["""FNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FNetConfig"""]}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_a : Dict = ["""FNetTokenizer"""]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_a : List[str] = ["""FNetTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_a : Tuple = [
"""FNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""FNetForMaskedLM""",
"""FNetForMultipleChoice""",
"""FNetForNextSentencePrediction""",
"""FNetForPreTraining""",
"""FNetForQuestionAnswering""",
"""FNetForSequenceClassification""",
"""FNetForTokenClassification""",
"""FNetLayer""",
"""FNetModel""",
"""FNetPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_fnet import FNetTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_fnet_fast import FNetTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_fnet import (
FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
FNetForMaskedLM,
FNetForMultipleChoice,
FNetForNextSentencePrediction,
FNetForPreTraining,
FNetForQuestionAnswering,
FNetForSequenceClassification,
FNetForTokenClassification,
FNetLayer,
FNetModel,
FNetPreTrainedModel,
)
else:
import sys
_a : Union[str, Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 689 |
'''simple docstring'''
from collections import Counter
from timeit import timeit
def _lowerCAmelCase ( lowercase = "" , ) -> bool:
return sum(c % 2 for c in Counter(input_str.replace(""" """ , """""" ).lower() ).values() ) < 2
def _lowerCAmelCase ( lowercase = "" ) -> bool:
if len(lowercase ) == 0:
return True
__lowerCAmelCase = input_str.replace(""" """ , """""" ).lower()
# character_freq_dict: Stores the frequency of every character in the input string
__lowerCAmelCase = {}
for character in lower_case_input_str:
__lowerCAmelCase = character_freq_dict.get(lowercase , 0 ) + 1
__lowerCAmelCase = 0
for character_count in character_freq_dict.values():
if character_count % 2:
odd_char += 1
if odd_char > 1:
return False
return True
def _lowerCAmelCase ( lowercase = "" ) -> None:
print("""\nFor string = """ , lowercase , """:""" )
print(
"""> can_string_be_rearranged_as_palindrome_counter()""" , """\tans =""" , can_string_be_rearranged_as_palindrome_counter(lowercase ) , """\ttime =""" , timeit(
"""z.can_string_be_rearranged_as_palindrome_counter(z.check_str)""" , setup="""import __main__ as z""" , ) , """seconds""" , )
print(
"""> can_string_be_rearranged_as_palindrome()""" , """\tans =""" , can_string_be_rearranged_as_palindrome(lowercase ) , """\ttime =""" , timeit(
"""z.can_string_be_rearranged_as_palindrome(z.check_str)""" , setup="""import __main__ as z""" , ) , """seconds""" , )
if __name__ == "__main__":
_a : int = input(
"""Enter string to determine if it can be rearranged as a palindrome or not: """
).strip()
benchmark(check_str)
_a : Optional[int] = can_string_be_rearranged_as_palindrome_counter(check_str)
print(f'{check_str} can {"" if status else "not "}be rearranged as a palindrome')
| 689 | 1 |
'''simple docstring'''
from datasets.utils.patching import _PatchedModuleObj, patch_submodule
from . import _test_patching
def _lowerCAmelCase ( ) -> int:
import os as original_os
from os import path as original_path
from os import rename as original_rename
from os.path import dirname as original_dirname
from os.path import join as original_join
assert _test_patching.os is original_os
assert _test_patching.path is original_path
assert _test_patching.join is original_join
assert _test_patching.renamed_os is original_os
assert _test_patching.renamed_path is original_path
assert _test_patching.renamed_join is original_join
__lowerCAmelCase = """__test_patch_submodule_mock__"""
with patch_submodule(_test_patching , """os.path.join""" , lowercase ):
# Every way to access os.path.join must be patched, and the rest must stay untouched
# check os.path.join
assert isinstance(_test_patching.os , _PatchedModuleObj )
assert isinstance(_test_patching.os.path , _PatchedModuleObj )
assert _test_patching.os.path.join is mock
# check path.join
assert isinstance(_test_patching.path , _PatchedModuleObj )
assert _test_patching.path.join is mock
# check join
assert _test_patching.join is mock
# check that the other attributes are untouched
assert _test_patching.os.rename is original_rename
assert _test_patching.path.dirname is original_dirname
assert _test_patching.os.path.dirname is original_dirname
# Even renamed modules or objects must be patched
# check renamed_os.path.join
assert isinstance(_test_patching.renamed_os , _PatchedModuleObj )
assert isinstance(_test_patching.renamed_os.path , _PatchedModuleObj )
assert _test_patching.renamed_os.path.join is mock
# check renamed_path.join
assert isinstance(_test_patching.renamed_path , _PatchedModuleObj )
assert _test_patching.renamed_path.join is mock
# check renamed_join
assert _test_patching.renamed_join is mock
# check that the other attributes are untouched
assert _test_patching.renamed_os.rename is original_rename
assert _test_patching.renamed_path.dirname is original_dirname
assert _test_patching.renamed_os.path.dirname is original_dirname
# check that everthing is back to normal when the patch is over
assert _test_patching.os is original_os
assert _test_patching.path is original_path
assert _test_patching.join is original_join
assert _test_patching.renamed_os is original_os
assert _test_patching.renamed_path is original_path
assert _test_patching.renamed_join is original_join
def _lowerCAmelCase ( ) -> int:
assert _test_patching.open is open
__lowerCAmelCase = """__test_patch_submodule_builtin_mock__"""
# _test_patching has "open" in its globals
assert _test_patching.open is open
with patch_submodule(_test_patching , """open""" , lowercase ):
assert _test_patching.open is mock
# check that everthing is back to normal when the patch is over
assert _test_patching.open is open
def _lowerCAmelCase ( ) -> str:
# pandas.read_csv is not present in _test_patching
__lowerCAmelCase = """__test_patch_submodule_missing_mock__"""
with patch_submodule(_test_patching , """pandas.read_csv""" , lowercase ):
pass
def _lowerCAmelCase ( ) -> Any:
# builtin should always be mocked even if they're not in the globals
# in case they're loaded at one point
__lowerCAmelCase = """__test_patch_submodule_missing_builtin_mock__"""
# _test_patching doesn't have "len" in its globals
assert getattr(_test_patching , """len""" , lowercase ) is None
with patch_submodule(_test_patching , """len""" , lowercase ):
assert _test_patching.len is mock
assert _test_patching.len is len
def _lowerCAmelCase ( ) -> Optional[Any]:
__lowerCAmelCase = """__test_patch_submodule_start_and_stop_mock__"""
__lowerCAmelCase = patch_submodule(_test_patching , """open""" , lowercase )
assert _test_patching.open is open
patch.start()
assert _test_patching.open is mock
patch.stop()
assert _test_patching.open is open
def _lowerCAmelCase ( ) -> Dict:
from os import rename as original_rename
from os.path import dirname as original_dirname
from os.path import join as original_join
__lowerCAmelCase = """__test_patch_submodule_successive_join__"""
__lowerCAmelCase = """__test_patch_submodule_successive_dirname__"""
__lowerCAmelCase = """__test_patch_submodule_successive_rename__"""
assert _test_patching.os.path.join is original_join
assert _test_patching.os.path.dirname is original_dirname
assert _test_patching.os.rename is original_rename
with patch_submodule(_test_patching , """os.path.join""" , lowercase ):
with patch_submodule(_test_patching , """os.rename""" , lowercase ):
with patch_submodule(_test_patching , """os.path.dirname""" , lowercase ):
assert _test_patching.os.path.join is mock_join
assert _test_patching.os.path.dirname is mock_dirname
assert _test_patching.os.rename is mock_rename
# try another order
with patch_submodule(_test_patching , """os.rename""" , lowercase ):
with patch_submodule(_test_patching , """os.path.join""" , lowercase ):
with patch_submodule(_test_patching , """os.path.dirname""" , lowercase ):
assert _test_patching.os.path.join is mock_join
assert _test_patching.os.path.dirname is mock_dirname
assert _test_patching.os.rename is mock_rename
assert _test_patching.os.path.join is original_join
assert _test_patching.os.path.dirname is original_dirname
assert _test_patching.os.rename is original_rename
def _lowerCAmelCase ( ) -> Any:
__lowerCAmelCase = """__test_patch_submodule_doesnt_exist_mock__"""
with patch_submodule(_test_patching , """__module_that_doesn_exist__.__attribute_that_doesn_exist__""" , lowercase ):
pass
with patch_submodule(_test_patching , """os.__attribute_that_doesn_exist__""" , lowercase ):
pass
| 689 |
'''simple docstring'''
import argparse
import json
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
VideoMAEConfig,
VideoMAEForPreTraining,
VideoMAEForVideoClassification,
VideoMAEImageProcessor,
)
def _lowerCAmelCase ( lowercase ) -> List[Any]:
__lowerCAmelCase = VideoMAEConfig()
set_architecture_configs(lowercase , lowercase )
if "finetuned" not in model_name:
__lowerCAmelCase = False
if "finetuned" in model_name:
__lowerCAmelCase = """huggingface/label-files"""
if "kinetics" in model_name:
__lowerCAmelCase = 400
__lowerCAmelCase = """kinetics400-id2label.json"""
elif "ssv2" in model_name:
__lowerCAmelCase = 174
__lowerCAmelCase = """something-something-v2-id2label.json"""
else:
raise ValueError("""Model name should either contain 'kinetics' or 'ssv2' in case it's fine-tuned.""" )
__lowerCAmelCase = json.load(open(hf_hub_download(lowercase , lowercase , repo_type="""dataset""" ) , """r""" ) )
__lowerCAmelCase = {int(lowercase ): v for k, v in idalabel.items()}
__lowerCAmelCase = idalabel
__lowerCAmelCase = {v: k for k, v in idalabel.items()}
return config
def _lowerCAmelCase ( lowercase , lowercase ) -> Any:
if "small" in model_name:
__lowerCAmelCase = 384
__lowerCAmelCase = 1536
__lowerCAmelCase = 12
__lowerCAmelCase = 16
__lowerCAmelCase = 12
__lowerCAmelCase = 3
__lowerCAmelCase = 192
__lowerCAmelCase = 768
elif "large" in model_name:
__lowerCAmelCase = 1024
__lowerCAmelCase = 4096
__lowerCAmelCase = 24
__lowerCAmelCase = 16
__lowerCAmelCase = 12
__lowerCAmelCase = 8
__lowerCAmelCase = 512
__lowerCAmelCase = 2048
elif "huge" in model_name:
__lowerCAmelCase = 1280
__lowerCAmelCase = 5120
__lowerCAmelCase = 32
__lowerCAmelCase = 16
__lowerCAmelCase = 12
__lowerCAmelCase = 8
__lowerCAmelCase = 640
__lowerCAmelCase = 2560
elif "base" not in model_name:
raise ValueError("""Model name should include either \"small\", \"base\", \"large\", or \"huge\"""" )
def _lowerCAmelCase ( lowercase ) -> List[str]:
if "encoder." in name:
__lowerCAmelCase = name.replace("""encoder.""" , """""" )
if "cls_token" in name:
__lowerCAmelCase = name.replace("""cls_token""" , """videomae.embeddings.cls_token""" )
if "decoder_pos_embed" in name:
__lowerCAmelCase = name.replace("""decoder_pos_embed""" , """decoder.decoder_pos_embed""" )
if "pos_embed" in name and "decoder" not in name:
__lowerCAmelCase = name.replace("""pos_embed""" , """videomae.embeddings.position_embeddings""" )
if "patch_embed.proj" in name:
__lowerCAmelCase = name.replace("""patch_embed.proj""" , """videomae.embeddings.patch_embeddings.projection""" )
if "patch_embed.norm" in name:
__lowerCAmelCase = name.replace("""patch_embed.norm""" , """videomae.embeddings.norm""" )
if "decoder.blocks" in name:
__lowerCAmelCase = name.replace("""decoder.blocks""" , """decoder.decoder_layers""" )
if "blocks" in name:
__lowerCAmelCase = name.replace("""blocks""" , """videomae.encoder.layer""" )
if "attn.proj" in name:
__lowerCAmelCase = name.replace("""attn.proj""" , """attention.output.dense""" )
if "attn" in name and "bias" not in name:
__lowerCAmelCase = name.replace("""attn""" , """attention.self""" )
if "attn" in name:
__lowerCAmelCase = name.replace("""attn""" , """attention.attention""" )
if "norm1" in name:
__lowerCAmelCase = name.replace("""norm1""" , """layernorm_before""" )
if "norm2" in name:
__lowerCAmelCase = name.replace("""norm2""" , """layernorm_after""" )
if "mlp.fc1" in name:
__lowerCAmelCase = name.replace("""mlp.fc1""" , """intermediate.dense""" )
if "mlp.fc2" in name:
__lowerCAmelCase = name.replace("""mlp.fc2""" , """output.dense""" )
if "decoder_embed" in name:
__lowerCAmelCase = name.replace("""decoder_embed""" , """decoder.decoder_embed""" )
if "decoder_norm" in name:
__lowerCAmelCase = name.replace("""decoder_norm""" , """decoder.decoder_norm""" )
if "decoder_pred" in name:
__lowerCAmelCase = name.replace("""decoder_pred""" , """decoder.decoder_pred""" )
if "norm.weight" in name and "decoder" not in name and "fc" not in name:
__lowerCAmelCase = name.replace("""norm.weight""" , """videomae.layernorm.weight""" )
if "norm.bias" in name and "decoder" not in name and "fc" not in name:
__lowerCAmelCase = name.replace("""norm.bias""" , """videomae.layernorm.bias""" )
if "head" in name and "decoder" not in name:
__lowerCAmelCase = name.replace("""head""" , """classifier""" )
return name
def _lowerCAmelCase ( lowercase , lowercase ) -> List[Any]:
for key in orig_state_dict.copy().keys():
__lowerCAmelCase = orig_state_dict.pop(lowercase )
if key.startswith("""encoder.""" ):
__lowerCAmelCase = key.replace("""encoder.""" , """""" )
if "qkv" in key:
__lowerCAmelCase = key.split(""".""" )
if key.startswith("""decoder.blocks""" ):
__lowerCAmelCase = config.decoder_hidden_size
__lowerCAmelCase = int(key_split[2] )
__lowerCAmelCase = """decoder.decoder_layers."""
if "weight" in key:
__lowerCAmelCase = val[:dim, :]
__lowerCAmelCase = val[dim : dim * 2, :]
__lowerCAmelCase = val[-dim:, :]
else:
__lowerCAmelCase = config.hidden_size
__lowerCAmelCase = int(key_split[1] )
__lowerCAmelCase = """videomae.encoder.layer."""
if "weight" in key:
__lowerCAmelCase = val[:dim, :]
__lowerCAmelCase = val[dim : dim * 2, :]
__lowerCAmelCase = val[-dim:, :]
else:
__lowerCAmelCase = val
return orig_state_dict
def _lowerCAmelCase ( ) -> str:
__lowerCAmelCase = hf_hub_download(
repo_id="""hf-internal-testing/spaghetti-video""" , filename="""eating_spaghetti.npy""" , repo_type="""dataset""" )
__lowerCAmelCase = np.load(lowercase )
return list(lowercase )
def _lowerCAmelCase ( lowercase , lowercase , lowercase , lowercase ) -> Optional[Any]:
__lowerCAmelCase = get_videomae_config(lowercase )
if "finetuned" in model_name:
__lowerCAmelCase = VideoMAEForVideoClassification(lowercase )
else:
__lowerCAmelCase = VideoMAEForPreTraining(lowercase )
# download original checkpoint, hosted on Google Drive
__lowerCAmelCase = """pytorch_model.bin"""
gdown.cached_download(lowercase , lowercase , quiet=lowercase )
__lowerCAmelCase = torch.load(lowercase , map_location="""cpu""" )
if "model" in files:
__lowerCAmelCase = files["""model"""]
else:
__lowerCAmelCase = files["""module"""]
__lowerCAmelCase = convert_state_dict(lowercase , lowercase )
model.load_state_dict(lowercase )
model.eval()
# verify model on basic input
__lowerCAmelCase = VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5] , image_std=[0.5, 0.5, 0.5] )
__lowerCAmelCase = prepare_video()
__lowerCAmelCase = image_processor(lowercase , return_tensors="""pt""" )
if "finetuned" not in model_name:
__lowerCAmelCase = hf_hub_download(repo_id="""hf-internal-testing/bool-masked-pos""" , filename="""bool_masked_pos.pt""" )
__lowerCAmelCase = torch.load(lowercase )
__lowerCAmelCase = model(**lowercase )
__lowerCAmelCase = outputs.logits
__lowerCAmelCase = [
"""videomae-small-finetuned-kinetics""",
"""videomae-small-finetuned-ssv2""",
# Kinetics-400 checkpoints (short = pretrained only for 800 epochs instead of 1600)
"""videomae-base-short""",
"""videomae-base-short-finetuned-kinetics""",
"""videomae-base""",
"""videomae-base-finetuned-kinetics""",
"""videomae-large""",
"""videomae-large-finetuned-kinetics""",
"""videomae-huge-finetuned-kinetics""",
# Something-Something-v2 checkpoints (short = pretrained only for 800 epochs instead of 2400)
"""videomae-base-short-ssv2""",
"""videomae-base-short-finetuned-ssv2""",
"""videomae-base-ssv2""",
"""videomae-base-finetuned-ssv2""",
]
# NOTE: logits were tested with image_mean and image_std equal to [0.5, 0.5, 0.5] and [0.5, 0.5, 0.5]
if model_name == "videomae-small-finetuned-kinetics":
__lowerCAmelCase = torch.Size([1, 400] )
__lowerCAmelCase = torch.tensor([-0.92_91, -0.40_61, -0.93_07] )
elif model_name == "videomae-small-finetuned-ssv2":
__lowerCAmelCase = torch.Size([1, 174] )
__lowerCAmelCase = torch.tensor([0.26_71, -0.46_89, -0.82_35] )
elif model_name == "videomae-base":
__lowerCAmelCase = torch.Size([1, 1408, 1536] )
__lowerCAmelCase = torch.tensor([[0.77_39, 0.79_68, 0.70_89], [0.67_01, 0.74_87, 0.62_09], [0.42_87, 0.51_58, 0.47_73]] )
elif model_name == "videomae-base-short":
__lowerCAmelCase = torch.Size([1, 1408, 1536] )
__lowerCAmelCase = torch.tensor([[0.79_94, 0.96_12, 0.85_08], [0.74_01, 0.89_58, 0.83_02], [0.58_62, 0.74_68, 0.73_25]] )
# we verified the loss both for normalized and unnormalized targets for this one
__lowerCAmelCase = torch.tensor([0.51_42] ) if config.norm_pix_loss else torch.tensor([0.64_69] )
elif model_name == "videomae-large":
__lowerCAmelCase = torch.Size([1, 1408, 1536] )
__lowerCAmelCase = torch.tensor([[0.71_49, 0.79_97, 0.69_66], [0.67_68, 0.78_69, 0.69_48], [0.51_39, 0.62_21, 0.56_05]] )
elif model_name == "videomae-large-finetuned-kinetics":
__lowerCAmelCase = torch.Size([1, 400] )
__lowerCAmelCase = torch.tensor([0.07_71, 0.00_11, -0.36_25] )
elif model_name == "videomae-huge-finetuned-kinetics":
__lowerCAmelCase = torch.Size([1, 400] )
__lowerCAmelCase = torch.tensor([0.24_33, 0.16_32, -0.48_94] )
elif model_name == "videomae-base-short-finetuned-kinetics":
__lowerCAmelCase = torch.Size([1, 400] )
__lowerCAmelCase = torch.tensor([0.65_88, 0.09_90, -0.24_93] )
elif model_name == "videomae-base-finetuned-kinetics":
__lowerCAmelCase = torch.Size([1, 400] )
__lowerCAmelCase = torch.tensor([0.36_69, -0.06_88, -0.24_21] )
elif model_name == "videomae-base-short-ssv2":
__lowerCAmelCase = torch.Size([1, 1408, 1536] )
__lowerCAmelCase = torch.tensor([[0.47_12, 0.52_96, 0.57_86], [0.22_78, 0.27_29, 0.40_26], [0.03_52, 0.07_30, 0.25_06]] )
elif model_name == "videomae-base-short-finetuned-ssv2":
__lowerCAmelCase = torch.Size([1, 174] )
__lowerCAmelCase = torch.tensor([-0.05_37, -0.15_39, -0.32_66] )
elif model_name == "videomae-base-ssv2":
__lowerCAmelCase = torch.Size([1, 1408, 1536] )
__lowerCAmelCase = torch.tensor([[0.81_31, 0.87_27, 0.85_46], [0.73_66, 0.93_77, 0.88_70], [0.59_35, 0.88_74, 0.85_64]] )
elif model_name == "videomae-base-finetuned-ssv2":
__lowerCAmelCase = torch.Size([1, 174] )
__lowerCAmelCase = torch.tensor([0.19_61, -0.83_37, -0.63_89] )
else:
raise ValueError(f'Model name not supported. Should be one of {model_names}' )
# verify logits
assert logits.shape == expected_shape
if "finetuned" in model_name:
assert torch.allclose(logits[0, :3] , lowercase , atol=1e-4 )
else:
print("""Logits:""" , logits[0, :3, :3] )
assert torch.allclose(logits[0, :3, :3] , lowercase , atol=1e-4 )
print("""Logits ok!""" )
# verify loss, if applicable
if model_name == "videomae-base-short":
__lowerCAmelCase = outputs.loss
assert torch.allclose(lowercase , lowercase , atol=1e-4 )
print("""Loss ok!""" )
if pytorch_dump_folder_path is not None:
print(f'Saving model and image processor to {pytorch_dump_folder_path}' )
image_processor.save_pretrained(lowercase )
model.save_pretrained(lowercase )
if push_to_hub:
print("""Pushing to the hub...""" )
model.push_to_hub(lowercase , organization="""nielsr""" )
if __name__ == "__main__":
_a : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--checkpoint_url""",
default="""https://drive.google.com/u/1/uc?id=1tEhLyskjb755TJ65ptsrafUG2llSwQE1&export=download&confirm=t&uuid=aa3276eb-fb7e-482a-adec-dc7171df14c4""",
type=str,
help=(
"""URL of the original PyTorch checkpoint (on Google Drive) you'd like to convert. Should be a direct"""
""" download link."""
),
)
parser.add_argument(
"""--pytorch_dump_folder_path""",
default="""/Users/nielsrogge/Documents/VideoMAE/Test""",
type=str,
help="""Path to the output PyTorch model directory.""",
)
parser.add_argument("""--model_name""", default="""videomae-base""", type=str, help="""Name of the model.""")
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
)
_a : int = parser.parse_args()
convert_videomae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 689 | 1 |
'''simple docstring'''
import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel
from ...utils import logging
_a : Any = logging.get_logger(__name__)
class _UpperCAmelCase ( lowerCAmelCase_ ):
a : int =CLIPConfig
a : Optional[int] =["""CLIPEncoderLayer"""]
def __init__( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
super().__init__(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = CLIPVisionModelWithProjection(config.vision_config )
__lowerCAmelCase = nn.Linear(config.vision_config.projection_dim,1 )
__lowerCAmelCase = nn.Linear(config.vision_config.projection_dim,1 )
@torch.no_grad()
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=0.5,__SCREAMING_SNAKE_CASE=0.5 ):
'''simple docstring'''
__lowerCAmelCase = self.vision_model(__SCREAMING_SNAKE_CASE )[0]
__lowerCAmelCase = self.p_head(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = nsfw_detected.flatten()
__lowerCAmelCase = nsfw_detected > p_threshold
__lowerCAmelCase = nsfw_detected.tolist()
if any(__SCREAMING_SNAKE_CASE ):
logger.warning(
"""Potential NSFW content was detected in one or more images. A black image will be returned instead."""
""" Try again with a different prompt and/or seed.""" )
for idx, nsfw_detected_ in enumerate(__SCREAMING_SNAKE_CASE ):
if nsfw_detected_:
__lowerCAmelCase = np.zeros(images[idx].shape )
__lowerCAmelCase = self.w_head(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = watermark_detected.flatten()
__lowerCAmelCase = watermark_detected > w_threshold
__lowerCAmelCase = watermark_detected.tolist()
if any(__SCREAMING_SNAKE_CASE ):
logger.warning(
"""Potential watermarked content was detected in one or more images. A black image will be returned instead."""
""" Try again with a different prompt and/or seed.""" )
for idx, watermark_detected_ in enumerate(__SCREAMING_SNAKE_CASE ):
if watermark_detected_:
__lowerCAmelCase = np.zeros(images[idx].shape )
return images, nsfw_detected, watermark_detected
| 689 |
'''simple docstring'''
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
_a : Tuple = """\
"""
_a : Tuple = """
Perplexity (PPL) is one of the most common metrics for evaluating language models.
It is defined as the exponentiated average negative log-likelihood of a sequence.
For more information, see https://huggingface.co/docs/transformers/perplexity
"""
_a : Optional[Any] = """
Args:
model_id (str): model used for calculating Perplexity
NOTE: Perplexity can only be calculated for causal language models.
This includes models such as gpt2, causal variations of bert,
causal versions of t5, and more (the full list can be found
in the AutoModelForCausalLM documentation here:
https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )
input_texts (list of str): input text, each separate text snippet
is one list entry.
batch_size (int): the batch size to run texts through the model. Defaults to 16.
add_start_token (bool): whether to add the start token to the texts,
so the perplexity can include the probability of the first word. Defaults to True.
device (str): device to run on, defaults to 'cuda' when available
Returns:
perplexity: dictionary containing the perplexity scores for the texts
in the input list, as well as the mean perplexity. If one of the input texts is
longer than the max input length of the model, then it is truncated to the
max length for the perplexity computation.
Examples:
Example 1:
>>> perplexity = datasets.load_metric(\"perplexity\")
>>> input_texts = [\"lorem ipsum\", \"Happy Birthday!\", \"Bienvenue\"]
>>> results = perplexity.compute(model_id='gpt2',
... add_start_token=False,
... input_texts=input_texts) # doctest:+ELLIPSIS
>>> print(list(results.keys()))
['perplexities', 'mean_perplexity']
>>> print(round(results[\"mean_perplexity\"], 2))
78.22
>>> print(round(results[\"perplexities\"][0], 2))
11.11
Example 2:
>>> perplexity = datasets.load_metric(\"perplexity\")
>>> input_texts = datasets.load_dataset(\"wikitext\",
... \"wikitext-2-raw-v1\",
... split=\"test\")[\"text\"][:50] # doctest:+ELLIPSIS
[...]
>>> input_texts = [s for s in input_texts if s!='']
>>> results = perplexity.compute(model_id='gpt2',
... input_texts=input_texts) # doctest:+ELLIPSIS
>>> print(list(results.keys()))
['perplexities', 'mean_perplexity']
>>> print(round(results[\"mean_perplexity\"], 2))
60.35
>>> print(round(results[\"perplexities\"][0], 2))
81.12
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _UpperCAmelCase ( datasets.Metric ):
def lowerCamelCase__ ( self ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION,citation=_CITATION,inputs_description=_KWARGS_DESCRIPTION,features=datasets.Features(
{
"""input_texts""": datasets.Value("""string""" ),
} ),reference_urls=["""https://huggingface.co/docs/transformers/perplexity"""],)
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE = 16,__SCREAMING_SNAKE_CASE = True,__SCREAMING_SNAKE_CASE=None ):
'''simple docstring'''
if device is not None:
assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
if device == "gpu":
__lowerCAmelCase = """cuda"""
else:
__lowerCAmelCase = """cuda""" if torch.cuda.is_available() else """cpu"""
__lowerCAmelCase = AutoModelForCausalLM.from_pretrained(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = model.to(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE )
# if batch_size > 1 (which generally leads to padding being required), and
# if there is not an already assigned pad_token, assign an existing
# special token to also be the padding token
if tokenizer.pad_token is None and batch_size > 1:
__lowerCAmelCase = list(tokenizer.special_tokens_map_extended.values() )
# check that the model already has at least one special token defined
assert (
len(__SCREAMING_SNAKE_CASE ) > 0
), "If batch_size > 1, model must have at least one special token to use for padding. Please use a different model or set batch_size=1."
# assign one of the special tokens to also be the pad token
tokenizer.add_special_tokens({"""pad_token""": existing_special_tokens[0]} )
if add_start_token:
# leave room for <BOS> token to be added:
assert (
tokenizer.bos_token is not None
), "Input model must already have a BOS token if using add_start_token=True. Please use a different model, or set add_start_token=False"
__lowerCAmelCase = model.config.max_length - 1
else:
__lowerCAmelCase = model.config.max_length
__lowerCAmelCase = tokenizer(
__SCREAMING_SNAKE_CASE,add_special_tokens=__SCREAMING_SNAKE_CASE,padding=__SCREAMING_SNAKE_CASE,truncation=__SCREAMING_SNAKE_CASE,max_length=__SCREAMING_SNAKE_CASE,return_tensors="""pt""",return_attention_mask=__SCREAMING_SNAKE_CASE,).to(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = encodings["""input_ids"""]
__lowerCAmelCase = encodings["""attention_mask"""]
# check that each input is long enough:
if add_start_token:
assert torch.all(torch.ge(attn_masks.sum(1 ),1 ) ), "Each input text must be at least one token long."
else:
assert torch.all(
torch.ge(attn_masks.sum(1 ),2 ) ), "When add_start_token=False, each input text must be at least two tokens long. Run with add_start_token=True if inputting strings of only one token, and remove all empty input strings."
__lowerCAmelCase = []
__lowerCAmelCase = CrossEntropyLoss(reduction="""none""" )
for start_index in logging.tqdm(range(0,len(__SCREAMING_SNAKE_CASE ),__SCREAMING_SNAKE_CASE ) ):
__lowerCAmelCase = min(start_index + batch_size,len(__SCREAMING_SNAKE_CASE ) )
__lowerCAmelCase = encoded_texts[start_index:end_index]
__lowerCAmelCase = attn_masks[start_index:end_index]
if add_start_token:
__lowerCAmelCase = torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0 ) ).to(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = torch.cat([bos_tokens_tensor, encoded_batch],dim=1 )
__lowerCAmelCase = torch.cat(
[torch.ones(bos_tokens_tensor.size(),dtype=torch.intaa ).to(__SCREAMING_SNAKE_CASE ), attn_mask],dim=1 )
__lowerCAmelCase = encoded_batch
with torch.no_grad():
__lowerCAmelCase = model(__SCREAMING_SNAKE_CASE,attention_mask=__SCREAMING_SNAKE_CASE ).logits
__lowerCAmelCase = out_logits[..., :-1, :].contiguous()
__lowerCAmelCase = labels[..., 1:].contiguous()
__lowerCAmelCase = attn_mask[..., 1:].contiguous()
__lowerCAmelCase = torch.expa(
(loss_fct(shift_logits.transpose(1,2 ),__SCREAMING_SNAKE_CASE ) * shift_attention_mask_batch).sum(1 )
/ shift_attention_mask_batch.sum(1 ) )
ppls += perplexity_batch.tolist()
return {"perplexities": ppls, "mean_perplexity": np.mean(__SCREAMING_SNAKE_CASE )}
| 689 | 1 |
'''simple docstring'''
from importlib import import_module
from .logging import get_logger
_a : int = get_logger(__name__)
class _UpperCAmelCase :
def __init__( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=None ):
'''simple docstring'''
__lowerCAmelCase = attrs or []
if module is not None:
for key in module.__dict__:
if key in attrs or not key.startswith("""__""" ):
setattr(self,__SCREAMING_SNAKE_CASE,getattr(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE ) )
__lowerCAmelCase = module._original_module if isinstance(__SCREAMING_SNAKE_CASE,_PatchedModuleObj ) else module
class _UpperCAmelCase :
a : Optional[Any] =[]
def __init__( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=None ):
'''simple docstring'''
__lowerCAmelCase = obj
__lowerCAmelCase = target
__lowerCAmelCase = new
__lowerCAmelCase = target.split(""".""" )[0]
__lowerCAmelCase = {}
__lowerCAmelCase = attrs or []
def __enter__( self ):
'''simple docstring'''
*__lowerCAmelCase , __lowerCAmelCase = self.target.split(""".""" )
# Patch modules:
# it's used to patch attributes of submodules like "os.path.join";
# in this case we need to patch "os" and "os.path"
for i in range(len(__SCREAMING_SNAKE_CASE ) ):
try:
__lowerCAmelCase = import_module(""".""".join(submodules[: i + 1] ) )
except ModuleNotFoundError:
continue
# We iterate over all the globals in self.obj in case we find "os" or "os.path"
for attr in self.obj.__dir__():
__lowerCAmelCase = getattr(self.obj,__SCREAMING_SNAKE_CASE )
# We don't check for the name of the global, but rather if its value *is* "os" or "os.path".
# This allows to patch renamed modules like "from os import path as ospath".
if obj_attr is submodule or (
(isinstance(__SCREAMING_SNAKE_CASE,_PatchedModuleObj ) and obj_attr._original_module is submodule)
):
__lowerCAmelCase = obj_attr
# patch at top level
setattr(self.obj,__SCREAMING_SNAKE_CASE,_PatchedModuleObj(__SCREAMING_SNAKE_CASE,attrs=self.attrs ) )
__lowerCAmelCase = getattr(self.obj,__SCREAMING_SNAKE_CASE )
# construct lower levels patches
for key in submodules[i + 1 :]:
setattr(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,_PatchedModuleObj(getattr(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE ),attrs=self.attrs ) )
__lowerCAmelCase = getattr(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
# finally set the target attribute
setattr(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,self.new )
# Patch attribute itself:
# it's used for builtins like "open",
# and also to patch "os.path.join" we may also need to patch "join"
# itself if it was imported as "from os.path import join".
if submodules: # if it's an attribute of a submodule like "os.path.join"
try:
__lowerCAmelCase = getattr(import_module(""".""".join(__SCREAMING_SNAKE_CASE ) ),__SCREAMING_SNAKE_CASE )
except (AttributeError, ModuleNotFoundError):
return
# We iterate over all the globals in self.obj in case we find "os.path.join"
for attr in self.obj.__dir__():
# We don't check for the name of the global, but rather if its value *is* "os.path.join".
# This allows to patch renamed attributes like "from os.path import join as pjoin".
if getattr(self.obj,__SCREAMING_SNAKE_CASE ) is attr_value:
__lowerCAmelCase = getattr(self.obj,__SCREAMING_SNAKE_CASE )
setattr(self.obj,__SCREAMING_SNAKE_CASE,self.new )
elif target_attr in globals()["__builtins__"]: # if it'a s builtin like "open"
__lowerCAmelCase = globals()["""__builtins__"""][target_attr]
setattr(self.obj,__SCREAMING_SNAKE_CASE,self.new )
else:
raise RuntimeError(f'Tried to patch attribute {target_attr} instead of a submodule.' )
def __exit__( self,*__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
for attr in list(self.original ):
setattr(self.obj,__SCREAMING_SNAKE_CASE,self.original.pop(__SCREAMING_SNAKE_CASE ) )
def lowerCamelCase__ ( self ):
'''simple docstring'''
self.__enter__()
self._active_patches.append(self )
def lowerCamelCase__ ( self ):
'''simple docstring'''
try:
self._active_patches.remove(self )
except ValueError:
# If the patch hasn't been started this will fail
return None
return self.__exit__()
| 689 |
'''simple docstring'''
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
import torch
if is_tf_available():
import tensorflow as tf
class _UpperCAmelCase ( lowerCAmelCase_ ):
a : Union[str, Any] =["""image_processor"""]
a : Dict ="""SamImageProcessor"""
def __init__( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
super().__init__(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = self.image_processor
__lowerCAmelCase = -10
__lowerCAmelCase = self.image_processor.size["""longest_edge"""]
def __call__( self,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE = None,**__SCREAMING_SNAKE_CASE,):
'''simple docstring'''
__lowerCAmelCase = self.image_processor(
__SCREAMING_SNAKE_CASE,return_tensors=__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE,)
# pop arguments that are not used in the foward but used nevertheless
__lowerCAmelCase = encoding_image_processor["""original_sizes"""]
if hasattr(__SCREAMING_SNAKE_CASE,"""numpy""" ): # Checks if Torch or TF tensor
__lowerCAmelCase = original_sizes.numpy()
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = self._check_and_preprocess_points(
input_points=__SCREAMING_SNAKE_CASE,input_labels=__SCREAMING_SNAKE_CASE,input_boxes=__SCREAMING_SNAKE_CASE,)
__lowerCAmelCase = self._normalize_and_convert(
__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,input_points=__SCREAMING_SNAKE_CASE,input_labels=__SCREAMING_SNAKE_CASE,input_boxes=__SCREAMING_SNAKE_CASE,return_tensors=__SCREAMING_SNAKE_CASE,)
return encoding_image_processor
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE="pt",):
'''simple docstring'''
if input_points is not None:
if len(__SCREAMING_SNAKE_CASE ) != len(__SCREAMING_SNAKE_CASE ):
__lowerCAmelCase = [
self._normalize_coordinates(self.target_size,__SCREAMING_SNAKE_CASE,original_sizes[0] ) for point in input_points
]
else:
__lowerCAmelCase = [
self._normalize_coordinates(self.target_size,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
for point, original_size in zip(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
]
# check that all arrays have the same shape
if not all(point.shape == input_points[0].shape for point in input_points ):
if input_labels is not None:
__lowerCAmelCase , __lowerCAmelCase = self._pad_points_and_labels(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = np.array(__SCREAMING_SNAKE_CASE )
if input_labels is not None:
__lowerCAmelCase = np.array(__SCREAMING_SNAKE_CASE )
if input_boxes is not None:
if len(__SCREAMING_SNAKE_CASE ) != len(__SCREAMING_SNAKE_CASE ):
__lowerCAmelCase = [
self._normalize_coordinates(self.target_size,__SCREAMING_SNAKE_CASE,original_sizes[0],is_bounding_box=__SCREAMING_SNAKE_CASE )
for box in input_boxes
]
else:
__lowerCAmelCase = [
self._normalize_coordinates(self.target_size,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,is_bounding_box=__SCREAMING_SNAKE_CASE )
for box, original_size in zip(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
]
__lowerCAmelCase = np.array(__SCREAMING_SNAKE_CASE )
if input_boxes is not None:
if return_tensors == "pt":
__lowerCAmelCase = torch.from_numpy(__SCREAMING_SNAKE_CASE )
# boxes batch size of 1 by default
__lowerCAmelCase = input_boxes.unsqueeze(1 ) if len(input_boxes.shape ) != 3 else input_boxes
elif return_tensors == "tf":
__lowerCAmelCase = tf.convert_to_tensor(__SCREAMING_SNAKE_CASE )
# boxes batch size of 1 by default
__lowerCAmelCase = tf.expand_dims(__SCREAMING_SNAKE_CASE,1 ) if len(input_boxes.shape ) != 3 else input_boxes
encoding_image_processor.update({"""input_boxes""": input_boxes} )
if input_points is not None:
if return_tensors == "pt":
__lowerCAmelCase = torch.from_numpy(__SCREAMING_SNAKE_CASE )
# point batch size of 1 by default
__lowerCAmelCase = input_points.unsqueeze(1 ) if len(input_points.shape ) != 4 else input_points
elif return_tensors == "tf":
__lowerCAmelCase = tf.convert_to_tensor(__SCREAMING_SNAKE_CASE )
# point batch size of 1 by default
__lowerCAmelCase = tf.expand_dims(__SCREAMING_SNAKE_CASE,1 ) if len(input_points.shape ) != 4 else input_points
encoding_image_processor.update({"""input_points""": input_points} )
if input_labels is not None:
if return_tensors == "pt":
__lowerCAmelCase = torch.from_numpy(__SCREAMING_SNAKE_CASE )
# point batch size of 1 by default
__lowerCAmelCase = input_labels.unsqueeze(1 ) if len(input_labels.shape ) != 3 else input_labels
elif return_tensors == "tf":
__lowerCAmelCase = tf.convert_to_tensor(__SCREAMING_SNAKE_CASE )
# point batch size of 1 by default
__lowerCAmelCase = tf.expand_dims(__SCREAMING_SNAKE_CASE,1 ) if len(input_labels.shape ) != 3 else input_labels
encoding_image_processor.update({"""input_labels""": input_labels} )
return encoding_image_processor
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase = max([point.shape[0] for point in input_points] )
__lowerCAmelCase = []
for i, point in enumerate(__SCREAMING_SNAKE_CASE ):
if point.shape[0] != expected_nb_points:
__lowerCAmelCase = np.concatenate(
[point, np.zeros((expected_nb_points - point.shape[0], 2) ) + self.point_pad_value],axis=0 )
__lowerCAmelCase = np.append(input_labels[i],[self.point_pad_value] )
processed_input_points.append(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = processed_input_points
return input_points, input_labels
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=False ):
'''simple docstring'''
__lowerCAmelCase , __lowerCAmelCase = original_size
__lowerCAmelCase , __lowerCAmelCase = self.image_processor._get_preprocess_shape(__SCREAMING_SNAKE_CASE,longest_edge=__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = deepcopy(__SCREAMING_SNAKE_CASE ).astype(__SCREAMING_SNAKE_CASE )
if is_bounding_box:
__lowerCAmelCase = coords.reshape(-1,2,2 )
__lowerCAmelCase = coords[..., 0] * (new_w / old_w)
__lowerCAmelCase = coords[..., 1] * (new_h / old_h)
if is_bounding_box:
__lowerCAmelCase = coords.reshape(-1,4 )
return coords
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=None,):
'''simple docstring'''
if input_points is not None:
if hasattr(__SCREAMING_SNAKE_CASE,"""numpy""" ): # Checks for TF or Torch tensor
__lowerCAmelCase = input_points.numpy().tolist()
if not isinstance(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE ) or not isinstance(input_points[0],__SCREAMING_SNAKE_CASE ):
raise ValueError("""Input points must be a list of list of floating points.""" )
__lowerCAmelCase = [np.array(__SCREAMING_SNAKE_CASE ) for input_point in input_points]
else:
__lowerCAmelCase = None
if input_labels is not None:
if hasattr(__SCREAMING_SNAKE_CASE,"""numpy""" ):
__lowerCAmelCase = input_labels.numpy().tolist()
if not isinstance(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE ) or not isinstance(input_labels[0],__SCREAMING_SNAKE_CASE ):
raise ValueError("""Input labels must be a list of list integers.""" )
__lowerCAmelCase = [np.array(__SCREAMING_SNAKE_CASE ) for label in input_labels]
else:
__lowerCAmelCase = None
if input_boxes is not None:
if hasattr(__SCREAMING_SNAKE_CASE,"""numpy""" ):
__lowerCAmelCase = input_boxes.numpy().tolist()
if (
not isinstance(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
or not isinstance(input_boxes[0],__SCREAMING_SNAKE_CASE )
or not isinstance(input_boxes[0][0],__SCREAMING_SNAKE_CASE )
):
raise ValueError("""Input boxes must be a list of list of list of floating points.""" )
__lowerCAmelCase = [np.array(__SCREAMING_SNAKE_CASE ).astype(np.floataa ) for box in input_boxes]
else:
__lowerCAmelCase = None
return input_points, input_labels, input_boxes
@property
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = self.image_processor.model_input_names
return list(dict.fromkeys(__SCREAMING_SNAKE_CASE ) )
def lowerCamelCase__ ( self,*__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
return self.image_processor.post_process_masks(*__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE )
| 689 | 1 |
'''simple docstring'''
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class _UpperCAmelCase :
def __init__( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
if dst_width < 0 or dst_height < 0:
raise ValueError("""Destination width/height should be > 0""" )
__lowerCAmelCase = img
__lowerCAmelCase = img.shape[1]
__lowerCAmelCase = img.shape[0]
__lowerCAmelCase = dst_width
__lowerCAmelCase = dst_height
__lowerCAmelCase = self.src_w / self.dst_w
__lowerCAmelCase = self.src_h / self.dst_h
__lowerCAmelCase = __lowerCAmelCase = (
np.ones((self.dst_h, self.dst_w, 3),np.uinta ) * 2_55
)
def lowerCamelCase__ ( self ):
'''simple docstring'''
for i in range(self.dst_h ):
for j in range(self.dst_w ):
__lowerCAmelCase = self.img[self.get_y(__SCREAMING_SNAKE_CASE )][self.get_x(__SCREAMING_SNAKE_CASE )]
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
return int(self.ratio_x * x )
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
return int(self.ratio_y * y )
if __name__ == "__main__":
_a ,_a : Any = 8_0_0, 6_0_0
_a : int = imread("""image_data/lena.jpg""", 1)
_a : Any = NearestNeighbour(im, dst_w, dst_h)
n.process()
imshow(
f'Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}', n.output
)
waitKey(0)
destroyAllWindows()
| 689 |
'''simple docstring'''
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequenceClassification,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
TapexTokenizer,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version
from transformers.utils.versions import require_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("""4.17.0.dev0""")
require_version("""datasets>=1.8.0""", """To fix: pip install -r examples/pytorch/text-classification/requirements.txt""")
_a : int = logging.getLogger(__name__)
@dataclass
class _UpperCAmelCase :
a : Optional[str] =field(
default="""tab_fact""" , metadata={"""help""": """The name of the dataset to use (via the datasets library)."""} )
a : Optional[str] =field(
default="""tab_fact""" , metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""} , )
a : int =field(
default=10_24 , metadata={
"""help""": (
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
)
} , )
a : bool =field(
default=lowerCAmelCase_ , metadata={"""help""": """Overwrite the cached preprocessed datasets or not."""} )
a : bool =field(
default=lowerCAmelCase_ , metadata={
"""help""": (
"""Whether to pad all samples to `max_seq_length`. """
"""If False, will pad the samples dynamically when batching to the maximum length in the batch."""
)
} , )
a : Optional[int] =field(
default=lowerCAmelCase_ , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of training examples to this """
"""value if set."""
)
} , )
a : Optional[int] =field(
default=lowerCAmelCase_ , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of evaluation examples to this """
"""value if set."""
)
} , )
a : Optional[int] =field(
default=lowerCAmelCase_ , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of prediction examples to this """
"""value if set."""
)
} , )
a : Optional[str] =field(
default=lowerCAmelCase_ , metadata={"""help""": """A csv or a json file containing the training data."""} )
a : Optional[str] =field(
default=lowerCAmelCase_ , metadata={"""help""": """A csv or a json file containing the validation data."""} )
a : Optional[str] =field(default=lowerCAmelCase_ , metadata={"""help""": """A csv or a json file containing the test data."""} )
def lowerCamelCase__ ( self ):
'''simple docstring'''
if self.dataset_name is not None:
pass
elif self.train_file is None or self.validation_file is None:
raise ValueError("""Need either a GLUE task, a training/validation file or a dataset name.""" )
else:
__lowerCAmelCase = self.train_file.split(""".""" )[-1]
assert train_extension in ["csv", "json"], "`train_file` should be a csv or a json file."
__lowerCAmelCase = self.validation_file.split(""".""" )[-1]
assert (
validation_extension == train_extension
), "`validation_file` should have the same extension (csv or json) as `train_file`."
@dataclass
class _UpperCAmelCase :
a : str =field(
default=lowerCAmelCase_ , metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""} )
a : Optional[str] =field(
default=lowerCAmelCase_ , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""} )
a : Optional[str] =field(
default=lowerCAmelCase_ , metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""} )
a : Optional[str] =field(
default=lowerCAmelCase_ , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} , )
a : bool =field(
default=lowerCAmelCase_ , metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""} , )
a : str =field(
default="""main""" , metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""} , )
a : bool =field(
default=lowerCAmelCase_ , metadata={
"""help""": (
"""Will use the token generated when running `huggingface-cli login` (necessary to use this script """
"""with private models)."""
)
} , )
def _lowerCAmelCase ( ) -> Optional[Any]:
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
__lowerCAmelCase = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith(""".json""" ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = parser.parse_args_into_dataclasses()
# Setup logging
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , handlers=[logging.StreamHandler(sys.stdout )] , )
__lowerCAmelCase = training_args.get_process_log_level()
logger.setLevel(lowercase )
datasets.utils.logging.set_verbosity(lowercase )
transformers.utils.logging.set_verbosity(lowercase )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
f'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
+ f'distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}' )
logger.info(f'Training/evaluation parameters {training_args}' )
# Detecting last checkpoint.
__lowerCAmelCase = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
__lowerCAmelCase = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
f'Output directory ({training_args.output_dir}) already exists and is not empty. '
"""Use --overwrite_output_dir to overcome.""" )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
f'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
"""the `--output_dir` or add `--overwrite_output_dir` to train from scratch.""" )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
# or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub).
#
# For JSON files, this script will use the `question` column for the input question and `table` column for the corresponding table.
#
# If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this
# single column. You can easily tweak this behavior (see below)
#
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
# download the dataset.
if data_args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
__lowerCAmelCase = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , cache_dir=model_args.cache_dir )
else:
# Loading a dataset from your local files.
# CSV/JSON training and evaluation files are needed.
__lowerCAmelCase = {"""train""": data_args.train_file, """validation""": data_args.validation_file}
# Get the test dataset: you can provide your own CSV/JSON test file (see below)
# when you use `do_predict` without specifying a GLUE benchmark task.
if training_args.do_predict:
if data_args.test_file is not None:
__lowerCAmelCase = data_args.train_file.split(""".""" )[-1]
__lowerCAmelCase = data_args.test_file.split(""".""" )[-1]
assert (
test_extension == train_extension
), "`test_file` should have the same extension (csv or json) as `train_file`."
__lowerCAmelCase = data_args.test_file
else:
raise ValueError("""Need either a GLUE task or a test file for `do_predict`.""" )
for key in data_files.keys():
logger.info(f'load a local file for {key}: {data_files[key]}' )
if data_args.train_file.endswith(""".csv""" ):
# Loading a dataset from local csv files
__lowerCAmelCase = load_dataset("""csv""" , data_files=lowercase , cache_dir=model_args.cache_dir )
else:
# Loading a dataset from local json files
__lowerCAmelCase = load_dataset("""json""" , data_files=lowercase , cache_dir=model_args.cache_dir )
# See more about loading any type of standard or custom dataset at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Labels
__lowerCAmelCase = raw_datasets["""train"""].features["""label"""].names
__lowerCAmelCase = len(lowercase )
# Load pretrained model and tokenizer
#
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
__lowerCAmelCase = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=lowercase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# load tapex tokenizer
__lowerCAmelCase = TapexTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , add_prefix_space=lowercase , )
__lowerCAmelCase = BartForSequenceClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool(""".ckpt""" in model_args.model_name_or_path ) , config=lowercase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# Padding strategy
if data_args.pad_to_max_length:
__lowerCAmelCase = """max_length"""
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
__lowerCAmelCase = False
# Some models have set the order of the labels to use, so let's make sure we do use it.
__lowerCAmelCase = {"""Refused""": 0, """Entailed""": 1}
__lowerCAmelCase = {0: """Refused""", 1: """Entailed"""}
if data_args.max_seq_length > tokenizer.model_max_length:
logger.warning(
f'The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the'
f'model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.' )
__lowerCAmelCase = min(data_args.max_seq_length , tokenizer.model_max_length )
def preprocess_tabfact_function(lowercase ):
# Tokenize the texts
def _convert_table_text_to_pandas(lowercase ):
__lowerCAmelCase = [_table_row.split("""#""" ) for _table_row in _table_text.strip("""\n""" ).split("""\n""" )]
__lowerCAmelCase = pd.DataFrame.from_records(_table_content[1:] , columns=_table_content[0] )
return _table_pd
__lowerCAmelCase = examples["""statement"""]
__lowerCAmelCase = list(map(_convert_table_text_to_pandas , examples["""table_text"""] ) )
__lowerCAmelCase = tokenizer(lowercase , lowercase , padding=lowercase , max_length=lowercase , truncation=lowercase )
__lowerCAmelCase = examples["""label"""]
return result
with training_args.main_process_first(desc="""dataset map pre-processing""" ):
__lowerCAmelCase = raw_datasets.map(
lowercase , batched=lowercase , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on dataset""" , )
if training_args.do_train:
if "train" not in raw_datasets:
raise ValueError("""--do_train requires a train dataset""" )
__lowerCAmelCase = raw_datasets["""train"""]
if data_args.max_train_samples is not None:
__lowerCAmelCase = train_dataset.select(range(data_args.max_train_samples ) )
if training_args.do_eval:
if "validation" not in raw_datasets and "validation_matched" not in raw_datasets:
raise ValueError("""--do_eval requires a validation dataset""" )
__lowerCAmelCase = raw_datasets["""validation"""]
if data_args.max_eval_samples is not None:
__lowerCAmelCase = eval_dataset.select(range(data_args.max_eval_samples ) )
if training_args.do_predict or data_args.test_file is not None:
if "test" not in raw_datasets and "test_matched" not in raw_datasets:
raise ValueError("""--do_predict requires a test dataset""" )
__lowerCAmelCase = raw_datasets["""test"""]
if data_args.max_predict_samples is not None:
__lowerCAmelCase = predict_dataset.select(range(data_args.max_predict_samples ) )
# Log a few random samples from the training set:
if training_args.do_train:
for index in random.sample(range(len(lowercase ) ) , 3 ):
logger.info(f'Sample {index} of the training set: {train_dataset[index]}.' )
# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(lowercase ):
__lowerCAmelCase = p.predictions[0] if isinstance(p.predictions , lowercase ) else p.predictions
__lowerCAmelCase = np.argmax(lowercase , axis=1 )
return {"accuracy": (preds == p.label_ids).astype(np.floataa ).mean().item()}
# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
if data_args.pad_to_max_length:
__lowerCAmelCase = default_data_collator
elif training_args.fpaa:
__lowerCAmelCase = DataCollatorWithPadding(lowercase , pad_to_multiple_of=8 )
else:
__lowerCAmelCase = None
# Initialize our Trainer
__lowerCAmelCase = Trainer(
model=lowercase , args=lowercase , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , compute_metrics=lowercase , tokenizer=lowercase , data_collator=lowercase , )
# Training
if training_args.do_train:
__lowerCAmelCase = None
if training_args.resume_from_checkpoint is not None:
__lowerCAmelCase = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
__lowerCAmelCase = last_checkpoint
__lowerCAmelCase = trainer.train(resume_from_checkpoint=lowercase )
__lowerCAmelCase = train_result.metrics
__lowerCAmelCase = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(lowercase )
)
__lowerCAmelCase = min(lowercase , len(lowercase ) )
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics("""train""" , lowercase )
trainer.save_metrics("""train""" , lowercase )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info("""*** Evaluate ***""" )
__lowerCAmelCase = trainer.evaluate(eval_dataset=lowercase )
__lowerCAmelCase = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(lowercase )
__lowerCAmelCase = min(lowercase , len(lowercase ) )
trainer.log_metrics("""eval""" , lowercase )
trainer.save_metrics("""eval""" , lowercase )
if training_args.do_predict:
logger.info("""*** Predict ***""" )
# Removing the `label` columns because it contains -1 and Trainer won't like that.
__lowerCAmelCase = predict_dataset.remove_columns("""label""" )
__lowerCAmelCase = trainer.predict(lowercase , metric_key_prefix="""predict""" ).predictions
__lowerCAmelCase = np.argmax(lowercase , axis=1 )
__lowerCAmelCase = os.path.join(training_args.output_dir , """predict_results_tabfact.txt""" )
if trainer.is_world_process_zero():
with open(lowercase , """w""" ) as writer:
logger.info("""***** Predict Results *****""" )
writer.write("""index\tprediction\n""" )
for index, item in enumerate(lowercase ):
__lowerCAmelCase = label_list[item]
writer.write(f'{index}\t{item}\n' )
__lowerCAmelCase = {"""finetuned_from""": model_args.model_name_or_path, """tasks""": """text-classification"""}
if training_args.push_to_hub:
trainer.push_to_hub(**lowercase )
else:
trainer.create_model_card(**lowercase )
def _lowerCAmelCase ( lowercase ) -> str:
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 689 | 1 |
'''simple docstring'''
def _lowerCAmelCase ( lowercase , lowercase ) -> int:
return 1 if input_a == input_a else 0
def _lowerCAmelCase ( ) -> None:
assert xnor_gate(0 , 0 ) == 1
assert xnor_gate(0 , 1 ) == 0
assert xnor_gate(1 , 0 ) == 0
assert xnor_gate(1 , 1 ) == 1
if __name__ == "__main__":
print(xnor_gate(0, 0))
print(xnor_gate(0, 1))
print(xnor_gate(1, 0))
print(xnor_gate(1, 1))
| 689 |
'''simple docstring'''
import os
import sys
import unittest
_a : List[Any] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init # noqa: E402
# Align TRANSFORMERS_PATH in check_dummies with the current path
_a : Union[str, Any] = os.path.join(git_repo_path, """src""", """diffusers""")
class _UpperCAmelCase ( unittest.TestCase ):
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = find_backend(""" if not is_torch_available():""" )
self.assertEqual(__SCREAMING_SNAKE_CASE,"""torch""" )
# backend_with_underscore = find_backend(" if not is_tensorflow_text_available():")
# self.assertEqual(backend_with_underscore, "tensorflow_text")
__lowerCAmelCase = find_backend(""" if not (is_torch_available() and is_transformers_available()):""" )
self.assertEqual(__SCREAMING_SNAKE_CASE,"""torch_and_transformers""" )
# double_backend_with_underscore = find_backend(
# " if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
# )
# self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")
__lowerCAmelCase = find_backend(
""" if not (is_torch_available() and is_transformers_available() and is_onnx_available()):""" )
self.assertEqual(__SCREAMING_SNAKE_CASE,"""torch_and_transformers_and_onnx""" )
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = read_init()
# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
self.assertIn("""torch""",__SCREAMING_SNAKE_CASE )
self.assertIn("""torch_and_transformers""",__SCREAMING_SNAKE_CASE )
self.assertIn("""flax_and_transformers""",__SCREAMING_SNAKE_CASE )
self.assertIn("""torch_and_transformers_and_onnx""",__SCREAMING_SNAKE_CASE )
# Likewise, we can't assert on the exact content of a key
self.assertIn("""UNet2DModel""",objects["""torch"""] )
self.assertIn("""FlaxUNet2DConditionModel""",objects["""flax"""] )
self.assertIn("""StableDiffusionPipeline""",objects["""torch_and_transformers"""] )
self.assertIn("""FlaxStableDiffusionPipeline""",objects["""flax_and_transformers"""] )
self.assertIn("""LMSDiscreteScheduler""",objects["""torch_and_scipy"""] )
self.assertIn("""OnnxStableDiffusionPipeline""",objects["""torch_and_transformers_and_onnx"""] )
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = create_dummy_object("""CONSTANT""","""'torch'""" )
self.assertEqual(__SCREAMING_SNAKE_CASE,"""\nCONSTANT = None\n""" )
__lowerCAmelCase = create_dummy_object("""function""","""'torch'""" )
self.assertEqual(
__SCREAMING_SNAKE_CASE,"""\ndef function(*args, **kwargs):\n requires_backends(function, 'torch')\n""" )
__lowerCAmelCase = """
class FakeClass(metaclass=DummyObject):
_backends = 'torch'
def __init__(self, *args, **kwargs):
requires_backends(self, 'torch')
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, 'torch')
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, 'torch')
"""
__lowerCAmelCase = create_dummy_object("""FakeClass""","""'torch'""" )
self.assertEqual(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = """# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends
CONSTANT = None
def function(*args, **kwargs):
requires_backends(function, [\"torch\"])
class FakeClass(metaclass=DummyObject):
_backends = [\"torch\"]
def __init__(self, *args, **kwargs):
requires_backends(self, [\"torch\"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, [\"torch\"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, [\"torch\"])
"""
__lowerCAmelCase = create_dummy_files({"""torch""": ["""CONSTANT""", """function""", """FakeClass"""]} )
self.assertEqual(dummy_files["""torch"""],__SCREAMING_SNAKE_CASE )
| 689 | 1 |
'''simple docstring'''
from collections import deque
def _lowerCAmelCase ( lowercase ) -> Dict:
__lowerCAmelCase = len(lowercase )
__lowerCAmelCase = deque()
__lowerCAmelCase = [False for _ in range(lowercase )]
__lowerCAmelCase = [-1 for _ in range(lowercase )]
__lowerCAmelCase = index_of[:]
def strong_connect(lowercase , lowercase , lowercase ):
__lowerCAmelCase = index # the number when this node is seen
__lowerCAmelCase = index # lowest rank node reachable from here
index += 1
stack.append(lowercase )
__lowerCAmelCase = True
for w in g[v]:
if index_of[w] == -1:
__lowerCAmelCase = strong_connect(lowercase , lowercase , lowercase )
__lowerCAmelCase = (
lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
)
elif on_stack[w]:
__lowerCAmelCase = (
lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
)
if lowlink_of[v] == index_of[v]:
__lowerCAmelCase = []
__lowerCAmelCase = stack.pop()
__lowerCAmelCase = False
component.append(lowercase )
while w != v:
__lowerCAmelCase = stack.pop()
__lowerCAmelCase = False
component.append(lowercase )
components.append(lowercase )
return index
__lowerCAmelCase = []
for v in range(lowercase ):
if index_of[v] == -1:
strong_connect(lowercase , 0 , lowercase )
return components
def _lowerCAmelCase ( lowercase , lowercase ) -> str:
__lowerCAmelCase = [[] for _ in range(lowercase )]
for u, v in edges:
g[u].append(lowercase )
return g
if __name__ == "__main__":
# Test
_a : Any = 7
_a : Tuple = [0, 0, 1, 2, 3, 3, 4, 4, 6]
_a : Optional[int] = [1, 3, 2, 0, 1, 4, 5, 6, 5]
_a : Optional[Any] = [(u, v) for u, v in zip(source, target)]
_a : Optional[int] = create_graph(n_vertices, edges)
assert [[5], [6], [4], [3, 2, 1, 0]] == tarjan(g)
| 689 |
'''simple docstring'''
def _lowerCAmelCase ( lowercase ) -> tuple[int, int]:
try:
__lowerCAmelCase = float(lowercase )
except ValueError:
raise ValueError("""Please enter a valid number""" )
__lowerCAmelCase = decimal - int(lowercase )
if fractional_part == 0:
return int(lowercase ), 1
else:
__lowerCAmelCase = len(str(lowercase ).split(""".""" )[1] )
__lowerCAmelCase = int(decimal * (10**number_of_frac_digits) )
__lowerCAmelCase = 10**number_of_frac_digits
__lowerCAmelCase , __lowerCAmelCase = denominator, numerator
while True:
__lowerCAmelCase = dividend % divisor
if remainder == 0:
break
__lowerCAmelCase , __lowerCAmelCase = divisor, remainder
__lowerCAmelCase , __lowerCAmelCase = numerator / divisor, denominator / divisor
return int(lowercase ), int(lowercase )
if __name__ == "__main__":
print(f'{decimal_to_fraction(2) = }')
print(f'{decimal_to_fraction(89.0) = }')
print(f'{decimal_to_fraction("67") = }')
print(f'{decimal_to_fraction("45.0") = }')
print(f'{decimal_to_fraction(1.5) = }')
print(f'{decimal_to_fraction("6.25") = }')
print(f'{decimal_to_fraction("78td") = }')
| 689 | 1 |
'''simple docstring'''
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequenceClassification,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
TapexTokenizer,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version
from transformers.utils.versions import require_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("""4.17.0.dev0""")
require_version("""datasets>=1.8.0""", """To fix: pip install -r examples/pytorch/text-classification/requirements.txt""")
_a : int = logging.getLogger(__name__)
@dataclass
class _UpperCAmelCase :
a : Optional[str] =field(
default="""tab_fact""" , metadata={"""help""": """The name of the dataset to use (via the datasets library)."""} )
a : Optional[str] =field(
default="""tab_fact""" , metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""} , )
a : int =field(
default=10_24 , metadata={
"""help""": (
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
)
} , )
a : bool =field(
default=lowerCAmelCase_ , metadata={"""help""": """Overwrite the cached preprocessed datasets or not."""} )
a : bool =field(
default=lowerCAmelCase_ , metadata={
"""help""": (
"""Whether to pad all samples to `max_seq_length`. """
"""If False, will pad the samples dynamically when batching to the maximum length in the batch."""
)
} , )
a : Optional[int] =field(
default=lowerCAmelCase_ , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of training examples to this """
"""value if set."""
)
} , )
a : Optional[int] =field(
default=lowerCAmelCase_ , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of evaluation examples to this """
"""value if set."""
)
} , )
a : Optional[int] =field(
default=lowerCAmelCase_ , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of prediction examples to this """
"""value if set."""
)
} , )
a : Optional[str] =field(
default=lowerCAmelCase_ , metadata={"""help""": """A csv or a json file containing the training data."""} )
a : Optional[str] =field(
default=lowerCAmelCase_ , metadata={"""help""": """A csv or a json file containing the validation data."""} )
a : Optional[str] =field(default=lowerCAmelCase_ , metadata={"""help""": """A csv or a json file containing the test data."""} )
def lowerCamelCase__ ( self ):
'''simple docstring'''
if self.dataset_name is not None:
pass
elif self.train_file is None or self.validation_file is None:
raise ValueError("""Need either a GLUE task, a training/validation file or a dataset name.""" )
else:
__lowerCAmelCase = self.train_file.split(""".""" )[-1]
assert train_extension in ["csv", "json"], "`train_file` should be a csv or a json file."
__lowerCAmelCase = self.validation_file.split(""".""" )[-1]
assert (
validation_extension == train_extension
), "`validation_file` should have the same extension (csv or json) as `train_file`."
@dataclass
class _UpperCAmelCase :
a : str =field(
default=lowerCAmelCase_ , metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""} )
a : Optional[str] =field(
default=lowerCAmelCase_ , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""} )
a : Optional[str] =field(
default=lowerCAmelCase_ , metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""} )
a : Optional[str] =field(
default=lowerCAmelCase_ , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} , )
a : bool =field(
default=lowerCAmelCase_ , metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""} , )
a : str =field(
default="""main""" , metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""} , )
a : bool =field(
default=lowerCAmelCase_ , metadata={
"""help""": (
"""Will use the token generated when running `huggingface-cli login` (necessary to use this script """
"""with private models)."""
)
} , )
def _lowerCAmelCase ( ) -> Optional[Any]:
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
__lowerCAmelCase = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith(""".json""" ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = parser.parse_args_into_dataclasses()
# Setup logging
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , handlers=[logging.StreamHandler(sys.stdout )] , )
__lowerCAmelCase = training_args.get_process_log_level()
logger.setLevel(lowercase )
datasets.utils.logging.set_verbosity(lowercase )
transformers.utils.logging.set_verbosity(lowercase )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
f'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
+ f'distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}' )
logger.info(f'Training/evaluation parameters {training_args}' )
# Detecting last checkpoint.
__lowerCAmelCase = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
__lowerCAmelCase = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
f'Output directory ({training_args.output_dir}) already exists and is not empty. '
"""Use --overwrite_output_dir to overcome.""" )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
f'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
"""the `--output_dir` or add `--overwrite_output_dir` to train from scratch.""" )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
# or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub).
#
# For JSON files, this script will use the `question` column for the input question and `table` column for the corresponding table.
#
# If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this
# single column. You can easily tweak this behavior (see below)
#
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
# download the dataset.
if data_args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
__lowerCAmelCase = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , cache_dir=model_args.cache_dir )
else:
# Loading a dataset from your local files.
# CSV/JSON training and evaluation files are needed.
__lowerCAmelCase = {"""train""": data_args.train_file, """validation""": data_args.validation_file}
# Get the test dataset: you can provide your own CSV/JSON test file (see below)
# when you use `do_predict` without specifying a GLUE benchmark task.
if training_args.do_predict:
if data_args.test_file is not None:
__lowerCAmelCase = data_args.train_file.split(""".""" )[-1]
__lowerCAmelCase = data_args.test_file.split(""".""" )[-1]
assert (
test_extension == train_extension
), "`test_file` should have the same extension (csv or json) as `train_file`."
__lowerCAmelCase = data_args.test_file
else:
raise ValueError("""Need either a GLUE task or a test file for `do_predict`.""" )
for key in data_files.keys():
logger.info(f'load a local file for {key}: {data_files[key]}' )
if data_args.train_file.endswith(""".csv""" ):
# Loading a dataset from local csv files
__lowerCAmelCase = load_dataset("""csv""" , data_files=lowercase , cache_dir=model_args.cache_dir )
else:
# Loading a dataset from local json files
__lowerCAmelCase = load_dataset("""json""" , data_files=lowercase , cache_dir=model_args.cache_dir )
# See more about loading any type of standard or custom dataset at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Labels
__lowerCAmelCase = raw_datasets["""train"""].features["""label"""].names
__lowerCAmelCase = len(lowercase )
# Load pretrained model and tokenizer
#
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
__lowerCAmelCase = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=lowercase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# load tapex tokenizer
__lowerCAmelCase = TapexTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , add_prefix_space=lowercase , )
__lowerCAmelCase = BartForSequenceClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool(""".ckpt""" in model_args.model_name_or_path ) , config=lowercase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# Padding strategy
if data_args.pad_to_max_length:
__lowerCAmelCase = """max_length"""
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
__lowerCAmelCase = False
# Some models have set the order of the labels to use, so let's make sure we do use it.
__lowerCAmelCase = {"""Refused""": 0, """Entailed""": 1}
__lowerCAmelCase = {0: """Refused""", 1: """Entailed"""}
if data_args.max_seq_length > tokenizer.model_max_length:
logger.warning(
f'The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the'
f'model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.' )
__lowerCAmelCase = min(data_args.max_seq_length , tokenizer.model_max_length )
def preprocess_tabfact_function(lowercase ):
# Tokenize the texts
def _convert_table_text_to_pandas(lowercase ):
__lowerCAmelCase = [_table_row.split("""#""" ) for _table_row in _table_text.strip("""\n""" ).split("""\n""" )]
__lowerCAmelCase = pd.DataFrame.from_records(_table_content[1:] , columns=_table_content[0] )
return _table_pd
__lowerCAmelCase = examples["""statement"""]
__lowerCAmelCase = list(map(_convert_table_text_to_pandas , examples["""table_text"""] ) )
__lowerCAmelCase = tokenizer(lowercase , lowercase , padding=lowercase , max_length=lowercase , truncation=lowercase )
__lowerCAmelCase = examples["""label"""]
return result
with training_args.main_process_first(desc="""dataset map pre-processing""" ):
__lowerCAmelCase = raw_datasets.map(
lowercase , batched=lowercase , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on dataset""" , )
if training_args.do_train:
if "train" not in raw_datasets:
raise ValueError("""--do_train requires a train dataset""" )
__lowerCAmelCase = raw_datasets["""train"""]
if data_args.max_train_samples is not None:
__lowerCAmelCase = train_dataset.select(range(data_args.max_train_samples ) )
if training_args.do_eval:
if "validation" not in raw_datasets and "validation_matched" not in raw_datasets:
raise ValueError("""--do_eval requires a validation dataset""" )
__lowerCAmelCase = raw_datasets["""validation"""]
if data_args.max_eval_samples is not None:
__lowerCAmelCase = eval_dataset.select(range(data_args.max_eval_samples ) )
if training_args.do_predict or data_args.test_file is not None:
if "test" not in raw_datasets and "test_matched" not in raw_datasets:
raise ValueError("""--do_predict requires a test dataset""" )
__lowerCAmelCase = raw_datasets["""test"""]
if data_args.max_predict_samples is not None:
__lowerCAmelCase = predict_dataset.select(range(data_args.max_predict_samples ) )
# Log a few random samples from the training set:
if training_args.do_train:
for index in random.sample(range(len(lowercase ) ) , 3 ):
logger.info(f'Sample {index} of the training set: {train_dataset[index]}.' )
# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(lowercase ):
__lowerCAmelCase = p.predictions[0] if isinstance(p.predictions , lowercase ) else p.predictions
__lowerCAmelCase = np.argmax(lowercase , axis=1 )
return {"accuracy": (preds == p.label_ids).astype(np.floataa ).mean().item()}
# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
if data_args.pad_to_max_length:
__lowerCAmelCase = default_data_collator
elif training_args.fpaa:
__lowerCAmelCase = DataCollatorWithPadding(lowercase , pad_to_multiple_of=8 )
else:
__lowerCAmelCase = None
# Initialize our Trainer
__lowerCAmelCase = Trainer(
model=lowercase , args=lowercase , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , compute_metrics=lowercase , tokenizer=lowercase , data_collator=lowercase , )
# Training
if training_args.do_train:
__lowerCAmelCase = None
if training_args.resume_from_checkpoint is not None:
__lowerCAmelCase = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
__lowerCAmelCase = last_checkpoint
__lowerCAmelCase = trainer.train(resume_from_checkpoint=lowercase )
__lowerCAmelCase = train_result.metrics
__lowerCAmelCase = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(lowercase )
)
__lowerCAmelCase = min(lowercase , len(lowercase ) )
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics("""train""" , lowercase )
trainer.save_metrics("""train""" , lowercase )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info("""*** Evaluate ***""" )
__lowerCAmelCase = trainer.evaluate(eval_dataset=lowercase )
__lowerCAmelCase = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(lowercase )
__lowerCAmelCase = min(lowercase , len(lowercase ) )
trainer.log_metrics("""eval""" , lowercase )
trainer.save_metrics("""eval""" , lowercase )
if training_args.do_predict:
logger.info("""*** Predict ***""" )
# Removing the `label` columns because it contains -1 and Trainer won't like that.
__lowerCAmelCase = predict_dataset.remove_columns("""label""" )
__lowerCAmelCase = trainer.predict(lowercase , metric_key_prefix="""predict""" ).predictions
__lowerCAmelCase = np.argmax(lowercase , axis=1 )
__lowerCAmelCase = os.path.join(training_args.output_dir , """predict_results_tabfact.txt""" )
if trainer.is_world_process_zero():
with open(lowercase , """w""" ) as writer:
logger.info("""***** Predict Results *****""" )
writer.write("""index\tprediction\n""" )
for index, item in enumerate(lowercase ):
__lowerCAmelCase = label_list[item]
writer.write(f'{index}\t{item}\n' )
__lowerCAmelCase = {"""finetuned_from""": model_args.model_name_or_path, """tasks""": """text-classification"""}
if training_args.push_to_hub:
trainer.push_to_hub(**lowercase )
else:
trainer.create_model_card(**lowercase )
def _lowerCAmelCase ( lowercase ) -> str:
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 689 |
'''simple docstring'''
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
_a : Dict = _symbol_database.Default()
_a : Union[str, Any] = _descriptor_pool.Default().AddSerializedFile(
b"""\n\x19sentencepiece_model.proto\x12\rsentencepiece\"\x80\x0c\n\x0bTrainerSpec\x12\r\n\x05input\x18\x01 \x03(\t\x12\x14\n\x0cinput_format\x18\x07 \x01(\t\x12\x14\n\x0cmodel_prefix\x18\x02 \x01(\t\x12\x41\n\nmodel_type\x18\x03 \x01(\x0e\x32$.sentencepiece.TrainerSpec.ModelType:\x07UNIGRAM\x12\x18\n\nvocab_size\x18\x04 \x01(\x05:\x04\x38\x30\x30\x30\x12\x17\n\x0f\x61\x63\x63\x65pt_language\x18\x05 \x03(\t\x12 \n\x15self_test_sample_size\x18\x06 \x01(\x05:\x01\x30\x12*\n\x1b\x65nable_differential_privacy\x18\x32 \x01(\x08:\x05\x66\x61lse\x12+\n differential_privacy_noise_level\x18\x33 \x01(\x02:\x01\x30\x12\x32\n\'differential_privacy_clipping_threshold\x18\x34 \x01(\x04:\x01\x30\x12\"\n\x12\x63haracter_coverage\x18\n \x01(\x02:\x06\x30.9995\x12\x1e\n\x13input_sentence_size\x18\x0b \x01(\x04:\x01\x30\x12$\n\x16shuffle_input_sentence\x18\x13 \x01(\x08:\x04true\x12 \n\x14mining_sentence_size\x18\x0c \x01(\x05\x42\x02\x18\x01\x12\"\n\x16training_sentence_size\x18\r \x01(\x05\x42\x02\x18\x01\x12(\n\x17seed_sentencepiece_size\x18\x0e \x01(\x05:\x07\x31\x30\x30\x30\x30\x30\x30\x12\x1e\n\x10shrinking_factor\x18\x0f \x01(\x02:\x04\x30.75\x12!\n\x13max_sentence_length\x18\x12 \x01(\x05:\x04\x34\x31\x39\x32\x12\x17\n\x0bnum_threads\x18\x10 \x01(\x05:\x02\x31\x36\x12\x1d\n\x12num_sub_iterations\x18\x11 \x01(\x05:\x01\x32\x12$\n\x18max_sentencepiece_length\x18\x14 \x01(\x05:\x02\x31\x36\x12%\n\x17split_by_unicode_script\x18\x15 \x01(\x08:\x04true\x12\x1d\n\x0fsplit_by_number\x18\x17 \x01(\x08:\x04true\x12!\n\x13split_by_whitespace\x18\x16 \x01(\x08:\x04true\x12)\n\x1atreat_whitespace_as_suffix\x18\x18 \x01(\x08:\x05\x66\x61lse\x12+\n\x1c\x61llow_whitespace_only_pieces\x18\x1a \x01(\x08:\x05\x66\x61lse\x12\x1b\n\x0csplit_digits\x18\x19 \x01(\x08:\x05\x66\x61lse\x12#\n\x19pretokenization_delimiter\x18\x35 \x01(\t:\x00\x12\x17\n\x0f\x63ontrol_symbols\x18\x1e \x03(\t\x12\x1c\n\x14user_defined_symbols\x18\x1f \x03(\t\x12\x16\n\x0erequired_chars\x18$ \x01(\t\x12\x1c\n\rbyte_fallback\x18# \x01(\x08:\x05\x66\x61lse\x12+\n\x1dvocabulary_output_piece_score\x18 \x01(\x08:\x04true\x12\x1e\n\x10hard_vocab_limit\x18! \x01(\x08:\x04true\x12\x1c\n\ruse_all_vocab\x18\" \x01(\x08:\x05\x66\x61lse\x12\x11\n\x06unk_id\x18( \x01(\x05:\x01\x30\x12\x11\n\x06\x62os_id\x18) \x01(\x05:\x01\x31\x12\x11\n\x06\x65os_id\x18* \x01(\x05:\x01\x32\x12\x12\n\x06pad_id\x18+ \x01(\x05:\x02-1\x12\x18\n\tunk_piece\x18- \x01(\t:\x05<unk>\x12\x16\n\tbos_piece\x18. \x01(\t:\x03<s>\x12\x17\n\teos_piece\x18/ \x01(\t:\x04</s>\x12\x18\n\tpad_piece\x18\x30 \x01(\t:\x05<pad>\x12\x1a\n\x0bunk_surface\x18, \x01(\t:\x05 \xe2\x81\x87 \x12+\n\x1ctrain_extremely_large_corpus\x18\x31 \x01(\x08:\x05\x66\x61lse\"5\n\tModelType\x12\x0b\n\x07UNIGRAM\x10\x01\x12\x07\n\x03\x42PE\x10\x02\x12\x08\n\x04WORD\x10\x03\x12\x08\n\x04\x43HAR\x10\x04*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"\xd1\x01\n\x0eNormalizerSpec\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x1c\n\x14precompiled_charsmap\x18\x02 \x01(\x0c\x12\x1e\n\x10\x61\x64\x64_dummy_prefix\x18\x03 \x01(\x08:\x04true\x12&\n\x18remove_extra_whitespaces\x18\x04 \x01(\x08:\x04true\x12 \n\x12\x65scape_whitespaces\x18\x05 \x01(\x08:\x04true\x12\x1e\n\x16normalization_rule_tsv\x18\x06 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"y\n\x0cSelfTestData\x12\x33\n\x07samples\x18\x01 \x03(\x0b\x32\".sentencepiece.SelfTestData.Sample\x1a)\n\x06Sample\x12\r\n\x05input\x18\x01 \x01(\t\x12\x10\n\x08\x65xpected\x18\x02 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"\xfe\x03\n\nModelProto\x12\x37\n\x06pieces\x18\x01 \x03(\x0b\x32\'.sentencepiece.ModelProto.SentencePiece\x12\x30\n\x0ctrainer_spec\x18\x02 \x01(\x0b\x32\x1a.sentencepiece.TrainerSpec\x12\x36\n\x0fnormalizer_spec\x18\x03 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x12\x33\n\x0eself_test_data\x18\x04 \x01(\x0b\x32\x1b.sentencepiece.SelfTestData\x12\x38\n\x11\x64\x65normalizer_spec\x18\x05 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x1a\xd2\x01\n\rSentencePiece\x12\r\n\x05piece\x18\x01 \x01(\t\x12\r\n\x05score\x18\x02 \x01(\x02\x12\x42\n\x04type\x18\x03 \x01(\x0e\x32,.sentencepiece.ModelProto.SentencePiece.Type:\x06NORMAL\"T\n\x04Type\x12\n\n\x06NORMAL\x10\x01\x12\x0b\n\x07UNKNOWN\x10\x02\x12\x0b\n\x07\x43ONTROL\x10\x03\x12\x10\n\x0cUSER_DEFINED\x10\x04\x12\x08\n\x04\x42YTE\x10\x06\x12\n\n\x06UNUSED\x10\x05*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\x42\x02H\x03"""
)
_a : str = globals()
_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, """sentencepiece_model_pb2""", _globals)
if _descriptor._USE_C_DESCRIPTORS is False:
_a : str = None
_a : Union[str, Any] = b"""H\003"""
# (generated by protobuf compiler, but `_TRAINERSPEC` is not defined)
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._serialized_options = b"\030\001"
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._serialized_options = b"\030\001"
_a : Optional[int] = 4_5
_a : List[Any] = 1_5_8_1
_a : str = 1_5_1_7
_a : Optional[Any] = 1_5_7_0
_a : List[str] = 1_5_8_4
_a : List[Any] = 1_7_9_3
_a : Union[str, Any] = 1_7_9_5
_a : Tuple = 1_9_1_6
_a : List[Any] = 1_8_6_4
_a : Any = 1_9_0_5
_a : Optional[Any] = 1_9_1_9
_a : Optional[int] = 2_4_2_9
_a : Tuple = 2_2_0_8
_a : Optional[Any] = 2_4_1_8
_a : List[Any] = 2_3_2_3
_a : str = 2_4_0_7
# @@protoc_insertion_point(module_scope)
| 689 | 1 |
'''simple docstring'''
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
_a : str = {
# 1536-bit
5: {
"""prime""": int(
"""FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"""
+ """29024E088A67CC74020BBEA63B139B22514A08798E3404DD"""
+ """EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"""
+ """E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"""
+ """EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"""
+ """C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"""
+ """83655D23DCA3AD961C62F356208552BB9ED529077096966D"""
+ """670C354E4ABC9804F1746C08CA237327FFFFFFFFFFFFFFFF""",
base=1_6,
),
"""generator""": 2,
},
# 2048-bit
1_4: {
"""prime""": int(
"""FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"""
+ """29024E088A67CC74020BBEA63B139B22514A08798E3404DD"""
+ """EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"""
+ """E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"""
+ """EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"""
+ """C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"""
+ """83655D23DCA3AD961C62F356208552BB9ED529077096966D"""
+ """670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"""
+ """E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"""
+ """DE2BCBF6955817183995497CEA956AE515D2261898FA0510"""
+ """15728E5A8AACAA68FFFFFFFFFFFFFFFF""",
base=1_6,
),
"""generator""": 2,
},
# 3072-bit
1_5: {
"""prime""": int(
"""FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"""
+ """29024E088A67CC74020BBEA63B139B22514A08798E3404DD"""
+ """EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"""
+ """E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"""
+ """EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"""
+ """C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"""
+ """83655D23DCA3AD961C62F356208552BB9ED529077096966D"""
+ """670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"""
+ """E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"""
+ """DE2BCBF6955817183995497CEA956AE515D2261898FA0510"""
+ """15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"""
+ """ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"""
+ """ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"""
+ """F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"""
+ """BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"""
+ """43DB5BFCE0FD108E4B82D120A93AD2CAFFFFFFFFFFFFFFFF""",
base=1_6,
),
"""generator""": 2,
},
# 4096-bit
1_6: {
"""prime""": int(
"""FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"""
+ """29024E088A67CC74020BBEA63B139B22514A08798E3404DD"""
+ """EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"""
+ """E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"""
+ """EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"""
+ """C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"""
+ """83655D23DCA3AD961C62F356208552BB9ED529077096966D"""
+ """670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"""
+ """E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"""
+ """DE2BCBF6955817183995497CEA956AE515D2261898FA0510"""
+ """15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"""
+ """ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"""
+ """ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"""
+ """F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"""
+ """BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"""
+ """43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7"""
+ """88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA"""
+ """2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6"""
+ """287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED"""
+ """1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9"""
+ """93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934063199"""
+ """FFFFFFFFFFFFFFFF""",
base=1_6,
),
"""generator""": 2,
},
# 6144-bit
1_7: {
"""prime""": int(
"""FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD129024E08"""
+ """8A67CC74020BBEA63B139B22514A08798E3404DDEF9519B3CD3A431B"""
+ """302B0A6DF25F14374FE1356D6D51C245E485B576625E7EC6F44C42E9"""
+ """A637ED6B0BFF5CB6F406B7EDEE386BFB5A899FA5AE9F24117C4B1FE6"""
+ """49286651ECE45B3DC2007CB8A163BF0598DA48361C55D39A69163FA8"""
+ """FD24CF5F83655D23DCA3AD961C62F356208552BB9ED529077096966D"""
+ """670C354E4ABC9804F1746C08CA18217C32905E462E36CE3BE39E772C"""
+ """180E86039B2783A2EC07A28FB5C55DF06F4C52C9DE2BCBF695581718"""
+ """3995497CEA956AE515D2261898FA051015728E5A8AAAC42DAD33170D"""
+ """04507A33A85521ABDF1CBA64ECFB850458DBEF0A8AEA71575D060C7D"""
+ """B3970F85A6E1E4C7ABF5AE8CDB0933D71E8C94E04A25619DCEE3D226"""
+ """1AD2EE6BF12FFA06D98A0864D87602733EC86A64521F2B18177B200C"""
+ """BBE117577A615D6C770988C0BAD946E208E24FA074E5AB3143DB5BFC"""
+ """E0FD108E4B82D120A92108011A723C12A787E6D788719A10BDBA5B26"""
+ """99C327186AF4E23C1A946834B6150BDA2583E9CA2AD44CE8DBBBC2DB"""
+ """04DE8EF92E8EFC141FBECAA6287C59474E6BC05D99B2964FA090C3A2"""
+ """233BA186515BE7ED1F612970CEE2D7AFB81BDD762170481CD0069127"""
+ """D5B05AA993B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492"""
+ """36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BDF8FF9406"""
+ """AD9E530EE5DB382F413001AEB06A53ED9027D831179727B0865A8918"""
+ """DA3EDBEBCF9B14ED44CE6CBACED4BB1BDB7F1447E6CC254B33205151"""
+ """2BD7AF426FB8F401378CD2BF5983CA01C64B92ECF032EA15D1721D03"""
+ """F482D7CE6E74FEF6D55E702F46980C82B5A84031900B1C9E59E7C97F"""
+ """BEC7E8F323A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA"""
+ """CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE32806A1D58B"""
+ """B7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55CDA56C9EC2EF29632"""
+ """387FE8D76E3C0468043E8F663F4860EE12BF2D5B0B7474D6E694F91E"""
+ """6DCC4024FFFFFFFFFFFFFFFF""",
base=1_6,
),
"""generator""": 2,
},
# 8192-bit
1_8: {
"""prime""": int(
"""FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"""
+ """29024E088A67CC74020BBEA63B139B22514A08798E3404DD"""
+ """EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"""
+ """E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"""
+ """EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"""
+ """C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"""
+ """83655D23DCA3AD961C62F356208552BB9ED529077096966D"""
+ """670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"""
+ """E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"""
+ """DE2BCBF6955817183995497CEA956AE515D2261898FA0510"""
+ """15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"""
+ """ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"""
+ """ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"""
+ """F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"""
+ """BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"""
+ """43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7"""
+ """88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA"""
+ """2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6"""
+ """287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED"""
+ """1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9"""
+ """93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492"""
+ """36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BD"""
+ """F8FF9406AD9E530EE5DB382F413001AEB06A53ED9027D831"""
+ """179727B0865A8918DA3EDBEBCF9B14ED44CE6CBACED4BB1B"""
+ """DB7F1447E6CC254B332051512BD7AF426FB8F401378CD2BF"""
+ """5983CA01C64B92ECF032EA15D1721D03F482D7CE6E74FEF6"""
+ """D55E702F46980C82B5A84031900B1C9E59E7C97FBEC7E8F3"""
+ """23A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA"""
+ """CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE328"""
+ """06A1D58BB7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55C"""
+ """DA56C9EC2EF29632387FE8D76E3C0468043E8F663F4860EE"""
+ """12BF2D5B0B7474D6E694F91E6DBE115974A3926F12FEE5E4"""
+ """38777CB6A932DF8CD8BEC4D073B931BA3BC832B68D9DD300"""
+ """741FA7BF8AFC47ED2576F6936BA424663AAB639C5AE4F568"""
+ """3423B4742BF1C978238F16CBE39D652DE3FDB8BEFC848AD9"""
+ """22222E04A4037C0713EB57A81A23F0C73473FC646CEA306B"""
+ """4BCBC8862F8385DDFA9D4B7FA2C087E879683303ED5BDD3A"""
+ """062B3CF5B3A278A66D2A13F83F44F82DDF310EE074AB6A36"""
+ """4597E899A0255DC164F31CC50846851DF9AB48195DED7EA1"""
+ """B1D510BD7EE74D73FAF36BC31ECFA268359046F4EB879F92"""
+ """4009438B481C6CD7889A002ED5EE382BC9190DA6FC026E47"""
+ """9558E4475677E9AA9E3050E2765694DFC81F56E880B96E71"""
+ """60C980DD98EDD3DFFFFFFFFFFFFFFFFF""",
base=1_6,
),
"""generator""": 2,
},
}
class _UpperCAmelCase :
def __init__( self,__SCREAMING_SNAKE_CASE = 14 ):
'''simple docstring'''
if group not in primes:
raise ValueError("""Unsupported Group""" )
__lowerCAmelCase = primes[group]["""prime"""]
__lowerCAmelCase = primes[group]["""generator"""]
__lowerCAmelCase = int(hexlify(urandom(32 ) ),base=16 )
def lowerCamelCase__ ( self ):
'''simple docstring'''
return hex(self.__private_key )[2:]
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = pow(self.generator,self.__private_key,self.prime )
return hex(__SCREAMING_SNAKE_CASE )[2:]
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
return (
2 <= key <= self.prime - 2
and pow(__SCREAMING_SNAKE_CASE,(self.prime - 1) // 2,self.prime ) == 1
)
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase = int(__SCREAMING_SNAKE_CASE,base=16 )
if not self.is_valid_public_key(__SCREAMING_SNAKE_CASE ):
raise ValueError("""Invalid public key""" )
__lowerCAmelCase = pow(__SCREAMING_SNAKE_CASE,self.__private_key,self.prime )
return shaaaa(str(__SCREAMING_SNAKE_CASE ).encode() ).hexdigest()
@staticmethod
def lowerCamelCase__ ( __SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
return (
2 <= remote_public_key_str <= prime - 2
and pow(__SCREAMING_SNAKE_CASE,(prime - 1) // 2,__SCREAMING_SNAKE_CASE ) == 1
)
@staticmethod
def lowerCamelCase__ ( __SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE = 14 ):
'''simple docstring'''
__lowerCAmelCase = int(__SCREAMING_SNAKE_CASE,base=16 )
__lowerCAmelCase = int(__SCREAMING_SNAKE_CASE,base=16 )
__lowerCAmelCase = primes[group]["""prime"""]
if not DiffieHellman.is_valid_public_key_static(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE ):
raise ValueError("""Invalid public key""" )
__lowerCAmelCase = pow(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
return shaaaa(str(__SCREAMING_SNAKE_CASE ).encode() ).hexdigest()
if __name__ == "__main__":
import doctest
doctest.testmod()
| 689 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block
@dataclass
class _UpperCAmelCase ( lowerCAmelCase_ ):
a : torch.FloatTensor
class _UpperCAmelCase ( nn.Module ):
def __init__( self,__SCREAMING_SNAKE_CASE=3,__SCREAMING_SNAKE_CASE=3,__SCREAMING_SNAKE_CASE=("DownEncoderBlock2D",),__SCREAMING_SNAKE_CASE=(64,),__SCREAMING_SNAKE_CASE=2,__SCREAMING_SNAKE_CASE=32,__SCREAMING_SNAKE_CASE="silu",__SCREAMING_SNAKE_CASE=True,):
'''simple docstring'''
super().__init__()
__lowerCAmelCase = layers_per_block
__lowerCAmelCase = torch.nn.Convad(
__SCREAMING_SNAKE_CASE,block_out_channels[0],kernel_size=3,stride=1,padding=1,)
__lowerCAmelCase = None
__lowerCAmelCase = nn.ModuleList([] )
# down
__lowerCAmelCase = block_out_channels[0]
for i, down_block_type in enumerate(__SCREAMING_SNAKE_CASE ):
__lowerCAmelCase = output_channel
__lowerCAmelCase = block_out_channels[i]
__lowerCAmelCase = i == len(__SCREAMING_SNAKE_CASE ) - 1
__lowerCAmelCase = get_down_block(
__SCREAMING_SNAKE_CASE,num_layers=self.layers_per_block,in_channels=__SCREAMING_SNAKE_CASE,out_channels=__SCREAMING_SNAKE_CASE,add_downsample=not is_final_block,resnet_eps=1e-6,downsample_padding=0,resnet_act_fn=__SCREAMING_SNAKE_CASE,resnet_groups=__SCREAMING_SNAKE_CASE,attention_head_dim=__SCREAMING_SNAKE_CASE,temb_channels=__SCREAMING_SNAKE_CASE,)
self.down_blocks.append(__SCREAMING_SNAKE_CASE )
# mid
__lowerCAmelCase = UNetMidBlockaD(
in_channels=block_out_channels[-1],resnet_eps=1e-6,resnet_act_fn=__SCREAMING_SNAKE_CASE,output_scale_factor=1,resnet_time_scale_shift="""default""",attention_head_dim=block_out_channels[-1],resnet_groups=__SCREAMING_SNAKE_CASE,temb_channels=__SCREAMING_SNAKE_CASE,)
# out
__lowerCAmelCase = nn.GroupNorm(num_channels=block_out_channels[-1],num_groups=__SCREAMING_SNAKE_CASE,eps=1e-6 )
__lowerCAmelCase = nn.SiLU()
__lowerCAmelCase = 2 * out_channels if double_z else out_channels
__lowerCAmelCase = nn.Convad(block_out_channels[-1],__SCREAMING_SNAKE_CASE,3,padding=1 )
__lowerCAmelCase = False
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase = x
__lowerCAmelCase = self.conv_in(__SCREAMING_SNAKE_CASE )
if self.training and self.gradient_checkpointing:
def create_custom_forward(__SCREAMING_SNAKE_CASE ):
def custom_forward(*__SCREAMING_SNAKE_CASE ):
return module(*__SCREAMING_SNAKE_CASE )
return custom_forward
# down
if is_torch_version(""">=""","""1.11.0""" ):
for down_block in self.down_blocks:
__lowerCAmelCase = torch.utils.checkpoint.checkpoint(
create_custom_forward(__SCREAMING_SNAKE_CASE ),__SCREAMING_SNAKE_CASE,use_reentrant=__SCREAMING_SNAKE_CASE )
# middle
__lowerCAmelCase = torch.utils.checkpoint.checkpoint(
create_custom_forward(self.mid_block ),__SCREAMING_SNAKE_CASE,use_reentrant=__SCREAMING_SNAKE_CASE )
else:
for down_block in self.down_blocks:
__lowerCAmelCase = torch.utils.checkpoint.checkpoint(create_custom_forward(__SCREAMING_SNAKE_CASE ),__SCREAMING_SNAKE_CASE )
# middle
__lowerCAmelCase = torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block ),__SCREAMING_SNAKE_CASE )
else:
# down
for down_block in self.down_blocks:
__lowerCAmelCase = down_block(__SCREAMING_SNAKE_CASE )
# middle
__lowerCAmelCase = self.mid_block(__SCREAMING_SNAKE_CASE )
# post-process
__lowerCAmelCase = self.conv_norm_out(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = self.conv_act(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = self.conv_out(__SCREAMING_SNAKE_CASE )
return sample
class _UpperCAmelCase ( nn.Module ):
def __init__( self,__SCREAMING_SNAKE_CASE=3,__SCREAMING_SNAKE_CASE=3,__SCREAMING_SNAKE_CASE=("UpDecoderBlock2D",),__SCREAMING_SNAKE_CASE=(64,),__SCREAMING_SNAKE_CASE=2,__SCREAMING_SNAKE_CASE=32,__SCREAMING_SNAKE_CASE="silu",__SCREAMING_SNAKE_CASE="group",):
'''simple docstring'''
super().__init__()
__lowerCAmelCase = layers_per_block
__lowerCAmelCase = nn.Convad(
__SCREAMING_SNAKE_CASE,block_out_channels[-1],kernel_size=3,stride=1,padding=1,)
__lowerCAmelCase = None
__lowerCAmelCase = nn.ModuleList([] )
__lowerCAmelCase = in_channels if norm_type == """spatial""" else None
# mid
__lowerCAmelCase = UNetMidBlockaD(
in_channels=block_out_channels[-1],resnet_eps=1e-6,resnet_act_fn=__SCREAMING_SNAKE_CASE,output_scale_factor=1,resnet_time_scale_shift="""default""" if norm_type == """group""" else norm_type,attention_head_dim=block_out_channels[-1],resnet_groups=__SCREAMING_SNAKE_CASE,temb_channels=__SCREAMING_SNAKE_CASE,)
# up
__lowerCAmelCase = list(reversed(__SCREAMING_SNAKE_CASE ) )
__lowerCAmelCase = reversed_block_out_channels[0]
for i, up_block_type in enumerate(__SCREAMING_SNAKE_CASE ):
__lowerCAmelCase = output_channel
__lowerCAmelCase = reversed_block_out_channels[i]
__lowerCAmelCase = i == len(__SCREAMING_SNAKE_CASE ) - 1
__lowerCAmelCase = get_up_block(
__SCREAMING_SNAKE_CASE,num_layers=self.layers_per_block + 1,in_channels=__SCREAMING_SNAKE_CASE,out_channels=__SCREAMING_SNAKE_CASE,prev_output_channel=__SCREAMING_SNAKE_CASE,add_upsample=not is_final_block,resnet_eps=1e-6,resnet_act_fn=__SCREAMING_SNAKE_CASE,resnet_groups=__SCREAMING_SNAKE_CASE,attention_head_dim=__SCREAMING_SNAKE_CASE,temb_channels=__SCREAMING_SNAKE_CASE,resnet_time_scale_shift=__SCREAMING_SNAKE_CASE,)
self.up_blocks.append(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = output_channel
# out
if norm_type == "spatial":
__lowerCAmelCase = SpatialNorm(block_out_channels[0],__SCREAMING_SNAKE_CASE )
else:
__lowerCAmelCase = nn.GroupNorm(num_channels=block_out_channels[0],num_groups=__SCREAMING_SNAKE_CASE,eps=1e-6 )
__lowerCAmelCase = nn.SiLU()
__lowerCAmelCase = nn.Convad(block_out_channels[0],__SCREAMING_SNAKE_CASE,3,padding=1 )
__lowerCAmelCase = False
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=None ):
'''simple docstring'''
__lowerCAmelCase = z
__lowerCAmelCase = self.conv_in(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = next(iter(self.up_blocks.parameters() ) ).dtype
if self.training and self.gradient_checkpointing:
def create_custom_forward(__SCREAMING_SNAKE_CASE ):
def custom_forward(*__SCREAMING_SNAKE_CASE ):
return module(*__SCREAMING_SNAKE_CASE )
return custom_forward
if is_torch_version(""">=""","""1.11.0""" ):
# middle
__lowerCAmelCase = torch.utils.checkpoint.checkpoint(
create_custom_forward(self.mid_block ),__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,use_reentrant=__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = sample.to(__SCREAMING_SNAKE_CASE )
# up
for up_block in self.up_blocks:
__lowerCAmelCase = torch.utils.checkpoint.checkpoint(
create_custom_forward(__SCREAMING_SNAKE_CASE ),__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,use_reentrant=__SCREAMING_SNAKE_CASE )
else:
# middle
__lowerCAmelCase = torch.utils.checkpoint.checkpoint(
create_custom_forward(self.mid_block ),__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = sample.to(__SCREAMING_SNAKE_CASE )
# up
for up_block in self.up_blocks:
__lowerCAmelCase = torch.utils.checkpoint.checkpoint(create_custom_forward(__SCREAMING_SNAKE_CASE ),__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
else:
# middle
__lowerCAmelCase = self.mid_block(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = sample.to(__SCREAMING_SNAKE_CASE )
# up
for up_block in self.up_blocks:
__lowerCAmelCase = up_block(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
# post-process
if latent_embeds is None:
__lowerCAmelCase = self.conv_norm_out(__SCREAMING_SNAKE_CASE )
else:
__lowerCAmelCase = self.conv_norm_out(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = self.conv_act(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = self.conv_out(__SCREAMING_SNAKE_CASE )
return sample
class _UpperCAmelCase ( nn.Module ):
def __init__( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE="random",__SCREAMING_SNAKE_CASE=False,__SCREAMING_SNAKE_CASE=True ):
'''simple docstring'''
super().__init__()
__lowerCAmelCase = n_e
__lowerCAmelCase = vq_embed_dim
__lowerCAmelCase = beta
__lowerCAmelCase = legacy
__lowerCAmelCase = nn.Embedding(self.n_e,self.vq_embed_dim )
self.embedding.weight.data.uniform_(-1.0 / self.n_e,1.0 / self.n_e )
__lowerCAmelCase = remap
if self.remap is not None:
self.register_buffer("""used""",torch.tensor(np.load(self.remap ) ) )
__lowerCAmelCase = self.used.shape[0]
__lowerCAmelCase = unknown_index # "random" or "extra" or integer
if self.unknown_index == "extra":
__lowerCAmelCase = self.re_embed
__lowerCAmelCase = self.re_embed + 1
print(
f'Remapping {self.n_e} indices to {self.re_embed} indices. '
f'Using {self.unknown_index} for unknown indices.' )
else:
__lowerCAmelCase = n_e
__lowerCAmelCase = sane_index_shape
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase = inds.shape
assert len(__SCREAMING_SNAKE_CASE ) > 1
__lowerCAmelCase = inds.reshape(ishape[0],-1 )
__lowerCAmelCase = self.used.to(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = (inds[:, :, None] == used[None, None, ...]).long()
__lowerCAmelCase = match.argmax(-1 )
__lowerCAmelCase = match.sum(2 ) < 1
if self.unknown_index == "random":
__lowerCAmelCase = torch.randint(0,self.re_embed,size=new[unknown].shape ).to(device=new.device )
else:
__lowerCAmelCase = self.unknown_index
return new.reshape(__SCREAMING_SNAKE_CASE )
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase = inds.shape
assert len(__SCREAMING_SNAKE_CASE ) > 1
__lowerCAmelCase = inds.reshape(ishape[0],-1 )
__lowerCAmelCase = self.used.to(__SCREAMING_SNAKE_CASE )
if self.re_embed > self.used.shape[0]: # extra token
__lowerCAmelCase = 0 # simply set to zero
__lowerCAmelCase = torch.gather(used[None, :][inds.shape[0] * [0], :],1,__SCREAMING_SNAKE_CASE )
return back.reshape(__SCREAMING_SNAKE_CASE )
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase = z.permute(0,2,3,1 ).contiguous()
__lowerCAmelCase = z.view(-1,self.vq_embed_dim )
# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
__lowerCAmelCase = torch.argmin(torch.cdist(__SCREAMING_SNAKE_CASE,self.embedding.weight ),dim=1 )
__lowerCAmelCase = self.embedding(__SCREAMING_SNAKE_CASE ).view(z.shape )
__lowerCAmelCase = None
__lowerCAmelCase = None
# compute loss for embedding
if not self.legacy:
__lowerCAmelCase = self.beta * torch.mean((z_q.detach() - z) ** 2 ) + torch.mean((z_q - z.detach()) ** 2 )
else:
__lowerCAmelCase = torch.mean((z_q.detach() - z) ** 2 ) + self.beta * torch.mean((z_q - z.detach()) ** 2 )
# preserve gradients
__lowerCAmelCase = z + (z_q - z).detach()
# reshape back to match original input shape
__lowerCAmelCase = z_q.permute(0,3,1,2 ).contiguous()
if self.remap is not None:
__lowerCAmelCase = min_encoding_indices.reshape(z.shape[0],-1 ) # add batch axis
__lowerCAmelCase = self.remap_to_used(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = min_encoding_indices.reshape(-1,1 ) # flatten
if self.sane_index_shape:
__lowerCAmelCase = min_encoding_indices.reshape(z_q.shape[0],z_q.shape[2],z_q.shape[3] )
return z_q, loss, (perplexity, min_encodings, min_encoding_indices)
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
if self.remap is not None:
__lowerCAmelCase = indices.reshape(shape[0],-1 ) # add batch axis
__lowerCAmelCase = self.unmap_to_all(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = indices.reshape(-1 ) # flatten again
# get quantized latent vectors
__lowerCAmelCase = self.embedding(__SCREAMING_SNAKE_CASE )
if shape is not None:
__lowerCAmelCase = z_q.view(__SCREAMING_SNAKE_CASE )
# reshape back to match original input shape
__lowerCAmelCase = z_q.permute(0,3,1,2 ).contiguous()
return z_q
class _UpperCAmelCase ( lowerCAmelCase_ ):
def __init__( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=False ):
'''simple docstring'''
__lowerCAmelCase = parameters
__lowerCAmelCase , __lowerCAmelCase = torch.chunk(__SCREAMING_SNAKE_CASE,2,dim=1 )
__lowerCAmelCase = torch.clamp(self.logvar,-30.0,20.0 )
__lowerCAmelCase = deterministic
__lowerCAmelCase = torch.exp(0.5 * self.logvar )
__lowerCAmelCase = torch.exp(self.logvar )
if self.deterministic:
__lowerCAmelCase = __lowerCAmelCase = torch.zeros_like(
self.mean,device=self.parameters.device,dtype=self.parameters.dtype )
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE = None ):
'''simple docstring'''
__lowerCAmelCase = randn_tensor(
self.mean.shape,generator=__SCREAMING_SNAKE_CASE,device=self.parameters.device,dtype=self.parameters.dtype )
__lowerCAmelCase = self.mean + self.std * sample
return x
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE=None ):
'''simple docstring'''
if self.deterministic:
return torch.Tensor([0.0] )
else:
if other is None:
return 0.5 * torch.sum(torch.pow(self.mean,2 ) + self.var - 1.0 - self.logvar,dim=[1, 2, 3] )
else:
return 0.5 * torch.sum(
torch.pow(self.mean - other.mean,2 ) / other.var
+ self.var / other.var
- 1.0
- self.logvar
+ other.logvar,dim=[1, 2, 3],)
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=[1, 2, 3] ):
'''simple docstring'''
if self.deterministic:
return torch.Tensor([0.0] )
__lowerCAmelCase = np.log(2.0 * np.pi )
return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean,2 ) / self.var,dim=__SCREAMING_SNAKE_CASE )
def lowerCamelCase__ ( self ):
'''simple docstring'''
return self.mean
| 689 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_a : Tuple = {
"""configuration_whisper""": ["""WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """WhisperConfig""", """WhisperOnnxConfig"""],
"""feature_extraction_whisper""": ["""WhisperFeatureExtractor"""],
"""processing_whisper""": ["""WhisperProcessor"""],
"""tokenization_whisper""": ["""WhisperTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_a : Optional[Any] = ["""WhisperTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_a : Optional[int] = [
"""WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""WhisperForConditionalGeneration""",
"""WhisperModel""",
"""WhisperPreTrainedModel""",
"""WhisperForAudioClassification""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_a : Union[str, Any] = [
"""TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFWhisperForConditionalGeneration""",
"""TFWhisperModel""",
"""TFWhisperPreTrainedModel""",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_a : Dict = [
"""FlaxWhisperForConditionalGeneration""",
"""FlaxWhisperModel""",
"""FlaxWhisperPreTrainedModel""",
"""FlaxWhisperForAudioClassification""",
]
if TYPE_CHECKING:
from .configuration_whisper import WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP, WhisperConfig, WhisperOnnxConfig
from .feature_extraction_whisper import WhisperFeatureExtractor
from .processing_whisper import WhisperProcessor
from .tokenization_whisper import WhisperTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_whisper_fast import WhisperTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_whisper import (
WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
WhisperForAudioClassification,
WhisperForConditionalGeneration,
WhisperModel,
WhisperPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_whisper import (
TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
TFWhisperForConditionalGeneration,
TFWhisperModel,
TFWhisperPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_whisper import (
FlaxWhisperForAudioClassification,
FlaxWhisperForConditionalGeneration,
FlaxWhisperModel,
FlaxWhisperPreTrainedModel,
)
else:
import sys
_a : Dict = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 689 |
'''simple docstring'''
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
_a : Optional[int] = logging.get_logger(__name__)
_a : int = list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
_a : str = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class _UpperCAmelCase :
a : str =field(
default=lowerCAmelCase_ , metadata={"""help""": """Model type selected in the list: """ + """, """.join(lowerCAmelCase_ )} )
a : str =field(
default=lowerCAmelCase_ , metadata={"""help""": """The input data dir. Should contain the .json files for the SQuAD task."""} )
a : int =field(
default=1_28 , metadata={
"""help""": (
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
)
} , )
a : int =field(
default=1_28 , metadata={"""help""": """When splitting up a long document into chunks, how much stride to take between chunks."""} , )
a : int =field(
default=64 , metadata={
"""help""": (
"""The maximum number of tokens for the question. Questions longer than this will """
"""be truncated to this length."""
)
} , )
a : int =field(
default=30 , metadata={
"""help""": (
"""The maximum length of an answer that can be generated. This is needed because the start """
"""and end predictions are not conditioned on one another."""
)
} , )
a : bool =field(
default=lowerCAmelCase_ , metadata={"""help""": """Overwrite the cached training and evaluation sets"""} )
a : bool =field(
default=lowerCAmelCase_ , metadata={"""help""": """If true, the SQuAD examples contain some that do not have an answer."""} )
a : float =field(
default=0.0 , metadata={"""help""": """If null_score - best_non_null is greater than the threshold predict null."""} )
a : int =field(
default=20 , metadata={"""help""": """If null_score - best_non_null is greater than the threshold predict null."""} )
a : int =field(
default=0 , metadata={
"""help""": (
"""language id of input for language-specific xlm models (see"""
""" tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)"""
)
} , )
a : int =field(default=1 , metadata={"""help""": """multiple threads for converting example to features"""} )
class _UpperCAmelCase ( lowerCAmelCase_ ):
a : Optional[Any] ="""train"""
a : Optional[int] ="""dev"""
class _UpperCAmelCase ( lowerCAmelCase_ ):
a : SquadDataTrainingArguments
a : List[SquadFeatures]
a : Split
a : bool
def __init__( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = Split.train,__SCREAMING_SNAKE_CASE = False,__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = "pt",):
'''simple docstring'''
__lowerCAmelCase = args
__lowerCAmelCase = is_language_sensitive
__lowerCAmelCase = SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
if isinstance(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE ):
try:
__lowerCAmelCase = Split[mode]
except KeyError:
raise KeyError("""mode is not a valid split name""" )
__lowerCAmelCase = mode
# Load data features from cache or dataset file
__lowerCAmelCase = """v2""" if args.version_2_with_negative else """v1"""
__lowerCAmelCase = os.path.join(
cache_dir if cache_dir is not None else args.data_dir,f'cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{version_tag}',)
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
__lowerCAmelCase = cached_features_file + """.lock"""
with FileLock(__SCREAMING_SNAKE_CASE ):
if os.path.exists(__SCREAMING_SNAKE_CASE ) and not args.overwrite_cache:
__lowerCAmelCase = time.time()
__lowerCAmelCase = torch.load(__SCREAMING_SNAKE_CASE )
# Legacy cache files have only features, while new cache files
# will have dataset and examples also.
__lowerCAmelCase = self.old_features["""features"""]
__lowerCAmelCase = self.old_features.get("""dataset""",__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = self.old_features.get("""examples""",__SCREAMING_SNAKE_CASE )
logger.info(
f'Loading features from cached file {cached_features_file} [took %.3f s]',time.time() - start )
if self.dataset is None or self.examples is None:
logger.warning(
f'Deleting cached file {cached_features_file} will allow dataset and examples to be cached in'
""" future run""" )
else:
if mode == Split.dev:
__lowerCAmelCase = self.processor.get_dev_examples(args.data_dir )
else:
__lowerCAmelCase = self.processor.get_train_examples(args.data_dir )
__lowerCAmelCase , __lowerCAmelCase = squad_convert_examples_to_features(
examples=self.examples,tokenizer=__SCREAMING_SNAKE_CASE,max_seq_length=args.max_seq_length,doc_stride=args.doc_stride,max_query_length=args.max_query_length,is_training=mode == Split.train,threads=args.threads,return_dataset=__SCREAMING_SNAKE_CASE,)
__lowerCAmelCase = time.time()
torch.save(
{"""features""": self.features, """dataset""": self.dataset, """examples""": self.examples},__SCREAMING_SNAKE_CASE,)
# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
logger.info(
f'Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]' )
def __len__( self ):
'''simple docstring'''
return len(self.features )
def __getitem__( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase = self.features[i]
__lowerCAmelCase = torch.tensor(feature.input_ids,dtype=torch.long )
__lowerCAmelCase = torch.tensor(feature.attention_mask,dtype=torch.long )
__lowerCAmelCase = torch.tensor(feature.token_type_ids,dtype=torch.long )
__lowerCAmelCase = torch.tensor(feature.cls_index,dtype=torch.long )
__lowerCAmelCase = torch.tensor(feature.p_mask,dtype=torch.float )
__lowerCAmelCase = torch.tensor(feature.is_impossible,dtype=torch.float )
__lowerCAmelCase = {
"""input_ids""": input_ids,
"""attention_mask""": attention_mask,
"""token_type_ids""": token_type_ids,
}
if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
del inputs["token_type_ids"]
if self.args.model_type in ["xlnet", "xlm"]:
inputs.update({"""cls_index""": cls_index, """p_mask""": p_mask} )
if self.args.version_2_with_negative:
inputs.update({"""is_impossible""": is_impossible} )
if self.is_language_sensitive:
inputs.update({"""langs""": (torch.ones(input_ids.shape,dtype=torch.intaa ) * self.args.lang_id)} )
if self.mode == Split.train:
__lowerCAmelCase = torch.tensor(feature.start_position,dtype=torch.long )
__lowerCAmelCase = torch.tensor(feature.end_position,dtype=torch.long )
inputs.update({"""start_positions""": start_positions, """end_positions""": end_positions} )
return inputs
| 689 | 1 |
'''simple docstring'''
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_flax_cross_test,
require_flax,
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import is_flax_available, is_torch_available, is_vision_available
from ...test_modeling_flax_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_flax_bert import FlaxBertModelTester
from ..clip.test_modeling_flax_clip import FlaxCLIPVisionModelTester
from ..vit.test_modeling_flax_vit import FlaxViTModelTester
if is_flax_available():
from transformers import (
FlaxBertModel,
FlaxCLIPVisionModel,
FlaxVisionTextDualEncoderModel,
FlaxViTModel,
VisionTextDualEncoderConfig,
VisionTextDualEncoderProcessor,
)
from transformers.modeling_flax_pytorch_utils import (
convert_pytorch_state_dict_to_flax,
load_flax_weights_in_pytorch_model,
)
if is_torch_available():
import torch
from transformers import VisionTextDualEncoderModel
if is_vision_available():
from PIL import Image
def _lowerCAmelCase ( lowercase ) -> Tuple:
if isinstance(lowercase , collections.abc.Iterable ):
return x
return (x, x)
@require_flax
class _UpperCAmelCase :
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
pass
def lowerCamelCase__ ( self ):
'''simple docstring'''
pass
def lowerCamelCase__ ( self ):
'''simple docstring'''
pass
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase = np.abs((a - b) ).max()
self.assertLessEqual(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,f'Difference between torch and flax is {diff} (>= {tol}).' )
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=None,**__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase = VisionTextDualEncoderConfig.from_vision_text_configs(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = FlaxVisionTextDualEncoderModel(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = model(input_ids=__SCREAMING_SNAKE_CASE,pixel_values=__SCREAMING_SNAKE_CASE,attention_mask=__SCREAMING_SNAKE_CASE )
self.assertEqual(output["""text_embeds"""].shape,(input_ids.shape[0], config.projection_dim) )
self.assertEqual(output["""image_embeds"""].shape,(pixel_values.shape[0], config.projection_dim) )
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=None,**__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase , __lowerCAmelCase = self.get_vision_text_model(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = {"""vision_model""": vision_model, """text_model""": text_model}
__lowerCAmelCase = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = model(input_ids=__SCREAMING_SNAKE_CASE,pixel_values=__SCREAMING_SNAKE_CASE,attention_mask=__SCREAMING_SNAKE_CASE )
self.assertEqual(output["""text_embeds"""].shape,(input_ids.shape[0], model.config.projection_dim) )
self.assertEqual(output["""image_embeds"""].shape,(pixel_values.shape[0], model.config.projection_dim) )
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=None,**__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase , __lowerCAmelCase = self.get_vision_text_model(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = {"""vision_model""": vision_model, """text_model""": text_model}
__lowerCAmelCase = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = model(input_ids=__SCREAMING_SNAKE_CASE,pixel_values=__SCREAMING_SNAKE_CASE,attention_mask=__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = output[0]
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = FlaxVisionTextDualEncoderModel.from_pretrained(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = model(input_ids=__SCREAMING_SNAKE_CASE,pixel_values=__SCREAMING_SNAKE_CASE,attention_mask=__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = after_output[0]
__lowerCAmelCase = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(__SCREAMING_SNAKE_CASE,1e-3 )
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=None,**__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase , __lowerCAmelCase = self.get_vision_text_model(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = {"""vision_model""": vision_model, """text_model""": text_model}
__lowerCAmelCase = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = model(
input_ids=__SCREAMING_SNAKE_CASE,pixel_values=__SCREAMING_SNAKE_CASE,attention_mask=__SCREAMING_SNAKE_CASE,output_attentions=__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = output.vision_model_output.attentions
self.assertEqual(len(__SCREAMING_SNAKE_CASE ),vision_config.num_hidden_layers )
# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
__lowerCAmelCase = to_atuple(vision_model.config.image_size )
__lowerCAmelCase = to_atuple(vision_model.config.patch_size )
__lowerCAmelCase = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
__lowerCAmelCase = num_patches + 1
self.assertEqual(vision_attentions[0].shape[-3:],(vision_config.num_attention_heads, seq_len, seq_len) )
__lowerCAmelCase = output.text_model_output.attentions
self.assertEqual(len(__SCREAMING_SNAKE_CASE ),text_config.num_hidden_layers )
self.assertEqual(
text_attentions[0].shape[-3:],(text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]),)
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
pt_model.to(__SCREAMING_SNAKE_CASE )
pt_model.eval()
# prepare inputs
__lowerCAmelCase = inputs_dict
__lowerCAmelCase = {k: torch.tensor(v.tolist() ) for k, v in flax_inputs.items()}
with torch.no_grad():
__lowerCAmelCase = pt_model(**__SCREAMING_SNAKE_CASE ).to_tuple()
__lowerCAmelCase = fx_model(**__SCREAMING_SNAKE_CASE ).to_tuple()
self.assertEqual(len(__SCREAMING_SNAKE_CASE ),len(__SCREAMING_SNAKE_CASE ),"""Output lengths differ between Flax and PyTorch""" )
for fx_output, pt_output in zip(fx_outputs[:4],pt_outputs[:4] ):
self.assert_almost_equals(__SCREAMING_SNAKE_CASE,pt_output.numpy(),4e-2 )
# PT -> Flax
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = FlaxVisionTextDualEncoderModel.from_pretrained(__SCREAMING_SNAKE_CASE,from_pt=__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = fx_model_loaded(**__SCREAMING_SNAKE_CASE ).to_tuple()
self.assertEqual(len(__SCREAMING_SNAKE_CASE ),len(__SCREAMING_SNAKE_CASE ),"""Output lengths differ between Flax and PyTorch""" )
for fx_output_loaded, pt_output in zip(fx_outputs_loaded[:4],pt_outputs[:4] ):
self.assert_almost_equals(__SCREAMING_SNAKE_CASE,pt_output.numpy(),4e-2 )
# Flax -> PT
with tempfile.TemporaryDirectory() as tmpdirname:
fx_model.save_pretrained(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = VisionTextDualEncoderModel.from_pretrained(__SCREAMING_SNAKE_CASE,from_flax=__SCREAMING_SNAKE_CASE )
pt_model_loaded.to(__SCREAMING_SNAKE_CASE )
pt_model_loaded.eval()
with torch.no_grad():
__lowerCAmelCase = pt_model_loaded(**__SCREAMING_SNAKE_CASE ).to_tuple()
self.assertEqual(len(__SCREAMING_SNAKE_CASE ),len(__SCREAMING_SNAKE_CASE ),"""Output lengths differ between Flax and PyTorch""" )
for fx_output, pt_output_loaded in zip(fx_outputs[:4],pt_outputs_loaded[:4] ):
self.assert_almost_equals(__SCREAMING_SNAKE_CASE,pt_output_loaded.numpy(),4e-2 )
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase = VisionTextDualEncoderConfig.from_vision_text_configs(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = VisionTextDualEncoderModel(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = FlaxVisionTextDualEncoderModel(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = convert_pytorch_state_dict_to_flax(pt_model.state_dict(),__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = fx_state
self.check_pt_flax_equivalence(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase = VisionTextDualEncoderConfig.from_vision_text_configs(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = VisionTextDualEncoderModel(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = FlaxVisionTextDualEncoderModel(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = load_flax_weights_in_pytorch_model(__SCREAMING_SNAKE_CASE,fx_model.params )
self.check_pt_flax_equivalence(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = self.prepare_config_and_inputs()
self.check_model_from_pretrained_configs(**__SCREAMING_SNAKE_CASE )
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = self.prepare_config_and_inputs()
self.check_vision_text_dual_encoder_from_pretrained(**__SCREAMING_SNAKE_CASE )
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = self.prepare_config_and_inputs()
self.check_save_load(**__SCREAMING_SNAKE_CASE )
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = self.prepare_config_and_inputs()
self.check_vision_text_output_attention(**__SCREAMING_SNAKE_CASE )
@is_pt_flax_cross_test
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = self.prepare_config_and_inputs()
__lowerCAmelCase = config_inputs_dict.pop("""vision_config""" )
__lowerCAmelCase = config_inputs_dict.pop("""text_config""" )
__lowerCAmelCase = config_inputs_dict
self.check_equivalence_pt_to_flax(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
self.check_equivalence_flax_to_pt(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
@slow
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase , __lowerCAmelCase = self.get_pretrained_model_and_inputs()
__lowerCAmelCase = model_a(**__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = outputs[0]
with tempfile.TemporaryDirectory() as tmp_dirname:
model_a.save_pretrained(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = FlaxVisionTextDualEncoderModel.from_pretrained(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = model_a(**__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = after_outputs[0]
__lowerCAmelCase = np.amax(np.abs(out_a - out_a ) )
self.assertLessEqual(__SCREAMING_SNAKE_CASE,1e-5 )
@require_flax
class _UpperCAmelCase ( lowerCAmelCase_ , unittest.TestCase ):
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
"""hf-internal-testing/tiny-random-vit""","""hf-internal-testing/tiny-bert""",vision_from_pt=__SCREAMING_SNAKE_CASE,text_from_pt=__SCREAMING_SNAKE_CASE,)
__lowerCAmelCase = 13
__lowerCAmelCase = floats_tensor(
[
batch_size,
model.config.vision_config.num_channels,
model.config.vision_config.image_size,
model.config.vision_config.image_size,
] )
__lowerCAmelCase = ids_tensor([batch_size, 4],model.config.text_config.vocab_size )
__lowerCAmelCase = random_attention_mask([batch_size, 4] )
__lowerCAmelCase = {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}
return model, inputs
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase = FlaxViTModel(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = FlaxBertModel(__SCREAMING_SNAKE_CASE )
return vision_model, text_model
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = FlaxViTModelTester(self )
__lowerCAmelCase = FlaxBertModelTester(self )
__lowerCAmelCase = vit_model_tester.prepare_config_and_inputs()
__lowerCAmelCase = bert_model_tester.prepare_config_and_inputs()
__lowerCAmelCase , __lowerCAmelCase = vision_config_and_inputs
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = text_config_and_inputs
# make sure that cross attention layers are added
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": attention_mask,
"input_ids": input_ids,
"token_type_ids": token_type_ids,
}
@require_torch
class _UpperCAmelCase ( lowerCAmelCase_ , unittest.TestCase ):
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
"""hf-internal-testing/tiny-random-clip""","""hf-internal-testing/tiny-bert""",vision_from_pt=__SCREAMING_SNAKE_CASE,text_from_pt=__SCREAMING_SNAKE_CASE,)
__lowerCAmelCase = 13
__lowerCAmelCase = floats_tensor(
[
batch_size,
model.config.vision_config.num_channels,
model.config.vision_config.image_size,
model.config.vision_config.image_size,
] )
__lowerCAmelCase = ids_tensor([batch_size, 4],model.config.text_config.vocab_size )
__lowerCAmelCase = random_attention_mask([batch_size, 4] )
__lowerCAmelCase = {"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}
return model, inputs
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase = FlaxCLIPVisionModel(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = FlaxBertModel(__SCREAMING_SNAKE_CASE )
return vision_model, text_model
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = FlaxCLIPVisionModelTester(self )
__lowerCAmelCase = FlaxBertModelTester(self )
__lowerCAmelCase = clip_model_tester.prepare_config_and_inputs()
__lowerCAmelCase = bert_model_tester.prepare_config_and_inputs()
__lowerCAmelCase , __lowerCAmelCase = vision_config_and_inputs
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = text_config_and_inputs
# make sure that cross attention layers are added
return {
"text_config": text_config,
"vision_config": vision_config,
"pixel_values": pixel_values,
"attention_mask": attention_mask,
"input_ids": input_ids,
"token_type_ids": token_type_ids,
}
@require_flax
@require_vision
class _UpperCAmelCase ( unittest.TestCase ):
@slow
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = FlaxVisionTextDualEncoderModel.from_pretrained("""clip-italian/clip-italian""",logit_scale_init_value=1.0 )
__lowerCAmelCase = VisionTextDualEncoderProcessor.from_pretrained("""clip-italian/clip-italian""" )
__lowerCAmelCase = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
__lowerCAmelCase = processor(
text=["""una foto di un gatto""", """una foto di un cane"""],images=__SCREAMING_SNAKE_CASE,padding=__SCREAMING_SNAKE_CASE,return_tensors="""np""" )
__lowerCAmelCase = model(**__SCREAMING_SNAKE_CASE )
# verify the logits
self.assertEqual(outputs.logits_per_image.shape,(inputs.pixel_values.shape[0], inputs.input_ids.shape[0]) )
self.assertEqual(
outputs.logits_per_text.shape,(inputs.input_ids.shape[0], inputs.pixel_values.shape[0]),)
__lowerCAmelCase = np.array([[1.228_4727, 0.310_4122]] )
self.assertTrue(np.allclose(outputs.logits_per_image,__SCREAMING_SNAKE_CASE,atol=1e-3 ) )
| 689 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def _lowerCAmelCase ( lowercase ) -> Optional[Any]:
# vision encoder
if "img_encoder.pos_embed" in name:
__lowerCAmelCase = name.replace("""img_encoder.pos_embed""" , """vision_model.embeddings.position_embeddings""" )
if "img_encoder.patch_embed.proj" in name:
__lowerCAmelCase = name.replace("""img_encoder.patch_embed.proj""" , """vision_model.embeddings.patch_embeddings.projection""" )
if "img_encoder.patch_embed.norm" in name:
__lowerCAmelCase = name.replace("""img_encoder.patch_embed.norm""" , """vision_model.embeddings.layernorm""" )
if "img_encoder.layers" in name:
__lowerCAmelCase = name.replace("""img_encoder.layers""" , """vision_model.encoder.stages""" )
if "blocks" in name and "res" not in name:
__lowerCAmelCase = name.replace("""blocks""" , """layers""" )
if "attn" in name and "pre_assign" not in name:
__lowerCAmelCase = name.replace("""attn""" , """self_attn""" )
if "proj" in name and "self_attn" in name and "text" not in name:
__lowerCAmelCase = name.replace("""proj""" , """out_proj""" )
if "pre_assign_attn.attn.proj" in name:
__lowerCAmelCase = name.replace("""pre_assign_attn.attn.proj""" , """pre_assign_attn.attn.out_proj""" )
if "norm1" in name:
__lowerCAmelCase = name.replace("""norm1""" , """layer_norm1""" )
if "norm2" in name and "pre_assign" not in name:
__lowerCAmelCase = name.replace("""norm2""" , """layer_norm2""" )
if "img_encoder.norm" in name:
__lowerCAmelCase = name.replace("""img_encoder.norm""" , """vision_model.layernorm""" )
# text encoder
if "text_encoder.token_embedding" in name:
__lowerCAmelCase = name.replace("""text_encoder.token_embedding""" , """text_model.embeddings.token_embedding""" )
if "text_encoder.positional_embedding" in name:
__lowerCAmelCase = name.replace("""text_encoder.positional_embedding""" , """text_model.embeddings.position_embedding.weight""" )
if "text_encoder.transformer.resblocks." in name:
__lowerCAmelCase = name.replace("""text_encoder.transformer.resblocks.""" , """text_model.encoder.layers.""" )
if "ln_1" in name:
__lowerCAmelCase = name.replace("""ln_1""" , """layer_norm1""" )
if "ln_2" in name:
__lowerCAmelCase = name.replace("""ln_2""" , """layer_norm2""" )
if "c_fc" in name:
__lowerCAmelCase = name.replace("""c_fc""" , """fc1""" )
if "c_proj" in name:
__lowerCAmelCase = name.replace("""c_proj""" , """fc2""" )
if "text_encoder" in name:
__lowerCAmelCase = name.replace("""text_encoder""" , """text_model""" )
if "ln_final" in name:
__lowerCAmelCase = name.replace("""ln_final""" , """final_layer_norm""" )
# projection layers
if "img_projector.linear_hidden." in name:
__lowerCAmelCase = name.replace("""img_projector.linear_hidden.""" , """visual_projection.""" )
if "img_projector.linear_out." in name:
__lowerCAmelCase = name.replace("""img_projector.linear_out.""" , """visual_projection.3.""" )
if "text_projector.linear_hidden" in name:
__lowerCAmelCase = name.replace("""text_projector.linear_hidden""" , """text_projection""" )
if "text_projector.linear_out" in name:
__lowerCAmelCase = name.replace("""text_projector.linear_out""" , """text_projection.3""" )
return name
def _lowerCAmelCase ( lowercase , lowercase ) -> Dict:
for key in orig_state_dict.copy().keys():
__lowerCAmelCase = orig_state_dict.pop(lowercase )
if "qkv" in key:
# weights and biases of the key, value and query projections of vision encoder's attention layers require special treatment:
# we need to split them up into separate matrices/vectors
__lowerCAmelCase = key.split(""".""" )
__lowerCAmelCase , __lowerCAmelCase = int(key_split[2] ), int(key_split[4] )
__lowerCAmelCase = config.vision_config.hidden_size
if "weight" in key:
__lowerCAmelCase = val[:dim, :]
__lowerCAmelCase = val[dim : dim * 2, :]
__lowerCAmelCase = val[-dim:, :]
else:
__lowerCAmelCase = val[:dim]
__lowerCAmelCase = val[dim : dim * 2]
__lowerCAmelCase = val[-dim:]
elif "in_proj" in key:
# weights and biases of the key, value and query projections of text encoder's attention layers require special treatment:
# we need to split them up into separate matrices/vectors
__lowerCAmelCase = key.split(""".""" )
__lowerCAmelCase = int(key_split[3] )
__lowerCAmelCase = config.text_config.hidden_size
if "weight" in key:
__lowerCAmelCase = val[:dim, :]
__lowerCAmelCase = val[
dim : dim * 2, :
]
__lowerCAmelCase = val[-dim:, :]
else:
__lowerCAmelCase = val[:dim]
__lowerCAmelCase = val[dim : dim * 2]
__lowerCAmelCase = val[-dim:]
else:
__lowerCAmelCase = rename_key(lowercase )
# squeeze if necessary
if (
"text_projection.0" in new_name
or "text_projection.3" in new_name
or "visual_projection.0" in new_name
or "visual_projection.3" in new_name
):
__lowerCAmelCase = val.squeeze_()
else:
__lowerCAmelCase = val
return orig_state_dict
def _lowerCAmelCase ( ) -> str:
__lowerCAmelCase = """http://images.cocodataset.org/val2017/000000039769.jpg"""
__lowerCAmelCase = Image.open(requests.get(lowercase , stream=lowercase ).raw )
return im
@torch.no_grad()
def _lowerCAmelCase ( lowercase , lowercase , lowercase="groupvit-gcc-yfcc" , lowercase=False ) -> List[Any]:
__lowerCAmelCase = GroupViTConfig()
__lowerCAmelCase = GroupViTModel(lowercase ).eval()
__lowerCAmelCase = torch.load(lowercase , map_location="""cpu""" )["""model"""]
__lowerCAmelCase = convert_state_dict(lowercase , lowercase )
__lowerCAmelCase , __lowerCAmelCase = model.load_state_dict(lowercase , strict=lowercase )
assert missing_keys == ["text_model.embeddings.position_ids"]
assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(lowercase ) == 0)
# verify result
__lowerCAmelCase = CLIPProcessor.from_pretrained("""openai/clip-vit-base-patch32""" )
__lowerCAmelCase = prepare_img()
__lowerCAmelCase = processor(text=["""a photo of a cat""", """a photo of a dog"""] , images=lowercase , padding=lowercase , return_tensors="""pt""" )
with torch.no_grad():
__lowerCAmelCase = model(**lowercase )
if model_name == "groupvit-gcc-yfcc":
__lowerCAmelCase = torch.tensor([[13.35_23, 6.36_29]] )
elif model_name == "groupvit-gcc-redcaps":
__lowerCAmelCase = torch.tensor([[16.18_73, 8.62_30]] )
else:
raise ValueError(f'Model name {model_name} not supported.' )
assert torch.allclose(outputs.logits_per_image , lowercase , atol=1e-3 )
processor.save_pretrained(lowercase )
model.save_pretrained(lowercase )
print("""Successfully saved processor and model to""" , lowercase )
if push_to_hub:
print("""Pushing to the hub...""" )
processor.push_to_hub(lowercase , organization="""nielsr""" )
model.push_to_hub(lowercase , organization="""nielsr""" )
if __name__ == "__main__":
_a : int = argparse.ArgumentParser()
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to dump the processor and PyTorch model."""
)
parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to GroupViT checkpoint""")
parser.add_argument(
"""--model_name""",
default="""groupvit-gccy-fcc""",
type=str,
help="""Name of the model. Expecting either 'groupvit-gcc-yfcc' or 'groupvit-gcc-redcaps'""",
)
parser.add_argument(
"""--push_to_hub""",
action="""store_true""",
help="""Whether or not to push the converted model and processor to the 🤗 hub using the provided `model_name`.""",
)
_a : List[str] = parser.parse_args()
convert_groupvit_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 689 | 1 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class _UpperCAmelCase ( lowerCAmelCase_ ):
a : Optional[torch.FloatTensor] =None
a : torch.FloatTensor =None
a : Optional[Tuple[torch.FloatTensor]] =None
a : Optional[Tuple[torch.FloatTensor]] =None
class _UpperCAmelCase ( lowerCAmelCase_ ):
def __init__( self,__SCREAMING_SNAKE_CASE=1,__SCREAMING_SNAKE_CASE=0,__SCREAMING_SNAKE_CASE=2,__SCREAMING_SNAKE_CASE=5_12,__SCREAMING_SNAKE_CASE="cls",__SCREAMING_SNAKE_CASE=False,__SCREAMING_SNAKE_CASE=True,**__SCREAMING_SNAKE_CASE,):
'''simple docstring'''
super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE,bos_token_id=__SCREAMING_SNAKE_CASE,eos_token_id=__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = project_dim
__lowerCAmelCase = pooler_fn
__lowerCAmelCase = learn_encoder
__lowerCAmelCase = use_attention_mask
class _UpperCAmelCase ( lowerCAmelCase_ ):
a : Any =[R"""pooler""", R"""logit_scale"""]
a : Union[str, Any] =[R"""position_ids""", R"""predictions.decoder.bias"""]
a : Optional[int] ="""roberta"""
a : List[str] =RobertaSeriesConfig
def __init__( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
super().__init__(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = XLMRobertaModel(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = nn.Linear(config.hidden_size,config.project_dim )
__lowerCAmelCase = getattr(__SCREAMING_SNAKE_CASE,"""has_pre_transformation""",__SCREAMING_SNAKE_CASE )
if self.has_pre_transformation:
__lowerCAmelCase = nn.Linear(config.hidden_size,config.project_dim )
__lowerCAmelCase = nn.LayerNorm(config.hidden_size,eps=config.layer_norm_eps )
self.post_init()
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = None,):
'''simple docstring'''
__lowerCAmelCase = return_dict if return_dict is not None else self.config.use_return_dict
__lowerCAmelCase = self.base_model(
input_ids=__SCREAMING_SNAKE_CASE,attention_mask=__SCREAMING_SNAKE_CASE,token_type_ids=__SCREAMING_SNAKE_CASE,position_ids=__SCREAMING_SNAKE_CASE,head_mask=__SCREAMING_SNAKE_CASE,inputs_embeds=__SCREAMING_SNAKE_CASE,encoder_hidden_states=__SCREAMING_SNAKE_CASE,encoder_attention_mask=__SCREAMING_SNAKE_CASE,output_attentions=__SCREAMING_SNAKE_CASE,output_hidden_states=True if self.has_pre_transformation else output_hidden_states,return_dict=__SCREAMING_SNAKE_CASE,)
if self.has_pre_transformation:
__lowerCAmelCase = outputs["""hidden_states"""][-2]
__lowerCAmelCase = self.pre_LN(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = self.transformation_pre(__SCREAMING_SNAKE_CASE )
return TransformationModelOutput(
projection_state=__SCREAMING_SNAKE_CASE,last_hidden_state=outputs.last_hidden_state,hidden_states=outputs.hidden_states,attentions=outputs.attentions,)
else:
__lowerCAmelCase = self.transformation(outputs.last_hidden_state )
return TransformationModelOutput(
projection_state=__SCREAMING_SNAKE_CASE,last_hidden_state=outputs.last_hidden_state,hidden_states=outputs.hidden_states,attentions=outputs.attentions,)
| 689 |
'''simple docstring'''
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGeneration,
MusicgenProcessor,
TaEncoderModel,
)
from transformers.models.musicgen.modeling_musicgen import MusicgenForCausalLM
from transformers.utils import logging
logging.set_verbosity_info()
_a : Tuple = logging.get_logger(__name__)
_a : Optional[int] = ["""model.decoder.embed_positions.weights"""]
def _lowerCAmelCase ( lowercase ) -> Optional[Any]:
if "emb" in name:
__lowerCAmelCase = name.replace("""emb""" , """model.decoder.embed_tokens""" )
if "transformer" in name:
__lowerCAmelCase = name.replace("""transformer""" , """model.decoder""" )
if "cross_attention" in name:
__lowerCAmelCase = name.replace("""cross_attention""" , """encoder_attn""" )
if "linear1" in name:
__lowerCAmelCase = name.replace("""linear1""" , """fc1""" )
if "linear2" in name:
__lowerCAmelCase = name.replace("""linear2""" , """fc2""" )
if "norm1" in name:
__lowerCAmelCase = name.replace("""norm1""" , """self_attn_layer_norm""" )
if "norm_cross" in name:
__lowerCAmelCase = name.replace("""norm_cross""" , """encoder_attn_layer_norm""" )
if "norm2" in name:
__lowerCAmelCase = name.replace("""norm2""" , """final_layer_norm""" )
if "out_norm" in name:
__lowerCAmelCase = name.replace("""out_norm""" , """model.decoder.layer_norm""" )
if "linears" in name:
__lowerCAmelCase = name.replace("""linears""" , """lm_heads""" )
if "condition_provider.conditioners.description.output_proj" in name:
__lowerCAmelCase = name.replace("""condition_provider.conditioners.description.output_proj""" , """enc_to_dec_proj""" )
return name
def _lowerCAmelCase ( lowercase , lowercase ) -> Tuple[Dict, Dict]:
__lowerCAmelCase = list(state_dict.keys() )
__lowerCAmelCase = {}
for key in keys:
__lowerCAmelCase = state_dict.pop(lowercase )
__lowerCAmelCase = rename_keys(lowercase )
if "in_proj_weight" in key:
# split fused qkv proj
__lowerCAmelCase = val[:hidden_size, :]
__lowerCAmelCase = val[hidden_size : 2 * hidden_size, :]
__lowerCAmelCase = val[-hidden_size:, :]
elif "enc_to_dec_proj" in key:
__lowerCAmelCase = val
else:
__lowerCAmelCase = val
return state_dict, enc_dec_proj_state_dict
def _lowerCAmelCase ( lowercase ) -> MusicgenDecoderConfig:
if checkpoint == "small":
# default config values
__lowerCAmelCase = 1024
__lowerCAmelCase = 24
__lowerCAmelCase = 16
elif checkpoint == "medium":
__lowerCAmelCase = 1536
__lowerCAmelCase = 48
__lowerCAmelCase = 24
elif checkpoint == "large":
__lowerCAmelCase = 2048
__lowerCAmelCase = 48
__lowerCAmelCase = 32
else:
raise ValueError(f'Checkpoint should be one of `[\'small\', \'medium\', \'large\']`, got {checkpoint}.' )
__lowerCAmelCase = MusicgenDecoderConfig(
hidden_size=lowercase , ffn_dim=hidden_size * 4 , num_hidden_layers=lowercase , num_attention_heads=lowercase , )
return config
@torch.no_grad()
def _lowerCAmelCase ( lowercase , lowercase=None , lowercase=None , lowercase="cpu" ) -> Optional[Any]:
__lowerCAmelCase = MusicGen.get_pretrained(lowercase , device=lowercase )
__lowerCAmelCase = decoder_config_from_checkpoint(lowercase )
__lowerCAmelCase = fairseq_model.lm.state_dict()
__lowerCAmelCase , __lowerCAmelCase = rename_state_dict(
lowercase , hidden_size=decoder_config.hidden_size )
__lowerCAmelCase = TaEncoderModel.from_pretrained("""t5-base""" )
__lowerCAmelCase = EncodecModel.from_pretrained("""facebook/encodec_32khz""" )
__lowerCAmelCase = MusicgenForCausalLM(lowercase ).eval()
# load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
__lowerCAmelCase , __lowerCAmelCase = decoder.load_state_dict(lowercase , strict=lowercase )
for key in missing_keys.copy():
if key.startswith(("""text_encoder""", """audio_encoder""") ) or key in EXPECTED_MISSING_KEYS:
missing_keys.remove(lowercase )
if len(lowercase ) > 0:
raise ValueError(f'Missing key(s) in state_dict: {missing_keys}' )
if len(lowercase ) > 0:
raise ValueError(f'Unexpected key(s) in state_dict: {unexpected_keys}' )
# init the composite model
__lowerCAmelCase = MusicgenForConditionalGeneration(text_encoder=lowercase , audio_encoder=lowercase , decoder=lowercase )
# load the pre-trained enc-dec projection (from the decoder state dict)
model.enc_to_dec_proj.load_state_dict(lowercase )
# check we can do a forward pass
__lowerCAmelCase = torch.arange(0 , 8 , dtype=torch.long ).reshape(2 , -1 )
__lowerCAmelCase = input_ids.reshape(2 * 4 , -1 )
with torch.no_grad():
__lowerCAmelCase = model(input_ids=lowercase , decoder_input_ids=lowercase ).logits
if logits.shape != (8, 1, 2048):
raise ValueError("""Incorrect shape for logits""" )
# now construct the processor
__lowerCAmelCase = AutoTokenizer.from_pretrained("""t5-base""" )
__lowerCAmelCase = AutoFeatureExtractor.from_pretrained("""facebook/encodec_32khz""" , padding_side="""left""" )
__lowerCAmelCase = MusicgenProcessor(feature_extractor=lowercase , tokenizer=lowercase )
# set the appropriate bos/pad token ids
__lowerCAmelCase = 2048
__lowerCAmelCase = 2048
# set other default generation config params
__lowerCAmelCase = int(30 * audio_encoder.config.frame_rate )
__lowerCAmelCase = True
__lowerCAmelCase = 3.0
if pytorch_dump_folder is not None:
Path(lowercase ).mkdir(exist_ok=lowercase )
logger.info(f'Saving model {checkpoint} to {pytorch_dump_folder}' )
model.save_pretrained(lowercase )
processor.save_pretrained(lowercase )
if repo_id:
logger.info(f'Pushing model {checkpoint} to {repo_id}' )
model.push_to_hub(lowercase )
processor.push_to_hub(lowercase )
if __name__ == "__main__":
_a : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--checkpoint""",
default="""small""",
type=str,
help="""Checkpoint size of the MusicGen model you'd like to convert. Can be one of: `['small', 'medium', 'large']`.""",
)
parser.add_argument(
"""--pytorch_dump_folder""",
required=True,
default=None,
type=str,
help="""Path to the output PyTorch model directory.""",
)
parser.add_argument(
"""--push_to_hub""", default=None, type=str, help="""Where to upload the converted model on the 🤗 hub."""
)
parser.add_argument(
"""--device""", default="""cpu""", type=str, help="""Torch device to run the conversion, either cpu or cuda."""
)
_a : List[Any] = parser.parse_args()
convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)
| 689 | 1 |
'''simple docstring'''
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_space_optuna,
default_hp_space_ray,
default_hp_space_sigopt,
default_hp_space_wandb,
)
from .utils import logging
_a : List[str] = logging.get_logger(__name__)
class _UpperCAmelCase :
a : str
a : str =None
@staticmethod
def lowerCamelCase__ ( ):
'''simple docstring'''
raise NotImplementedError
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
raise NotImplementedError
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
raise NotImplementedError
def lowerCamelCase__ ( self ):
'''simple docstring'''
if not self.is_available():
raise RuntimeError(
f'You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}.' )
@classmethod
def lowerCamelCase__ ( cls ):
'''simple docstring'''
return f'`pip install {cls.pip_package or cls.name}`'
class _UpperCAmelCase ( lowerCAmelCase_ ):
a : Optional[Any] ="""optuna"""
@staticmethod
def lowerCamelCase__ ( ):
'''simple docstring'''
return is_optuna_available()
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
return run_hp_search_optuna(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE )
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
return default_hp_space_optuna(__SCREAMING_SNAKE_CASE )
class _UpperCAmelCase ( lowerCAmelCase_ ):
a : List[str] ="""ray"""
a : List[str] ="""'ray[tune]'"""
@staticmethod
def lowerCamelCase__ ( ):
'''simple docstring'''
return is_ray_available()
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
return run_hp_search_ray(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE )
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
return default_hp_space_ray(__SCREAMING_SNAKE_CASE )
class _UpperCAmelCase ( lowerCAmelCase_ ):
a : Optional[int] ="""sigopt"""
@staticmethod
def lowerCamelCase__ ( ):
'''simple docstring'''
return is_sigopt_available()
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
return run_hp_search_sigopt(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE )
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
return default_hp_space_sigopt(__SCREAMING_SNAKE_CASE )
class _UpperCAmelCase ( lowerCAmelCase_ ):
a : Dict ="""wandb"""
@staticmethod
def lowerCamelCase__ ( ):
'''simple docstring'''
return is_wandb_available()
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
return run_hp_search_wandb(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE )
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
return default_hp_space_wandb(__SCREAMING_SNAKE_CASE )
_a : str = {
HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}
def _lowerCAmelCase ( ) -> str:
__lowerCAmelCase = [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
if len(lowercase ) > 0:
__lowerCAmelCase = available_backends[0].name
if len(lowercase ) > 1:
logger.info(
f'{len(lowercase )} hyperparameter search backends available. Using {name} as the default.' )
return name
raise RuntimeError(
"""No hyperparameter search backend available.\n"""
+ """\n""".join(
f' - To install {backend.name} run {backend.pip_install()}'
for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() ) )
| 689 |
'''simple docstring'''
from collections import deque
def _lowerCAmelCase ( lowercase ) -> Dict:
__lowerCAmelCase = len(lowercase )
__lowerCAmelCase = deque()
__lowerCAmelCase = [False for _ in range(lowercase )]
__lowerCAmelCase = [-1 for _ in range(lowercase )]
__lowerCAmelCase = index_of[:]
def strong_connect(lowercase , lowercase , lowercase ):
__lowerCAmelCase = index # the number when this node is seen
__lowerCAmelCase = index # lowest rank node reachable from here
index += 1
stack.append(lowercase )
__lowerCAmelCase = True
for w in g[v]:
if index_of[w] == -1:
__lowerCAmelCase = strong_connect(lowercase , lowercase , lowercase )
__lowerCAmelCase = (
lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
)
elif on_stack[w]:
__lowerCAmelCase = (
lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
)
if lowlink_of[v] == index_of[v]:
__lowerCAmelCase = []
__lowerCAmelCase = stack.pop()
__lowerCAmelCase = False
component.append(lowercase )
while w != v:
__lowerCAmelCase = stack.pop()
__lowerCAmelCase = False
component.append(lowercase )
components.append(lowercase )
return index
__lowerCAmelCase = []
for v in range(lowercase ):
if index_of[v] == -1:
strong_connect(lowercase , 0 , lowercase )
return components
def _lowerCAmelCase ( lowercase , lowercase ) -> str:
__lowerCAmelCase = [[] for _ in range(lowercase )]
for u, v in edges:
g[u].append(lowercase )
return g
if __name__ == "__main__":
# Test
_a : Any = 7
_a : Tuple = [0, 0, 1, 2, 3, 3, 4, 4, 6]
_a : Optional[int] = [1, 3, 2, 0, 1, 4, 5, 6, 5]
_a : Optional[Any] = [(u, v) for u, v in zip(source, target)]
_a : Optional[int] = create_graph(n_vertices, edges)
assert [[5], [6], [4], [3, 2, 1, 0]] == tarjan(g)
| 689 | 1 |
'''simple docstring'''
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
_a : Dict = _symbol_database.Default()
_a : Union[str, Any] = _descriptor_pool.Default().AddSerializedFile(
b"""\n\x19sentencepiece_model.proto\x12\rsentencepiece\"\x80\x0c\n\x0bTrainerSpec\x12\r\n\x05input\x18\x01 \x03(\t\x12\x14\n\x0cinput_format\x18\x07 \x01(\t\x12\x14\n\x0cmodel_prefix\x18\x02 \x01(\t\x12\x41\n\nmodel_type\x18\x03 \x01(\x0e\x32$.sentencepiece.TrainerSpec.ModelType:\x07UNIGRAM\x12\x18\n\nvocab_size\x18\x04 \x01(\x05:\x04\x38\x30\x30\x30\x12\x17\n\x0f\x61\x63\x63\x65pt_language\x18\x05 \x03(\t\x12 \n\x15self_test_sample_size\x18\x06 \x01(\x05:\x01\x30\x12*\n\x1b\x65nable_differential_privacy\x18\x32 \x01(\x08:\x05\x66\x61lse\x12+\n differential_privacy_noise_level\x18\x33 \x01(\x02:\x01\x30\x12\x32\n\'differential_privacy_clipping_threshold\x18\x34 \x01(\x04:\x01\x30\x12\"\n\x12\x63haracter_coverage\x18\n \x01(\x02:\x06\x30.9995\x12\x1e\n\x13input_sentence_size\x18\x0b \x01(\x04:\x01\x30\x12$\n\x16shuffle_input_sentence\x18\x13 \x01(\x08:\x04true\x12 \n\x14mining_sentence_size\x18\x0c \x01(\x05\x42\x02\x18\x01\x12\"\n\x16training_sentence_size\x18\r \x01(\x05\x42\x02\x18\x01\x12(\n\x17seed_sentencepiece_size\x18\x0e \x01(\x05:\x07\x31\x30\x30\x30\x30\x30\x30\x12\x1e\n\x10shrinking_factor\x18\x0f \x01(\x02:\x04\x30.75\x12!\n\x13max_sentence_length\x18\x12 \x01(\x05:\x04\x34\x31\x39\x32\x12\x17\n\x0bnum_threads\x18\x10 \x01(\x05:\x02\x31\x36\x12\x1d\n\x12num_sub_iterations\x18\x11 \x01(\x05:\x01\x32\x12$\n\x18max_sentencepiece_length\x18\x14 \x01(\x05:\x02\x31\x36\x12%\n\x17split_by_unicode_script\x18\x15 \x01(\x08:\x04true\x12\x1d\n\x0fsplit_by_number\x18\x17 \x01(\x08:\x04true\x12!\n\x13split_by_whitespace\x18\x16 \x01(\x08:\x04true\x12)\n\x1atreat_whitespace_as_suffix\x18\x18 \x01(\x08:\x05\x66\x61lse\x12+\n\x1c\x61llow_whitespace_only_pieces\x18\x1a \x01(\x08:\x05\x66\x61lse\x12\x1b\n\x0csplit_digits\x18\x19 \x01(\x08:\x05\x66\x61lse\x12#\n\x19pretokenization_delimiter\x18\x35 \x01(\t:\x00\x12\x17\n\x0f\x63ontrol_symbols\x18\x1e \x03(\t\x12\x1c\n\x14user_defined_symbols\x18\x1f \x03(\t\x12\x16\n\x0erequired_chars\x18$ \x01(\t\x12\x1c\n\rbyte_fallback\x18# \x01(\x08:\x05\x66\x61lse\x12+\n\x1dvocabulary_output_piece_score\x18 \x01(\x08:\x04true\x12\x1e\n\x10hard_vocab_limit\x18! \x01(\x08:\x04true\x12\x1c\n\ruse_all_vocab\x18\" \x01(\x08:\x05\x66\x61lse\x12\x11\n\x06unk_id\x18( \x01(\x05:\x01\x30\x12\x11\n\x06\x62os_id\x18) \x01(\x05:\x01\x31\x12\x11\n\x06\x65os_id\x18* \x01(\x05:\x01\x32\x12\x12\n\x06pad_id\x18+ \x01(\x05:\x02-1\x12\x18\n\tunk_piece\x18- \x01(\t:\x05<unk>\x12\x16\n\tbos_piece\x18. \x01(\t:\x03<s>\x12\x17\n\teos_piece\x18/ \x01(\t:\x04</s>\x12\x18\n\tpad_piece\x18\x30 \x01(\t:\x05<pad>\x12\x1a\n\x0bunk_surface\x18, \x01(\t:\x05 \xe2\x81\x87 \x12+\n\x1ctrain_extremely_large_corpus\x18\x31 \x01(\x08:\x05\x66\x61lse\"5\n\tModelType\x12\x0b\n\x07UNIGRAM\x10\x01\x12\x07\n\x03\x42PE\x10\x02\x12\x08\n\x04WORD\x10\x03\x12\x08\n\x04\x43HAR\x10\x04*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"\xd1\x01\n\x0eNormalizerSpec\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x1c\n\x14precompiled_charsmap\x18\x02 \x01(\x0c\x12\x1e\n\x10\x61\x64\x64_dummy_prefix\x18\x03 \x01(\x08:\x04true\x12&\n\x18remove_extra_whitespaces\x18\x04 \x01(\x08:\x04true\x12 \n\x12\x65scape_whitespaces\x18\x05 \x01(\x08:\x04true\x12\x1e\n\x16normalization_rule_tsv\x18\x06 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"y\n\x0cSelfTestData\x12\x33\n\x07samples\x18\x01 \x03(\x0b\x32\".sentencepiece.SelfTestData.Sample\x1a)\n\x06Sample\x12\r\n\x05input\x18\x01 \x01(\t\x12\x10\n\x08\x65xpected\x18\x02 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"\xfe\x03\n\nModelProto\x12\x37\n\x06pieces\x18\x01 \x03(\x0b\x32\'.sentencepiece.ModelProto.SentencePiece\x12\x30\n\x0ctrainer_spec\x18\x02 \x01(\x0b\x32\x1a.sentencepiece.TrainerSpec\x12\x36\n\x0fnormalizer_spec\x18\x03 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x12\x33\n\x0eself_test_data\x18\x04 \x01(\x0b\x32\x1b.sentencepiece.SelfTestData\x12\x38\n\x11\x64\x65normalizer_spec\x18\x05 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x1a\xd2\x01\n\rSentencePiece\x12\r\n\x05piece\x18\x01 \x01(\t\x12\r\n\x05score\x18\x02 \x01(\x02\x12\x42\n\x04type\x18\x03 \x01(\x0e\x32,.sentencepiece.ModelProto.SentencePiece.Type:\x06NORMAL\"T\n\x04Type\x12\n\n\x06NORMAL\x10\x01\x12\x0b\n\x07UNKNOWN\x10\x02\x12\x0b\n\x07\x43ONTROL\x10\x03\x12\x10\n\x0cUSER_DEFINED\x10\x04\x12\x08\n\x04\x42YTE\x10\x06\x12\n\n\x06UNUSED\x10\x05*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\x42\x02H\x03"""
)
_a : str = globals()
_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, """sentencepiece_model_pb2""", _globals)
if _descriptor._USE_C_DESCRIPTORS is False:
_a : str = None
_a : Union[str, Any] = b"""H\003"""
# (generated by protobuf compiler, but `_TRAINERSPEC` is not defined)
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._serialized_options = b"\030\001"
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._serialized_options = b"\030\001"
_a : Optional[int] = 4_5
_a : List[Any] = 1_5_8_1
_a : str = 1_5_1_7
_a : Optional[Any] = 1_5_7_0
_a : List[str] = 1_5_8_4
_a : List[Any] = 1_7_9_3
_a : Union[str, Any] = 1_7_9_5
_a : Tuple = 1_9_1_6
_a : List[Any] = 1_8_6_4
_a : Any = 1_9_0_5
_a : Optional[Any] = 1_9_1_9
_a : Optional[int] = 2_4_2_9
_a : Tuple = 2_2_0_8
_a : Optional[Any] = 2_4_1_8
_a : List[Any] = 2_3_2_3
_a : str = 2_4_0_7
# @@protoc_insertion_point(module_scope)
| 689 |
'''simple docstring'''
from argparse import ArgumentParser
from .env import EnvironmentCommand
def _lowerCAmelCase ( ) -> Union[str, Any]:
__lowerCAmelCase = ArgumentParser("""Diffusers CLI tool""" , usage="""diffusers-cli <command> [<args>]""" )
__lowerCAmelCase = parser.add_subparsers(help="""diffusers-cli command helpers""" )
# Register commands
EnvironmentCommand.register_subcommand(lowercase )
# Let's go
__lowerCAmelCase = parser.parse_args()
if not hasattr(lowercase , """func""" ):
parser.print_help()
exit(1 )
# Run
__lowerCAmelCase = args.func(lowercase )
service.run()
if __name__ == "__main__":
main()
| 689 | 1 |
'''simple docstring'''
from datetime import datetime as dt
import os
from github import Github
_a : Optional[int] = [
"""good first issue""",
"""good second issue""",
"""good difficult issue""",
"""feature request""",
"""new model""",
"""wip""",
]
def _lowerCAmelCase ( ) -> Dict:
__lowerCAmelCase = Github(os.environ["""GITHUB_TOKEN"""] )
__lowerCAmelCase = g.get_repo("""huggingface/transformers""" )
__lowerCAmelCase = repo.get_issues(state="""open""" )
for issue in open_issues:
__lowerCAmelCase = sorted([comment for comment in issue.get_comments()] , key=lambda lowercase : i.created_at , reverse=lowercase )
__lowerCAmelCase = comments[0] if len(lowercase ) > 0 else None
if (
last_comment is not None
and last_comment.user.login == "github-actions[bot]"
and (dt.utcnow() - issue.updated_at).days > 7
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.")
issue.edit(state="""closed""" )
elif (
(dt.utcnow() - issue.updated_at).days > 23
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# print(f"Would add stale comment to {issue.number}")
issue.create_comment(
"""This issue has been automatically marked as stale because it has not had """
"""recent activity. If you think this still needs to be addressed """
"""please comment on this thread.\n\nPlease note that issues that do not follow the """
"""[contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) """
"""are likely to be ignored.""" )
if __name__ == "__main__":
main()
| 689 |
'''simple docstring'''
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
_a : List[Any] = logging.get_logger(__name__)
_a : int = {
"""post_extract_proj""": """feature_projection.projection""",
"""encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
"""self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
"""self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
"""self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
"""self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
"""self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
"""fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
"""fc2""": """encoder.layers.*.feed_forward.output_dense""",
"""final_layer_norm""": """encoder.layers.*.final_layer_norm""",
"""encoder.layer_norm""": """encoder.layer_norm""",
"""encoder.layer_norm_for_extract""": """layer_norm_for_extract""",
"""w2v_model.layer_norm""": """feature_projection.layer_norm""",
"""quantizer.weight_proj""": """quantizer.weight_proj""",
"""quantizer.vars""": """quantizer.codevectors""",
"""project_q""": """project_q""",
"""final_proj""": """project_hid""",
"""w2v_encoder.proj""": """lm_head""",
"""label_embs_concat""": """label_embeddings_concat""",
"""mask_emb""": """masked_spec_embed""",
"""spk_proj""": """speaker_proj""",
}
_a : Any = [
"""lm_head""",
"""quantizer.weight_proj""",
"""quantizer.codevectors""",
"""project_q""",
"""project_hid""",
"""label_embeddings_concat""",
"""speaker_proj""",
"""layer_norm_for_extract""",
]
def _lowerCAmelCase ( lowercase , lowercase , lowercase , lowercase , lowercase ) -> str:
for attribute in key.split(""".""" ):
__lowerCAmelCase = getattr(lowercase , lowercase )
if weight_type is not None:
__lowerCAmelCase = getattr(lowercase , lowercase ).shape
else:
__lowerCAmelCase = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
f'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
f' {value.shape} for {full_name}' )
if weight_type == "weight":
__lowerCAmelCase = value
elif weight_type == "weight_g":
__lowerCAmelCase = value
elif weight_type == "weight_v":
__lowerCAmelCase = value
elif weight_type == "bias":
__lowerCAmelCase = value
else:
__lowerCAmelCase = value
logger.info(f'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.' )
def _lowerCAmelCase ( lowercase , lowercase ) -> List[Any]:
__lowerCAmelCase = []
__lowerCAmelCase = fairseq_model.state_dict()
__lowerCAmelCase = hf_model.unispeech_sat.feature_extractor
for name, value in fairseq_dict.items():
__lowerCAmelCase = False
if "conv_layers" in name:
load_conv_layer(
lowercase , lowercase , lowercase , lowercase , hf_model.config.feat_extract_norm == """group""" , )
__lowerCAmelCase = True
else:
for key, mapped_key in MAPPING.items():
__lowerCAmelCase = """unispeech_sat.""" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split("""w2v_model.""" )[-1] == name.split(""".""" )[0]:
if "layer_norm_for_extract" in name and (".".join(name.split(""".""" )[:-1] ) != key):
# special case since naming is very similar
continue
__lowerCAmelCase = True
if "*" in mapped_key:
__lowerCAmelCase = name.split(lowercase )[0].split(""".""" )[-2]
__lowerCAmelCase = mapped_key.replace("""*""" , lowercase )
if "weight_g" in name:
__lowerCAmelCase = """weight_g"""
elif "weight_v" in name:
__lowerCAmelCase = """weight_v"""
elif "bias" in name:
__lowerCAmelCase = """bias"""
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
__lowerCAmelCase = """weight"""
else:
__lowerCAmelCase = None
set_recursively(lowercase , lowercase , lowercase , lowercase , lowercase )
continue
if not is_used:
unused_weights.append(lowercase )
logger.warning(f'Unused weights: {unused_weights}' )
def _lowerCAmelCase ( lowercase , lowercase , lowercase , lowercase , lowercase ) -> Optional[Any]:
__lowerCAmelCase = full_name.split("""conv_layers.""" )[-1]
__lowerCAmelCase = name.split(""".""" )
__lowerCAmelCase = int(items[0] )
__lowerCAmelCase = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.' )
__lowerCAmelCase = value
logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.' )
__lowerCAmelCase = value
logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.' )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor[layer_id].layer_norm.bias.data.shape} was found.' )
__lowerCAmelCase = value
logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.' )
__lowerCAmelCase = value
logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
else:
unused_weights.append(lowercase )
@torch.no_grad()
def _lowerCAmelCase ( lowercase , lowercase , lowercase=None , lowercase=None , lowercase=True ) -> Dict:
if config_path is not None:
__lowerCAmelCase = UniSpeechSatConfig.from_pretrained(lowercase )
else:
__lowerCAmelCase = UniSpeechSatConfig()
__lowerCAmelCase = """"""
if is_finetuned:
__lowerCAmelCase = UniSpeechSatForCTC(lowercase )
else:
__lowerCAmelCase = UniSpeechSatForPreTraining(lowercase )
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={"""data""": """/""".join(dict_path.split("""/""" )[:-1] )} )
__lowerCAmelCase = model[0].eval()
recursively_load_weights(lowercase , lowercase )
hf_wavavec.save_pretrained(lowercase )
if __name__ == "__main__":
_a : List[str] = argparse.ArgumentParser()
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
parser.add_argument(
"""--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
)
_a : Union[str, Any] = parser.parse_args()
convert_unispeech_sat_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 689 | 1 |
'''simple docstring'''
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class _UpperCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ ):
@register_to_config
def __init__( self,__SCREAMING_SNAKE_CASE = 7_68,):
'''simple docstring'''
super().__init__()
__lowerCAmelCase = nn.Parameter(torch.zeros(1,__SCREAMING_SNAKE_CASE ) )
__lowerCAmelCase = nn.Parameter(torch.ones(1,__SCREAMING_SNAKE_CASE ) )
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = None,):
'''simple docstring'''
__lowerCAmelCase = nn.Parameter(self.mean.to(__SCREAMING_SNAKE_CASE ).to(__SCREAMING_SNAKE_CASE ) )
__lowerCAmelCase = nn.Parameter(self.std.to(__SCREAMING_SNAKE_CASE ).to(__SCREAMING_SNAKE_CASE ) )
return self
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase = (embeds - self.mean) * 1.0 / self.std
return embeds
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase = (embeds * self.std) + self.mean
return embeds
| 689 |
'''simple docstring'''
from scipy.stats import spearmanr
import datasets
_a : str = """
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlations imply that as data in dataset x increases, so
does data in dataset y. Negative correlations imply that as x increases,
y decreases. Correlations of -1 or +1 imply an exact monotonic relationship.
Unlike the Pearson correlation, the Spearman correlation does not
assume that both datasets are normally distributed.
The p-value roughly indicates the probability of an uncorrelated system
producing datasets that have a Spearman correlation at least as extreme
as the one computed from these datasets. The p-values are not entirely
reliable but are probably reasonable for datasets larger than 500 or so.
"""
_a : Dict = """
Args:
predictions (`List[float]`): Predicted labels, as returned by a model.
references (`List[float]`): Ground truth labels.
return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns
only the spearmanr score. Defaults to `False`.
Returns:
spearmanr (`float`): Spearman correlation coefficient.
p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.
Examples:
Example 1:
>>> spearmanr_metric = datasets.load_metric(\"spearmanr\")
>>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])
>>> print(results)
{'spearmanr': -0.7}
Example 2:
>>> spearmanr_metric = datasets.load_metric(\"spearmanr\")
>>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],
... predictions=[10, 9, 2.5, 6, 4],
... return_pvalue=True)
>>> print(results['spearmanr'])
-0.7
>>> print(round(results['spearmanr_pvalue'], 2))
0.19
"""
_a : List[str] = r"""\
@book{kokoska2000crc,
title={CRC standard probability and statistics tables and formulae},
author={Kokoska, Stephen and Zwillinger, Daniel},
year={2000},
publisher={Crc Press}
}
@article{2020SciPy-NMeth,
author = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
Haberland, Matt and Reddy, Tyler and Cournapeau, David and
Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
Kern, Robert and Larson, Eric and Carey, C J and
Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and
{VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
Harris, Charles R. and Archibald, Anne M. and
Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and
{van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
title = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
Computing in Python}},
journal = {Nature Methods},
year = {2020},
volume = {17},
pages = {261--272},
adsurl = {https://rdcu.be/b08Wh},
doi = {10.1038/s41592-019-0686-2},
}
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _UpperCAmelCase ( datasets.Metric ):
def lowerCamelCase__ ( self ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION,citation=_CITATION,inputs_description=_KWARGS_DESCRIPTION,features=datasets.Features(
{
"""predictions""": datasets.Value("""float""" ),
"""references""": datasets.Value("""float""" ),
} ),reference_urls=["""https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.spearmanr.html"""],)
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=False ):
'''simple docstring'''
__lowerCAmelCase = spearmanr(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
if return_pvalue:
return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
else:
return {"spearmanr": results[0]}
| 689 | 1 |
'''simple docstring'''
from typing import Any
import numpy as np
def _lowerCAmelCase ( lowercase ) -> bool:
return np.array_equal(lowercase , matrix.conjugate().T )
def _lowerCAmelCase ( lowercase , lowercase ) -> Any:
__lowerCAmelCase = v.conjugate().T
__lowerCAmelCase = v_star.dot(lowercase )
assert isinstance(lowercase , np.ndarray )
return (v_star_dot.dot(lowercase )) / (v_star.dot(lowercase ))
def _lowerCAmelCase ( ) -> None:
__lowerCAmelCase = np.array([[2, 2 + 1j, 4], [2 - 1j, 3, 1j], [4, -1j, 1]] )
__lowerCAmelCase = np.array([[1], [2], [3]] )
assert is_hermitian(lowercase ), f'{a} is not hermitian.'
print(rayleigh_quotient(lowercase , lowercase ) )
__lowerCAmelCase = np.array([[1, 2, 4], [2, 3, -1], [4, -1, 1]] )
assert is_hermitian(lowercase ), f'{a} is not hermitian.'
assert rayleigh_quotient(lowercase , lowercase ) == float(3 )
if __name__ == "__main__":
import doctest
doctest.testmod()
tests()
| 689 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _UpperCAmelCase ( metaclass=lowerCAmelCase_ ):
a : List[str] =["""onnx"""]
def __init__( self,*__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
requires_backends(self,["""onnx"""] )
@classmethod
def lowerCamelCase__ ( cls,*__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
requires_backends(cls,["""onnx"""] )
@classmethod
def lowerCamelCase__ ( cls,*__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
requires_backends(cls,["""onnx"""] )
| 689 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel
@require_tf
class _UpperCAmelCase :
a : Optional[int] =BlenderbotSmallConfig
a : Optional[int] ={}
a : Optional[int] ="""gelu"""
def __init__( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=13,__SCREAMING_SNAKE_CASE=7,__SCREAMING_SNAKE_CASE=True,__SCREAMING_SNAKE_CASE=False,__SCREAMING_SNAKE_CASE=99,__SCREAMING_SNAKE_CASE=32,__SCREAMING_SNAKE_CASE=2,__SCREAMING_SNAKE_CASE=4,__SCREAMING_SNAKE_CASE=37,__SCREAMING_SNAKE_CASE=0.1,__SCREAMING_SNAKE_CASE=0.1,__SCREAMING_SNAKE_CASE=20,__SCREAMING_SNAKE_CASE=2,__SCREAMING_SNAKE_CASE=1,__SCREAMING_SNAKE_CASE=0,):
'''simple docstring'''
__lowerCAmelCase = parent
__lowerCAmelCase = batch_size
__lowerCAmelCase = seq_length
__lowerCAmelCase = is_training
__lowerCAmelCase = use_labels
__lowerCAmelCase = vocab_size
__lowerCAmelCase = hidden_size
__lowerCAmelCase = num_hidden_layers
__lowerCAmelCase = num_attention_heads
__lowerCAmelCase = intermediate_size
__lowerCAmelCase = hidden_dropout_prob
__lowerCAmelCase = attention_probs_dropout_prob
__lowerCAmelCase = max_position_embeddings
__lowerCAmelCase = eos_token_id
__lowerCAmelCase = pad_token_id
__lowerCAmelCase = bos_token_id
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length - 1],self.vocab_size )
__lowerCAmelCase = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ),1 )
__lowerCAmelCase = tf.concat([input_ids, eos_tensor],axis=1 )
__lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length],self.vocab_size )
__lowerCAmelCase = self.config_cls(
vocab_size=self.vocab_size,d_model=self.hidden_size,encoder_layers=self.num_hidden_layers,decoder_layers=self.num_hidden_layers,encoder_attention_heads=self.num_attention_heads,decoder_attention_heads=self.num_attention_heads,encoder_ffn_dim=self.intermediate_size,decoder_ffn_dim=self.intermediate_size,dropout=self.hidden_dropout_prob,attention_dropout=self.attention_probs_dropout_prob,max_position_embeddings=self.max_position_embeddings,eos_token_ids=[2],bos_token_id=self.bos_token_id,pad_token_id=self.pad_token_id,decoder_start_token_id=self.pad_token_id,**self.config_updates,)
__lowerCAmelCase = prepare_blenderbot_small_inputs_dict(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
return config, inputs_dict
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase = TFBlenderbotSmallModel(config=__SCREAMING_SNAKE_CASE ).get_decoder()
__lowerCAmelCase = inputs_dict["""input_ids"""]
__lowerCAmelCase = input_ids[:1, :]
__lowerCAmelCase = inputs_dict["""attention_mask"""][:1, :]
__lowerCAmelCase = inputs_dict["""head_mask"""]
__lowerCAmelCase = 1
# first forward pass
__lowerCAmelCase = model(__SCREAMING_SNAKE_CASE,attention_mask=__SCREAMING_SNAKE_CASE,head_mask=__SCREAMING_SNAKE_CASE,use_cache=__SCREAMING_SNAKE_CASE )
__lowerCAmelCase , __lowerCAmelCase = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
__lowerCAmelCase = ids_tensor((self.batch_size, 3),config.vocab_size )
__lowerCAmelCase = tf.cast(ids_tensor((self.batch_size, 3),2 ),tf.inta )
# append to next input_ids and
__lowerCAmelCase = tf.concat([input_ids, next_tokens],axis=-1 )
__lowerCAmelCase = tf.concat([attention_mask, next_attn_mask],axis=-1 )
__lowerCAmelCase = model(__SCREAMING_SNAKE_CASE,attention_mask=__SCREAMING_SNAKE_CASE )[0]
__lowerCAmelCase = model(__SCREAMING_SNAKE_CASE,attention_mask=__SCREAMING_SNAKE_CASE,past_key_values=__SCREAMING_SNAKE_CASE )[0]
self.parent.assertEqual(next_tokens.shape[1],output_from_past.shape[1] )
# select random slice
__lowerCAmelCase = int(ids_tensor((1,),output_from_past.shape[-1] ) )
__lowerCAmelCase = output_from_no_past[:, -3:, random_slice_idx]
__lowerCAmelCase = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,rtol=1e-3 )
def _lowerCAmelCase ( lowercase , lowercase , lowercase , lowercase=None , lowercase=None , lowercase=None , lowercase=None , lowercase=None , ) -> int:
if attention_mask is None:
__lowerCAmelCase = tf.cast(tf.math.not_equal(lowercase , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
__lowerCAmelCase = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
__lowerCAmelCase = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
__lowerCAmelCase = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
__lowerCAmelCase = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
@require_tf
class _UpperCAmelCase ( lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase ):
a : Optional[int] =(
(TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
)
a : Optional[Any] =(TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
a : Optional[int] =(
{
"""conversational""": TFBlenderbotSmallForConditionalGeneration,
"""feature-extraction""": TFBlenderbotSmallModel,
"""summarization""": TFBlenderbotSmallForConditionalGeneration,
"""text2text-generation""": TFBlenderbotSmallForConditionalGeneration,
"""translation""": TFBlenderbotSmallForConditionalGeneration,
}
if is_tf_available()
else {}
)
a : Tuple =True
a : Union[str, Any] =False
a : List[str] =False
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = TFBlenderbotSmallModelTester(self )
__lowerCAmelCase = ConfigTester(self,config_class=__SCREAMING_SNAKE_CASE )
def lowerCamelCase__ ( self ):
'''simple docstring'''
self.config_tester.run_common_tests()
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*__SCREAMING_SNAKE_CASE )
@require_tokenizers
@require_tf
class _UpperCAmelCase ( unittest.TestCase ):
a : Any =[
"""Social anxiety\nWow, I am never shy. Do you have anxiety?\nYes. I end up sweating and blushing and feel like """
""" i'm going to throw up.\nand why is that?"""
]
a : Dict ="""facebook/blenderbot_small-90M"""
@cached_property
def lowerCamelCase__ ( self ):
'''simple docstring'''
return BlenderbotSmallTokenizer.from_pretrained("""facebook/blenderbot-90M""" )
@cached_property
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
return model
@slow
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = self.tokenizer(self.src_text,return_tensors="""tf""" )
__lowerCAmelCase = self.model.generate(
model_inputs.input_ids,attention_mask=model_inputs.attention_mask,num_beams=2,use_cache=__SCREAMING_SNAKE_CASE,)
__lowerCAmelCase = self.tokenizer.batch_decode(generated_ids.numpy(),skip_special_tokens=__SCREAMING_SNAKE_CASE )[0]
assert generated_words in (
"i don't know. i just feel like i'm going to throw up. it's not fun.",
"i'm not sure. i just feel like i've been feeling like i have to be in a certain place",
"i'm not sure. i just feel like i've been in a bad situation.",
)
| 689 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
_a : int = logging.get_logger(__name__)
_a : Optional[int] = {
"""EleutherAI/gpt-j-6B""": """https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json""",
# See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}
class _UpperCAmelCase ( lowerCAmelCase_ ):
a : List[str] ="""gptj"""
a : Optional[int] ={
"""max_position_embeddings""": """n_positions""",
"""hidden_size""": """n_embd""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self,__SCREAMING_SNAKE_CASE=5_04_00,__SCREAMING_SNAKE_CASE=20_48,__SCREAMING_SNAKE_CASE=40_96,__SCREAMING_SNAKE_CASE=28,__SCREAMING_SNAKE_CASE=16,__SCREAMING_SNAKE_CASE=64,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE="gelu_new",__SCREAMING_SNAKE_CASE=0.0,__SCREAMING_SNAKE_CASE=0.0,__SCREAMING_SNAKE_CASE=0.0,__SCREAMING_SNAKE_CASE=1e-5,__SCREAMING_SNAKE_CASE=0.02,__SCREAMING_SNAKE_CASE=True,__SCREAMING_SNAKE_CASE=5_02_56,__SCREAMING_SNAKE_CASE=5_02_56,__SCREAMING_SNAKE_CASE=False,**__SCREAMING_SNAKE_CASE,):
'''simple docstring'''
__lowerCAmelCase = vocab_size
__lowerCAmelCase = n_positions
__lowerCAmelCase = n_embd
__lowerCAmelCase = n_layer
__lowerCAmelCase = n_head
__lowerCAmelCase = n_inner
__lowerCAmelCase = rotary_dim
__lowerCAmelCase = activation_function
__lowerCAmelCase = resid_pdrop
__lowerCAmelCase = embd_pdrop
__lowerCAmelCase = attn_pdrop
__lowerCAmelCase = layer_norm_epsilon
__lowerCAmelCase = initializer_range
__lowerCAmelCase = use_cache
__lowerCAmelCase = bos_token_id
__lowerCAmelCase = eos_token_id
super().__init__(
bos_token_id=__SCREAMING_SNAKE_CASE,eos_token_id=__SCREAMING_SNAKE_CASE,tie_word_embeddings=__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE )
class _UpperCAmelCase ( lowerCAmelCase_ ):
def __init__( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE = "default",__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = False,):
'''simple docstring'''
super().__init__(__SCREAMING_SNAKE_CASE,task=__SCREAMING_SNAKE_CASE,patching_specs=__SCREAMING_SNAKE_CASE,use_past=__SCREAMING_SNAKE_CASE )
if not getattr(self._config,"""pad_token_id""",__SCREAMING_SNAKE_CASE ):
# TODO: how to do that better?
__lowerCAmelCase = 0
@property
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}} )
if self.use_past:
self.fill_with_past_key_values_(__SCREAMING_SNAKE_CASE,direction="""inputs""" )
__lowerCAmelCase = {0: """batch""", 1: """past_sequence + sequence"""}
else:
__lowerCAmelCase = {0: """batch""", 1: """sequence"""}
return common_inputs
@property
def lowerCamelCase__ ( self ):
'''simple docstring'''
return self._config.n_layer
@property
def lowerCamelCase__ ( self ):
'''simple docstring'''
return self._config.n_head
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE = -1,__SCREAMING_SNAKE_CASE = -1,__SCREAMING_SNAKE_CASE = False,__SCREAMING_SNAKE_CASE = None,):
'''simple docstring'''
__lowerCAmelCase = super(__SCREAMING_SNAKE_CASE,self ).generate_dummy_inputs(
__SCREAMING_SNAKE_CASE,batch_size=__SCREAMING_SNAKE_CASE,seq_length=__SCREAMING_SNAKE_CASE,is_pair=__SCREAMING_SNAKE_CASE,framework=__SCREAMING_SNAKE_CASE )
# We need to order the input in the way they appears in the forward()
__lowerCAmelCase = OrderedDict({"""input_ids""": common_inputs["""input_ids"""]} )
# Need to add the past_keys
if self.use_past:
if not is_torch_available():
raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed.""" )
else:
import torch
__lowerCAmelCase , __lowerCAmelCase = common_inputs["""input_ids"""].shape
# Not using the same length for past_key_values
__lowerCAmelCase = seqlen + 2
__lowerCAmelCase = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
__lowerCAmelCase = [
(torch.zeros(__SCREAMING_SNAKE_CASE ), torch.zeros(__SCREAMING_SNAKE_CASE )) for _ in range(self.num_layers )
]
__lowerCAmelCase = common_inputs["""attention_mask"""]
if self.use_past:
__lowerCAmelCase = ordered_inputs["""attention_mask"""].dtype
__lowerCAmelCase = torch.cat(
[ordered_inputs["""attention_mask"""], torch.ones(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,dtype=__SCREAMING_SNAKE_CASE )],dim=1 )
return ordered_inputs
@property
def lowerCamelCase__ ( self ):
'''simple docstring'''
return 13
| 689 | 1 |
'''simple docstring'''
import collections
import importlib.util
import os
import re
from pathlib import Path
_a : Any = """src/transformers"""
# Matches is_xxx_available()
_a : Any = re.compile(r"""is\_([a-z_]*)_available()""")
# Catches a one-line _import_struct = {xxx}
_a : List[Any] = re.compile(r"""^_import_structure\s+=\s+\{([^\}]+)\}""")
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
_a : List[str] = re.compile(r"""\s+\"\S*\":\s+\[([^\]]*)\]""")
# Catches a line if not is_foo_available
_a : Dict = re.compile(r"""^\s*if\s+not\s+is\_[a-z_]*\_available\(\)""")
# Catches a line _import_struct["bla"].append("foo")
_a : Dict = re.compile(r"""^\s*_import_structure\[\"\S*\"\]\.append\(\"(\S*)\"\)""")
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
_a : Any = re.compile(r"""^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]""")
# Catches a line with an object between quotes and a comma: "MyModel",
_a : int = re.compile("""^\s+\"([^\"]+)\",""")
# Catches a line with objects between brackets only: ["foo", "bar"],
_a : Tuple = re.compile("""^\s+\[([^\]]+)\]""")
# Catches a line with from foo import bar, bla, boo
_a : int = re.compile(r"""\s+from\s+\S*\s+import\s+([^\(\s].*)\n""")
# Catches a line with try:
_a : Any = re.compile(r"""^\s*try:""")
# Catches a line with else:
_a : Tuple = re.compile(r"""^\s*else:""")
def _lowerCAmelCase ( lowercase ) -> Any:
if _re_test_backend.search(lowercase ) is None:
return None
__lowerCAmelCase = [b[0] for b in _re_backend.findall(lowercase )]
backends.sort()
return "_and_".join(lowercase )
def _lowerCAmelCase ( lowercase ) -> Optional[Any]:
with open(lowercase , """r""" , encoding="""utf-8""" , newline="""\n""" ) as f:
__lowerCAmelCase = f.readlines()
__lowerCAmelCase = 0
while line_index < len(lowercase ) and not lines[line_index].startswith("""_import_structure = {""" ):
line_index += 1
# If this is a traditional init, just return.
if line_index >= len(lowercase ):
return None
# First grab the objects without a specific backend in _import_structure
__lowerCAmelCase = []
while not lines[line_index].startswith("""if TYPE_CHECKING""" ) and find_backend(lines[line_index] ) is None:
__lowerCAmelCase = lines[line_index]
# If we have everything on a single line, let's deal with it.
if _re_one_line_import_struct.search(lowercase ):
__lowerCAmelCase = _re_one_line_import_struct.search(lowercase ).groups()[0]
__lowerCAmelCase = re.findall("""\[([^\]]+)\]""" , lowercase )
for imp in imports:
objects.extend([obj[1:-1] for obj in imp.split(""", """ )] )
line_index += 1
continue
__lowerCAmelCase = _re_import_struct_key_value.search(lowercase )
if single_line_import_search is not None:
__lowerCAmelCase = [obj[1:-1] for obj in single_line_import_search.groups()[0].split(""", """ ) if len(lowercase ) > 0]
objects.extend(lowercase )
elif line.startswith(""" """ * 8 + """\"""" ):
objects.append(line[9:-3] )
line_index += 1
__lowerCAmelCase = {"""none""": objects}
# Let's continue with backend-specific objects in _import_structure
while not lines[line_index].startswith("""if TYPE_CHECKING""" ):
# If the line is an if not is_backend_available, we grab all objects associated.
__lowerCAmelCase = find_backend(lines[line_index] )
# Check if the backend declaration is inside a try block:
if _re_try.search(lines[line_index - 1] ) is None:
__lowerCAmelCase = None
if backend is not None:
line_index += 1
# Scroll until we hit the else block of try-except-else
while _re_else.search(lines[line_index] ) is None:
line_index += 1
line_index += 1
__lowerCAmelCase = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(""" """ * 4 ):
__lowerCAmelCase = lines[line_index]
if _re_import_struct_add_one.search(lowercase ) is not None:
objects.append(_re_import_struct_add_one.search(lowercase ).groups()[0] )
elif _re_import_struct_add_many.search(lowercase ) is not None:
__lowerCAmelCase = _re_import_struct_add_many.search(lowercase ).groups()[0].split(""", """ )
__lowerCAmelCase = [obj[1:-1] for obj in imports if len(lowercase ) > 0]
objects.extend(lowercase )
elif _re_between_brackets.search(lowercase ) is not None:
__lowerCAmelCase = _re_between_brackets.search(lowercase ).groups()[0].split(""", """ )
__lowerCAmelCase = [obj[1:-1] for obj in imports if len(lowercase ) > 0]
objects.extend(lowercase )
elif _re_quote_object.search(lowercase ) is not None:
objects.append(_re_quote_object.search(lowercase ).groups()[0] )
elif line.startswith(""" """ * 8 + """\"""" ):
objects.append(line[9:-3] )
elif line.startswith(""" """ * 12 + """\"""" ):
objects.append(line[13:-3] )
line_index += 1
__lowerCAmelCase = objects
else:
line_index += 1
# At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
__lowerCAmelCase = []
while (
line_index < len(lowercase )
and find_backend(lines[line_index] ) is None
and not lines[line_index].startswith("""else""" )
):
__lowerCAmelCase = lines[line_index]
__lowerCAmelCase = _re_import.search(lowercase )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(""", """ ) )
elif line.startswith(""" """ * 8 ):
objects.append(line[8:-2] )
line_index += 1
__lowerCAmelCase = {"""none""": objects}
# Let's continue with backend-specific objects
while line_index < len(lowercase ):
# If the line is an if is_backend_available, we grab all objects associated.
__lowerCAmelCase = find_backend(lines[line_index] )
# Check if the backend declaration is inside a try block:
if _re_try.search(lines[line_index - 1] ) is None:
__lowerCAmelCase = None
if backend is not None:
line_index += 1
# Scroll until we hit the else block of try-except-else
while _re_else.search(lines[line_index] ) is None:
line_index += 1
line_index += 1
__lowerCAmelCase = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(""" """ * 8 ):
__lowerCAmelCase = lines[line_index]
__lowerCAmelCase = _re_import.search(lowercase )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(""", """ ) )
elif line.startswith(""" """ * 12 ):
objects.append(line[12:-2] )
line_index += 1
__lowerCAmelCase = objects
else:
line_index += 1
return import_dict_objects, type_hint_objects
def _lowerCAmelCase ( lowercase , lowercase ) -> List[str]:
def find_duplicates(lowercase ):
return [k for k, v in collections.Counter(lowercase ).items() if v > 1]
if list(import_dict_objects.keys() ) != list(type_hint_objects.keys() ):
return ["Both sides of the init do not have the same backends!"]
__lowerCAmelCase = []
for key in import_dict_objects.keys():
__lowerCAmelCase = find_duplicates(import_dict_objects[key] )
if duplicate_imports:
errors.append(f'Duplicate _import_structure definitions for: {duplicate_imports}' )
__lowerCAmelCase = find_duplicates(type_hint_objects[key] )
if duplicate_type_hints:
errors.append(f'Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}' )
if sorted(set(import_dict_objects[key] ) ) != sorted(set(type_hint_objects[key] ) ):
__lowerCAmelCase = """base imports""" if key == """none""" else f'{key} backend'
errors.append(f'Differences for {name}:' )
for a in type_hint_objects[key]:
if a not in import_dict_objects[key]:
errors.append(f' {a} in TYPE_HINT but not in _import_structure.' )
for a in import_dict_objects[key]:
if a not in type_hint_objects[key]:
errors.append(f' {a} in _import_structure but not in TYPE_HINT.' )
return errors
def _lowerCAmelCase ( ) -> Optional[int]:
__lowerCAmelCase = []
for root, _, files in os.walk(lowercase ):
if "__init__.py" in files:
__lowerCAmelCase = os.path.join(lowercase , """__init__.py""" )
__lowerCAmelCase = parse_init(lowercase )
if objects is not None:
__lowerCAmelCase = analyze_results(*lowercase )
if len(lowercase ) > 0:
__lowerCAmelCase = f'Problem in {fname}, both halves do not define the same objects.\n{errors[0]}'
failures.append("""\n""".join(lowercase ) )
if len(lowercase ) > 0:
raise ValueError("""\n\n""".join(lowercase ) )
def _lowerCAmelCase ( ) -> int:
__lowerCAmelCase = []
for path, directories, files in os.walk(lowercase ):
for folder in directories:
# Ignore private modules
if folder.startswith("""_""" ):
directories.remove(lowercase )
continue
# Ignore leftovers from branches (empty folders apart from pycache)
if len(list((Path(lowercase ) / folder).glob("""*.py""" ) ) ) == 0:
continue
__lowerCAmelCase = str((Path(lowercase ) / folder).relative_to(lowercase ) )
__lowerCAmelCase = short_path.replace(os.path.sep , """.""" )
submodules.append(lowercase )
for fname in files:
if fname == "__init__.py":
continue
__lowerCAmelCase = str((Path(lowercase ) / fname).relative_to(lowercase ) )
__lowerCAmelCase = short_path.replace(""".py""" , """""" ).replace(os.path.sep , """.""" )
if len(submodule.split(""".""" ) ) == 1:
submodules.append(lowercase )
return submodules
_a : Optional[Any] = [
"""convert_pytorch_checkpoint_to_tf2""",
"""modeling_flax_pytorch_utils""",
]
def _lowerCAmelCase ( ) -> Any:
# This is to make sure the transformers module imported is the one in the repo.
__lowerCAmelCase = importlib.util.spec_from_file_location(
"""transformers""" , os.path.join(lowercase , """__init__.py""" ) , submodule_search_locations=[PATH_TO_TRANSFORMERS] , )
__lowerCAmelCase = spec.loader.load_module()
__lowerCAmelCase = [
module
for module in get_transformers_submodules()
if module not in IGNORE_SUBMODULES and module not in transformers._import_structure.keys()
]
if len(lowercase ) > 0:
__lowerCAmelCase = """\n""".join(f'- {module}' for module in module_not_registered )
raise ValueError(
"""The following submodules are not properly registered in the main init of Transformers:\n"""
f'{list_of_modules}\n'
"""Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value.""" )
if __name__ == "__main__":
check_all_inits()
check_submodules()
| 689 |
'''simple docstring'''
def _lowerCAmelCase ( lowercase = 5000_0000 ) -> int:
__lowerCAmelCase = set()
__lowerCAmelCase = int((limit - 24) ** (1 / 2) )
__lowerCAmelCase = set(range(3 , prime_square_limit + 1 , 2 ) )
primes.add(2 )
for p in range(3 , prime_square_limit + 1 , 2 ):
if p not in primes:
continue
primes.difference_update(set(range(p * p , prime_square_limit + 1 , lowercase ) ) )
for primea in primes:
__lowerCAmelCase = primea * primea
for primea in primes:
__lowerCAmelCase = primea * primea * primea
if square + cube >= limit - 16:
break
for primea in primes:
__lowerCAmelCase = primea * primea * primea * primea
__lowerCAmelCase = square + cube + tetr
if total >= limit:
break
ret.add(lowercase )
return len(lowercase )
if __name__ == "__main__":
print(f'{solution() = }')
| 689 | 1 |
'''simple docstring'''
def _lowerCAmelCase ( lowercase , lowercase , lowercase ) -> int:
if exponent == 1:
return base
if exponent % 2 == 0:
__lowerCAmelCase = _modexpt(lowercase , exponent // 2 , lowercase ) % modulo_value
return (x * x) % modulo_value
else:
return (base * _modexpt(lowercase , exponent - 1 , lowercase )) % modulo_value
def _lowerCAmelCase ( lowercase = 1777 , lowercase = 1855 , lowercase = 8 ) -> int:
__lowerCAmelCase = base
for _ in range(1 , lowercase ):
__lowerCAmelCase = _modexpt(lowercase , lowercase , 10**digits )
return result
if __name__ == "__main__":
print(f'{solution() = }')
| 689 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, load_numpy, skip_mps, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
@skip_mps
class _UpperCAmelCase ( lowerCAmelCase_ , unittest.TestCase ):
a : Optional[int] =TextToVideoSDPipeline
a : Optional[int] =TEXT_TO_IMAGE_PARAMS
a : Any =TEXT_TO_IMAGE_BATCH_PARAMS
# No `output_type`.
a : Union[str, Any] =frozenset(
[
"""num_inference_steps""",
"""generator""",
"""latents""",
"""return_dict""",
"""callback""",
"""callback_steps""",
] )
def lowerCamelCase__ ( self ):
'''simple docstring'''
torch.manual_seed(0 )
__lowerCAmelCase = UNetaDConditionModel(
block_out_channels=(32, 64, 64, 64),layers_per_block=2,sample_size=32,in_channels=4,out_channels=4,down_block_types=("""CrossAttnDownBlock3D""", """CrossAttnDownBlock3D""", """CrossAttnDownBlock3D""", """DownBlock3D"""),up_block_types=("""UpBlock3D""", """CrossAttnUpBlock3D""", """CrossAttnUpBlock3D""", """CrossAttnUpBlock3D"""),cross_attention_dim=32,attention_head_dim=4,)
__lowerCAmelCase = DDIMScheduler(
beta_start=0.0_0085,beta_end=0.012,beta_schedule="""scaled_linear""",clip_sample=__SCREAMING_SNAKE_CASE,set_alpha_to_one=__SCREAMING_SNAKE_CASE,)
torch.manual_seed(0 )
__lowerCAmelCase = AutoencoderKL(
block_out_channels=[32, 64],in_channels=3,out_channels=3,down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""],up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""],latent_channels=4,sample_size=1_28,)
torch.manual_seed(0 )
__lowerCAmelCase = CLIPTextConfig(
bos_token_id=0,eos_token_id=2,hidden_size=32,intermediate_size=37,layer_norm_eps=1e-05,num_attention_heads=4,num_hidden_layers=5,pad_token_id=1,vocab_size=10_00,hidden_act="""gelu""",projection_dim=5_12,)
__lowerCAmelCase = CLIPTextModel(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
__lowerCAmelCase = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
}
return components
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=0 ):
'''simple docstring'''
if str(__SCREAMING_SNAKE_CASE ).startswith("""mps""" ):
__lowerCAmelCase = torch.manual_seed(__SCREAMING_SNAKE_CASE )
else:
__lowerCAmelCase = torch.Generator(device=__SCREAMING_SNAKE_CASE ).manual_seed(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 6.0,
"""output_type""": """pt""",
}
return inputs
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = """cpu""" # ensure determinism for the device-dependent torch.Generator
__lowerCAmelCase = self.get_dummy_components()
__lowerCAmelCase = TextToVideoSDPipeline(**__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = sd_pipe.to(__SCREAMING_SNAKE_CASE )
sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = self.get_dummy_inputs(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = """np"""
__lowerCAmelCase = sd_pipe(**__SCREAMING_SNAKE_CASE ).frames
__lowerCAmelCase = frames[0][-3:, -3:, -1]
assert frames[0].shape == (64, 64, 3)
__lowerCAmelCase = np.array([158.0, 160.0, 153.0, 125.0, 100.0, 121.0, 111.0, 93.0, 113.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def lowerCamelCase__ ( self ):
'''simple docstring'''
self._test_attention_slicing_forward_pass(test_mean_pixel_difference=__SCREAMING_SNAKE_CASE,expected_max_diff=3e-3 )
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available(),reason="""XFormers attention is only available with CUDA and `xformers` installed""",)
def lowerCamelCase__ ( self ):
'''simple docstring'''
self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=__SCREAMING_SNAKE_CASE,expected_max_diff=1e-2 )
@unittest.skip(reason="""Batching needs to be properly figured out first for this pipeline.""" )
def lowerCamelCase__ ( self ):
'''simple docstring'''
pass
@unittest.skip(reason="""Batching needs to be properly figured out first for this pipeline.""" )
def lowerCamelCase__ ( self ):
'''simple docstring'''
pass
@unittest.skip(reason="""`num_images_per_prompt` argument is not supported for this pipeline.""" )
def lowerCamelCase__ ( self ):
'''simple docstring'''
pass
def lowerCamelCase__ ( self ):
'''simple docstring'''
return super().test_progress_bar()
@slow
@skip_mps
class _UpperCAmelCase ( unittest.TestCase ):
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video.npy""" )
__lowerCAmelCase = TextToVideoSDPipeline.from_pretrained("""damo-vilab/text-to-video-ms-1.7b""" )
__lowerCAmelCase = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
__lowerCAmelCase = pipe.to("""cuda""" )
__lowerCAmelCase = """Spiderman is surfing"""
__lowerCAmelCase = torch.Generator(device="""cpu""" ).manual_seed(0 )
__lowerCAmelCase = pipe(__SCREAMING_SNAKE_CASE,generator=__SCREAMING_SNAKE_CASE,num_inference_steps=25,output_type="""pt""" ).frames
__lowerCAmelCase = video_frames.cpu().numpy()
assert np.abs(expected_video - video ).mean() < 5e-2
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video_2step.npy""" )
__lowerCAmelCase = TextToVideoSDPipeline.from_pretrained("""damo-vilab/text-to-video-ms-1.7b""" )
__lowerCAmelCase = pipe.to("""cuda""" )
__lowerCAmelCase = """Spiderman is surfing"""
__lowerCAmelCase = torch.Generator(device="""cpu""" ).manual_seed(0 )
__lowerCAmelCase = pipe(__SCREAMING_SNAKE_CASE,generator=__SCREAMING_SNAKE_CASE,num_inference_steps=2,output_type="""pt""" ).frames
__lowerCAmelCase = video_frames.cpu().numpy()
assert np.abs(expected_video - video ).mean() < 5e-2
| 689 | 1 |
'''simple docstring'''
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class _UpperCAmelCase ( nn.Module ):
def __init__( self,__SCREAMING_SNAKE_CASE = 16,__SCREAMING_SNAKE_CASE = 88,__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = 1,__SCREAMING_SNAKE_CASE = 0.0,__SCREAMING_SNAKE_CASE = 32,__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = False,__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = "geglu",__SCREAMING_SNAKE_CASE = None,):
'''simple docstring'''
super().__init__()
__lowerCAmelCase = nn.ModuleList(
[
TransformeraDModel(
num_attention_heads=__SCREAMING_SNAKE_CASE,attention_head_dim=__SCREAMING_SNAKE_CASE,in_channels=__SCREAMING_SNAKE_CASE,num_layers=__SCREAMING_SNAKE_CASE,dropout=__SCREAMING_SNAKE_CASE,norm_num_groups=__SCREAMING_SNAKE_CASE,cross_attention_dim=__SCREAMING_SNAKE_CASE,attention_bias=__SCREAMING_SNAKE_CASE,sample_size=__SCREAMING_SNAKE_CASE,num_vector_embeds=__SCREAMING_SNAKE_CASE,activation_fn=__SCREAMING_SNAKE_CASE,num_embeds_ada_norm=__SCREAMING_SNAKE_CASE,)
for _ in range(2 )
] )
# Variables that can be set by a pipeline:
# The ratio of transformer1 to transformer2's output states to be combined during inference
__lowerCAmelCase = 0.5
# The shape of `encoder_hidden_states` is expected to be
# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
__lowerCAmelCase = [77, 2_57]
# Which transformer to use to encode which condition.
# E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
__lowerCAmelCase = [1, 0]
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE = True,):
'''simple docstring'''
__lowerCAmelCase = hidden_states
__lowerCAmelCase = []
__lowerCAmelCase = 0
# attention_mask is not used yet
for i in range(2 ):
# for each of the two transformers, pass the corresponding condition tokens
__lowerCAmelCase = encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
__lowerCAmelCase = self.transformer_index_for_condition[i]
__lowerCAmelCase = self.transformers[transformer_index](
__SCREAMING_SNAKE_CASE,encoder_hidden_states=__SCREAMING_SNAKE_CASE,timestep=__SCREAMING_SNAKE_CASE,cross_attention_kwargs=__SCREAMING_SNAKE_CASE,return_dict=__SCREAMING_SNAKE_CASE,)[0]
encoded_states.append(encoded_state - input_states )
tokens_start += self.condition_lengths[i]
__lowerCAmelCase = encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
__lowerCAmelCase = output_states + input_states
if not return_dict:
return (output_states,)
return TransformeraDModelOutput(sample=__SCREAMING_SNAKE_CASE )
| 689 |
'''simple docstring'''
from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def _lowerCAmelCase ( lowercase ) -> Optional[int]:
if not is_accelerate_available():
return method
__lowerCAmelCase = version.parse(accelerate.__version__ ).base_version
if version.parse(lowercase ) < version.parse("""0.17.0""" ):
return method
def wrapper(self , *lowercase , **lowercase ):
if hasattr(self , """_hf_hook""" ) and hasattr(self._hf_hook , """pre_forward""" ):
self._hf_hook.pre_forward(self )
return method(self , *lowercase , **lowercase )
return wrapper
| 689 | 1 |
'''simple docstring'''
from pathlib import Path
import numpy as np
from PIL import Image
def _lowerCAmelCase ( lowercase ) -> np.ndarray:
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
return 0.29_89 * r + 0.58_70 * g + 0.11_40 * b
def _lowerCAmelCase ( lowercase ) -> np.ndarray:
return (gray > 127) & (gray <= 255)
def _lowerCAmelCase ( lowercase , lowercase ) -> np.ndarray:
__lowerCAmelCase = np.zeros_like(lowercase )
__lowerCAmelCase = np.zeros(
(image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1) )
# Copy image to padded image
__lowerCAmelCase = image
# Iterate over image & apply kernel
for x in range(image.shape[1] ):
for y in range(image.shape[0] ):
__lowerCAmelCase = (
kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
).sum()
__lowerCAmelCase = int(summation > 0 )
return output
if __name__ == "__main__":
# read original image
_a : Any = Path(__file__).resolve().parent / """image_data""" / """lena.jpg"""
_a : Optional[int] = np.array(Image.open(lena_path))
# kernel to be applied
_a : Dict = np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
_a : Union[str, Any] = dilation(gray_to_binary(rgb_to_gray(lena)), structuring_element)
# Save the output image
_a : List[Any] = Image.fromarray(output).convert("""RGB""")
pil_img.save("""result_dilation.png""")
| 689 |
'''simple docstring'''
import argparse
import torch
from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline
def _lowerCAmelCase ( lowercase , lowercase , lowercase , lowercase , lowercase ) -> Optional[int]:
# load base model
__lowerCAmelCase = StableDiffusionPipeline.from_pretrained(lowercase , torch_dtype=torch.floataa )
# load LoRA weight from .safetensors
__lowerCAmelCase = load_file(lowercase )
__lowerCAmelCase = []
# directly update weight in diffusers model
for key in state_dict:
# it is suggested to print out the key, it usually will be something like below
# "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"
# as we have set the alpha beforehand, so just skip
if ".alpha" in key or key in visited:
continue
if "text" in key:
__lowerCAmelCase = key.split(""".""" )[0].split(LORA_PREFIX_TEXT_ENCODER + """_""" )[-1].split("""_""" )
__lowerCAmelCase = pipeline.text_encoder
else:
__lowerCAmelCase = key.split(""".""" )[0].split(LORA_PREFIX_UNET + """_""" )[-1].split("""_""" )
__lowerCAmelCase = pipeline.unet
# find the target layer
__lowerCAmelCase = layer_infos.pop(0 )
while len(lowercase ) > -1:
try:
__lowerCAmelCase = curr_layer.__getattr__(lowercase )
if len(lowercase ) > 0:
__lowerCAmelCase = layer_infos.pop(0 )
elif len(lowercase ) == 0:
break
except Exception:
if len(lowercase ) > 0:
temp_name += "_" + layer_infos.pop(0 )
else:
__lowerCAmelCase = layer_infos.pop(0 )
__lowerCAmelCase = []
if "lora_down" in key:
pair_keys.append(key.replace("""lora_down""" , """lora_up""" ) )
pair_keys.append(lowercase )
else:
pair_keys.append(lowercase )
pair_keys.append(key.replace("""lora_up""" , """lora_down""" ) )
# update weight
if len(state_dict[pair_keys[0]].shape ) == 4:
__lowerCAmelCase = state_dict[pair_keys[0]].squeeze(3 ).squeeze(2 ).to(torch.floataa )
__lowerCAmelCase = state_dict[pair_keys[1]].squeeze(3 ).squeeze(2 ).to(torch.floataa )
curr_layer.weight.data += alpha * torch.mm(lowercase , lowercase ).unsqueeze(2 ).unsqueeze(3 )
else:
__lowerCAmelCase = state_dict[pair_keys[0]].to(torch.floataa )
__lowerCAmelCase = state_dict[pair_keys[1]].to(torch.floataa )
curr_layer.weight.data += alpha * torch.mm(lowercase , lowercase )
# update visited list
for item in pair_keys:
visited.append(lowercase )
return pipeline
if __name__ == "__main__":
_a : Union[str, Any] = argparse.ArgumentParser()
parser.add_argument(
"""--base_model_path""", default=None, type=str, required=True, help="""Path to the base model in diffusers format."""
)
parser.add_argument(
"""--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
)
parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")
parser.add_argument(
"""--lora_prefix_unet""", default="""lora_unet""", type=str, help="""The prefix of UNet weight in safetensors"""
)
parser.add_argument(
"""--lora_prefix_text_encoder""",
default="""lora_te""",
type=str,
help="""The prefix of text encoder weight in safetensors""",
)
parser.add_argument("""--alpha""", default=0.75, type=float, help="""The merging ratio in W = W0 + alpha * deltaW""")
parser.add_argument(
"""--to_safetensors""", action="""store_true""", help="""Whether to store pipeline in safetensors format or not."""
)
parser.add_argument("""--device""", type=str, help="""Device to use (e.g. cpu, cuda:0, cuda:1, etc.)""")
_a : Optional[int] = parser.parse_args()
_a : Dict = args.base_model_path
_a : Optional[Any] = args.checkpoint_path
_a : Union[str, Any] = args.dump_path
_a : Optional[int] = args.lora_prefix_unet
_a : int = args.lora_prefix_text_encoder
_a : str = args.alpha
_a : Any = convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)
_a : Tuple = pipe.to(args.device)
pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
| 689 | 1 |
'''simple docstring'''
from __future__ import annotations
def _lowerCAmelCase ( lowercase , lowercase , lowercase ) -> int | float:
if len(lowercase ) == 0:
raise ValueError("""find_max() arg is an empty sequence""" )
if (
left >= len(lowercase )
or left < -len(lowercase )
or right >= len(lowercase )
or right < -len(lowercase )
):
raise IndexError("""list index out of range""" )
if left == right:
return nums[left]
__lowerCAmelCase = (left + right) >> 1 # the middle
__lowerCAmelCase = find_max(lowercase , lowercase , lowercase ) # find max in range[left, mid]
__lowerCAmelCase = find_max(lowercase , mid + 1 , lowercase ) # find max in range[mid + 1, right]
return left_max if left_max >= right_max else right_max
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
| 689 |
'''simple docstring'''
from collections import Counter
from timeit import timeit
def _lowerCAmelCase ( lowercase = "" , ) -> bool:
return sum(c % 2 for c in Counter(input_str.replace(""" """ , """""" ).lower() ).values() ) < 2
def _lowerCAmelCase ( lowercase = "" ) -> bool:
if len(lowercase ) == 0:
return True
__lowerCAmelCase = input_str.replace(""" """ , """""" ).lower()
# character_freq_dict: Stores the frequency of every character in the input string
__lowerCAmelCase = {}
for character in lower_case_input_str:
__lowerCAmelCase = character_freq_dict.get(lowercase , 0 ) + 1
__lowerCAmelCase = 0
for character_count in character_freq_dict.values():
if character_count % 2:
odd_char += 1
if odd_char > 1:
return False
return True
def _lowerCAmelCase ( lowercase = "" ) -> None:
print("""\nFor string = """ , lowercase , """:""" )
print(
"""> can_string_be_rearranged_as_palindrome_counter()""" , """\tans =""" , can_string_be_rearranged_as_palindrome_counter(lowercase ) , """\ttime =""" , timeit(
"""z.can_string_be_rearranged_as_palindrome_counter(z.check_str)""" , setup="""import __main__ as z""" , ) , """seconds""" , )
print(
"""> can_string_be_rearranged_as_palindrome()""" , """\tans =""" , can_string_be_rearranged_as_palindrome(lowercase ) , """\ttime =""" , timeit(
"""z.can_string_be_rearranged_as_palindrome(z.check_str)""" , setup="""import __main__ as z""" , ) , """seconds""" , )
if __name__ == "__main__":
_a : int = input(
"""Enter string to determine if it can be rearranged as a palindrome or not: """
).strip()
benchmark(check_str)
_a : Optional[int] = can_string_be_rearranged_as_palindrome_counter(check_str)
print(f'{check_str} can {"" if status else "not "}be rearranged as a palindrome')
| 689 | 1 |
'''simple docstring'''
from graphs.minimum_spanning_tree_kruskal import kruskal
def _lowerCAmelCase ( ) -> str:
__lowerCAmelCase = 9
__lowerCAmelCase = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
[2, 8, 2],
[8, 6, 6],
[2, 3, 7],
[2, 5, 4],
[6, 5, 2],
[3, 5, 14],
[3, 4, 9],
[5, 4, 10],
[1, 7, 11],
]
__lowerCAmelCase = kruskal(lowercase , lowercase )
__lowerCAmelCase = [
[7, 6, 1],
[2, 8, 2],
[6, 5, 2],
[0, 1, 4],
[2, 5, 4],
[2, 3, 7],
[0, 7, 8],
[3, 4, 9],
]
assert sorted(lowercase ) == sorted(lowercase )
| 689 |
'''simple docstring'''
import argparse
import json
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
VideoMAEConfig,
VideoMAEForPreTraining,
VideoMAEForVideoClassification,
VideoMAEImageProcessor,
)
def _lowerCAmelCase ( lowercase ) -> List[Any]:
__lowerCAmelCase = VideoMAEConfig()
set_architecture_configs(lowercase , lowercase )
if "finetuned" not in model_name:
__lowerCAmelCase = False
if "finetuned" in model_name:
__lowerCAmelCase = """huggingface/label-files"""
if "kinetics" in model_name:
__lowerCAmelCase = 400
__lowerCAmelCase = """kinetics400-id2label.json"""
elif "ssv2" in model_name:
__lowerCAmelCase = 174
__lowerCAmelCase = """something-something-v2-id2label.json"""
else:
raise ValueError("""Model name should either contain 'kinetics' or 'ssv2' in case it's fine-tuned.""" )
__lowerCAmelCase = json.load(open(hf_hub_download(lowercase , lowercase , repo_type="""dataset""" ) , """r""" ) )
__lowerCAmelCase = {int(lowercase ): v for k, v in idalabel.items()}
__lowerCAmelCase = idalabel
__lowerCAmelCase = {v: k for k, v in idalabel.items()}
return config
def _lowerCAmelCase ( lowercase , lowercase ) -> Any:
if "small" in model_name:
__lowerCAmelCase = 384
__lowerCAmelCase = 1536
__lowerCAmelCase = 12
__lowerCAmelCase = 16
__lowerCAmelCase = 12
__lowerCAmelCase = 3
__lowerCAmelCase = 192
__lowerCAmelCase = 768
elif "large" in model_name:
__lowerCAmelCase = 1024
__lowerCAmelCase = 4096
__lowerCAmelCase = 24
__lowerCAmelCase = 16
__lowerCAmelCase = 12
__lowerCAmelCase = 8
__lowerCAmelCase = 512
__lowerCAmelCase = 2048
elif "huge" in model_name:
__lowerCAmelCase = 1280
__lowerCAmelCase = 5120
__lowerCAmelCase = 32
__lowerCAmelCase = 16
__lowerCAmelCase = 12
__lowerCAmelCase = 8
__lowerCAmelCase = 640
__lowerCAmelCase = 2560
elif "base" not in model_name:
raise ValueError("""Model name should include either \"small\", \"base\", \"large\", or \"huge\"""" )
def _lowerCAmelCase ( lowercase ) -> List[str]:
if "encoder." in name:
__lowerCAmelCase = name.replace("""encoder.""" , """""" )
if "cls_token" in name:
__lowerCAmelCase = name.replace("""cls_token""" , """videomae.embeddings.cls_token""" )
if "decoder_pos_embed" in name:
__lowerCAmelCase = name.replace("""decoder_pos_embed""" , """decoder.decoder_pos_embed""" )
if "pos_embed" in name and "decoder" not in name:
__lowerCAmelCase = name.replace("""pos_embed""" , """videomae.embeddings.position_embeddings""" )
if "patch_embed.proj" in name:
__lowerCAmelCase = name.replace("""patch_embed.proj""" , """videomae.embeddings.patch_embeddings.projection""" )
if "patch_embed.norm" in name:
__lowerCAmelCase = name.replace("""patch_embed.norm""" , """videomae.embeddings.norm""" )
if "decoder.blocks" in name:
__lowerCAmelCase = name.replace("""decoder.blocks""" , """decoder.decoder_layers""" )
if "blocks" in name:
__lowerCAmelCase = name.replace("""blocks""" , """videomae.encoder.layer""" )
if "attn.proj" in name:
__lowerCAmelCase = name.replace("""attn.proj""" , """attention.output.dense""" )
if "attn" in name and "bias" not in name:
__lowerCAmelCase = name.replace("""attn""" , """attention.self""" )
if "attn" in name:
__lowerCAmelCase = name.replace("""attn""" , """attention.attention""" )
if "norm1" in name:
__lowerCAmelCase = name.replace("""norm1""" , """layernorm_before""" )
if "norm2" in name:
__lowerCAmelCase = name.replace("""norm2""" , """layernorm_after""" )
if "mlp.fc1" in name:
__lowerCAmelCase = name.replace("""mlp.fc1""" , """intermediate.dense""" )
if "mlp.fc2" in name:
__lowerCAmelCase = name.replace("""mlp.fc2""" , """output.dense""" )
if "decoder_embed" in name:
__lowerCAmelCase = name.replace("""decoder_embed""" , """decoder.decoder_embed""" )
if "decoder_norm" in name:
__lowerCAmelCase = name.replace("""decoder_norm""" , """decoder.decoder_norm""" )
if "decoder_pred" in name:
__lowerCAmelCase = name.replace("""decoder_pred""" , """decoder.decoder_pred""" )
if "norm.weight" in name and "decoder" not in name and "fc" not in name:
__lowerCAmelCase = name.replace("""norm.weight""" , """videomae.layernorm.weight""" )
if "norm.bias" in name and "decoder" not in name and "fc" not in name:
__lowerCAmelCase = name.replace("""norm.bias""" , """videomae.layernorm.bias""" )
if "head" in name and "decoder" not in name:
__lowerCAmelCase = name.replace("""head""" , """classifier""" )
return name
def _lowerCAmelCase ( lowercase , lowercase ) -> List[Any]:
for key in orig_state_dict.copy().keys():
__lowerCAmelCase = orig_state_dict.pop(lowercase )
if key.startswith("""encoder.""" ):
__lowerCAmelCase = key.replace("""encoder.""" , """""" )
if "qkv" in key:
__lowerCAmelCase = key.split(""".""" )
if key.startswith("""decoder.blocks""" ):
__lowerCAmelCase = config.decoder_hidden_size
__lowerCAmelCase = int(key_split[2] )
__lowerCAmelCase = """decoder.decoder_layers."""
if "weight" in key:
__lowerCAmelCase = val[:dim, :]
__lowerCAmelCase = val[dim : dim * 2, :]
__lowerCAmelCase = val[-dim:, :]
else:
__lowerCAmelCase = config.hidden_size
__lowerCAmelCase = int(key_split[1] )
__lowerCAmelCase = """videomae.encoder.layer."""
if "weight" in key:
__lowerCAmelCase = val[:dim, :]
__lowerCAmelCase = val[dim : dim * 2, :]
__lowerCAmelCase = val[-dim:, :]
else:
__lowerCAmelCase = val
return orig_state_dict
def _lowerCAmelCase ( ) -> str:
__lowerCAmelCase = hf_hub_download(
repo_id="""hf-internal-testing/spaghetti-video""" , filename="""eating_spaghetti.npy""" , repo_type="""dataset""" )
__lowerCAmelCase = np.load(lowercase )
return list(lowercase )
def _lowerCAmelCase ( lowercase , lowercase , lowercase , lowercase ) -> Optional[Any]:
__lowerCAmelCase = get_videomae_config(lowercase )
if "finetuned" in model_name:
__lowerCAmelCase = VideoMAEForVideoClassification(lowercase )
else:
__lowerCAmelCase = VideoMAEForPreTraining(lowercase )
# download original checkpoint, hosted on Google Drive
__lowerCAmelCase = """pytorch_model.bin"""
gdown.cached_download(lowercase , lowercase , quiet=lowercase )
__lowerCAmelCase = torch.load(lowercase , map_location="""cpu""" )
if "model" in files:
__lowerCAmelCase = files["""model"""]
else:
__lowerCAmelCase = files["""module"""]
__lowerCAmelCase = convert_state_dict(lowercase , lowercase )
model.load_state_dict(lowercase )
model.eval()
# verify model on basic input
__lowerCAmelCase = VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5] , image_std=[0.5, 0.5, 0.5] )
__lowerCAmelCase = prepare_video()
__lowerCAmelCase = image_processor(lowercase , return_tensors="""pt""" )
if "finetuned" not in model_name:
__lowerCAmelCase = hf_hub_download(repo_id="""hf-internal-testing/bool-masked-pos""" , filename="""bool_masked_pos.pt""" )
__lowerCAmelCase = torch.load(lowercase )
__lowerCAmelCase = model(**lowercase )
__lowerCAmelCase = outputs.logits
__lowerCAmelCase = [
"""videomae-small-finetuned-kinetics""",
"""videomae-small-finetuned-ssv2""",
# Kinetics-400 checkpoints (short = pretrained only for 800 epochs instead of 1600)
"""videomae-base-short""",
"""videomae-base-short-finetuned-kinetics""",
"""videomae-base""",
"""videomae-base-finetuned-kinetics""",
"""videomae-large""",
"""videomae-large-finetuned-kinetics""",
"""videomae-huge-finetuned-kinetics""",
# Something-Something-v2 checkpoints (short = pretrained only for 800 epochs instead of 2400)
"""videomae-base-short-ssv2""",
"""videomae-base-short-finetuned-ssv2""",
"""videomae-base-ssv2""",
"""videomae-base-finetuned-ssv2""",
]
# NOTE: logits were tested with image_mean and image_std equal to [0.5, 0.5, 0.5] and [0.5, 0.5, 0.5]
if model_name == "videomae-small-finetuned-kinetics":
__lowerCAmelCase = torch.Size([1, 400] )
__lowerCAmelCase = torch.tensor([-0.92_91, -0.40_61, -0.93_07] )
elif model_name == "videomae-small-finetuned-ssv2":
__lowerCAmelCase = torch.Size([1, 174] )
__lowerCAmelCase = torch.tensor([0.26_71, -0.46_89, -0.82_35] )
elif model_name == "videomae-base":
__lowerCAmelCase = torch.Size([1, 1408, 1536] )
__lowerCAmelCase = torch.tensor([[0.77_39, 0.79_68, 0.70_89], [0.67_01, 0.74_87, 0.62_09], [0.42_87, 0.51_58, 0.47_73]] )
elif model_name == "videomae-base-short":
__lowerCAmelCase = torch.Size([1, 1408, 1536] )
__lowerCAmelCase = torch.tensor([[0.79_94, 0.96_12, 0.85_08], [0.74_01, 0.89_58, 0.83_02], [0.58_62, 0.74_68, 0.73_25]] )
# we verified the loss both for normalized and unnormalized targets for this one
__lowerCAmelCase = torch.tensor([0.51_42] ) if config.norm_pix_loss else torch.tensor([0.64_69] )
elif model_name == "videomae-large":
__lowerCAmelCase = torch.Size([1, 1408, 1536] )
__lowerCAmelCase = torch.tensor([[0.71_49, 0.79_97, 0.69_66], [0.67_68, 0.78_69, 0.69_48], [0.51_39, 0.62_21, 0.56_05]] )
elif model_name == "videomae-large-finetuned-kinetics":
__lowerCAmelCase = torch.Size([1, 400] )
__lowerCAmelCase = torch.tensor([0.07_71, 0.00_11, -0.36_25] )
elif model_name == "videomae-huge-finetuned-kinetics":
__lowerCAmelCase = torch.Size([1, 400] )
__lowerCAmelCase = torch.tensor([0.24_33, 0.16_32, -0.48_94] )
elif model_name == "videomae-base-short-finetuned-kinetics":
__lowerCAmelCase = torch.Size([1, 400] )
__lowerCAmelCase = torch.tensor([0.65_88, 0.09_90, -0.24_93] )
elif model_name == "videomae-base-finetuned-kinetics":
__lowerCAmelCase = torch.Size([1, 400] )
__lowerCAmelCase = torch.tensor([0.36_69, -0.06_88, -0.24_21] )
elif model_name == "videomae-base-short-ssv2":
__lowerCAmelCase = torch.Size([1, 1408, 1536] )
__lowerCAmelCase = torch.tensor([[0.47_12, 0.52_96, 0.57_86], [0.22_78, 0.27_29, 0.40_26], [0.03_52, 0.07_30, 0.25_06]] )
elif model_name == "videomae-base-short-finetuned-ssv2":
__lowerCAmelCase = torch.Size([1, 174] )
__lowerCAmelCase = torch.tensor([-0.05_37, -0.15_39, -0.32_66] )
elif model_name == "videomae-base-ssv2":
__lowerCAmelCase = torch.Size([1, 1408, 1536] )
__lowerCAmelCase = torch.tensor([[0.81_31, 0.87_27, 0.85_46], [0.73_66, 0.93_77, 0.88_70], [0.59_35, 0.88_74, 0.85_64]] )
elif model_name == "videomae-base-finetuned-ssv2":
__lowerCAmelCase = torch.Size([1, 174] )
__lowerCAmelCase = torch.tensor([0.19_61, -0.83_37, -0.63_89] )
else:
raise ValueError(f'Model name not supported. Should be one of {model_names}' )
# verify logits
assert logits.shape == expected_shape
if "finetuned" in model_name:
assert torch.allclose(logits[0, :3] , lowercase , atol=1e-4 )
else:
print("""Logits:""" , logits[0, :3, :3] )
assert torch.allclose(logits[0, :3, :3] , lowercase , atol=1e-4 )
print("""Logits ok!""" )
# verify loss, if applicable
if model_name == "videomae-base-short":
__lowerCAmelCase = outputs.loss
assert torch.allclose(lowercase , lowercase , atol=1e-4 )
print("""Loss ok!""" )
if pytorch_dump_folder_path is not None:
print(f'Saving model and image processor to {pytorch_dump_folder_path}' )
image_processor.save_pretrained(lowercase )
model.save_pretrained(lowercase )
if push_to_hub:
print("""Pushing to the hub...""" )
model.push_to_hub(lowercase , organization="""nielsr""" )
if __name__ == "__main__":
_a : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--checkpoint_url""",
default="""https://drive.google.com/u/1/uc?id=1tEhLyskjb755TJ65ptsrafUG2llSwQE1&export=download&confirm=t&uuid=aa3276eb-fb7e-482a-adec-dc7171df14c4""",
type=str,
help=(
"""URL of the original PyTorch checkpoint (on Google Drive) you'd like to convert. Should be a direct"""
""" download link."""
),
)
parser.add_argument(
"""--pytorch_dump_folder_path""",
default="""/Users/nielsrogge/Documents/VideoMAE/Test""",
type=str,
help="""Path to the output PyTorch model directory.""",
)
parser.add_argument("""--model_name""", default="""videomae-base""", type=str, help="""Name of the model.""")
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
)
_a : int = parser.parse_args()
convert_videomae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 689 | 1 |
'''simple docstring'''
def _lowerCAmelCase ( lowercase ) -> bool:
return credit_card_number.startswith(("""34""", """35""", """37""", """4""", """5""", """6""") )
def _lowerCAmelCase ( lowercase ) -> bool:
__lowerCAmelCase = credit_card_number
__lowerCAmelCase = 0
__lowerCAmelCase = len(lowercase ) - 2
for i in range(lowercase , -1 , -2 ):
# double the value of every second digit
__lowerCAmelCase = int(cc_number[i] )
digit *= 2
# If doubling of a number results in a two digit number
# i.e greater than 9(e.g., 6 × 2 = 12),
# then add the digits of the product (e.g., 12: 1 + 2 = 3, 15: 1 + 5 = 6),
# to get a single digit number.
if digit > 9:
digit %= 10
digit += 1
__lowerCAmelCase = cc_number[:i] + str(lowercase ) + cc_number[i + 1 :]
total += digit
# Sum up the remaining digits
for i in range(len(lowercase ) - 1 , -1 , -2 ):
total += int(cc_number[i] )
return total % 10 == 0
def _lowerCAmelCase ( lowercase ) -> bool:
__lowerCAmelCase = f'{credit_card_number} is an invalid credit card number because'
if not credit_card_number.isdigit():
print(f'{error_message} it has nonnumerical characters.' )
return False
if not 13 <= len(lowercase ) <= 16:
print(f'{error_message} of its length.' )
return False
if not validate_initial_digits(lowercase ):
print(f'{error_message} of its first two digits.' )
return False
if not luhn_validation(lowercase ):
print(f'{error_message} it fails the Luhn check.' )
return False
print(f'{credit_card_number} is a valid credit card number.' )
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
validate_credit_card_number("""4111111111111111""")
validate_credit_card_number("""32323""")
| 689 |
'''simple docstring'''
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
_a : Tuple = """\
"""
_a : Tuple = """
Perplexity (PPL) is one of the most common metrics for evaluating language models.
It is defined as the exponentiated average negative log-likelihood of a sequence.
For more information, see https://huggingface.co/docs/transformers/perplexity
"""
_a : Optional[Any] = """
Args:
model_id (str): model used for calculating Perplexity
NOTE: Perplexity can only be calculated for causal language models.
This includes models such as gpt2, causal variations of bert,
causal versions of t5, and more (the full list can be found
in the AutoModelForCausalLM documentation here:
https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )
input_texts (list of str): input text, each separate text snippet
is one list entry.
batch_size (int): the batch size to run texts through the model. Defaults to 16.
add_start_token (bool): whether to add the start token to the texts,
so the perplexity can include the probability of the first word. Defaults to True.
device (str): device to run on, defaults to 'cuda' when available
Returns:
perplexity: dictionary containing the perplexity scores for the texts
in the input list, as well as the mean perplexity. If one of the input texts is
longer than the max input length of the model, then it is truncated to the
max length for the perplexity computation.
Examples:
Example 1:
>>> perplexity = datasets.load_metric(\"perplexity\")
>>> input_texts = [\"lorem ipsum\", \"Happy Birthday!\", \"Bienvenue\"]
>>> results = perplexity.compute(model_id='gpt2',
... add_start_token=False,
... input_texts=input_texts) # doctest:+ELLIPSIS
>>> print(list(results.keys()))
['perplexities', 'mean_perplexity']
>>> print(round(results[\"mean_perplexity\"], 2))
78.22
>>> print(round(results[\"perplexities\"][0], 2))
11.11
Example 2:
>>> perplexity = datasets.load_metric(\"perplexity\")
>>> input_texts = datasets.load_dataset(\"wikitext\",
... \"wikitext-2-raw-v1\",
... split=\"test\")[\"text\"][:50] # doctest:+ELLIPSIS
[...]
>>> input_texts = [s for s in input_texts if s!='']
>>> results = perplexity.compute(model_id='gpt2',
... input_texts=input_texts) # doctest:+ELLIPSIS
>>> print(list(results.keys()))
['perplexities', 'mean_perplexity']
>>> print(round(results[\"mean_perplexity\"], 2))
60.35
>>> print(round(results[\"perplexities\"][0], 2))
81.12
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _UpperCAmelCase ( datasets.Metric ):
def lowerCamelCase__ ( self ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION,citation=_CITATION,inputs_description=_KWARGS_DESCRIPTION,features=datasets.Features(
{
"""input_texts""": datasets.Value("""string""" ),
} ),reference_urls=["""https://huggingface.co/docs/transformers/perplexity"""],)
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE = 16,__SCREAMING_SNAKE_CASE = True,__SCREAMING_SNAKE_CASE=None ):
'''simple docstring'''
if device is not None:
assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
if device == "gpu":
__lowerCAmelCase = """cuda"""
else:
__lowerCAmelCase = """cuda""" if torch.cuda.is_available() else """cpu"""
__lowerCAmelCase = AutoModelForCausalLM.from_pretrained(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = model.to(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE )
# if batch_size > 1 (which generally leads to padding being required), and
# if there is not an already assigned pad_token, assign an existing
# special token to also be the padding token
if tokenizer.pad_token is None and batch_size > 1:
__lowerCAmelCase = list(tokenizer.special_tokens_map_extended.values() )
# check that the model already has at least one special token defined
assert (
len(__SCREAMING_SNAKE_CASE ) > 0
), "If batch_size > 1, model must have at least one special token to use for padding. Please use a different model or set batch_size=1."
# assign one of the special tokens to also be the pad token
tokenizer.add_special_tokens({"""pad_token""": existing_special_tokens[0]} )
if add_start_token:
# leave room for <BOS> token to be added:
assert (
tokenizer.bos_token is not None
), "Input model must already have a BOS token if using add_start_token=True. Please use a different model, or set add_start_token=False"
__lowerCAmelCase = model.config.max_length - 1
else:
__lowerCAmelCase = model.config.max_length
__lowerCAmelCase = tokenizer(
__SCREAMING_SNAKE_CASE,add_special_tokens=__SCREAMING_SNAKE_CASE,padding=__SCREAMING_SNAKE_CASE,truncation=__SCREAMING_SNAKE_CASE,max_length=__SCREAMING_SNAKE_CASE,return_tensors="""pt""",return_attention_mask=__SCREAMING_SNAKE_CASE,).to(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = encodings["""input_ids"""]
__lowerCAmelCase = encodings["""attention_mask"""]
# check that each input is long enough:
if add_start_token:
assert torch.all(torch.ge(attn_masks.sum(1 ),1 ) ), "Each input text must be at least one token long."
else:
assert torch.all(
torch.ge(attn_masks.sum(1 ),2 ) ), "When add_start_token=False, each input text must be at least two tokens long. Run with add_start_token=True if inputting strings of only one token, and remove all empty input strings."
__lowerCAmelCase = []
__lowerCAmelCase = CrossEntropyLoss(reduction="""none""" )
for start_index in logging.tqdm(range(0,len(__SCREAMING_SNAKE_CASE ),__SCREAMING_SNAKE_CASE ) ):
__lowerCAmelCase = min(start_index + batch_size,len(__SCREAMING_SNAKE_CASE ) )
__lowerCAmelCase = encoded_texts[start_index:end_index]
__lowerCAmelCase = attn_masks[start_index:end_index]
if add_start_token:
__lowerCAmelCase = torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0 ) ).to(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = torch.cat([bos_tokens_tensor, encoded_batch],dim=1 )
__lowerCAmelCase = torch.cat(
[torch.ones(bos_tokens_tensor.size(),dtype=torch.intaa ).to(__SCREAMING_SNAKE_CASE ), attn_mask],dim=1 )
__lowerCAmelCase = encoded_batch
with torch.no_grad():
__lowerCAmelCase = model(__SCREAMING_SNAKE_CASE,attention_mask=__SCREAMING_SNAKE_CASE ).logits
__lowerCAmelCase = out_logits[..., :-1, :].contiguous()
__lowerCAmelCase = labels[..., 1:].contiguous()
__lowerCAmelCase = attn_mask[..., 1:].contiguous()
__lowerCAmelCase = torch.expa(
(loss_fct(shift_logits.transpose(1,2 ),__SCREAMING_SNAKE_CASE ) * shift_attention_mask_batch).sum(1 )
/ shift_attention_mask_batch.sum(1 ) )
ppls += perplexity_batch.tolist()
return {"perplexities": ppls, "mean_perplexity": np.mean(__SCREAMING_SNAKE_CASE )}
| 689 | 1 |
'''simple docstring'''
import re
import string
import numpy as np
import datasets
_a : str = """
Returns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.
"""
_a : List[Any] = """
Args:
predictions: List of predicted texts.
references: List of reference texts.
regexes_to_ignore: List, defaults to None. Regex expressions of characters to
ignore when calculating the exact matches. Note: these regexes are removed
from the input data before the changes based on the options below (e.g. ignore_case,
ignore_punctuation, ignore_numbers) are applied.
ignore_case: Boolean, defaults to False. If true, turns everything
to lowercase so that capitalization differences are ignored.
ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before
comparing predictions and references.
ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before
comparing predictions and references.
Returns:
exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.
Examples:
>>> exact_match = datasets.load_metric(\"exact_match\")
>>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]
>>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]
>>> results = exact_match.compute(references=refs, predictions=preds)
>>> print(round(results[\"exact_match\"], 1))
25.0
>>> exact_match = datasets.load_metric(\"exact_match\")
>>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]
>>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]
>>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\"], ignore_case=True, ignore_punctuation=True)
>>> print(round(results[\"exact_match\"], 1))
50.0
>>> exact_match = datasets.load_metric(\"exact_match\")
>>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]
>>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]
>>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True)
>>> print(round(results[\"exact_match\"], 1))
75.0
>>> exact_match = datasets.load_metric(\"exact_match\")
>>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]
>>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]
>>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)
>>> print(round(results[\"exact_match\"], 1))
100.0
>>> exact_match = datasets.load_metric(\"exact_match\")
>>> refs = [\"The cat sat on the mat.\", \"Theaters are great.\", \"It's like comparing oranges and apples.\"]
>>> preds = [\"The cat sat on the mat?\", \"Theaters are great.\", \"It's like comparing apples and oranges.\"]
>>> results = exact_match.compute(references=refs, predictions=preds)
>>> print(round(results[\"exact_match\"], 1))
33.3
"""
_a : List[Any] = """
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _UpperCAmelCase ( datasets.Metric ):
def lowerCamelCase__ ( self ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION,citation=_CITATION,inputs_description=_KWARGS_DESCRIPTION,features=datasets.Features(
{
"""predictions""": datasets.Value("""string""",id="""sequence""" ),
"""references""": datasets.Value("""string""",id="""sequence""" ),
} ),reference_urls=[],)
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=False,__SCREAMING_SNAKE_CASE=False,__SCREAMING_SNAKE_CASE=False,):
'''simple docstring'''
if regexes_to_ignore is not None:
for s in regexes_to_ignore:
__lowerCAmelCase = np.array([re.sub(__SCREAMING_SNAKE_CASE,"""""",__SCREAMING_SNAKE_CASE ) for x in predictions] )
__lowerCAmelCase = np.array([re.sub(__SCREAMING_SNAKE_CASE,"""""",__SCREAMING_SNAKE_CASE ) for x in references] )
else:
__lowerCAmelCase = np.asarray(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = np.asarray(__SCREAMING_SNAKE_CASE )
if ignore_case:
__lowerCAmelCase = np.char.lower(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = np.char.lower(__SCREAMING_SNAKE_CASE )
if ignore_punctuation:
__lowerCAmelCase = string.punctuation.maketrans("""""","""""",string.punctuation )
__lowerCAmelCase = np.char.translate(__SCREAMING_SNAKE_CASE,table=__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = np.char.translate(__SCREAMING_SNAKE_CASE,table=__SCREAMING_SNAKE_CASE )
if ignore_numbers:
__lowerCAmelCase = string.digits.maketrans("""""","""""",string.digits )
__lowerCAmelCase = np.char.translate(__SCREAMING_SNAKE_CASE,table=__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = np.char.translate(__SCREAMING_SNAKE_CASE,table=__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = predictions == references
return {"exact_match": np.mean(__SCREAMING_SNAKE_CASE ) * 1_00}
| 689 |
'''simple docstring'''
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
import torch
if is_tf_available():
import tensorflow as tf
class _UpperCAmelCase ( lowerCAmelCase_ ):
a : Union[str, Any] =["""image_processor"""]
a : Dict ="""SamImageProcessor"""
def __init__( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
super().__init__(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = self.image_processor
__lowerCAmelCase = -10
__lowerCAmelCase = self.image_processor.size["""longest_edge"""]
def __call__( self,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE = None,**__SCREAMING_SNAKE_CASE,):
'''simple docstring'''
__lowerCAmelCase = self.image_processor(
__SCREAMING_SNAKE_CASE,return_tensors=__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE,)
# pop arguments that are not used in the foward but used nevertheless
__lowerCAmelCase = encoding_image_processor["""original_sizes"""]
if hasattr(__SCREAMING_SNAKE_CASE,"""numpy""" ): # Checks if Torch or TF tensor
__lowerCAmelCase = original_sizes.numpy()
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = self._check_and_preprocess_points(
input_points=__SCREAMING_SNAKE_CASE,input_labels=__SCREAMING_SNAKE_CASE,input_boxes=__SCREAMING_SNAKE_CASE,)
__lowerCAmelCase = self._normalize_and_convert(
__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,input_points=__SCREAMING_SNAKE_CASE,input_labels=__SCREAMING_SNAKE_CASE,input_boxes=__SCREAMING_SNAKE_CASE,return_tensors=__SCREAMING_SNAKE_CASE,)
return encoding_image_processor
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE="pt",):
'''simple docstring'''
if input_points is not None:
if len(__SCREAMING_SNAKE_CASE ) != len(__SCREAMING_SNAKE_CASE ):
__lowerCAmelCase = [
self._normalize_coordinates(self.target_size,__SCREAMING_SNAKE_CASE,original_sizes[0] ) for point in input_points
]
else:
__lowerCAmelCase = [
self._normalize_coordinates(self.target_size,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
for point, original_size in zip(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
]
# check that all arrays have the same shape
if not all(point.shape == input_points[0].shape for point in input_points ):
if input_labels is not None:
__lowerCAmelCase , __lowerCAmelCase = self._pad_points_and_labels(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = np.array(__SCREAMING_SNAKE_CASE )
if input_labels is not None:
__lowerCAmelCase = np.array(__SCREAMING_SNAKE_CASE )
if input_boxes is not None:
if len(__SCREAMING_SNAKE_CASE ) != len(__SCREAMING_SNAKE_CASE ):
__lowerCAmelCase = [
self._normalize_coordinates(self.target_size,__SCREAMING_SNAKE_CASE,original_sizes[0],is_bounding_box=__SCREAMING_SNAKE_CASE )
for box in input_boxes
]
else:
__lowerCAmelCase = [
self._normalize_coordinates(self.target_size,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,is_bounding_box=__SCREAMING_SNAKE_CASE )
for box, original_size in zip(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
]
__lowerCAmelCase = np.array(__SCREAMING_SNAKE_CASE )
if input_boxes is not None:
if return_tensors == "pt":
__lowerCAmelCase = torch.from_numpy(__SCREAMING_SNAKE_CASE )
# boxes batch size of 1 by default
__lowerCAmelCase = input_boxes.unsqueeze(1 ) if len(input_boxes.shape ) != 3 else input_boxes
elif return_tensors == "tf":
__lowerCAmelCase = tf.convert_to_tensor(__SCREAMING_SNAKE_CASE )
# boxes batch size of 1 by default
__lowerCAmelCase = tf.expand_dims(__SCREAMING_SNAKE_CASE,1 ) if len(input_boxes.shape ) != 3 else input_boxes
encoding_image_processor.update({"""input_boxes""": input_boxes} )
if input_points is not None:
if return_tensors == "pt":
__lowerCAmelCase = torch.from_numpy(__SCREAMING_SNAKE_CASE )
# point batch size of 1 by default
__lowerCAmelCase = input_points.unsqueeze(1 ) if len(input_points.shape ) != 4 else input_points
elif return_tensors == "tf":
__lowerCAmelCase = tf.convert_to_tensor(__SCREAMING_SNAKE_CASE )
# point batch size of 1 by default
__lowerCAmelCase = tf.expand_dims(__SCREAMING_SNAKE_CASE,1 ) if len(input_points.shape ) != 4 else input_points
encoding_image_processor.update({"""input_points""": input_points} )
if input_labels is not None:
if return_tensors == "pt":
__lowerCAmelCase = torch.from_numpy(__SCREAMING_SNAKE_CASE )
# point batch size of 1 by default
__lowerCAmelCase = input_labels.unsqueeze(1 ) if len(input_labels.shape ) != 3 else input_labels
elif return_tensors == "tf":
__lowerCAmelCase = tf.convert_to_tensor(__SCREAMING_SNAKE_CASE )
# point batch size of 1 by default
__lowerCAmelCase = tf.expand_dims(__SCREAMING_SNAKE_CASE,1 ) if len(input_labels.shape ) != 3 else input_labels
encoding_image_processor.update({"""input_labels""": input_labels} )
return encoding_image_processor
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase = max([point.shape[0] for point in input_points] )
__lowerCAmelCase = []
for i, point in enumerate(__SCREAMING_SNAKE_CASE ):
if point.shape[0] != expected_nb_points:
__lowerCAmelCase = np.concatenate(
[point, np.zeros((expected_nb_points - point.shape[0], 2) ) + self.point_pad_value],axis=0 )
__lowerCAmelCase = np.append(input_labels[i],[self.point_pad_value] )
processed_input_points.append(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = processed_input_points
return input_points, input_labels
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=False ):
'''simple docstring'''
__lowerCAmelCase , __lowerCAmelCase = original_size
__lowerCAmelCase , __lowerCAmelCase = self.image_processor._get_preprocess_shape(__SCREAMING_SNAKE_CASE,longest_edge=__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = deepcopy(__SCREAMING_SNAKE_CASE ).astype(__SCREAMING_SNAKE_CASE )
if is_bounding_box:
__lowerCAmelCase = coords.reshape(-1,2,2 )
__lowerCAmelCase = coords[..., 0] * (new_w / old_w)
__lowerCAmelCase = coords[..., 1] * (new_h / old_h)
if is_bounding_box:
__lowerCAmelCase = coords.reshape(-1,4 )
return coords
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=None,):
'''simple docstring'''
if input_points is not None:
if hasattr(__SCREAMING_SNAKE_CASE,"""numpy""" ): # Checks for TF or Torch tensor
__lowerCAmelCase = input_points.numpy().tolist()
if not isinstance(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE ) or not isinstance(input_points[0],__SCREAMING_SNAKE_CASE ):
raise ValueError("""Input points must be a list of list of floating points.""" )
__lowerCAmelCase = [np.array(__SCREAMING_SNAKE_CASE ) for input_point in input_points]
else:
__lowerCAmelCase = None
if input_labels is not None:
if hasattr(__SCREAMING_SNAKE_CASE,"""numpy""" ):
__lowerCAmelCase = input_labels.numpy().tolist()
if not isinstance(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE ) or not isinstance(input_labels[0],__SCREAMING_SNAKE_CASE ):
raise ValueError("""Input labels must be a list of list integers.""" )
__lowerCAmelCase = [np.array(__SCREAMING_SNAKE_CASE ) for label in input_labels]
else:
__lowerCAmelCase = None
if input_boxes is not None:
if hasattr(__SCREAMING_SNAKE_CASE,"""numpy""" ):
__lowerCAmelCase = input_boxes.numpy().tolist()
if (
not isinstance(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
or not isinstance(input_boxes[0],__SCREAMING_SNAKE_CASE )
or not isinstance(input_boxes[0][0],__SCREAMING_SNAKE_CASE )
):
raise ValueError("""Input boxes must be a list of list of list of floating points.""" )
__lowerCAmelCase = [np.array(__SCREAMING_SNAKE_CASE ).astype(np.floataa ) for box in input_boxes]
else:
__lowerCAmelCase = None
return input_points, input_labels, input_boxes
@property
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = self.image_processor.model_input_names
return list(dict.fromkeys(__SCREAMING_SNAKE_CASE ) )
def lowerCamelCase__ ( self,*__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
return self.image_processor.post_process_masks(*__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE )
| 689 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class _UpperCAmelCase ( lowerCAmelCase_ , unittest.TestCase ):
a : str =KandinskyInpaintPipeline
a : List[str] =["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
a : List[str] =[
"""prompt""",
"""negative_prompt""",
"""image_embeds""",
"""negative_image_embeds""",
"""image""",
"""mask_image""",
]
a : Optional[int] =[
"""generator""",
"""height""",
"""width""",
"""latents""",
"""guidance_scale""",
"""negative_prompt""",
"""num_inference_steps""",
"""return_dict""",
"""guidance_scale""",
"""num_images_per_prompt""",
"""output_type""",
"""return_dict""",
]
a : Union[str, Any] =False
@property
def lowerCamelCase__ ( self ):
'''simple docstring'''
return 32
@property
def lowerCamelCase__ ( self ):
'''simple docstring'''
return 32
@property
def lowerCamelCase__ ( self ):
'''simple docstring'''
return self.time_input_dim
@property
def lowerCamelCase__ ( self ):
'''simple docstring'''
return self.time_input_dim * 4
@property
def lowerCamelCase__ ( self ):
'''simple docstring'''
return 1_00
@property
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base""" )
return tokenizer
@property
def lowerCamelCase__ ( self ):
'''simple docstring'''
torch.manual_seed(0 )
__lowerCAmelCase = MCLIPConfig(
numDims=self.cross_attention_dim,transformerDimensions=self.text_embedder_hidden_size,hidden_size=self.text_embedder_hidden_size,intermediate_size=37,num_attention_heads=4,num_hidden_layers=5,vocab_size=10_05,)
__lowerCAmelCase = MultilingualCLIP(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = text_encoder.eval()
return text_encoder
@property
def lowerCamelCase__ ( self ):
'''simple docstring'''
torch.manual_seed(0 )
__lowerCAmelCase = {
"""in_channels""": 9,
# Out channels is double in channels because predicts mean and variance
"""out_channels""": 8,
"""addition_embed_type""": """text_image""",
"""down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
"""up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
"""mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
"""block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
"""layers_per_block""": 1,
"""encoder_hid_dim""": self.text_embedder_hidden_size,
"""encoder_hid_dim_type""": """text_image_proj""",
"""cross_attention_dim""": self.cross_attention_dim,
"""attention_head_dim""": 4,
"""resnet_time_scale_shift""": """scale_shift""",
"""class_embed_type""": None,
}
__lowerCAmelCase = UNetaDConditionModel(**__SCREAMING_SNAKE_CASE )
return model
@property
def lowerCamelCase__ ( self ):
'''simple docstring'''
return {
"block_out_channels": [32, 64],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def lowerCamelCase__ ( self ):
'''simple docstring'''
torch.manual_seed(0 )
__lowerCAmelCase = VQModel(**self.dummy_movq_kwargs )
return model
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = self.dummy_text_encoder
__lowerCAmelCase = self.dummy_tokenizer
__lowerCAmelCase = self.dummy_unet
__lowerCAmelCase = self.dummy_movq
__lowerCAmelCase = DDIMScheduler(
num_train_timesteps=10_00,beta_schedule="""linear""",beta_start=0.0_0085,beta_end=0.012,clip_sample=__SCREAMING_SNAKE_CASE,set_alpha_to_one=__SCREAMING_SNAKE_CASE,steps_offset=1,prediction_type="""epsilon""",thresholding=__SCREAMING_SNAKE_CASE,)
__lowerCAmelCase = {
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""unet""": unet,
"""scheduler""": scheduler,
"""movq""": movq,
}
return components
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=0 ):
'''simple docstring'''
__lowerCAmelCase = floats_tensor((1, self.cross_attention_dim),rng=random.Random(__SCREAMING_SNAKE_CASE ) ).to(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = floats_tensor((1, self.cross_attention_dim),rng=random.Random(seed + 1 ) ).to(__SCREAMING_SNAKE_CASE )
# create init_image
__lowerCAmelCase = floats_tensor((1, 3, 64, 64),rng=random.Random(__SCREAMING_SNAKE_CASE ) ).to(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = image.cpu().permute(0,2,3,1 )[0]
__lowerCAmelCase = Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE ) ).convert("""RGB""" ).resize((2_56, 2_56) )
# create mask
__lowerCAmelCase = np.ones((64, 64),dtype=np.floataa )
__lowerCAmelCase = 0
if str(__SCREAMING_SNAKE_CASE ).startswith("""mps""" ):
__lowerCAmelCase = torch.manual_seed(__SCREAMING_SNAKE_CASE )
else:
__lowerCAmelCase = torch.Generator(device=__SCREAMING_SNAKE_CASE ).manual_seed(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = {
"""prompt""": """horse""",
"""image""": init_image,
"""mask_image""": mask,
"""image_embeds""": image_embeds,
"""negative_image_embeds""": negative_image_embeds,
"""generator""": generator,
"""height""": 64,
"""width""": 64,
"""num_inference_steps""": 2,
"""guidance_scale""": 4.0,
"""output_type""": """np""",
}
return inputs
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = """cpu"""
__lowerCAmelCase = self.get_dummy_components()
__lowerCAmelCase = self.pipeline_class(**__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = pipe.to(__SCREAMING_SNAKE_CASE )
pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = pipe(**self.get_dummy_inputs(__SCREAMING_SNAKE_CASE ) )
__lowerCAmelCase = output.images
__lowerCAmelCase = pipe(
**self.get_dummy_inputs(__SCREAMING_SNAKE_CASE ),return_dict=__SCREAMING_SNAKE_CASE,)[0]
__lowerCAmelCase = image[0, -3:, -3:, -1]
__lowerCAmelCase = image_from_tuple[0, -3:, -3:, -1]
print(f'image.shape {image.shape}' )
assert image.shape == (1, 64, 64, 3)
__lowerCAmelCase = np.array(
[0.832_6919, 0.7379_0467, 0.2091_8581, 0.930_9612, 0.551_1791, 0.4371_3328, 0.551_3321, 0.4992_2934, 0.5949_7786] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
), f' expected_slice {expected_slice}, but got {image_slice.flatten()}'
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
), f' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'
def lowerCamelCase__ ( self ):
'''simple docstring'''
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
@slow
@require_torch_gpu
class _UpperCAmelCase ( unittest.TestCase ):
def lowerCamelCase__ ( self ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy""" )
__lowerCAmelCase = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png""" )
__lowerCAmelCase = np.ones((7_68, 7_68),dtype=np.floataa )
__lowerCAmelCase = 0
__lowerCAmelCase = """a hat"""
__lowerCAmelCase = KandinskyPriorPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-1-prior""",torch_dtype=torch.floataa )
pipe_prior.to(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = KandinskyInpaintPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-1-inpaint""",torch_dtype=torch.floataa )
__lowerCAmelCase = pipeline.to(__SCREAMING_SNAKE_CASE )
pipeline.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = torch.Generator(device="""cpu""" ).manual_seed(0 )
__lowerCAmelCase , __lowerCAmelCase = pipe_prior(
__SCREAMING_SNAKE_CASE,generator=__SCREAMING_SNAKE_CASE,num_inference_steps=5,negative_prompt="""""",).to_tuple()
__lowerCAmelCase = pipeline(
__SCREAMING_SNAKE_CASE,image=__SCREAMING_SNAKE_CASE,mask_image=__SCREAMING_SNAKE_CASE,image_embeds=__SCREAMING_SNAKE_CASE,negative_image_embeds=__SCREAMING_SNAKE_CASE,generator=__SCREAMING_SNAKE_CASE,num_inference_steps=1_00,height=7_68,width=7_68,output_type="""np""",)
__lowerCAmelCase = output.images[0]
assert image.shape == (7_68, 7_68, 3)
assert_mean_pixel_difference(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
| 689 |
'''simple docstring'''
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequenceClassification,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
TapexTokenizer,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version
from transformers.utils.versions import require_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("""4.17.0.dev0""")
require_version("""datasets>=1.8.0""", """To fix: pip install -r examples/pytorch/text-classification/requirements.txt""")
_a : int = logging.getLogger(__name__)
@dataclass
class _UpperCAmelCase :
a : Optional[str] =field(
default="""tab_fact""" , metadata={"""help""": """The name of the dataset to use (via the datasets library)."""} )
a : Optional[str] =field(
default="""tab_fact""" , metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""} , )
a : int =field(
default=10_24 , metadata={
"""help""": (
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
)
} , )
a : bool =field(
default=lowerCAmelCase_ , metadata={"""help""": """Overwrite the cached preprocessed datasets or not."""} )
a : bool =field(
default=lowerCAmelCase_ , metadata={
"""help""": (
"""Whether to pad all samples to `max_seq_length`. """
"""If False, will pad the samples dynamically when batching to the maximum length in the batch."""
)
} , )
a : Optional[int] =field(
default=lowerCAmelCase_ , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of training examples to this """
"""value if set."""
)
} , )
a : Optional[int] =field(
default=lowerCAmelCase_ , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of evaluation examples to this """
"""value if set."""
)
} , )
a : Optional[int] =field(
default=lowerCAmelCase_ , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of prediction examples to this """
"""value if set."""
)
} , )
a : Optional[str] =field(
default=lowerCAmelCase_ , metadata={"""help""": """A csv or a json file containing the training data."""} )
a : Optional[str] =field(
default=lowerCAmelCase_ , metadata={"""help""": """A csv or a json file containing the validation data."""} )
a : Optional[str] =field(default=lowerCAmelCase_ , metadata={"""help""": """A csv or a json file containing the test data."""} )
def lowerCamelCase__ ( self ):
'''simple docstring'''
if self.dataset_name is not None:
pass
elif self.train_file is None or self.validation_file is None:
raise ValueError("""Need either a GLUE task, a training/validation file or a dataset name.""" )
else:
__lowerCAmelCase = self.train_file.split(""".""" )[-1]
assert train_extension in ["csv", "json"], "`train_file` should be a csv or a json file."
__lowerCAmelCase = self.validation_file.split(""".""" )[-1]
assert (
validation_extension == train_extension
), "`validation_file` should have the same extension (csv or json) as `train_file`."
@dataclass
class _UpperCAmelCase :
a : str =field(
default=lowerCAmelCase_ , metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""} )
a : Optional[str] =field(
default=lowerCAmelCase_ , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""} )
a : Optional[str] =field(
default=lowerCAmelCase_ , metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""} )
a : Optional[str] =field(
default=lowerCAmelCase_ , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} , )
a : bool =field(
default=lowerCAmelCase_ , metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""} , )
a : str =field(
default="""main""" , metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""} , )
a : bool =field(
default=lowerCAmelCase_ , metadata={
"""help""": (
"""Will use the token generated when running `huggingface-cli login` (necessary to use this script """
"""with private models)."""
)
} , )
def _lowerCAmelCase ( ) -> Optional[Any]:
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
__lowerCAmelCase = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith(""".json""" ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = parser.parse_args_into_dataclasses()
# Setup logging
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , handlers=[logging.StreamHandler(sys.stdout )] , )
__lowerCAmelCase = training_args.get_process_log_level()
logger.setLevel(lowercase )
datasets.utils.logging.set_verbosity(lowercase )
transformers.utils.logging.set_verbosity(lowercase )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
f'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
+ f'distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}' )
logger.info(f'Training/evaluation parameters {training_args}' )
# Detecting last checkpoint.
__lowerCAmelCase = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
__lowerCAmelCase = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
f'Output directory ({training_args.output_dir}) already exists and is not empty. '
"""Use --overwrite_output_dir to overcome.""" )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
f'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
"""the `--output_dir` or add `--overwrite_output_dir` to train from scratch.""" )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
# or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub).
#
# For JSON files, this script will use the `question` column for the input question and `table` column for the corresponding table.
#
# If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this
# single column. You can easily tweak this behavior (see below)
#
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
# download the dataset.
if data_args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
__lowerCAmelCase = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , cache_dir=model_args.cache_dir )
else:
# Loading a dataset from your local files.
# CSV/JSON training and evaluation files are needed.
__lowerCAmelCase = {"""train""": data_args.train_file, """validation""": data_args.validation_file}
# Get the test dataset: you can provide your own CSV/JSON test file (see below)
# when you use `do_predict` without specifying a GLUE benchmark task.
if training_args.do_predict:
if data_args.test_file is not None:
__lowerCAmelCase = data_args.train_file.split(""".""" )[-1]
__lowerCAmelCase = data_args.test_file.split(""".""" )[-1]
assert (
test_extension == train_extension
), "`test_file` should have the same extension (csv or json) as `train_file`."
__lowerCAmelCase = data_args.test_file
else:
raise ValueError("""Need either a GLUE task or a test file for `do_predict`.""" )
for key in data_files.keys():
logger.info(f'load a local file for {key}: {data_files[key]}' )
if data_args.train_file.endswith(""".csv""" ):
# Loading a dataset from local csv files
__lowerCAmelCase = load_dataset("""csv""" , data_files=lowercase , cache_dir=model_args.cache_dir )
else:
# Loading a dataset from local json files
__lowerCAmelCase = load_dataset("""json""" , data_files=lowercase , cache_dir=model_args.cache_dir )
# See more about loading any type of standard or custom dataset at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Labels
__lowerCAmelCase = raw_datasets["""train"""].features["""label"""].names
__lowerCAmelCase = len(lowercase )
# Load pretrained model and tokenizer
#
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
__lowerCAmelCase = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=lowercase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# load tapex tokenizer
__lowerCAmelCase = TapexTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , add_prefix_space=lowercase , )
__lowerCAmelCase = BartForSequenceClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool(""".ckpt""" in model_args.model_name_or_path ) , config=lowercase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# Padding strategy
if data_args.pad_to_max_length:
__lowerCAmelCase = """max_length"""
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
__lowerCAmelCase = False
# Some models have set the order of the labels to use, so let's make sure we do use it.
__lowerCAmelCase = {"""Refused""": 0, """Entailed""": 1}
__lowerCAmelCase = {0: """Refused""", 1: """Entailed"""}
if data_args.max_seq_length > tokenizer.model_max_length:
logger.warning(
f'The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the'
f'model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.' )
__lowerCAmelCase = min(data_args.max_seq_length , tokenizer.model_max_length )
def preprocess_tabfact_function(lowercase ):
# Tokenize the texts
def _convert_table_text_to_pandas(lowercase ):
__lowerCAmelCase = [_table_row.split("""#""" ) for _table_row in _table_text.strip("""\n""" ).split("""\n""" )]
__lowerCAmelCase = pd.DataFrame.from_records(_table_content[1:] , columns=_table_content[0] )
return _table_pd
__lowerCAmelCase = examples["""statement"""]
__lowerCAmelCase = list(map(_convert_table_text_to_pandas , examples["""table_text"""] ) )
__lowerCAmelCase = tokenizer(lowercase , lowercase , padding=lowercase , max_length=lowercase , truncation=lowercase )
__lowerCAmelCase = examples["""label"""]
return result
with training_args.main_process_first(desc="""dataset map pre-processing""" ):
__lowerCAmelCase = raw_datasets.map(
lowercase , batched=lowercase , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on dataset""" , )
if training_args.do_train:
if "train" not in raw_datasets:
raise ValueError("""--do_train requires a train dataset""" )
__lowerCAmelCase = raw_datasets["""train"""]
if data_args.max_train_samples is not None:
__lowerCAmelCase = train_dataset.select(range(data_args.max_train_samples ) )
if training_args.do_eval:
if "validation" not in raw_datasets and "validation_matched" not in raw_datasets:
raise ValueError("""--do_eval requires a validation dataset""" )
__lowerCAmelCase = raw_datasets["""validation"""]
if data_args.max_eval_samples is not None:
__lowerCAmelCase = eval_dataset.select(range(data_args.max_eval_samples ) )
if training_args.do_predict or data_args.test_file is not None:
if "test" not in raw_datasets and "test_matched" not in raw_datasets:
raise ValueError("""--do_predict requires a test dataset""" )
__lowerCAmelCase = raw_datasets["""test"""]
if data_args.max_predict_samples is not None:
__lowerCAmelCase = predict_dataset.select(range(data_args.max_predict_samples ) )
# Log a few random samples from the training set:
if training_args.do_train:
for index in random.sample(range(len(lowercase ) ) , 3 ):
logger.info(f'Sample {index} of the training set: {train_dataset[index]}.' )
# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(lowercase ):
__lowerCAmelCase = p.predictions[0] if isinstance(p.predictions , lowercase ) else p.predictions
__lowerCAmelCase = np.argmax(lowercase , axis=1 )
return {"accuracy": (preds == p.label_ids).astype(np.floataa ).mean().item()}
# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
if data_args.pad_to_max_length:
__lowerCAmelCase = default_data_collator
elif training_args.fpaa:
__lowerCAmelCase = DataCollatorWithPadding(lowercase , pad_to_multiple_of=8 )
else:
__lowerCAmelCase = None
# Initialize our Trainer
__lowerCAmelCase = Trainer(
model=lowercase , args=lowercase , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , compute_metrics=lowercase , tokenizer=lowercase , data_collator=lowercase , )
# Training
if training_args.do_train:
__lowerCAmelCase = None
if training_args.resume_from_checkpoint is not None:
__lowerCAmelCase = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
__lowerCAmelCase = last_checkpoint
__lowerCAmelCase = trainer.train(resume_from_checkpoint=lowercase )
__lowerCAmelCase = train_result.metrics
__lowerCAmelCase = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(lowercase )
)
__lowerCAmelCase = min(lowercase , len(lowercase ) )
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics("""train""" , lowercase )
trainer.save_metrics("""train""" , lowercase )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info("""*** Evaluate ***""" )
__lowerCAmelCase = trainer.evaluate(eval_dataset=lowercase )
__lowerCAmelCase = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(lowercase )
__lowerCAmelCase = min(lowercase , len(lowercase ) )
trainer.log_metrics("""eval""" , lowercase )
trainer.save_metrics("""eval""" , lowercase )
if training_args.do_predict:
logger.info("""*** Predict ***""" )
# Removing the `label` columns because it contains -1 and Trainer won't like that.
__lowerCAmelCase = predict_dataset.remove_columns("""label""" )
__lowerCAmelCase = trainer.predict(lowercase , metric_key_prefix="""predict""" ).predictions
__lowerCAmelCase = np.argmax(lowercase , axis=1 )
__lowerCAmelCase = os.path.join(training_args.output_dir , """predict_results_tabfact.txt""" )
if trainer.is_world_process_zero():
with open(lowercase , """w""" ) as writer:
logger.info("""***** Predict Results *****""" )
writer.write("""index\tprediction\n""" )
for index, item in enumerate(lowercase ):
__lowerCAmelCase = label_list[item]
writer.write(f'{index}\t{item}\n' )
__lowerCAmelCase = {"""finetuned_from""": model_args.model_name_or_path, """tasks""": """text-classification"""}
if training_args.push_to_hub:
trainer.push_to_hub(**lowercase )
else:
trainer.create_model_card(**lowercase )
def _lowerCAmelCase ( lowercase ) -> str:
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 689 | 1 |
'''simple docstring'''
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGeneration,
MusicgenProcessor,
TaEncoderModel,
)
from transformers.models.musicgen.modeling_musicgen import MusicgenForCausalLM
from transformers.utils import logging
logging.set_verbosity_info()
_a : Tuple = logging.get_logger(__name__)
_a : Optional[int] = ["""model.decoder.embed_positions.weights"""]
def _lowerCAmelCase ( lowercase ) -> Optional[Any]:
if "emb" in name:
__lowerCAmelCase = name.replace("""emb""" , """model.decoder.embed_tokens""" )
if "transformer" in name:
__lowerCAmelCase = name.replace("""transformer""" , """model.decoder""" )
if "cross_attention" in name:
__lowerCAmelCase = name.replace("""cross_attention""" , """encoder_attn""" )
if "linear1" in name:
__lowerCAmelCase = name.replace("""linear1""" , """fc1""" )
if "linear2" in name:
__lowerCAmelCase = name.replace("""linear2""" , """fc2""" )
if "norm1" in name:
__lowerCAmelCase = name.replace("""norm1""" , """self_attn_layer_norm""" )
if "norm_cross" in name:
__lowerCAmelCase = name.replace("""norm_cross""" , """encoder_attn_layer_norm""" )
if "norm2" in name:
__lowerCAmelCase = name.replace("""norm2""" , """final_layer_norm""" )
if "out_norm" in name:
__lowerCAmelCase = name.replace("""out_norm""" , """model.decoder.layer_norm""" )
if "linears" in name:
__lowerCAmelCase = name.replace("""linears""" , """lm_heads""" )
if "condition_provider.conditioners.description.output_proj" in name:
__lowerCAmelCase = name.replace("""condition_provider.conditioners.description.output_proj""" , """enc_to_dec_proj""" )
return name
def _lowerCAmelCase ( lowercase , lowercase ) -> Tuple[Dict, Dict]:
__lowerCAmelCase = list(state_dict.keys() )
__lowerCAmelCase = {}
for key in keys:
__lowerCAmelCase = state_dict.pop(lowercase )
__lowerCAmelCase = rename_keys(lowercase )
if "in_proj_weight" in key:
# split fused qkv proj
__lowerCAmelCase = val[:hidden_size, :]
__lowerCAmelCase = val[hidden_size : 2 * hidden_size, :]
__lowerCAmelCase = val[-hidden_size:, :]
elif "enc_to_dec_proj" in key:
__lowerCAmelCase = val
else:
__lowerCAmelCase = val
return state_dict, enc_dec_proj_state_dict
def _lowerCAmelCase ( lowercase ) -> MusicgenDecoderConfig:
if checkpoint == "small":
# default config values
__lowerCAmelCase = 1024
__lowerCAmelCase = 24
__lowerCAmelCase = 16
elif checkpoint == "medium":
__lowerCAmelCase = 1536
__lowerCAmelCase = 48
__lowerCAmelCase = 24
elif checkpoint == "large":
__lowerCAmelCase = 2048
__lowerCAmelCase = 48
__lowerCAmelCase = 32
else:
raise ValueError(f'Checkpoint should be one of `[\'small\', \'medium\', \'large\']`, got {checkpoint}.' )
__lowerCAmelCase = MusicgenDecoderConfig(
hidden_size=lowercase , ffn_dim=hidden_size * 4 , num_hidden_layers=lowercase , num_attention_heads=lowercase , )
return config
@torch.no_grad()
def _lowerCAmelCase ( lowercase , lowercase=None , lowercase=None , lowercase="cpu" ) -> Optional[Any]:
__lowerCAmelCase = MusicGen.get_pretrained(lowercase , device=lowercase )
__lowerCAmelCase = decoder_config_from_checkpoint(lowercase )
__lowerCAmelCase = fairseq_model.lm.state_dict()
__lowerCAmelCase , __lowerCAmelCase = rename_state_dict(
lowercase , hidden_size=decoder_config.hidden_size )
__lowerCAmelCase = TaEncoderModel.from_pretrained("""t5-base""" )
__lowerCAmelCase = EncodecModel.from_pretrained("""facebook/encodec_32khz""" )
__lowerCAmelCase = MusicgenForCausalLM(lowercase ).eval()
# load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
__lowerCAmelCase , __lowerCAmelCase = decoder.load_state_dict(lowercase , strict=lowercase )
for key in missing_keys.copy():
if key.startswith(("""text_encoder""", """audio_encoder""") ) or key in EXPECTED_MISSING_KEYS:
missing_keys.remove(lowercase )
if len(lowercase ) > 0:
raise ValueError(f'Missing key(s) in state_dict: {missing_keys}' )
if len(lowercase ) > 0:
raise ValueError(f'Unexpected key(s) in state_dict: {unexpected_keys}' )
# init the composite model
__lowerCAmelCase = MusicgenForConditionalGeneration(text_encoder=lowercase , audio_encoder=lowercase , decoder=lowercase )
# load the pre-trained enc-dec projection (from the decoder state dict)
model.enc_to_dec_proj.load_state_dict(lowercase )
# check we can do a forward pass
__lowerCAmelCase = torch.arange(0 , 8 , dtype=torch.long ).reshape(2 , -1 )
__lowerCAmelCase = input_ids.reshape(2 * 4 , -1 )
with torch.no_grad():
__lowerCAmelCase = model(input_ids=lowercase , decoder_input_ids=lowercase ).logits
if logits.shape != (8, 1, 2048):
raise ValueError("""Incorrect shape for logits""" )
# now construct the processor
__lowerCAmelCase = AutoTokenizer.from_pretrained("""t5-base""" )
__lowerCAmelCase = AutoFeatureExtractor.from_pretrained("""facebook/encodec_32khz""" , padding_side="""left""" )
__lowerCAmelCase = MusicgenProcessor(feature_extractor=lowercase , tokenizer=lowercase )
# set the appropriate bos/pad token ids
__lowerCAmelCase = 2048
__lowerCAmelCase = 2048
# set other default generation config params
__lowerCAmelCase = int(30 * audio_encoder.config.frame_rate )
__lowerCAmelCase = True
__lowerCAmelCase = 3.0
if pytorch_dump_folder is not None:
Path(lowercase ).mkdir(exist_ok=lowercase )
logger.info(f'Saving model {checkpoint} to {pytorch_dump_folder}' )
model.save_pretrained(lowercase )
processor.save_pretrained(lowercase )
if repo_id:
logger.info(f'Pushing model {checkpoint} to {repo_id}' )
model.push_to_hub(lowercase )
processor.push_to_hub(lowercase )
if __name__ == "__main__":
_a : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--checkpoint""",
default="""small""",
type=str,
help="""Checkpoint size of the MusicGen model you'd like to convert. Can be one of: `['small', 'medium', 'large']`.""",
)
parser.add_argument(
"""--pytorch_dump_folder""",
required=True,
default=None,
type=str,
help="""Path to the output PyTorch model directory.""",
)
parser.add_argument(
"""--push_to_hub""", default=None, type=str, help="""Where to upload the converted model on the 🤗 hub."""
)
parser.add_argument(
"""--device""", default="""cpu""", type=str, help="""Torch device to run the conversion, either cpu or cuda."""
)
_a : List[Any] = parser.parse_args()
convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)
| 689 |
'''simple docstring'''
import os
import sys
import unittest
_a : List[Any] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init # noqa: E402
# Align TRANSFORMERS_PATH in check_dummies with the current path
_a : Union[str, Any] = os.path.join(git_repo_path, """src""", """diffusers""")
class _UpperCAmelCase ( unittest.TestCase ):
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = find_backend(""" if not is_torch_available():""" )
self.assertEqual(__SCREAMING_SNAKE_CASE,"""torch""" )
# backend_with_underscore = find_backend(" if not is_tensorflow_text_available():")
# self.assertEqual(backend_with_underscore, "tensorflow_text")
__lowerCAmelCase = find_backend(""" if not (is_torch_available() and is_transformers_available()):""" )
self.assertEqual(__SCREAMING_SNAKE_CASE,"""torch_and_transformers""" )
# double_backend_with_underscore = find_backend(
# " if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
# )
# self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")
__lowerCAmelCase = find_backend(
""" if not (is_torch_available() and is_transformers_available() and is_onnx_available()):""" )
self.assertEqual(__SCREAMING_SNAKE_CASE,"""torch_and_transformers_and_onnx""" )
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = read_init()
# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
self.assertIn("""torch""",__SCREAMING_SNAKE_CASE )
self.assertIn("""torch_and_transformers""",__SCREAMING_SNAKE_CASE )
self.assertIn("""flax_and_transformers""",__SCREAMING_SNAKE_CASE )
self.assertIn("""torch_and_transformers_and_onnx""",__SCREAMING_SNAKE_CASE )
# Likewise, we can't assert on the exact content of a key
self.assertIn("""UNet2DModel""",objects["""torch"""] )
self.assertIn("""FlaxUNet2DConditionModel""",objects["""flax"""] )
self.assertIn("""StableDiffusionPipeline""",objects["""torch_and_transformers"""] )
self.assertIn("""FlaxStableDiffusionPipeline""",objects["""flax_and_transformers"""] )
self.assertIn("""LMSDiscreteScheduler""",objects["""torch_and_scipy"""] )
self.assertIn("""OnnxStableDiffusionPipeline""",objects["""torch_and_transformers_and_onnx"""] )
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = create_dummy_object("""CONSTANT""","""'torch'""" )
self.assertEqual(__SCREAMING_SNAKE_CASE,"""\nCONSTANT = None\n""" )
__lowerCAmelCase = create_dummy_object("""function""","""'torch'""" )
self.assertEqual(
__SCREAMING_SNAKE_CASE,"""\ndef function(*args, **kwargs):\n requires_backends(function, 'torch')\n""" )
__lowerCAmelCase = """
class FakeClass(metaclass=DummyObject):
_backends = 'torch'
def __init__(self, *args, **kwargs):
requires_backends(self, 'torch')
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, 'torch')
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, 'torch')
"""
__lowerCAmelCase = create_dummy_object("""FakeClass""","""'torch'""" )
self.assertEqual(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = """# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends
CONSTANT = None
def function(*args, **kwargs):
requires_backends(function, [\"torch\"])
class FakeClass(metaclass=DummyObject):
_backends = [\"torch\"]
def __init__(self, *args, **kwargs):
requires_backends(self, [\"torch\"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, [\"torch\"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, [\"torch\"])
"""
__lowerCAmelCase = create_dummy_files({"""torch""": ["""CONSTANT""", """function""", """FakeClass"""]} )
self.assertEqual(dummy_files["""torch"""],__SCREAMING_SNAKE_CASE )
| 689 | 1 |
'''simple docstring'''
def _lowerCAmelCase ( lowercase ) -> list[list[float]]:
__lowerCAmelCase = []
for data in source_data:
for i, el in enumerate(lowercase ):
if len(lowercase ) < i + 1:
data_lists.append([] )
data_lists[i].append(float(lowercase ) )
return data_lists
def _lowerCAmelCase ( lowercase , lowercase ) -> list[list[float]]:
__lowerCAmelCase = []
for dlist, weight in zip(lowercase , lowercase ):
__lowerCAmelCase = min(lowercase )
__lowerCAmelCase = max(lowercase )
__lowerCAmelCase = []
# for weight 0 score is 1 - actual score
if weight == 0:
for item in dlist:
try:
score.append(1 - ((item - mind) / (maxd - mind)) )
except ZeroDivisionError:
score.append(1 )
elif weight == 1:
for item in dlist:
try:
score.append((item - mind) / (maxd - mind) )
except ZeroDivisionError:
score.append(0 )
# weight not 0 or 1
else:
__lowerCAmelCase = f'Invalid weight of {weight:f} provided'
raise ValueError(lowercase )
score_lists.append(lowercase )
return score_lists
def _lowerCAmelCase ( lowercase ) -> list[float]:
__lowerCAmelCase = [0 for i in range(len(score_lists[0] ) )]
for slist in score_lists:
for j, ele in enumerate(lowercase ):
__lowerCAmelCase = final_scores[j] + ele
return final_scores
def _lowerCAmelCase ( lowercase , lowercase ) -> list[list[float]]:
__lowerCAmelCase = get_data(lowercase )
__lowerCAmelCase = calculate_each_score(lowercase , lowercase )
__lowerCAmelCase = generate_final_scores(lowercase )
# append scores to source data
for i, ele in enumerate(lowercase ):
source_data[i].append(lowercase )
return source_data
| 689 |
'''simple docstring'''
def _lowerCAmelCase ( lowercase ) -> tuple[int, int]:
try:
__lowerCAmelCase = float(lowercase )
except ValueError:
raise ValueError("""Please enter a valid number""" )
__lowerCAmelCase = decimal - int(lowercase )
if fractional_part == 0:
return int(lowercase ), 1
else:
__lowerCAmelCase = len(str(lowercase ).split(""".""" )[1] )
__lowerCAmelCase = int(decimal * (10**number_of_frac_digits) )
__lowerCAmelCase = 10**number_of_frac_digits
__lowerCAmelCase , __lowerCAmelCase = denominator, numerator
while True:
__lowerCAmelCase = dividend % divisor
if remainder == 0:
break
__lowerCAmelCase , __lowerCAmelCase = divisor, remainder
__lowerCAmelCase , __lowerCAmelCase = numerator / divisor, denominator / divisor
return int(lowercase ), int(lowercase )
if __name__ == "__main__":
print(f'{decimal_to_fraction(2) = }')
print(f'{decimal_to_fraction(89.0) = }')
print(f'{decimal_to_fraction("67") = }')
print(f'{decimal_to_fraction("45.0") = }')
print(f'{decimal_to_fraction(1.5) = }')
print(f'{decimal_to_fraction("6.25") = }')
print(f'{decimal_to_fraction("78td") = }')
| 689 | 1 |
'''simple docstring'''
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a : Any = logging.get_logger(__name__)
_a : Dict = {
"""microsoft/xprophetnet-large-wiki100-cased""": (
"""https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/config.json"""
),
}
class _UpperCAmelCase ( lowerCAmelCase_ ):
a : Dict ="""xlm-prophetnet"""
a : Optional[Any] =["""past_key_values"""]
a : Union[str, Any] ={
"""num_attention_heads""": """num_encoder_attention_heads""",
}
def __init__( self,__SCREAMING_SNAKE_CASE = 0.1,__SCREAMING_SNAKE_CASE = "gelu",__SCREAMING_SNAKE_CASE = 3_05_22,__SCREAMING_SNAKE_CASE = 10_24,__SCREAMING_SNAKE_CASE = 40_96,__SCREAMING_SNAKE_CASE = 12,__SCREAMING_SNAKE_CASE = 16,__SCREAMING_SNAKE_CASE = 40_96,__SCREAMING_SNAKE_CASE = 12,__SCREAMING_SNAKE_CASE = 16,__SCREAMING_SNAKE_CASE = 0.1,__SCREAMING_SNAKE_CASE = 0.1,__SCREAMING_SNAKE_CASE = 5_12,__SCREAMING_SNAKE_CASE = 0.02,__SCREAMING_SNAKE_CASE = True,__SCREAMING_SNAKE_CASE = True,__SCREAMING_SNAKE_CASE = 0,__SCREAMING_SNAKE_CASE = 2,__SCREAMING_SNAKE_CASE = 32,__SCREAMING_SNAKE_CASE = 1_28,__SCREAMING_SNAKE_CASE = False,__SCREAMING_SNAKE_CASE = 0.0,__SCREAMING_SNAKE_CASE = True,__SCREAMING_SNAKE_CASE = 0,__SCREAMING_SNAKE_CASE = 1,__SCREAMING_SNAKE_CASE = 2,**__SCREAMING_SNAKE_CASE,):
'''simple docstring'''
__lowerCAmelCase = vocab_size
__lowerCAmelCase = hidden_size
__lowerCAmelCase = encoder_ffn_dim
__lowerCAmelCase = num_encoder_layers
__lowerCAmelCase = num_encoder_attention_heads
__lowerCAmelCase = decoder_ffn_dim
__lowerCAmelCase = num_decoder_layers
__lowerCAmelCase = num_decoder_attention_heads
__lowerCAmelCase = max_position_embeddings
__lowerCAmelCase = init_std # Normal(0, this parameter)
__lowerCAmelCase = activation_function
# parameters for xlmprophetnet
__lowerCAmelCase = ngram
__lowerCAmelCase = num_buckets
__lowerCAmelCase = relative_max_distance
__lowerCAmelCase = disable_ngram_loss
__lowerCAmelCase = eps
# 3 Types of Dropout
__lowerCAmelCase = attention_dropout
__lowerCAmelCase = activation_dropout
__lowerCAmelCase = dropout
__lowerCAmelCase = use_cache
super().__init__(
pad_token_id=__SCREAMING_SNAKE_CASE,bos_token_id=__SCREAMING_SNAKE_CASE,eos_token_id=__SCREAMING_SNAKE_CASE,is_encoder_decoder=__SCREAMING_SNAKE_CASE,add_cross_attention=__SCREAMING_SNAKE_CASE,decoder_start_token_id=__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE,)
@property
def lowerCamelCase__ ( self ):
'''simple docstring'''
return self.num_encoder_layers + self.num_decoder_layers
@num_hidden_layers.setter
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
raise NotImplementedError(
"""This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and"""
""" `num_decoder_layers`.""" )
| 689 |
'''simple docstring'''
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
_a : Dict = _symbol_database.Default()
_a : Union[str, Any] = _descriptor_pool.Default().AddSerializedFile(
b"""\n\x19sentencepiece_model.proto\x12\rsentencepiece\"\x80\x0c\n\x0bTrainerSpec\x12\r\n\x05input\x18\x01 \x03(\t\x12\x14\n\x0cinput_format\x18\x07 \x01(\t\x12\x14\n\x0cmodel_prefix\x18\x02 \x01(\t\x12\x41\n\nmodel_type\x18\x03 \x01(\x0e\x32$.sentencepiece.TrainerSpec.ModelType:\x07UNIGRAM\x12\x18\n\nvocab_size\x18\x04 \x01(\x05:\x04\x38\x30\x30\x30\x12\x17\n\x0f\x61\x63\x63\x65pt_language\x18\x05 \x03(\t\x12 \n\x15self_test_sample_size\x18\x06 \x01(\x05:\x01\x30\x12*\n\x1b\x65nable_differential_privacy\x18\x32 \x01(\x08:\x05\x66\x61lse\x12+\n differential_privacy_noise_level\x18\x33 \x01(\x02:\x01\x30\x12\x32\n\'differential_privacy_clipping_threshold\x18\x34 \x01(\x04:\x01\x30\x12\"\n\x12\x63haracter_coverage\x18\n \x01(\x02:\x06\x30.9995\x12\x1e\n\x13input_sentence_size\x18\x0b \x01(\x04:\x01\x30\x12$\n\x16shuffle_input_sentence\x18\x13 \x01(\x08:\x04true\x12 \n\x14mining_sentence_size\x18\x0c \x01(\x05\x42\x02\x18\x01\x12\"\n\x16training_sentence_size\x18\r \x01(\x05\x42\x02\x18\x01\x12(\n\x17seed_sentencepiece_size\x18\x0e \x01(\x05:\x07\x31\x30\x30\x30\x30\x30\x30\x12\x1e\n\x10shrinking_factor\x18\x0f \x01(\x02:\x04\x30.75\x12!\n\x13max_sentence_length\x18\x12 \x01(\x05:\x04\x34\x31\x39\x32\x12\x17\n\x0bnum_threads\x18\x10 \x01(\x05:\x02\x31\x36\x12\x1d\n\x12num_sub_iterations\x18\x11 \x01(\x05:\x01\x32\x12$\n\x18max_sentencepiece_length\x18\x14 \x01(\x05:\x02\x31\x36\x12%\n\x17split_by_unicode_script\x18\x15 \x01(\x08:\x04true\x12\x1d\n\x0fsplit_by_number\x18\x17 \x01(\x08:\x04true\x12!\n\x13split_by_whitespace\x18\x16 \x01(\x08:\x04true\x12)\n\x1atreat_whitespace_as_suffix\x18\x18 \x01(\x08:\x05\x66\x61lse\x12+\n\x1c\x61llow_whitespace_only_pieces\x18\x1a \x01(\x08:\x05\x66\x61lse\x12\x1b\n\x0csplit_digits\x18\x19 \x01(\x08:\x05\x66\x61lse\x12#\n\x19pretokenization_delimiter\x18\x35 \x01(\t:\x00\x12\x17\n\x0f\x63ontrol_symbols\x18\x1e \x03(\t\x12\x1c\n\x14user_defined_symbols\x18\x1f \x03(\t\x12\x16\n\x0erequired_chars\x18$ \x01(\t\x12\x1c\n\rbyte_fallback\x18# \x01(\x08:\x05\x66\x61lse\x12+\n\x1dvocabulary_output_piece_score\x18 \x01(\x08:\x04true\x12\x1e\n\x10hard_vocab_limit\x18! \x01(\x08:\x04true\x12\x1c\n\ruse_all_vocab\x18\" \x01(\x08:\x05\x66\x61lse\x12\x11\n\x06unk_id\x18( \x01(\x05:\x01\x30\x12\x11\n\x06\x62os_id\x18) \x01(\x05:\x01\x31\x12\x11\n\x06\x65os_id\x18* \x01(\x05:\x01\x32\x12\x12\n\x06pad_id\x18+ \x01(\x05:\x02-1\x12\x18\n\tunk_piece\x18- \x01(\t:\x05<unk>\x12\x16\n\tbos_piece\x18. \x01(\t:\x03<s>\x12\x17\n\teos_piece\x18/ \x01(\t:\x04</s>\x12\x18\n\tpad_piece\x18\x30 \x01(\t:\x05<pad>\x12\x1a\n\x0bunk_surface\x18, \x01(\t:\x05 \xe2\x81\x87 \x12+\n\x1ctrain_extremely_large_corpus\x18\x31 \x01(\x08:\x05\x66\x61lse\"5\n\tModelType\x12\x0b\n\x07UNIGRAM\x10\x01\x12\x07\n\x03\x42PE\x10\x02\x12\x08\n\x04WORD\x10\x03\x12\x08\n\x04\x43HAR\x10\x04*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"\xd1\x01\n\x0eNormalizerSpec\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x1c\n\x14precompiled_charsmap\x18\x02 \x01(\x0c\x12\x1e\n\x10\x61\x64\x64_dummy_prefix\x18\x03 \x01(\x08:\x04true\x12&\n\x18remove_extra_whitespaces\x18\x04 \x01(\x08:\x04true\x12 \n\x12\x65scape_whitespaces\x18\x05 \x01(\x08:\x04true\x12\x1e\n\x16normalization_rule_tsv\x18\x06 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"y\n\x0cSelfTestData\x12\x33\n\x07samples\x18\x01 \x03(\x0b\x32\".sentencepiece.SelfTestData.Sample\x1a)\n\x06Sample\x12\r\n\x05input\x18\x01 \x01(\t\x12\x10\n\x08\x65xpected\x18\x02 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"\xfe\x03\n\nModelProto\x12\x37\n\x06pieces\x18\x01 \x03(\x0b\x32\'.sentencepiece.ModelProto.SentencePiece\x12\x30\n\x0ctrainer_spec\x18\x02 \x01(\x0b\x32\x1a.sentencepiece.TrainerSpec\x12\x36\n\x0fnormalizer_spec\x18\x03 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x12\x33\n\x0eself_test_data\x18\x04 \x01(\x0b\x32\x1b.sentencepiece.SelfTestData\x12\x38\n\x11\x64\x65normalizer_spec\x18\x05 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x1a\xd2\x01\n\rSentencePiece\x12\r\n\x05piece\x18\x01 \x01(\t\x12\r\n\x05score\x18\x02 \x01(\x02\x12\x42\n\x04type\x18\x03 \x01(\x0e\x32,.sentencepiece.ModelProto.SentencePiece.Type:\x06NORMAL\"T\n\x04Type\x12\n\n\x06NORMAL\x10\x01\x12\x0b\n\x07UNKNOWN\x10\x02\x12\x0b\n\x07\x43ONTROL\x10\x03\x12\x10\n\x0cUSER_DEFINED\x10\x04\x12\x08\n\x04\x42YTE\x10\x06\x12\n\n\x06UNUSED\x10\x05*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\x42\x02H\x03"""
)
_a : str = globals()
_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, """sentencepiece_model_pb2""", _globals)
if _descriptor._USE_C_DESCRIPTORS is False:
_a : str = None
_a : Union[str, Any] = b"""H\003"""
# (generated by protobuf compiler, but `_TRAINERSPEC` is not defined)
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._serialized_options = b"\030\001"
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._serialized_options = b"\030\001"
_a : Optional[int] = 4_5
_a : List[Any] = 1_5_8_1
_a : str = 1_5_1_7
_a : Optional[Any] = 1_5_7_0
_a : List[str] = 1_5_8_4
_a : List[Any] = 1_7_9_3
_a : Union[str, Any] = 1_7_9_5
_a : Tuple = 1_9_1_6
_a : List[Any] = 1_8_6_4
_a : Any = 1_9_0_5
_a : Optional[Any] = 1_9_1_9
_a : Optional[int] = 2_4_2_9
_a : Tuple = 2_2_0_8
_a : Optional[Any] = 2_4_1_8
_a : List[Any] = 2_3_2_3
_a : str = 2_4_0_7
# @@protoc_insertion_point(module_scope)
| 689 | 1 |
'''simple docstring'''
def _lowerCAmelCase ( lowercase ) -> list:
if len(lowercase ) < 2:
return collection
def circle_sort_util(lowercase , lowercase , lowercase ) -> bool:
__lowerCAmelCase = False
if low == high:
return swapped
__lowerCAmelCase = low
__lowerCAmelCase = high
while left < right:
if collection[left] > collection[right]:
__lowerCAmelCase , __lowerCAmelCase = (
collection[right],
collection[left],
)
__lowerCAmelCase = True
left += 1
right -= 1
if left == right and collection[left] > collection[right + 1]:
__lowerCAmelCase , __lowerCAmelCase = (
collection[right + 1],
collection[left],
)
__lowerCAmelCase = True
__lowerCAmelCase = low + int((high - low) / 2 )
__lowerCAmelCase = circle_sort_util(lowercase , lowercase , lowercase )
__lowerCAmelCase = circle_sort_util(lowercase , mid + 1 , lowercase )
return swapped or left_swap or right_swap
__lowerCAmelCase = True
while is_not_sorted is True:
__lowerCAmelCase = circle_sort_util(lowercase , 0 , len(lowercase ) - 1 )
return collection
if __name__ == "__main__":
_a : str = input("""Enter numbers separated by a comma:\n""").strip()
_a : Optional[int] = [int(item) for item in user_input.split(""",""")]
print(circle_sort(unsorted))
| 689 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block
@dataclass
class _UpperCAmelCase ( lowerCAmelCase_ ):
a : torch.FloatTensor
class _UpperCAmelCase ( nn.Module ):
def __init__( self,__SCREAMING_SNAKE_CASE=3,__SCREAMING_SNAKE_CASE=3,__SCREAMING_SNAKE_CASE=("DownEncoderBlock2D",),__SCREAMING_SNAKE_CASE=(64,),__SCREAMING_SNAKE_CASE=2,__SCREAMING_SNAKE_CASE=32,__SCREAMING_SNAKE_CASE="silu",__SCREAMING_SNAKE_CASE=True,):
'''simple docstring'''
super().__init__()
__lowerCAmelCase = layers_per_block
__lowerCAmelCase = torch.nn.Convad(
__SCREAMING_SNAKE_CASE,block_out_channels[0],kernel_size=3,stride=1,padding=1,)
__lowerCAmelCase = None
__lowerCAmelCase = nn.ModuleList([] )
# down
__lowerCAmelCase = block_out_channels[0]
for i, down_block_type in enumerate(__SCREAMING_SNAKE_CASE ):
__lowerCAmelCase = output_channel
__lowerCAmelCase = block_out_channels[i]
__lowerCAmelCase = i == len(__SCREAMING_SNAKE_CASE ) - 1
__lowerCAmelCase = get_down_block(
__SCREAMING_SNAKE_CASE,num_layers=self.layers_per_block,in_channels=__SCREAMING_SNAKE_CASE,out_channels=__SCREAMING_SNAKE_CASE,add_downsample=not is_final_block,resnet_eps=1e-6,downsample_padding=0,resnet_act_fn=__SCREAMING_SNAKE_CASE,resnet_groups=__SCREAMING_SNAKE_CASE,attention_head_dim=__SCREAMING_SNAKE_CASE,temb_channels=__SCREAMING_SNAKE_CASE,)
self.down_blocks.append(__SCREAMING_SNAKE_CASE )
# mid
__lowerCAmelCase = UNetMidBlockaD(
in_channels=block_out_channels[-1],resnet_eps=1e-6,resnet_act_fn=__SCREAMING_SNAKE_CASE,output_scale_factor=1,resnet_time_scale_shift="""default""",attention_head_dim=block_out_channels[-1],resnet_groups=__SCREAMING_SNAKE_CASE,temb_channels=__SCREAMING_SNAKE_CASE,)
# out
__lowerCAmelCase = nn.GroupNorm(num_channels=block_out_channels[-1],num_groups=__SCREAMING_SNAKE_CASE,eps=1e-6 )
__lowerCAmelCase = nn.SiLU()
__lowerCAmelCase = 2 * out_channels if double_z else out_channels
__lowerCAmelCase = nn.Convad(block_out_channels[-1],__SCREAMING_SNAKE_CASE,3,padding=1 )
__lowerCAmelCase = False
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase = x
__lowerCAmelCase = self.conv_in(__SCREAMING_SNAKE_CASE )
if self.training and self.gradient_checkpointing:
def create_custom_forward(__SCREAMING_SNAKE_CASE ):
def custom_forward(*__SCREAMING_SNAKE_CASE ):
return module(*__SCREAMING_SNAKE_CASE )
return custom_forward
# down
if is_torch_version(""">=""","""1.11.0""" ):
for down_block in self.down_blocks:
__lowerCAmelCase = torch.utils.checkpoint.checkpoint(
create_custom_forward(__SCREAMING_SNAKE_CASE ),__SCREAMING_SNAKE_CASE,use_reentrant=__SCREAMING_SNAKE_CASE )
# middle
__lowerCAmelCase = torch.utils.checkpoint.checkpoint(
create_custom_forward(self.mid_block ),__SCREAMING_SNAKE_CASE,use_reentrant=__SCREAMING_SNAKE_CASE )
else:
for down_block in self.down_blocks:
__lowerCAmelCase = torch.utils.checkpoint.checkpoint(create_custom_forward(__SCREAMING_SNAKE_CASE ),__SCREAMING_SNAKE_CASE )
# middle
__lowerCAmelCase = torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block ),__SCREAMING_SNAKE_CASE )
else:
# down
for down_block in self.down_blocks:
__lowerCAmelCase = down_block(__SCREAMING_SNAKE_CASE )
# middle
__lowerCAmelCase = self.mid_block(__SCREAMING_SNAKE_CASE )
# post-process
__lowerCAmelCase = self.conv_norm_out(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = self.conv_act(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = self.conv_out(__SCREAMING_SNAKE_CASE )
return sample
class _UpperCAmelCase ( nn.Module ):
def __init__( self,__SCREAMING_SNAKE_CASE=3,__SCREAMING_SNAKE_CASE=3,__SCREAMING_SNAKE_CASE=("UpDecoderBlock2D",),__SCREAMING_SNAKE_CASE=(64,),__SCREAMING_SNAKE_CASE=2,__SCREAMING_SNAKE_CASE=32,__SCREAMING_SNAKE_CASE="silu",__SCREAMING_SNAKE_CASE="group",):
'''simple docstring'''
super().__init__()
__lowerCAmelCase = layers_per_block
__lowerCAmelCase = nn.Convad(
__SCREAMING_SNAKE_CASE,block_out_channels[-1],kernel_size=3,stride=1,padding=1,)
__lowerCAmelCase = None
__lowerCAmelCase = nn.ModuleList([] )
__lowerCAmelCase = in_channels if norm_type == """spatial""" else None
# mid
__lowerCAmelCase = UNetMidBlockaD(
in_channels=block_out_channels[-1],resnet_eps=1e-6,resnet_act_fn=__SCREAMING_SNAKE_CASE,output_scale_factor=1,resnet_time_scale_shift="""default""" if norm_type == """group""" else norm_type,attention_head_dim=block_out_channels[-1],resnet_groups=__SCREAMING_SNAKE_CASE,temb_channels=__SCREAMING_SNAKE_CASE,)
# up
__lowerCAmelCase = list(reversed(__SCREAMING_SNAKE_CASE ) )
__lowerCAmelCase = reversed_block_out_channels[0]
for i, up_block_type in enumerate(__SCREAMING_SNAKE_CASE ):
__lowerCAmelCase = output_channel
__lowerCAmelCase = reversed_block_out_channels[i]
__lowerCAmelCase = i == len(__SCREAMING_SNAKE_CASE ) - 1
__lowerCAmelCase = get_up_block(
__SCREAMING_SNAKE_CASE,num_layers=self.layers_per_block + 1,in_channels=__SCREAMING_SNAKE_CASE,out_channels=__SCREAMING_SNAKE_CASE,prev_output_channel=__SCREAMING_SNAKE_CASE,add_upsample=not is_final_block,resnet_eps=1e-6,resnet_act_fn=__SCREAMING_SNAKE_CASE,resnet_groups=__SCREAMING_SNAKE_CASE,attention_head_dim=__SCREAMING_SNAKE_CASE,temb_channels=__SCREAMING_SNAKE_CASE,resnet_time_scale_shift=__SCREAMING_SNAKE_CASE,)
self.up_blocks.append(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = output_channel
# out
if norm_type == "spatial":
__lowerCAmelCase = SpatialNorm(block_out_channels[0],__SCREAMING_SNAKE_CASE )
else:
__lowerCAmelCase = nn.GroupNorm(num_channels=block_out_channels[0],num_groups=__SCREAMING_SNAKE_CASE,eps=1e-6 )
__lowerCAmelCase = nn.SiLU()
__lowerCAmelCase = nn.Convad(block_out_channels[0],__SCREAMING_SNAKE_CASE,3,padding=1 )
__lowerCAmelCase = False
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=None ):
'''simple docstring'''
__lowerCAmelCase = z
__lowerCAmelCase = self.conv_in(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = next(iter(self.up_blocks.parameters() ) ).dtype
if self.training and self.gradient_checkpointing:
def create_custom_forward(__SCREAMING_SNAKE_CASE ):
def custom_forward(*__SCREAMING_SNAKE_CASE ):
return module(*__SCREAMING_SNAKE_CASE )
return custom_forward
if is_torch_version(""">=""","""1.11.0""" ):
# middle
__lowerCAmelCase = torch.utils.checkpoint.checkpoint(
create_custom_forward(self.mid_block ),__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,use_reentrant=__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = sample.to(__SCREAMING_SNAKE_CASE )
# up
for up_block in self.up_blocks:
__lowerCAmelCase = torch.utils.checkpoint.checkpoint(
create_custom_forward(__SCREAMING_SNAKE_CASE ),__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,use_reentrant=__SCREAMING_SNAKE_CASE )
else:
# middle
__lowerCAmelCase = torch.utils.checkpoint.checkpoint(
create_custom_forward(self.mid_block ),__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = sample.to(__SCREAMING_SNAKE_CASE )
# up
for up_block in self.up_blocks:
__lowerCAmelCase = torch.utils.checkpoint.checkpoint(create_custom_forward(__SCREAMING_SNAKE_CASE ),__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
else:
# middle
__lowerCAmelCase = self.mid_block(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = sample.to(__SCREAMING_SNAKE_CASE )
# up
for up_block in self.up_blocks:
__lowerCAmelCase = up_block(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
# post-process
if latent_embeds is None:
__lowerCAmelCase = self.conv_norm_out(__SCREAMING_SNAKE_CASE )
else:
__lowerCAmelCase = self.conv_norm_out(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = self.conv_act(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = self.conv_out(__SCREAMING_SNAKE_CASE )
return sample
class _UpperCAmelCase ( nn.Module ):
def __init__( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE="random",__SCREAMING_SNAKE_CASE=False,__SCREAMING_SNAKE_CASE=True ):
'''simple docstring'''
super().__init__()
__lowerCAmelCase = n_e
__lowerCAmelCase = vq_embed_dim
__lowerCAmelCase = beta
__lowerCAmelCase = legacy
__lowerCAmelCase = nn.Embedding(self.n_e,self.vq_embed_dim )
self.embedding.weight.data.uniform_(-1.0 / self.n_e,1.0 / self.n_e )
__lowerCAmelCase = remap
if self.remap is not None:
self.register_buffer("""used""",torch.tensor(np.load(self.remap ) ) )
__lowerCAmelCase = self.used.shape[0]
__lowerCAmelCase = unknown_index # "random" or "extra" or integer
if self.unknown_index == "extra":
__lowerCAmelCase = self.re_embed
__lowerCAmelCase = self.re_embed + 1
print(
f'Remapping {self.n_e} indices to {self.re_embed} indices. '
f'Using {self.unknown_index} for unknown indices.' )
else:
__lowerCAmelCase = n_e
__lowerCAmelCase = sane_index_shape
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase = inds.shape
assert len(__SCREAMING_SNAKE_CASE ) > 1
__lowerCAmelCase = inds.reshape(ishape[0],-1 )
__lowerCAmelCase = self.used.to(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = (inds[:, :, None] == used[None, None, ...]).long()
__lowerCAmelCase = match.argmax(-1 )
__lowerCAmelCase = match.sum(2 ) < 1
if self.unknown_index == "random":
__lowerCAmelCase = torch.randint(0,self.re_embed,size=new[unknown].shape ).to(device=new.device )
else:
__lowerCAmelCase = self.unknown_index
return new.reshape(__SCREAMING_SNAKE_CASE )
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase = inds.shape
assert len(__SCREAMING_SNAKE_CASE ) > 1
__lowerCAmelCase = inds.reshape(ishape[0],-1 )
__lowerCAmelCase = self.used.to(__SCREAMING_SNAKE_CASE )
if self.re_embed > self.used.shape[0]: # extra token
__lowerCAmelCase = 0 # simply set to zero
__lowerCAmelCase = torch.gather(used[None, :][inds.shape[0] * [0], :],1,__SCREAMING_SNAKE_CASE )
return back.reshape(__SCREAMING_SNAKE_CASE )
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase = z.permute(0,2,3,1 ).contiguous()
__lowerCAmelCase = z.view(-1,self.vq_embed_dim )
# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
__lowerCAmelCase = torch.argmin(torch.cdist(__SCREAMING_SNAKE_CASE,self.embedding.weight ),dim=1 )
__lowerCAmelCase = self.embedding(__SCREAMING_SNAKE_CASE ).view(z.shape )
__lowerCAmelCase = None
__lowerCAmelCase = None
# compute loss for embedding
if not self.legacy:
__lowerCAmelCase = self.beta * torch.mean((z_q.detach() - z) ** 2 ) + torch.mean((z_q - z.detach()) ** 2 )
else:
__lowerCAmelCase = torch.mean((z_q.detach() - z) ** 2 ) + self.beta * torch.mean((z_q - z.detach()) ** 2 )
# preserve gradients
__lowerCAmelCase = z + (z_q - z).detach()
# reshape back to match original input shape
__lowerCAmelCase = z_q.permute(0,3,1,2 ).contiguous()
if self.remap is not None:
__lowerCAmelCase = min_encoding_indices.reshape(z.shape[0],-1 ) # add batch axis
__lowerCAmelCase = self.remap_to_used(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = min_encoding_indices.reshape(-1,1 ) # flatten
if self.sane_index_shape:
__lowerCAmelCase = min_encoding_indices.reshape(z_q.shape[0],z_q.shape[2],z_q.shape[3] )
return z_q, loss, (perplexity, min_encodings, min_encoding_indices)
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
if self.remap is not None:
__lowerCAmelCase = indices.reshape(shape[0],-1 ) # add batch axis
__lowerCAmelCase = self.unmap_to_all(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = indices.reshape(-1 ) # flatten again
# get quantized latent vectors
__lowerCAmelCase = self.embedding(__SCREAMING_SNAKE_CASE )
if shape is not None:
__lowerCAmelCase = z_q.view(__SCREAMING_SNAKE_CASE )
# reshape back to match original input shape
__lowerCAmelCase = z_q.permute(0,3,1,2 ).contiguous()
return z_q
class _UpperCAmelCase ( lowerCAmelCase_ ):
def __init__( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=False ):
'''simple docstring'''
__lowerCAmelCase = parameters
__lowerCAmelCase , __lowerCAmelCase = torch.chunk(__SCREAMING_SNAKE_CASE,2,dim=1 )
__lowerCAmelCase = torch.clamp(self.logvar,-30.0,20.0 )
__lowerCAmelCase = deterministic
__lowerCAmelCase = torch.exp(0.5 * self.logvar )
__lowerCAmelCase = torch.exp(self.logvar )
if self.deterministic:
__lowerCAmelCase = __lowerCAmelCase = torch.zeros_like(
self.mean,device=self.parameters.device,dtype=self.parameters.dtype )
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE = None ):
'''simple docstring'''
__lowerCAmelCase = randn_tensor(
self.mean.shape,generator=__SCREAMING_SNAKE_CASE,device=self.parameters.device,dtype=self.parameters.dtype )
__lowerCAmelCase = self.mean + self.std * sample
return x
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE=None ):
'''simple docstring'''
if self.deterministic:
return torch.Tensor([0.0] )
else:
if other is None:
return 0.5 * torch.sum(torch.pow(self.mean,2 ) + self.var - 1.0 - self.logvar,dim=[1, 2, 3] )
else:
return 0.5 * torch.sum(
torch.pow(self.mean - other.mean,2 ) / other.var
+ self.var / other.var
- 1.0
- self.logvar
+ other.logvar,dim=[1, 2, 3],)
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=[1, 2, 3] ):
'''simple docstring'''
if self.deterministic:
return torch.Tensor([0.0] )
__lowerCAmelCase = np.log(2.0 * np.pi )
return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean,2 ) / self.var,dim=__SCREAMING_SNAKE_CASE )
def lowerCamelCase__ ( self ):
'''simple docstring'''
return self.mean
| 689 | 1 |
'''simple docstring'''
from collections import defaultdict
from math import ceil, sqrt
def _lowerCAmelCase ( lowercase = 100_0000 , lowercase = 10 ) -> int:
__lowerCAmelCase = defaultdict(lowercase )
for outer_width in range(3 , (t_limit // 4) + 2 ):
if outer_width * outer_width > t_limit:
__lowerCAmelCase = max(
ceil(sqrt(outer_width * outer_width - t_limit ) ) , 1 )
else:
__lowerCAmelCase = 1
hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2
for hole_width in range(lowercase , outer_width - 1 , 2 ):
count[outer_width * outer_width - hole_width * hole_width] += 1
return sum(1 for n in count.values() if 1 <= n <= 10 )
if __name__ == "__main__":
print(f'{solution() = }')
| 689 |
'''simple docstring'''
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
_a : Optional[int] = logging.get_logger(__name__)
_a : int = list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
_a : str = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class _UpperCAmelCase :
a : str =field(
default=lowerCAmelCase_ , metadata={"""help""": """Model type selected in the list: """ + """, """.join(lowerCAmelCase_ )} )
a : str =field(
default=lowerCAmelCase_ , metadata={"""help""": """The input data dir. Should contain the .json files for the SQuAD task."""} )
a : int =field(
default=1_28 , metadata={
"""help""": (
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
)
} , )
a : int =field(
default=1_28 , metadata={"""help""": """When splitting up a long document into chunks, how much stride to take between chunks."""} , )
a : int =field(
default=64 , metadata={
"""help""": (
"""The maximum number of tokens for the question. Questions longer than this will """
"""be truncated to this length."""
)
} , )
a : int =field(
default=30 , metadata={
"""help""": (
"""The maximum length of an answer that can be generated. This is needed because the start """
"""and end predictions are not conditioned on one another."""
)
} , )
a : bool =field(
default=lowerCAmelCase_ , metadata={"""help""": """Overwrite the cached training and evaluation sets"""} )
a : bool =field(
default=lowerCAmelCase_ , metadata={"""help""": """If true, the SQuAD examples contain some that do not have an answer."""} )
a : float =field(
default=0.0 , metadata={"""help""": """If null_score - best_non_null is greater than the threshold predict null."""} )
a : int =field(
default=20 , metadata={"""help""": """If null_score - best_non_null is greater than the threshold predict null."""} )
a : int =field(
default=0 , metadata={
"""help""": (
"""language id of input for language-specific xlm models (see"""
""" tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)"""
)
} , )
a : int =field(default=1 , metadata={"""help""": """multiple threads for converting example to features"""} )
class _UpperCAmelCase ( lowerCAmelCase_ ):
a : Optional[Any] ="""train"""
a : Optional[int] ="""dev"""
class _UpperCAmelCase ( lowerCAmelCase_ ):
a : SquadDataTrainingArguments
a : List[SquadFeatures]
a : Split
a : bool
def __init__( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = Split.train,__SCREAMING_SNAKE_CASE = False,__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = "pt",):
'''simple docstring'''
__lowerCAmelCase = args
__lowerCAmelCase = is_language_sensitive
__lowerCAmelCase = SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
if isinstance(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE ):
try:
__lowerCAmelCase = Split[mode]
except KeyError:
raise KeyError("""mode is not a valid split name""" )
__lowerCAmelCase = mode
# Load data features from cache or dataset file
__lowerCAmelCase = """v2""" if args.version_2_with_negative else """v1"""
__lowerCAmelCase = os.path.join(
cache_dir if cache_dir is not None else args.data_dir,f'cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{version_tag}',)
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
__lowerCAmelCase = cached_features_file + """.lock"""
with FileLock(__SCREAMING_SNAKE_CASE ):
if os.path.exists(__SCREAMING_SNAKE_CASE ) and not args.overwrite_cache:
__lowerCAmelCase = time.time()
__lowerCAmelCase = torch.load(__SCREAMING_SNAKE_CASE )
# Legacy cache files have only features, while new cache files
# will have dataset and examples also.
__lowerCAmelCase = self.old_features["""features"""]
__lowerCAmelCase = self.old_features.get("""dataset""",__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = self.old_features.get("""examples""",__SCREAMING_SNAKE_CASE )
logger.info(
f'Loading features from cached file {cached_features_file} [took %.3f s]',time.time() - start )
if self.dataset is None or self.examples is None:
logger.warning(
f'Deleting cached file {cached_features_file} will allow dataset and examples to be cached in'
""" future run""" )
else:
if mode == Split.dev:
__lowerCAmelCase = self.processor.get_dev_examples(args.data_dir )
else:
__lowerCAmelCase = self.processor.get_train_examples(args.data_dir )
__lowerCAmelCase , __lowerCAmelCase = squad_convert_examples_to_features(
examples=self.examples,tokenizer=__SCREAMING_SNAKE_CASE,max_seq_length=args.max_seq_length,doc_stride=args.doc_stride,max_query_length=args.max_query_length,is_training=mode == Split.train,threads=args.threads,return_dataset=__SCREAMING_SNAKE_CASE,)
__lowerCAmelCase = time.time()
torch.save(
{"""features""": self.features, """dataset""": self.dataset, """examples""": self.examples},__SCREAMING_SNAKE_CASE,)
# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
logger.info(
f'Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]' )
def __len__( self ):
'''simple docstring'''
return len(self.features )
def __getitem__( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase = self.features[i]
__lowerCAmelCase = torch.tensor(feature.input_ids,dtype=torch.long )
__lowerCAmelCase = torch.tensor(feature.attention_mask,dtype=torch.long )
__lowerCAmelCase = torch.tensor(feature.token_type_ids,dtype=torch.long )
__lowerCAmelCase = torch.tensor(feature.cls_index,dtype=torch.long )
__lowerCAmelCase = torch.tensor(feature.p_mask,dtype=torch.float )
__lowerCAmelCase = torch.tensor(feature.is_impossible,dtype=torch.float )
__lowerCAmelCase = {
"""input_ids""": input_ids,
"""attention_mask""": attention_mask,
"""token_type_ids""": token_type_ids,
}
if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
del inputs["token_type_ids"]
if self.args.model_type in ["xlnet", "xlm"]:
inputs.update({"""cls_index""": cls_index, """p_mask""": p_mask} )
if self.args.version_2_with_negative:
inputs.update({"""is_impossible""": is_impossible} )
if self.is_language_sensitive:
inputs.update({"""langs""": (torch.ones(input_ids.shape,dtype=torch.intaa ) * self.args.lang_id)} )
if self.mode == Split.train:
__lowerCAmelCase = torch.tensor(feature.start_position,dtype=torch.long )
__lowerCAmelCase = torch.tensor(feature.end_position,dtype=torch.long )
inputs.update({"""start_positions""": start_positions, """end_positions""": end_positions} )
return inputs
| 689 | 1 |
'''simple docstring'''
import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict
import requests
from slack_sdk import WebClient
_a : Dict = WebClient(token=os.environ["""CI_SLACK_BOT_TOKEN"""])
def _lowerCAmelCase ( lowercase ) -> str:
__lowerCAmelCase = test_results.split(""" """ )
__lowerCAmelCase = 0
__lowerCAmelCase = 0
# When the output is short enough, the output is surrounded by = signs: "== OUTPUT =="
# When it is too long, those signs are not present.
__lowerCAmelCase = expressions[-2] if """=""" in expressions[-1] else expressions[-1]
for i, expression in enumerate(lowercase ):
if "failed" in expression:
failed += int(expressions[i - 1] )
if "passed" in expression:
success += int(expressions[i - 1] )
return failed, success, time_spent
def _lowerCAmelCase ( lowercase ) -> Union[str, Any]:
__lowerCAmelCase = {}
__lowerCAmelCase = None
__lowerCAmelCase = False
for line in failures_short_lines.split("""\n""" ):
if re.search(R"""_ \[doctest\]""" , lowercase ):
__lowerCAmelCase = True
__lowerCAmelCase = line.split(""" """ )[2]
elif in_error and not line.split(""" """ )[0].isdigit():
__lowerCAmelCase = line
__lowerCAmelCase = False
return failures
class _UpperCAmelCase :
def __init__( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase = title
__lowerCAmelCase = doc_test_results["""time_spent"""].split(""",""" )[0]
__lowerCAmelCase = doc_test_results["""success"""]
__lowerCAmelCase = doc_test_results["""failures"""]
__lowerCAmelCase = self.n_success + self.n_failures
# Failures and success of the modeling tests
__lowerCAmelCase = doc_test_results
@property
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = [self._time_spent]
__lowerCAmelCase = 0
for time in time_spent:
__lowerCAmelCase = time.split(""":""" )
# Time can be formatted as xx:xx:xx, as .xx, or as x.xx if the time spent was less than a minute.
if len(__SCREAMING_SNAKE_CASE ) == 1:
__lowerCAmelCase = [0, 0, time_parts[0]]
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = int(time_parts[0] ), int(time_parts[1] ), float(time_parts[2] )
total_secs += hours * 36_00 + minutes * 60 + seconds
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = total_secs // 36_00, (total_secs % 36_00) // 60, total_secs % 60
return f'{int(__SCREAMING_SNAKE_CASE )}h{int(__SCREAMING_SNAKE_CASE )}m{int(__SCREAMING_SNAKE_CASE )}s'
@property
def lowerCamelCase__ ( self ):
'''simple docstring'''
return {"type": "header", "text": {"type": "plain_text", "text": self.title}}
@property
def lowerCamelCase__ ( self ):
'''simple docstring'''
return {
"type": "section",
"text": {
"type": "plain_text",
"text": f'🌞 There were no failures: all {self.n_tests} tests passed. The suite ran in {self.time}.',
"emoji": True,
},
"accessory": {
"type": "button",
"text": {"type": "plain_text", "text": "Check Action results", "emoji": True},
"url": f'https://github.com/huggingface/transformers/actions/runs/{os.environ["GITHUB_RUN_ID"]}',
},
}
@property
def lowerCamelCase__ ( self ):
'''simple docstring'''
return {
"type": "section",
"text": {
"type": "plain_text",
"text": (
f'There were {self.n_failures} failures, out of {self.n_tests} tests.\nThe suite ran in'
f' {self.time}.'
),
"emoji": True,
},
"accessory": {
"type": "button",
"text": {"type": "plain_text", "text": "Check Action results", "emoji": True},
"url": f'https://github.com/huggingface/transformers/actions/runs/{os.environ["GITHUB_RUN_ID"]}',
},
}
@property
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = 40
__lowerCAmelCase = {k: v["""failed"""] for k, v in doc_test_results.items() if isinstance(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )}
__lowerCAmelCase = """"""
for category, failures in category_failures.items():
if len(__SCREAMING_SNAKE_CASE ) == 0:
continue
if report != "":
report += "\n\n"
report += f'*{category} failures*:'.ljust(line_length // 2 ).rjust(line_length // 2 ) + "\n"
report += "`"
report += "`\n`".join(__SCREAMING_SNAKE_CASE )
report += "`"
return {
"type": "section",
"text": {
"type": "mrkdwn",
"text": f'The following examples had failures:\n\n\n{report}\n',
},
}
@property
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = [self.header]
if self.n_failures > 0:
blocks.append(self.failures )
if self.n_failures > 0:
blocks.extend([self.category_failures] )
if self.n_failures == 0:
blocks.append(self.no_failures )
return json.dumps(__SCREAMING_SNAKE_CASE )
@staticmethod
def lowerCamelCase__ ( ):
'''simple docstring'''
__lowerCAmelCase = [
{
"""type""": """section""",
"""text""": {
"""type""": """plain_text""",
"""text""": """There was an issue running the tests.""",
},
"""accessory""": {
"""type""": """button""",
"""text""": {"""type""": """plain_text""", """text""": """Check Action results""", """emoji""": True},
"""url""": f'https://github.com/huggingface/transformers/actions/runs/{os.environ["GITHUB_RUN_ID"]}',
},
}
]
print("""Sending the following payload""" )
print(json.dumps({"""blocks""": json.loads(__SCREAMING_SNAKE_CASE )} ) )
client.chat_postMessage(
channel=os.environ["""CI_SLACK_CHANNEL_ID_DAILY"""],text="""There was an issue running the tests.""",blocks=__SCREAMING_SNAKE_CASE,)
def lowerCamelCase__ ( self ):
'''simple docstring'''
print("""Sending the following payload""" )
print(json.dumps({"""blocks""": json.loads(self.payload )} ) )
__lowerCAmelCase = f'{self.n_failures} failures out of {self.n_tests} tests,' if self.n_failures else """All tests passed."""
__lowerCAmelCase = client.chat_postMessage(
channel=os.environ["""CI_SLACK_CHANNEL_ID_DAILY"""],blocks=self.payload,text=__SCREAMING_SNAKE_CASE,)
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase = """"""
for key, value in failures.items():
__lowerCAmelCase = value[:2_00] + """ [Truncated]""" if len(__SCREAMING_SNAKE_CASE ) > 2_50 else value
failures_text += f'*{key}*\n_{value}_\n\n'
__lowerCAmelCase = job_name
__lowerCAmelCase = {"""type""": """section""", """text""": {"""type""": """mrkdwn""", """text""": text}}
if job_link is not None:
__lowerCAmelCase = {
"""type""": """button""",
"""text""": {"""type""": """plain_text""", """text""": """GitHub Action job""", """emoji""": True},
"""url""": job_link,
}
return [
{"type": "header", "text": {"type": "plain_text", "text": title.upper(), "emoji": True}},
content,
{"type": "section", "text": {"type": "mrkdwn", "text": failures_text}},
]
def lowerCamelCase__ ( self ):
'''simple docstring'''
if self.thread_ts is None:
raise ValueError("""Can only post reply if a post has been made.""" )
__lowerCAmelCase = self.doc_test_results.pop("""job_link""" )
self.doc_test_results.pop("""failures""" )
self.doc_test_results.pop("""success""" )
self.doc_test_results.pop("""time_spent""" )
__lowerCAmelCase = sorted(self.doc_test_results.items(),key=lambda __SCREAMING_SNAKE_CASE : t[0] )
for job, job_result in sorted_dict:
if len(job_result["""failures"""] ):
__lowerCAmelCase = f'*Num failures* :{len(job_result["failed"] )} \n'
__lowerCAmelCase = job_result["""failures"""]
__lowerCAmelCase = self.get_reply_blocks(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,text=__SCREAMING_SNAKE_CASE )
print("""Sending the following reply""" )
print(json.dumps({"""blocks""": blocks} ) )
client.chat_postMessage(
channel=os.environ["""CI_SLACK_CHANNEL_ID_DAILY"""],text=f'Results for {job}',blocks=__SCREAMING_SNAKE_CASE,thread_ts=self.thread_ts["""ts"""],)
time.sleep(1 )
def _lowerCAmelCase ( ) -> str:
__lowerCAmelCase = os.environ["""GITHUB_RUN_ID"""]
__lowerCAmelCase = f'https://api.github.com/repos/huggingface/transformers/actions/runs/{run_id}/jobs?per_page=100'
__lowerCAmelCase = requests.get(lowercase ).json()
__lowerCAmelCase = {}
try:
jobs.update({job["""name"""]: job["""html_url"""] for job in result["""jobs"""]} )
__lowerCAmelCase = math.ceil((result["""total_count"""] - 100) / 100 )
for i in range(lowercase ):
__lowerCAmelCase = requests.get(url + f'&page={i + 2}' ).json()
jobs.update({job["""name"""]: job["""html_url"""] for job in result["""jobs"""]} )
return jobs
except Exception as e:
print("""Unknown error, could not fetch links.""" , lowercase )
return {}
def _lowerCAmelCase ( lowercase ) -> Dict:
__lowerCAmelCase = {}
if os.path.exists(lowercase ):
__lowerCAmelCase = os.listdir(lowercase )
for file in files:
try:
with open(os.path.join(lowercase , lowercase ) , encoding="""utf-8""" ) as f:
__lowerCAmelCase = f.read()
except UnicodeDecodeError as e:
raise ValueError(f'Could not open {os.path.join(lowercase , lowercase )}.' ) from e
return _artifact
def _lowerCAmelCase ( ) -> Any:
class _UpperCAmelCase :
def __init__( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase = name
__lowerCAmelCase = []
def __str__( self ):
'''simple docstring'''
return self.name
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
self.paths.append({"""name""": self.name, """path""": path} )
__lowerCAmelCase = {}
__lowerCAmelCase = filter(os.path.isdir , os.listdir() )
for directory in directories:
__lowerCAmelCase = directory
if artifact_name not in _available_artifacts:
__lowerCAmelCase = Artifact(lowercase )
_available_artifacts[artifact_name].add_path(lowercase )
return _available_artifacts
if __name__ == "__main__":
_a : List[str] = get_job_links()
_a : str = retrieve_available_artifacts()
_a : Union[str, Any] = collections.OrderedDict(
[
("""*.py""", """API Examples"""),
("""*.md""", """MD Examples"""),
]
)
# This dict will contain all the information relative to each doc test category:
# - failed: list of failed tests
# - failures: dict in the format 'test': 'error_message'
_a : Dict = {
v: {
"""failed""": [],
"""failures""": {},
}
for v in docs.values()
}
# Link to the GitHub Action job
_a : Dict = github_actions_job_links.get("""run_doctests""")
_a : List[str] = available_artifacts["""doc_tests_gpu_test_reports"""].paths[0]
_a : Tuple = retrieve_artifact(artifact_path["""name"""])
if "stats" in artifact:
_a ,_a ,_a : Optional[Any] = handle_test_results(artifact["""stats"""])
_a : List[str] = failed
_a : List[str] = success
_a : Optional[Any] = time_spent[1:-1] + """, """
_a : List[Any] = extract_first_line_failure(artifact["""failures_short"""])
for line in artifact["summary_short"].split("""\n"""):
if re.search("""FAILED""", line):
_a : List[str] = line.replace("""FAILED """, """""")
_a : List[Any] = line.split()[0].replace("""\n""", """""")
if "::" in line:
_a ,_a : int = line.split("""::""")
else:
_a ,_a : int = line, line
for file_regex in docs.keys():
if fnmatch(file_path, file_regex):
_a : str = docs[file_regex]
doc_test_results[category]["failed"].append(test)
_a : Dict = all_failures[test] if test in all_failures else """N/A"""
_a : List[Any] = failure
break
_a : Optional[Any] = Message("""🤗 Results of the doc tests.""", doc_test_results)
message.post()
message.post_reply()
| 689 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def _lowerCAmelCase ( lowercase ) -> Optional[Any]:
# vision encoder
if "img_encoder.pos_embed" in name:
__lowerCAmelCase = name.replace("""img_encoder.pos_embed""" , """vision_model.embeddings.position_embeddings""" )
if "img_encoder.patch_embed.proj" in name:
__lowerCAmelCase = name.replace("""img_encoder.patch_embed.proj""" , """vision_model.embeddings.patch_embeddings.projection""" )
if "img_encoder.patch_embed.norm" in name:
__lowerCAmelCase = name.replace("""img_encoder.patch_embed.norm""" , """vision_model.embeddings.layernorm""" )
if "img_encoder.layers" in name:
__lowerCAmelCase = name.replace("""img_encoder.layers""" , """vision_model.encoder.stages""" )
if "blocks" in name and "res" not in name:
__lowerCAmelCase = name.replace("""blocks""" , """layers""" )
if "attn" in name and "pre_assign" not in name:
__lowerCAmelCase = name.replace("""attn""" , """self_attn""" )
if "proj" in name and "self_attn" in name and "text" not in name:
__lowerCAmelCase = name.replace("""proj""" , """out_proj""" )
if "pre_assign_attn.attn.proj" in name:
__lowerCAmelCase = name.replace("""pre_assign_attn.attn.proj""" , """pre_assign_attn.attn.out_proj""" )
if "norm1" in name:
__lowerCAmelCase = name.replace("""norm1""" , """layer_norm1""" )
if "norm2" in name and "pre_assign" not in name:
__lowerCAmelCase = name.replace("""norm2""" , """layer_norm2""" )
if "img_encoder.norm" in name:
__lowerCAmelCase = name.replace("""img_encoder.norm""" , """vision_model.layernorm""" )
# text encoder
if "text_encoder.token_embedding" in name:
__lowerCAmelCase = name.replace("""text_encoder.token_embedding""" , """text_model.embeddings.token_embedding""" )
if "text_encoder.positional_embedding" in name:
__lowerCAmelCase = name.replace("""text_encoder.positional_embedding""" , """text_model.embeddings.position_embedding.weight""" )
if "text_encoder.transformer.resblocks." in name:
__lowerCAmelCase = name.replace("""text_encoder.transformer.resblocks.""" , """text_model.encoder.layers.""" )
if "ln_1" in name:
__lowerCAmelCase = name.replace("""ln_1""" , """layer_norm1""" )
if "ln_2" in name:
__lowerCAmelCase = name.replace("""ln_2""" , """layer_norm2""" )
if "c_fc" in name:
__lowerCAmelCase = name.replace("""c_fc""" , """fc1""" )
if "c_proj" in name:
__lowerCAmelCase = name.replace("""c_proj""" , """fc2""" )
if "text_encoder" in name:
__lowerCAmelCase = name.replace("""text_encoder""" , """text_model""" )
if "ln_final" in name:
__lowerCAmelCase = name.replace("""ln_final""" , """final_layer_norm""" )
# projection layers
if "img_projector.linear_hidden." in name:
__lowerCAmelCase = name.replace("""img_projector.linear_hidden.""" , """visual_projection.""" )
if "img_projector.linear_out." in name:
__lowerCAmelCase = name.replace("""img_projector.linear_out.""" , """visual_projection.3.""" )
if "text_projector.linear_hidden" in name:
__lowerCAmelCase = name.replace("""text_projector.linear_hidden""" , """text_projection""" )
if "text_projector.linear_out" in name:
__lowerCAmelCase = name.replace("""text_projector.linear_out""" , """text_projection.3""" )
return name
def _lowerCAmelCase ( lowercase , lowercase ) -> Dict:
for key in orig_state_dict.copy().keys():
__lowerCAmelCase = orig_state_dict.pop(lowercase )
if "qkv" in key:
# weights and biases of the key, value and query projections of vision encoder's attention layers require special treatment:
# we need to split them up into separate matrices/vectors
__lowerCAmelCase = key.split(""".""" )
__lowerCAmelCase , __lowerCAmelCase = int(key_split[2] ), int(key_split[4] )
__lowerCAmelCase = config.vision_config.hidden_size
if "weight" in key:
__lowerCAmelCase = val[:dim, :]
__lowerCAmelCase = val[dim : dim * 2, :]
__lowerCAmelCase = val[-dim:, :]
else:
__lowerCAmelCase = val[:dim]
__lowerCAmelCase = val[dim : dim * 2]
__lowerCAmelCase = val[-dim:]
elif "in_proj" in key:
# weights and biases of the key, value and query projections of text encoder's attention layers require special treatment:
# we need to split them up into separate matrices/vectors
__lowerCAmelCase = key.split(""".""" )
__lowerCAmelCase = int(key_split[3] )
__lowerCAmelCase = config.text_config.hidden_size
if "weight" in key:
__lowerCAmelCase = val[:dim, :]
__lowerCAmelCase = val[
dim : dim * 2, :
]
__lowerCAmelCase = val[-dim:, :]
else:
__lowerCAmelCase = val[:dim]
__lowerCAmelCase = val[dim : dim * 2]
__lowerCAmelCase = val[-dim:]
else:
__lowerCAmelCase = rename_key(lowercase )
# squeeze if necessary
if (
"text_projection.0" in new_name
or "text_projection.3" in new_name
or "visual_projection.0" in new_name
or "visual_projection.3" in new_name
):
__lowerCAmelCase = val.squeeze_()
else:
__lowerCAmelCase = val
return orig_state_dict
def _lowerCAmelCase ( ) -> str:
__lowerCAmelCase = """http://images.cocodataset.org/val2017/000000039769.jpg"""
__lowerCAmelCase = Image.open(requests.get(lowercase , stream=lowercase ).raw )
return im
@torch.no_grad()
def _lowerCAmelCase ( lowercase , lowercase , lowercase="groupvit-gcc-yfcc" , lowercase=False ) -> List[Any]:
__lowerCAmelCase = GroupViTConfig()
__lowerCAmelCase = GroupViTModel(lowercase ).eval()
__lowerCAmelCase = torch.load(lowercase , map_location="""cpu""" )["""model"""]
__lowerCAmelCase = convert_state_dict(lowercase , lowercase )
__lowerCAmelCase , __lowerCAmelCase = model.load_state_dict(lowercase , strict=lowercase )
assert missing_keys == ["text_model.embeddings.position_ids"]
assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(lowercase ) == 0)
# verify result
__lowerCAmelCase = CLIPProcessor.from_pretrained("""openai/clip-vit-base-patch32""" )
__lowerCAmelCase = prepare_img()
__lowerCAmelCase = processor(text=["""a photo of a cat""", """a photo of a dog"""] , images=lowercase , padding=lowercase , return_tensors="""pt""" )
with torch.no_grad():
__lowerCAmelCase = model(**lowercase )
if model_name == "groupvit-gcc-yfcc":
__lowerCAmelCase = torch.tensor([[13.35_23, 6.36_29]] )
elif model_name == "groupvit-gcc-redcaps":
__lowerCAmelCase = torch.tensor([[16.18_73, 8.62_30]] )
else:
raise ValueError(f'Model name {model_name} not supported.' )
assert torch.allclose(outputs.logits_per_image , lowercase , atol=1e-3 )
processor.save_pretrained(lowercase )
model.save_pretrained(lowercase )
print("""Successfully saved processor and model to""" , lowercase )
if push_to_hub:
print("""Pushing to the hub...""" )
processor.push_to_hub(lowercase , organization="""nielsr""" )
model.push_to_hub(lowercase , organization="""nielsr""" )
if __name__ == "__main__":
_a : int = argparse.ArgumentParser()
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to dump the processor and PyTorch model."""
)
parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to GroupViT checkpoint""")
parser.add_argument(
"""--model_name""",
default="""groupvit-gccy-fcc""",
type=str,
help="""Name of the model. Expecting either 'groupvit-gcc-yfcc' or 'groupvit-gcc-redcaps'""",
)
parser.add_argument(
"""--push_to_hub""",
action="""store_true""",
help="""Whether or not to push the converted model and processor to the 🤗 hub using the provided `model_name`.""",
)
_a : List[str] = parser.parse_args()
convert_groupvit_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 689 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_a : Tuple = logging.get_logger(__name__)
_a : List[Any] = {
"""facebook/xlm-roberta-xl""": """https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json""",
"""facebook/xlm-roberta-xxl""": """https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json""",
# See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}
class _UpperCAmelCase ( lowerCAmelCase_ ):
a : Any ="""xlm-roberta-xl"""
def __init__( self,__SCREAMING_SNAKE_CASE=25_08_80,__SCREAMING_SNAKE_CASE=25_60,__SCREAMING_SNAKE_CASE=36,__SCREAMING_SNAKE_CASE=32,__SCREAMING_SNAKE_CASE=1_02_40,__SCREAMING_SNAKE_CASE="gelu",__SCREAMING_SNAKE_CASE=0.1,__SCREAMING_SNAKE_CASE=0.1,__SCREAMING_SNAKE_CASE=5_14,__SCREAMING_SNAKE_CASE=1,__SCREAMING_SNAKE_CASE=0.02,__SCREAMING_SNAKE_CASE=1e-05,__SCREAMING_SNAKE_CASE=1,__SCREAMING_SNAKE_CASE=0,__SCREAMING_SNAKE_CASE=2,__SCREAMING_SNAKE_CASE="absolute",__SCREAMING_SNAKE_CASE=True,__SCREAMING_SNAKE_CASE=None,**__SCREAMING_SNAKE_CASE,):
'''simple docstring'''
super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE,bos_token_id=__SCREAMING_SNAKE_CASE,eos_token_id=__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = vocab_size
__lowerCAmelCase = hidden_size
__lowerCAmelCase = num_hidden_layers
__lowerCAmelCase = num_attention_heads
__lowerCAmelCase = hidden_act
__lowerCAmelCase = intermediate_size
__lowerCAmelCase = hidden_dropout_prob
__lowerCAmelCase = attention_probs_dropout_prob
__lowerCAmelCase = max_position_embeddings
__lowerCAmelCase = type_vocab_size
__lowerCAmelCase = initializer_range
__lowerCAmelCase = layer_norm_eps
__lowerCAmelCase = position_embedding_type
__lowerCAmelCase = use_cache
__lowerCAmelCase = classifier_dropout
class _UpperCAmelCase ( lowerCAmelCase_ ):
@property
def lowerCamelCase__ ( self ):
'''simple docstring'''
if self.task == "multiple-choice":
__lowerCAmelCase = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
__lowerCAmelCase = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
] )
| 689 |
'''simple docstring'''
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGeneration,
MusicgenProcessor,
TaEncoderModel,
)
from transformers.models.musicgen.modeling_musicgen import MusicgenForCausalLM
from transformers.utils import logging
logging.set_verbosity_info()
_a : Tuple = logging.get_logger(__name__)
_a : Optional[int] = ["""model.decoder.embed_positions.weights"""]
def _lowerCAmelCase ( lowercase ) -> Optional[Any]:
if "emb" in name:
__lowerCAmelCase = name.replace("""emb""" , """model.decoder.embed_tokens""" )
if "transformer" in name:
__lowerCAmelCase = name.replace("""transformer""" , """model.decoder""" )
if "cross_attention" in name:
__lowerCAmelCase = name.replace("""cross_attention""" , """encoder_attn""" )
if "linear1" in name:
__lowerCAmelCase = name.replace("""linear1""" , """fc1""" )
if "linear2" in name:
__lowerCAmelCase = name.replace("""linear2""" , """fc2""" )
if "norm1" in name:
__lowerCAmelCase = name.replace("""norm1""" , """self_attn_layer_norm""" )
if "norm_cross" in name:
__lowerCAmelCase = name.replace("""norm_cross""" , """encoder_attn_layer_norm""" )
if "norm2" in name:
__lowerCAmelCase = name.replace("""norm2""" , """final_layer_norm""" )
if "out_norm" in name:
__lowerCAmelCase = name.replace("""out_norm""" , """model.decoder.layer_norm""" )
if "linears" in name:
__lowerCAmelCase = name.replace("""linears""" , """lm_heads""" )
if "condition_provider.conditioners.description.output_proj" in name:
__lowerCAmelCase = name.replace("""condition_provider.conditioners.description.output_proj""" , """enc_to_dec_proj""" )
return name
def _lowerCAmelCase ( lowercase , lowercase ) -> Tuple[Dict, Dict]:
__lowerCAmelCase = list(state_dict.keys() )
__lowerCAmelCase = {}
for key in keys:
__lowerCAmelCase = state_dict.pop(lowercase )
__lowerCAmelCase = rename_keys(lowercase )
if "in_proj_weight" in key:
# split fused qkv proj
__lowerCAmelCase = val[:hidden_size, :]
__lowerCAmelCase = val[hidden_size : 2 * hidden_size, :]
__lowerCAmelCase = val[-hidden_size:, :]
elif "enc_to_dec_proj" in key:
__lowerCAmelCase = val
else:
__lowerCAmelCase = val
return state_dict, enc_dec_proj_state_dict
def _lowerCAmelCase ( lowercase ) -> MusicgenDecoderConfig:
if checkpoint == "small":
# default config values
__lowerCAmelCase = 1024
__lowerCAmelCase = 24
__lowerCAmelCase = 16
elif checkpoint == "medium":
__lowerCAmelCase = 1536
__lowerCAmelCase = 48
__lowerCAmelCase = 24
elif checkpoint == "large":
__lowerCAmelCase = 2048
__lowerCAmelCase = 48
__lowerCAmelCase = 32
else:
raise ValueError(f'Checkpoint should be one of `[\'small\', \'medium\', \'large\']`, got {checkpoint}.' )
__lowerCAmelCase = MusicgenDecoderConfig(
hidden_size=lowercase , ffn_dim=hidden_size * 4 , num_hidden_layers=lowercase , num_attention_heads=lowercase , )
return config
@torch.no_grad()
def _lowerCAmelCase ( lowercase , lowercase=None , lowercase=None , lowercase="cpu" ) -> Optional[Any]:
__lowerCAmelCase = MusicGen.get_pretrained(lowercase , device=lowercase )
__lowerCAmelCase = decoder_config_from_checkpoint(lowercase )
__lowerCAmelCase = fairseq_model.lm.state_dict()
__lowerCAmelCase , __lowerCAmelCase = rename_state_dict(
lowercase , hidden_size=decoder_config.hidden_size )
__lowerCAmelCase = TaEncoderModel.from_pretrained("""t5-base""" )
__lowerCAmelCase = EncodecModel.from_pretrained("""facebook/encodec_32khz""" )
__lowerCAmelCase = MusicgenForCausalLM(lowercase ).eval()
# load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
__lowerCAmelCase , __lowerCAmelCase = decoder.load_state_dict(lowercase , strict=lowercase )
for key in missing_keys.copy():
if key.startswith(("""text_encoder""", """audio_encoder""") ) or key in EXPECTED_MISSING_KEYS:
missing_keys.remove(lowercase )
if len(lowercase ) > 0:
raise ValueError(f'Missing key(s) in state_dict: {missing_keys}' )
if len(lowercase ) > 0:
raise ValueError(f'Unexpected key(s) in state_dict: {unexpected_keys}' )
# init the composite model
__lowerCAmelCase = MusicgenForConditionalGeneration(text_encoder=lowercase , audio_encoder=lowercase , decoder=lowercase )
# load the pre-trained enc-dec projection (from the decoder state dict)
model.enc_to_dec_proj.load_state_dict(lowercase )
# check we can do a forward pass
__lowerCAmelCase = torch.arange(0 , 8 , dtype=torch.long ).reshape(2 , -1 )
__lowerCAmelCase = input_ids.reshape(2 * 4 , -1 )
with torch.no_grad():
__lowerCAmelCase = model(input_ids=lowercase , decoder_input_ids=lowercase ).logits
if logits.shape != (8, 1, 2048):
raise ValueError("""Incorrect shape for logits""" )
# now construct the processor
__lowerCAmelCase = AutoTokenizer.from_pretrained("""t5-base""" )
__lowerCAmelCase = AutoFeatureExtractor.from_pretrained("""facebook/encodec_32khz""" , padding_side="""left""" )
__lowerCAmelCase = MusicgenProcessor(feature_extractor=lowercase , tokenizer=lowercase )
# set the appropriate bos/pad token ids
__lowerCAmelCase = 2048
__lowerCAmelCase = 2048
# set other default generation config params
__lowerCAmelCase = int(30 * audio_encoder.config.frame_rate )
__lowerCAmelCase = True
__lowerCAmelCase = 3.0
if pytorch_dump_folder is not None:
Path(lowercase ).mkdir(exist_ok=lowercase )
logger.info(f'Saving model {checkpoint} to {pytorch_dump_folder}' )
model.save_pretrained(lowercase )
processor.save_pretrained(lowercase )
if repo_id:
logger.info(f'Pushing model {checkpoint} to {repo_id}' )
model.push_to_hub(lowercase )
processor.push_to_hub(lowercase )
if __name__ == "__main__":
_a : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--checkpoint""",
default="""small""",
type=str,
help="""Checkpoint size of the MusicGen model you'd like to convert. Can be one of: `['small', 'medium', 'large']`.""",
)
parser.add_argument(
"""--pytorch_dump_folder""",
required=True,
default=None,
type=str,
help="""Path to the output PyTorch model directory.""",
)
parser.add_argument(
"""--push_to_hub""", default=None, type=str, help="""Where to upload the converted model on the 🤗 hub."""
)
parser.add_argument(
"""--device""", default="""cpu""", type=str, help="""Torch device to run the conversion, either cpu or cuda."""
)
_a : List[Any] = parser.parse_args()
convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)
| 689 | 1 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging
logging.set_verbosity_info()
_a : str = logging.get_logger(__name__)
def _lowerCAmelCase ( lowercase , lowercase=False , lowercase=False ) -> List[str]:
__lowerCAmelCase = """backbone.""" if is_semantic else """"""
__lowerCAmelCase = []
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((f'{prefix}blocks.{i}.norm1.weight', f'beit.encoder.layer.{i}.layernorm_before.weight') )
rename_keys.append((f'{prefix}blocks.{i}.norm1.bias', f'beit.encoder.layer.{i}.layernorm_before.bias') )
rename_keys.append(
(f'{prefix}blocks.{i}.attn.proj.weight', f'beit.encoder.layer.{i}.attention.output.dense.weight') )
rename_keys.append(
(f'{prefix}blocks.{i}.attn.proj.bias', f'beit.encoder.layer.{i}.attention.output.dense.bias') )
rename_keys.append((f'{prefix}blocks.{i}.norm2.weight', f'beit.encoder.layer.{i}.layernorm_after.weight') )
rename_keys.append((f'{prefix}blocks.{i}.norm2.bias', f'beit.encoder.layer.{i}.layernorm_after.bias') )
rename_keys.append((f'{prefix}blocks.{i}.mlp.fc1.weight', f'beit.encoder.layer.{i}.intermediate.dense.weight') )
rename_keys.append((f'{prefix}blocks.{i}.mlp.fc1.bias', f'beit.encoder.layer.{i}.intermediate.dense.bias') )
rename_keys.append((f'{prefix}blocks.{i}.mlp.fc2.weight', f'beit.encoder.layer.{i}.output.dense.weight') )
rename_keys.append((f'{prefix}blocks.{i}.mlp.fc2.bias', f'beit.encoder.layer.{i}.output.dense.bias') )
# projection layer + position embeddings
rename_keys.extend(
[
(f'{prefix}cls_token', """beit.embeddings.cls_token"""),
(f'{prefix}patch_embed.proj.weight', """beit.embeddings.patch_embeddings.projection.weight"""),
(f'{prefix}patch_embed.proj.bias', """beit.embeddings.patch_embeddings.projection.bias"""),
(f'{prefix}pos_embed', """beit.embeddings.position_embeddings"""),
] )
if has_lm_head:
# mask token + layernorm
rename_keys.extend(
[
("""mask_token""", """beit.embeddings.mask_token"""),
("""norm.weight""", """layernorm.weight"""),
("""norm.bias""", """layernorm.bias"""),
] )
else:
# layernorm + classification head
rename_keys.extend(
[
("""fc_norm.weight""", """beit.pooler.layernorm.weight"""),
("""fc_norm.bias""", """beit.pooler.layernorm.bias"""),
("""head.weight""", """classifier.weight"""),
("""head.bias""", """classifier.bias"""),
] )
return rename_keys
def _lowerCAmelCase ( lowercase , lowercase , lowercase=False , lowercase=False ) -> Tuple:
for i in range(config.num_hidden_layers ):
__lowerCAmelCase = """backbone.""" if is_semantic else """"""
# queries, keys and values
__lowerCAmelCase = state_dict.pop(f'{prefix}blocks.{i}.attn.qkv.weight' )
__lowerCAmelCase = state_dict.pop(f'{prefix}blocks.{i}.attn.q_bias' )
__lowerCAmelCase = state_dict.pop(f'{prefix}blocks.{i}.attn.v_bias' )
__lowerCAmelCase = in_proj_weight[
: config.hidden_size, :
]
__lowerCAmelCase = q_bias
__lowerCAmelCase = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
__lowerCAmelCase = in_proj_weight[
-config.hidden_size :, :
]
__lowerCAmelCase = v_bias
# gamma_1 and gamma_2
# we call them lambda because otherwise they are renamed when using .from_pretrained
__lowerCAmelCase = state_dict.pop(f'{prefix}blocks.{i}.gamma_1' )
__lowerCAmelCase = state_dict.pop(f'{prefix}blocks.{i}.gamma_2' )
__lowerCAmelCase = gamma_a
__lowerCAmelCase = gamma_a
def _lowerCAmelCase ( lowercase , lowercase , lowercase ) -> Dict:
__lowerCAmelCase = dct.pop(lowercase )
__lowerCAmelCase = val
def _lowerCAmelCase ( ) -> Tuple:
__lowerCAmelCase = """http://images.cocodataset.org/val2017/000000039769.jpg"""
__lowerCAmelCase = Image.open(requests.get(lowercase , stream=lowercase ).raw )
return im
@torch.no_grad()
def _lowerCAmelCase ( lowercase , lowercase , lowercase=False ) -> List[str]:
__lowerCAmelCase = False if """rvlcdip""" in checkpoint_url else True
__lowerCAmelCase = BeitConfig(use_absolute_position_embeddings=lowercase , use_mask_token=lowercase )
# size of the architecture
if "large" in checkpoint_url or "dit-l" in checkpoint_url:
__lowerCAmelCase = 1024
__lowerCAmelCase = 4096
__lowerCAmelCase = 24
__lowerCAmelCase = 16
# labels
if "rvlcdip" in checkpoint_url:
__lowerCAmelCase = 16
__lowerCAmelCase = """huggingface/label-files"""
__lowerCAmelCase = """rvlcdip-id2label.json"""
__lowerCAmelCase = json.load(open(hf_hub_download(lowercase , lowercase , repo_type="""dataset""" ) , """r""" ) )
__lowerCAmelCase = {int(lowercase ): v for k, v in idalabel.items()}
__lowerCAmelCase = idalabel
__lowerCAmelCase = {v: k for k, v in idalabel.items()}
# load state_dict of original model, remove and rename some keys
__lowerCAmelCase = torch.hub.load_state_dict_from_url(lowercase , map_location="""cpu""" )["""model"""]
__lowerCAmelCase = create_rename_keys(lowercase , has_lm_head=lowercase )
for src, dest in rename_keys:
rename_key(lowercase , lowercase , lowercase )
read_in_q_k_v(lowercase , lowercase , has_lm_head=lowercase )
# load HuggingFace model
__lowerCAmelCase = BeitForMaskedImageModeling(lowercase ) if has_lm_head else BeitForImageClassification(lowercase )
model.eval()
model.load_state_dict(lowercase )
# Check outputs on an image
__lowerCAmelCase = BeitImageProcessor(
size=config.image_size , resample=PILImageResampling.BILINEAR , do_center_crop=lowercase )
__lowerCAmelCase = prepare_img()
__lowerCAmelCase = image_processor(images=lowercase , return_tensors="""pt""" )
__lowerCAmelCase = encoding["""pixel_values"""]
__lowerCAmelCase = model(lowercase )
__lowerCAmelCase = outputs.logits
# verify logits
__lowerCAmelCase = [1, 16] if """rvlcdip""" in checkpoint_url else [1, 196, 8192]
assert logits.shape == torch.Size(lowercase ), "Shape of logits not as expected"
Path(lowercase ).mkdir(exist_ok=lowercase )
print(f'Saving model to {pytorch_dump_folder_path}' )
model.save_pretrained(lowercase )
print(f'Saving image processor to {pytorch_dump_folder_path}' )
image_processor.save_pretrained(lowercase )
if push_to_hub:
if has_lm_head:
__lowerCAmelCase = """dit-base""" if """base""" in checkpoint_url else """dit-large"""
else:
__lowerCAmelCase = """dit-base-finetuned-rvlcdip""" if """dit-b""" in checkpoint_url else """dit-large-finetuned-rvlcdip"""
image_processor.push_to_hub(
repo_path_or_name=Path(lowercase , lowercase ) , organization="""nielsr""" , commit_message="""Add image processor""" , use_temp_dir=lowercase , )
model.push_to_hub(
repo_path_or_name=Path(lowercase , lowercase ) , organization="""nielsr""" , commit_message="""Add model""" , use_temp_dir=lowercase , )
if __name__ == "__main__":
_a : Union[str, Any] = argparse.ArgumentParser()
parser.add_argument(
"""--checkpoint_url""",
default="""https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth""",
type=str,
help="""URL to the original PyTorch checkpoint (.pth file).""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
)
parser.add_argument(
"""--push_to_hub""",
action="""store_true""",
)
_a : List[str] = parser.parse_args()
convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)
| 689 |
'''simple docstring'''
from collections import deque
def _lowerCAmelCase ( lowercase ) -> Dict:
__lowerCAmelCase = len(lowercase )
__lowerCAmelCase = deque()
__lowerCAmelCase = [False for _ in range(lowercase )]
__lowerCAmelCase = [-1 for _ in range(lowercase )]
__lowerCAmelCase = index_of[:]
def strong_connect(lowercase , lowercase , lowercase ):
__lowerCAmelCase = index # the number when this node is seen
__lowerCAmelCase = index # lowest rank node reachable from here
index += 1
stack.append(lowercase )
__lowerCAmelCase = True
for w in g[v]:
if index_of[w] == -1:
__lowerCAmelCase = strong_connect(lowercase , lowercase , lowercase )
__lowerCAmelCase = (
lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
)
elif on_stack[w]:
__lowerCAmelCase = (
lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
)
if lowlink_of[v] == index_of[v]:
__lowerCAmelCase = []
__lowerCAmelCase = stack.pop()
__lowerCAmelCase = False
component.append(lowercase )
while w != v:
__lowerCAmelCase = stack.pop()
__lowerCAmelCase = False
component.append(lowercase )
components.append(lowercase )
return index
__lowerCAmelCase = []
for v in range(lowercase ):
if index_of[v] == -1:
strong_connect(lowercase , 0 , lowercase )
return components
def _lowerCAmelCase ( lowercase , lowercase ) -> str:
__lowerCAmelCase = [[] for _ in range(lowercase )]
for u, v in edges:
g[u].append(lowercase )
return g
if __name__ == "__main__":
# Test
_a : Any = 7
_a : Tuple = [0, 0, 1, 2, 3, 3, 4, 4, 6]
_a : Optional[int] = [1, 3, 2, 0, 1, 4, 5, 6, 5]
_a : Optional[Any] = [(u, v) for u, v in zip(source, target)]
_a : Optional[int] = create_graph(n_vertices, edges)
assert [[5], [6], [4], [3, 2, 1, 0]] == tarjan(g)
| 689 | 1 |
'''simple docstring'''
import torch
from transformers import AutoModel
class _UpperCAmelCase ( torch.nn.Module ):
def __init__( self,__SCREAMING_SNAKE_CASE="sayef/fsner-bert-base-uncased" ):
'''simple docstring'''
super(__SCREAMING_SNAKE_CASE,self ).__init__()
__lowerCAmelCase = AutoModel.from_pretrained(__SCREAMING_SNAKE_CASE,return_dict=__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = torch.nn.CosineSimilarity(3,1e-08 )
__lowerCAmelCase = torch.nn.Softmax(dim=1 )
def lowerCamelCase__ ( self,**__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
return self.bert(**__SCREAMING_SNAKE_CASE ).last_hidden_state
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
return token_embeddings.sum(2,keepdim=__SCREAMING_SNAKE_CASE )
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=1 ):
'''simple docstring'''
return self.softmax(T * self.cos(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE ) )
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase = W_supports["""sizes"""].tolist()
__lowerCAmelCase = W_supports["""start_token_id"""].item()
__lowerCAmelCase = W_supports["""end_token_id"""].item()
del W_supports["sizes"]
del W_supports["start_token_id"]
del W_supports["end_token_id"]
__lowerCAmelCase = self.BERT(**__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = self.BERT(**__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = None
__lowerCAmelCase = None
__lowerCAmelCase = W_supports["""input_ids"""] == start_token_id
__lowerCAmelCase = W_supports["""input_ids"""] == end_token_id
for i, size in enumerate(__SCREAMING_SNAKE_CASE ):
if i == 0:
__lowerCAmelCase = 0
else:
__lowerCAmelCase = support_sizes[i - 1]
__lowerCAmelCase = S[s : s + size][start_token_masks[s : s + size]]
__lowerCAmelCase = S[s : s + size][end_token_masks[s : s + size]]
__lowerCAmelCase = torch.matmul(q[i],s_start.T ).sum(1 ).softmax(0 )
__lowerCAmelCase = torch.matmul(q[i],s_end.T ).sum(1 ).softmax(0 )
if p_starts is not None:
__lowerCAmelCase = torch.vstack((p_starts, p_start) )
__lowerCAmelCase = torch.vstack((p_ends, p_end) )
else:
__lowerCAmelCase = p_start
__lowerCAmelCase = p_end
return p_starts, p_ends
| 689 |
'''simple docstring'''
from argparse import ArgumentParser
from .env import EnvironmentCommand
def _lowerCAmelCase ( ) -> Union[str, Any]:
__lowerCAmelCase = ArgumentParser("""Diffusers CLI tool""" , usage="""diffusers-cli <command> [<args>]""" )
__lowerCAmelCase = parser.add_subparsers(help="""diffusers-cli command helpers""" )
# Register commands
EnvironmentCommand.register_subcommand(lowercase )
# Let's go
__lowerCAmelCase = parser.parse_args()
if not hasattr(lowercase , """func""" ):
parser.print_help()
exit(1 )
# Run
__lowerCAmelCase = args.func(lowercase )
service.run()
if __name__ == "__main__":
main()
| 689 | 1 |
'''simple docstring'''
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
_a : List[str] = 1_0
def _lowerCAmelCase ( lowercase , lowercase , lowercase , lowercase ) -> int:
for i in range(lowercase , lowercase ):
if array[i] == target:
return i
return -1
def _lowerCAmelCase ( lowercase , lowercase ) -> int:
__lowerCAmelCase = 0
__lowerCAmelCase = len(lowercase )
while left <= right:
if right - left < precision:
return lin_search(lowercase , lowercase , lowercase , lowercase )
__lowerCAmelCase = (left + right) // 3 + 1
__lowerCAmelCase = 2 * (left + right) // 3 + 1
if array[one_third] == target:
return one_third
elif array[two_third] == target:
return two_third
elif target < array[one_third]:
__lowerCAmelCase = one_third - 1
elif array[two_third] < target:
__lowerCAmelCase = two_third + 1
else:
__lowerCAmelCase = one_third + 1
__lowerCAmelCase = two_third - 1
else:
return -1
def _lowerCAmelCase ( lowercase , lowercase , lowercase , lowercase ) -> int:
if left < right:
if right - left < precision:
return lin_search(lowercase , lowercase , lowercase , lowercase )
__lowerCAmelCase = (left + right) // 3 + 1
__lowerCAmelCase = 2 * (left + right) // 3 + 1
if array[one_third] == target:
return one_third
elif array[two_third] == target:
return two_third
elif target < array[one_third]:
return rec_ternary_search(lowercase , one_third - 1 , lowercase , lowercase )
elif array[two_third] < target:
return rec_ternary_search(two_third + 1 , lowercase , lowercase , lowercase )
else:
return rec_ternary_search(one_third + 1 , two_third - 1 , lowercase , lowercase )
else:
return -1
if __name__ == "__main__":
import doctest
doctest.testmod()
_a : Union[str, Any] = input("""Enter numbers separated by comma:\n""").strip()
_a : Union[str, Any] = [int(item.strip()) for item in user_input.split(""",""")]
assert collection == sorted(collection), f"List must be ordered.\n{collection}."
_a : str = int(input("""Enter the number to be found in the list:\n""").strip())
_a : str = ite_ternary_search(collection, target)
_a : int = rec_ternary_search(0, len(collection) - 1, collection, target)
if resulta != -1:
print(f'Iterative search: {target} found at positions: {resulta}')
print(f'Recursive search: {target} found at positions: {resulta}')
else:
print("""Not found""")
| 689 |
'''simple docstring'''
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
_a : List[Any] = logging.get_logger(__name__)
_a : int = {
"""post_extract_proj""": """feature_projection.projection""",
"""encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
"""self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
"""self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
"""self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
"""self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
"""self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
"""fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
"""fc2""": """encoder.layers.*.feed_forward.output_dense""",
"""final_layer_norm""": """encoder.layers.*.final_layer_norm""",
"""encoder.layer_norm""": """encoder.layer_norm""",
"""encoder.layer_norm_for_extract""": """layer_norm_for_extract""",
"""w2v_model.layer_norm""": """feature_projection.layer_norm""",
"""quantizer.weight_proj""": """quantizer.weight_proj""",
"""quantizer.vars""": """quantizer.codevectors""",
"""project_q""": """project_q""",
"""final_proj""": """project_hid""",
"""w2v_encoder.proj""": """lm_head""",
"""label_embs_concat""": """label_embeddings_concat""",
"""mask_emb""": """masked_spec_embed""",
"""spk_proj""": """speaker_proj""",
}
_a : Any = [
"""lm_head""",
"""quantizer.weight_proj""",
"""quantizer.codevectors""",
"""project_q""",
"""project_hid""",
"""label_embeddings_concat""",
"""speaker_proj""",
"""layer_norm_for_extract""",
]
def _lowerCAmelCase ( lowercase , lowercase , lowercase , lowercase , lowercase ) -> str:
for attribute in key.split(""".""" ):
__lowerCAmelCase = getattr(lowercase , lowercase )
if weight_type is not None:
__lowerCAmelCase = getattr(lowercase , lowercase ).shape
else:
__lowerCAmelCase = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
f'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
f' {value.shape} for {full_name}' )
if weight_type == "weight":
__lowerCAmelCase = value
elif weight_type == "weight_g":
__lowerCAmelCase = value
elif weight_type == "weight_v":
__lowerCAmelCase = value
elif weight_type == "bias":
__lowerCAmelCase = value
else:
__lowerCAmelCase = value
logger.info(f'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.' )
def _lowerCAmelCase ( lowercase , lowercase ) -> List[Any]:
__lowerCAmelCase = []
__lowerCAmelCase = fairseq_model.state_dict()
__lowerCAmelCase = hf_model.unispeech_sat.feature_extractor
for name, value in fairseq_dict.items():
__lowerCAmelCase = False
if "conv_layers" in name:
load_conv_layer(
lowercase , lowercase , lowercase , lowercase , hf_model.config.feat_extract_norm == """group""" , )
__lowerCAmelCase = True
else:
for key, mapped_key in MAPPING.items():
__lowerCAmelCase = """unispeech_sat.""" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split("""w2v_model.""" )[-1] == name.split(""".""" )[0]:
if "layer_norm_for_extract" in name and (".".join(name.split(""".""" )[:-1] ) != key):
# special case since naming is very similar
continue
__lowerCAmelCase = True
if "*" in mapped_key:
__lowerCAmelCase = name.split(lowercase )[0].split(""".""" )[-2]
__lowerCAmelCase = mapped_key.replace("""*""" , lowercase )
if "weight_g" in name:
__lowerCAmelCase = """weight_g"""
elif "weight_v" in name:
__lowerCAmelCase = """weight_v"""
elif "bias" in name:
__lowerCAmelCase = """bias"""
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
__lowerCAmelCase = """weight"""
else:
__lowerCAmelCase = None
set_recursively(lowercase , lowercase , lowercase , lowercase , lowercase )
continue
if not is_used:
unused_weights.append(lowercase )
logger.warning(f'Unused weights: {unused_weights}' )
def _lowerCAmelCase ( lowercase , lowercase , lowercase , lowercase , lowercase ) -> Optional[Any]:
__lowerCAmelCase = full_name.split("""conv_layers.""" )[-1]
__lowerCAmelCase = name.split(""".""" )
__lowerCAmelCase = int(items[0] )
__lowerCAmelCase = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.' )
__lowerCAmelCase = value
logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.' )
__lowerCAmelCase = value
logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.' )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor[layer_id].layer_norm.bias.data.shape} was found.' )
__lowerCAmelCase = value
logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.' )
__lowerCAmelCase = value
logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
else:
unused_weights.append(lowercase )
@torch.no_grad()
def _lowerCAmelCase ( lowercase , lowercase , lowercase=None , lowercase=None , lowercase=True ) -> Dict:
if config_path is not None:
__lowerCAmelCase = UniSpeechSatConfig.from_pretrained(lowercase )
else:
__lowerCAmelCase = UniSpeechSatConfig()
__lowerCAmelCase = """"""
if is_finetuned:
__lowerCAmelCase = UniSpeechSatForCTC(lowercase )
else:
__lowerCAmelCase = UniSpeechSatForPreTraining(lowercase )
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={"""data""": """/""".join(dict_path.split("""/""" )[:-1] )} )
__lowerCAmelCase = model[0].eval()
recursively_load_weights(lowercase , lowercase )
hf_wavavec.save_pretrained(lowercase )
if __name__ == "__main__":
_a : List[str] = argparse.ArgumentParser()
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
parser.add_argument(
"""--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
)
_a : Union[str, Any] = parser.parse_args()
convert_unispeech_sat_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 689 | 1 |
'''simple docstring'''
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_SIZE
from datasets.filesystems import (
is_remote_filesystem,
rename,
)
from datasets.iterable_dataset import _BaseExamplesIterable
from datasets.utils.py_utils import convert_file_size_to_int
_a : List[Any] = datasets.utils.logging.get_logger(__name__)
if TYPE_CHECKING:
import pyspark
@dataclass
class _UpperCAmelCase ( datasets.BuilderConfig ):
a : Optional[datasets.Features] =None
def _lowerCAmelCase ( lowercase , lowercase , ) -> List[Any]:
import pyspark
def generate_fn():
__lowerCAmelCase = df.select("""*""" , pyspark.sql.functions.spark_partition_id().alias("""part_id""" ) )
for partition_id in partition_order:
__lowerCAmelCase = df_with_partition_id.select("""*""" ).where(f'part_id = {partition_id}' ).drop("""part_id""" )
__lowerCAmelCase = partition_df.collect()
__lowerCAmelCase = 0
for row in rows:
yield f'{partition_id}_{row_id}', row.asDict()
row_id += 1
return generate_fn
class _UpperCAmelCase ( _BaseExamplesIterable ):
def __init__( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=None,):
'''simple docstring'''
__lowerCAmelCase = df
__lowerCAmelCase = partition_order or range(self.df.rdd.getNumPartitions() )
__lowerCAmelCase = _generate_iterable_examples(self.df,self.partition_order )
def __iter__( self ):
'''simple docstring'''
yield from self.generate_examples_fn()
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase = list(range(self.df.rdd.getNumPartitions() ) )
generator.shuffle(__SCREAMING_SNAKE_CASE )
return SparkExamplesIterable(self.df,partition_order=__SCREAMING_SNAKE_CASE )
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase = self.split_shard_indices_by_worker(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
return SparkExamplesIterable(self.df,partition_order=__SCREAMING_SNAKE_CASE )
@property
def lowerCamelCase__ ( self ):
'''simple docstring'''
return len(self.partition_order )
class _UpperCAmelCase ( datasets.DatasetBuilder ):
a : Optional[int] =SparkConfig
def __init__( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = None,**__SCREAMING_SNAKE_CASE,):
'''simple docstring'''
import pyspark
__lowerCAmelCase = pyspark.sql.SparkSession.builder.getOrCreate()
__lowerCAmelCase = df
__lowerCAmelCase = working_dir
super().__init__(
cache_dir=__SCREAMING_SNAKE_CASE,config_name=str(self.df.semanticHash() ),**__SCREAMING_SNAKE_CASE,)
def lowerCamelCase__ ( self ):
'''simple docstring'''
def create_cache_and_write_probe(__SCREAMING_SNAKE_CASE ):
# makedirs with exist_ok will recursively create the directory. It will not throw an error if directories
# already exist.
os.makedirs(self._cache_dir,exist_ok=__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = os.path.join(self._cache_dir,"""fs_test""" + uuid.uuida().hex )
# Opening the file in append mode will create a new file unless it already exists, in which case it will not
# change the file contents.
open(__SCREAMING_SNAKE_CASE,"""a""" )
return [probe_file]
if self._spark.conf.get("""spark.master""","""""" ).startswith("""local""" ):
return
# If the cluster is multi-node, make sure that the user provided a cache_dir and that it is on an NFS
# accessible to the driver.
# TODO: Stream batches to the driver using ArrowCollectSerializer instead of throwing an error.
if self._cache_dir:
__lowerCAmelCase = (
self._spark.sparkContext.parallelize(range(1 ),1 ).mapPartitions(__SCREAMING_SNAKE_CASE ).collect()
)
if os.path.isfile(probe[0] ):
return
raise ValueError(
"""When using Dataset.from_spark on a multi-node cluster, the driver and all workers should be able to access cache_dir""" )
def lowerCamelCase__ ( self ):
'''simple docstring'''
return datasets.DatasetInfo(features=self.config.features )
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
return [datasets.SplitGenerator(name=datasets.Split.TRAIN )]
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
import pyspark
def get_arrow_batch_size(__SCREAMING_SNAKE_CASE ):
for batch in it:
yield pa.RecordBatch.from_pydict({"""batch_bytes""": [batch.nbytes]} )
__lowerCAmelCase = self.df.count()
__lowerCAmelCase = df_num_rows if df_num_rows <= 1_00 else 1_00
# Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
__lowerCAmelCase = (
self.df.limit(__SCREAMING_SNAKE_CASE )
.repartition(1 )
.mapInArrow(__SCREAMING_SNAKE_CASE,"""batch_bytes: long""" )
.agg(pyspark.sql.functions.sum("""batch_bytes""" ).alias("""sample_bytes""" ) )
.collect()[0]
.sample_bytes
/ sample_num_rows
)
__lowerCAmelCase = approx_bytes_per_row * df_num_rows
if approx_total_size > max_shard_size:
# Make sure there is at least one row per partition.
__lowerCAmelCase = min(__SCREAMING_SNAKE_CASE,int(approx_total_size / max_shard_size ) )
__lowerCAmelCase = self.df.repartition(__SCREAMING_SNAKE_CASE )
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,):
'''simple docstring'''
import pyspark
__lowerCAmelCase = ParquetWriter if file_format == """parquet""" else ArrowWriter
__lowerCAmelCase = os.path.join(self._working_dir,os.path.basename(__SCREAMING_SNAKE_CASE ) ) if self._working_dir else fpath
__lowerCAmelCase = file_format == """parquet"""
# Define these so that we don't reference self in write_arrow, which will result in a pickling error due to
# pickling the SparkContext.
__lowerCAmelCase = self.config.features
__lowerCAmelCase = self._writer_batch_size
__lowerCAmelCase = self._fs.storage_options
def write_arrow(__SCREAMING_SNAKE_CASE ):
# Within the same SparkContext, no two task attempts will share the same attempt ID.
__lowerCAmelCase = pyspark.TaskContext().taskAttemptId()
__lowerCAmelCase = next(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
if first_batch is None:
# Some partitions might not receive any data.
return pa.RecordBatch.from_arrays(
[[task_id], [0], [0]],names=["""task_id""", """num_examples""", """num_bytes"""],)
__lowerCAmelCase = 0
__lowerCAmelCase = writer_class(
features=__SCREAMING_SNAKE_CASE,path=working_fpath.replace("""SSSSS""",f'{shard_id:05d}' ).replace("""TTTTT""",f'{task_id:05d}' ),writer_batch_size=__SCREAMING_SNAKE_CASE,storage_options=__SCREAMING_SNAKE_CASE,embed_local_files=__SCREAMING_SNAKE_CASE,)
__lowerCAmelCase = pa.Table.from_batches([first_batch] )
writer.write_table(__SCREAMING_SNAKE_CASE )
for batch in it:
if max_shard_size is not None and writer._num_bytes >= max_shard_size:
__lowerCAmelCase , __lowerCAmelCase = writer.finalize()
writer.close()
yield pa.RecordBatch.from_arrays(
[[task_id], [num_examples], [num_bytes]],names=["""task_id""", """num_examples""", """num_bytes"""],)
shard_id += 1
__lowerCAmelCase = writer_class(
features=writer._features,path=working_fpath.replace("""SSSSS""",f'{shard_id:05d}' ).replace("""TTTTT""",f'{task_id:05d}' ),writer_batch_size=__SCREAMING_SNAKE_CASE,storage_options=__SCREAMING_SNAKE_CASE,embed_local_files=__SCREAMING_SNAKE_CASE,)
__lowerCAmelCase = pa.Table.from_batches([batch] )
writer.write_table(__SCREAMING_SNAKE_CASE )
if writer._num_bytes > 0:
__lowerCAmelCase , __lowerCAmelCase = writer.finalize()
writer.close()
yield pa.RecordBatch.from_arrays(
[[task_id], [num_examples], [num_bytes]],names=["""task_id""", """num_examples""", """num_bytes"""],)
if working_fpath != fpath:
for file in os.listdir(os.path.dirname(__SCREAMING_SNAKE_CASE ) ):
__lowerCAmelCase = os.path.join(os.path.dirname(__SCREAMING_SNAKE_CASE ),os.path.basename(__SCREAMING_SNAKE_CASE ) )
shutil.move(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = (
self.df.mapInArrow(__SCREAMING_SNAKE_CASE,"""task_id: long, num_examples: long, num_bytes: long""" )
.groupBy("""task_id""" )
.agg(
pyspark.sql.functions.sum("""num_examples""" ).alias("""total_num_examples""" ),pyspark.sql.functions.sum("""num_bytes""" ).alias("""total_num_bytes""" ),pyspark.sql.functions.count("""num_bytes""" ).alias("""num_shards""" ),pyspark.sql.functions.collect_list("""num_examples""" ).alias("""shard_lengths""" ),)
.collect()
)
for row in stats:
yield row.task_id, (row.total_num_examples, row.total_num_bytes, row.num_shards, row.shard_lengths)
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE = "arrow",__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = None,**__SCREAMING_SNAKE_CASE,):
'''simple docstring'''
self._validate_cache_dir()
__lowerCAmelCase = convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE )
self._repartition_df_if_needed(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = not is_remote_filesystem(self._fs )
__lowerCAmelCase = os.path.join if is_local else posixpath.join
__lowerCAmelCase = """-TTTTT-SSSSS-of-NNNNN"""
__lowerCAmelCase = f'{self.name}-{split_generator.name}{SUFFIX}.{file_format}'
__lowerCAmelCase = path_join(self._output_dir,__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = 0
__lowerCAmelCase = 0
__lowerCAmelCase = 0
__lowerCAmelCase = []
__lowerCAmelCase = []
for task_id, content in self._prepare_split_single(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE ):
(
(
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) ,
) = content
if num_bytes > 0:
total_num_examples += num_examples
total_num_bytes += num_bytes
total_shards += num_shards
task_id_and_num_shards.append((task_id, num_shards) )
all_shard_lengths.extend(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = total_num_examples
__lowerCAmelCase = total_num_bytes
# should rename everything at the end
logger.debug(f'Renaming {total_shards} shards.' )
if total_shards > 1:
__lowerCAmelCase = all_shard_lengths
# Define fs outside of _rename_shard so that we don't reference self in the function, which will result in a
# pickling error due to pickling the SparkContext.
__lowerCAmelCase = self._fs
# use the -SSSSS-of-NNNNN pattern
def _rename_shard(
__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,):
rename(
__SCREAMING_SNAKE_CASE,fpath.replace("""SSSSS""",f'{shard_id:05d}' ).replace("""TTTTT""",f'{task_id:05d}' ),fpath.replace("""TTTTT-SSSSS""",f'{global_shard_id:05d}' ).replace("""NNNNN""",f'{total_shards:05d}' ),)
__lowerCAmelCase = []
__lowerCAmelCase = 0
for i in range(len(__SCREAMING_SNAKE_CASE ) ):
__lowerCAmelCase , __lowerCAmelCase = task_id_and_num_shards[i]
for shard_id in range(__SCREAMING_SNAKE_CASE ):
args.append([task_id, shard_id, global_shard_id] )
global_shard_id += 1
self._spark.sparkContext.parallelize(__SCREAMING_SNAKE_CASE,len(__SCREAMING_SNAKE_CASE ) ).map(lambda __SCREAMING_SNAKE_CASE : _rename_shard(*__SCREAMING_SNAKE_CASE ) ).collect()
else:
# don't use any pattern
__lowerCAmelCase = 0
__lowerCAmelCase = task_id_and_num_shards[0][0]
self._rename(
fpath.replace("""SSSSS""",f'{shard_id:05d}' ).replace("""TTTTT""",f'{task_id:05d}' ),fpath.replace(__SCREAMING_SNAKE_CASE,"""""" ),)
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,):
'''simple docstring'''
return SparkExamplesIterable(self.df )
| 689 |
'''simple docstring'''
from scipy.stats import spearmanr
import datasets
_a : str = """
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlations imply that as data in dataset x increases, so
does data in dataset y. Negative correlations imply that as x increases,
y decreases. Correlations of -1 or +1 imply an exact monotonic relationship.
Unlike the Pearson correlation, the Spearman correlation does not
assume that both datasets are normally distributed.
The p-value roughly indicates the probability of an uncorrelated system
producing datasets that have a Spearman correlation at least as extreme
as the one computed from these datasets. The p-values are not entirely
reliable but are probably reasonable for datasets larger than 500 or so.
"""
_a : Dict = """
Args:
predictions (`List[float]`): Predicted labels, as returned by a model.
references (`List[float]`): Ground truth labels.
return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns
only the spearmanr score. Defaults to `False`.
Returns:
spearmanr (`float`): Spearman correlation coefficient.
p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.
Examples:
Example 1:
>>> spearmanr_metric = datasets.load_metric(\"spearmanr\")
>>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])
>>> print(results)
{'spearmanr': -0.7}
Example 2:
>>> spearmanr_metric = datasets.load_metric(\"spearmanr\")
>>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],
... predictions=[10, 9, 2.5, 6, 4],
... return_pvalue=True)
>>> print(results['spearmanr'])
-0.7
>>> print(round(results['spearmanr_pvalue'], 2))
0.19
"""
_a : List[str] = r"""\
@book{kokoska2000crc,
title={CRC standard probability and statistics tables and formulae},
author={Kokoska, Stephen and Zwillinger, Daniel},
year={2000},
publisher={Crc Press}
}
@article{2020SciPy-NMeth,
author = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
Haberland, Matt and Reddy, Tyler and Cournapeau, David and
Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
Kern, Robert and Larson, Eric and Carey, C J and
Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and
{VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
Harris, Charles R. and Archibald, Anne M. and
Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and
{van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
title = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
Computing in Python}},
journal = {Nature Methods},
year = {2020},
volume = {17},
pages = {261--272},
adsurl = {https://rdcu.be/b08Wh},
doi = {10.1038/s41592-019-0686-2},
}
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _UpperCAmelCase ( datasets.Metric ):
def lowerCamelCase__ ( self ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION,citation=_CITATION,inputs_description=_KWARGS_DESCRIPTION,features=datasets.Features(
{
"""predictions""": datasets.Value("""float""" ),
"""references""": datasets.Value("""float""" ),
} ),reference_urls=["""https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.spearmanr.html"""],)
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=False ):
'''simple docstring'''
__lowerCAmelCase = spearmanr(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
if return_pvalue:
return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
else:
return {"spearmanr": results[0]}
| 689 | 1 |
'''simple docstring'''
import argparse
from collections import defaultdict
def _lowerCAmelCase ( lowercase , lowercase , lowercase , lowercase , lowercase ) -> Any:
__lowerCAmelCase = f'{file}_{class_name}_{test_name}'
done_test[_id] += 1
with open(lowercase , """r""" ) as f:
__lowerCAmelCase = f.readlines()
__lowerCAmelCase = f'class {class_name}('
__lowerCAmelCase = f'{4 * " "}def {test_name}('
__lowerCAmelCase = f'{8 * " "}{correct_line.split()[0]}'
__lowerCAmelCase = f'{16 * " "}{correct_line.split()[0]}'
__lowerCAmelCase = False
__lowerCAmelCase = False
__lowerCAmelCase = False
__lowerCAmelCase = False
__lowerCAmelCase = 0
__lowerCAmelCase = 0
__lowerCAmelCase = []
for line in lines:
if line.startswith(lowercase ):
__lowerCAmelCase = True
elif in_class and line.startswith(lowercase ):
__lowerCAmelCase = True
elif in_class and in_func and (line.startswith(lowercase ) or line.startswith(lowercase )):
__lowerCAmelCase = len(line.split(correct_line.split()[0] )[0] )
count += 1
if count == done_test[_id]:
__lowerCAmelCase = True
if in_class and in_func and in_line:
if ")" not in line:
continue
else:
__lowerCAmelCase = True
if in_class and in_func and in_line and insert_line:
new_lines.append(f'{spaces * " "}{correct_line}' )
__lowerCAmelCase = __lowerCAmelCase = __lowerCAmelCase = __lowerCAmelCase = False
else:
new_lines.append(lowercase )
with open(lowercase , """w""" ) as f:
for line in new_lines:
f.write(lowercase )
def _lowerCAmelCase ( lowercase , lowercase=None ) -> List[str]:
if fail is not None:
with open(lowercase , """r""" ) as f:
__lowerCAmelCase = {l.strip() for l in f.readlines()}
else:
__lowerCAmelCase = None
with open(lowercase , """r""" ) as f:
__lowerCAmelCase = f.readlines()
__lowerCAmelCase = defaultdict(lowercase )
for line in correct_lines:
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = line.split(""";""" )
if test_failures is None or "::".join([file, class_name, test_name] ) in test_failures:
overwrite_file(lowercase , lowercase , lowercase , lowercase , lowercase )
if __name__ == "__main__":
_a : Tuple = argparse.ArgumentParser()
parser.add_argument("""--correct_filename""", help="""filename of tests with expected result""")
parser.add_argument("""--fail_filename""", help="""filename of test failures""", type=str, default=None)
_a : str = parser.parse_args()
main(args.correct_filename, args.fail_filename)
| 689 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _UpperCAmelCase ( metaclass=lowerCAmelCase_ ):
a : List[str] =["""onnx"""]
def __init__( self,*__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
requires_backends(self,["""onnx"""] )
@classmethod
def lowerCamelCase__ ( cls,*__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
requires_backends(cls,["""onnx"""] )
@classmethod
def lowerCamelCase__ ( cls,*__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
requires_backends(cls,["""onnx"""] )
| 689 | 1 |
'''simple docstring'''
def _lowerCAmelCase ( lowercase = 5000_0000 ) -> int:
__lowerCAmelCase = set()
__lowerCAmelCase = int((limit - 24) ** (1 / 2) )
__lowerCAmelCase = set(range(3 , prime_square_limit + 1 , 2 ) )
primes.add(2 )
for p in range(3 , prime_square_limit + 1 , 2 ):
if p not in primes:
continue
primes.difference_update(set(range(p * p , prime_square_limit + 1 , lowercase ) ) )
for primea in primes:
__lowerCAmelCase = primea * primea
for primea in primes:
__lowerCAmelCase = primea * primea * primea
if square + cube >= limit - 16:
break
for primea in primes:
__lowerCAmelCase = primea * primea * primea * primea
__lowerCAmelCase = square + cube + tetr
if total >= limit:
break
ret.add(lowercase )
return len(lowercase )
if __name__ == "__main__":
print(f'{solution() = }')
| 689 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
_a : int = logging.get_logger(__name__)
_a : Optional[int] = {
"""EleutherAI/gpt-j-6B""": """https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json""",
# See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}
class _UpperCAmelCase ( lowerCAmelCase_ ):
a : List[str] ="""gptj"""
a : Optional[int] ={
"""max_position_embeddings""": """n_positions""",
"""hidden_size""": """n_embd""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self,__SCREAMING_SNAKE_CASE=5_04_00,__SCREAMING_SNAKE_CASE=20_48,__SCREAMING_SNAKE_CASE=40_96,__SCREAMING_SNAKE_CASE=28,__SCREAMING_SNAKE_CASE=16,__SCREAMING_SNAKE_CASE=64,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE="gelu_new",__SCREAMING_SNAKE_CASE=0.0,__SCREAMING_SNAKE_CASE=0.0,__SCREAMING_SNAKE_CASE=0.0,__SCREAMING_SNAKE_CASE=1e-5,__SCREAMING_SNAKE_CASE=0.02,__SCREAMING_SNAKE_CASE=True,__SCREAMING_SNAKE_CASE=5_02_56,__SCREAMING_SNAKE_CASE=5_02_56,__SCREAMING_SNAKE_CASE=False,**__SCREAMING_SNAKE_CASE,):
'''simple docstring'''
__lowerCAmelCase = vocab_size
__lowerCAmelCase = n_positions
__lowerCAmelCase = n_embd
__lowerCAmelCase = n_layer
__lowerCAmelCase = n_head
__lowerCAmelCase = n_inner
__lowerCAmelCase = rotary_dim
__lowerCAmelCase = activation_function
__lowerCAmelCase = resid_pdrop
__lowerCAmelCase = embd_pdrop
__lowerCAmelCase = attn_pdrop
__lowerCAmelCase = layer_norm_epsilon
__lowerCAmelCase = initializer_range
__lowerCAmelCase = use_cache
__lowerCAmelCase = bos_token_id
__lowerCAmelCase = eos_token_id
super().__init__(
bos_token_id=__SCREAMING_SNAKE_CASE,eos_token_id=__SCREAMING_SNAKE_CASE,tie_word_embeddings=__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE )
class _UpperCAmelCase ( lowerCAmelCase_ ):
def __init__( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE = "default",__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = False,):
'''simple docstring'''
super().__init__(__SCREAMING_SNAKE_CASE,task=__SCREAMING_SNAKE_CASE,patching_specs=__SCREAMING_SNAKE_CASE,use_past=__SCREAMING_SNAKE_CASE )
if not getattr(self._config,"""pad_token_id""",__SCREAMING_SNAKE_CASE ):
# TODO: how to do that better?
__lowerCAmelCase = 0
@property
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}} )
if self.use_past:
self.fill_with_past_key_values_(__SCREAMING_SNAKE_CASE,direction="""inputs""" )
__lowerCAmelCase = {0: """batch""", 1: """past_sequence + sequence"""}
else:
__lowerCAmelCase = {0: """batch""", 1: """sequence"""}
return common_inputs
@property
def lowerCamelCase__ ( self ):
'''simple docstring'''
return self._config.n_layer
@property
def lowerCamelCase__ ( self ):
'''simple docstring'''
return self._config.n_head
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE = -1,__SCREAMING_SNAKE_CASE = -1,__SCREAMING_SNAKE_CASE = False,__SCREAMING_SNAKE_CASE = None,):
'''simple docstring'''
__lowerCAmelCase = super(__SCREAMING_SNAKE_CASE,self ).generate_dummy_inputs(
__SCREAMING_SNAKE_CASE,batch_size=__SCREAMING_SNAKE_CASE,seq_length=__SCREAMING_SNAKE_CASE,is_pair=__SCREAMING_SNAKE_CASE,framework=__SCREAMING_SNAKE_CASE )
# We need to order the input in the way they appears in the forward()
__lowerCAmelCase = OrderedDict({"""input_ids""": common_inputs["""input_ids"""]} )
# Need to add the past_keys
if self.use_past:
if not is_torch_available():
raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed.""" )
else:
import torch
__lowerCAmelCase , __lowerCAmelCase = common_inputs["""input_ids"""].shape
# Not using the same length for past_key_values
__lowerCAmelCase = seqlen + 2
__lowerCAmelCase = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
__lowerCAmelCase = [
(torch.zeros(__SCREAMING_SNAKE_CASE ), torch.zeros(__SCREAMING_SNAKE_CASE )) for _ in range(self.num_layers )
]
__lowerCAmelCase = common_inputs["""attention_mask"""]
if self.use_past:
__lowerCAmelCase = ordered_inputs["""attention_mask"""].dtype
__lowerCAmelCase = torch.cat(
[ordered_inputs["""attention_mask"""], torch.ones(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,dtype=__SCREAMING_SNAKE_CASE )],dim=1 )
return ordered_inputs
@property
def lowerCamelCase__ ( self ):
'''simple docstring'''
return 13
| 689 | 1 |
'''simple docstring'''
import os
import sys
import unittest
_a : List[Any] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init # noqa: E402
# Align TRANSFORMERS_PATH in check_dummies with the current path
_a : Union[str, Any] = os.path.join(git_repo_path, """src""", """diffusers""")
class _UpperCAmelCase ( unittest.TestCase ):
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = find_backend(""" if not is_torch_available():""" )
self.assertEqual(__SCREAMING_SNAKE_CASE,"""torch""" )
# backend_with_underscore = find_backend(" if not is_tensorflow_text_available():")
# self.assertEqual(backend_with_underscore, "tensorflow_text")
__lowerCAmelCase = find_backend(""" if not (is_torch_available() and is_transformers_available()):""" )
self.assertEqual(__SCREAMING_SNAKE_CASE,"""torch_and_transformers""" )
# double_backend_with_underscore = find_backend(
# " if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
# )
# self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")
__lowerCAmelCase = find_backend(
""" if not (is_torch_available() and is_transformers_available() and is_onnx_available()):""" )
self.assertEqual(__SCREAMING_SNAKE_CASE,"""torch_and_transformers_and_onnx""" )
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = read_init()
# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
self.assertIn("""torch""",__SCREAMING_SNAKE_CASE )
self.assertIn("""torch_and_transformers""",__SCREAMING_SNAKE_CASE )
self.assertIn("""flax_and_transformers""",__SCREAMING_SNAKE_CASE )
self.assertIn("""torch_and_transformers_and_onnx""",__SCREAMING_SNAKE_CASE )
# Likewise, we can't assert on the exact content of a key
self.assertIn("""UNet2DModel""",objects["""torch"""] )
self.assertIn("""FlaxUNet2DConditionModel""",objects["""flax"""] )
self.assertIn("""StableDiffusionPipeline""",objects["""torch_and_transformers"""] )
self.assertIn("""FlaxStableDiffusionPipeline""",objects["""flax_and_transformers"""] )
self.assertIn("""LMSDiscreteScheduler""",objects["""torch_and_scipy"""] )
self.assertIn("""OnnxStableDiffusionPipeline""",objects["""torch_and_transformers_and_onnx"""] )
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = create_dummy_object("""CONSTANT""","""'torch'""" )
self.assertEqual(__SCREAMING_SNAKE_CASE,"""\nCONSTANT = None\n""" )
__lowerCAmelCase = create_dummy_object("""function""","""'torch'""" )
self.assertEqual(
__SCREAMING_SNAKE_CASE,"""\ndef function(*args, **kwargs):\n requires_backends(function, 'torch')\n""" )
__lowerCAmelCase = """
class FakeClass(metaclass=DummyObject):
_backends = 'torch'
def __init__(self, *args, **kwargs):
requires_backends(self, 'torch')
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, 'torch')
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, 'torch')
"""
__lowerCAmelCase = create_dummy_object("""FakeClass""","""'torch'""" )
self.assertEqual(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = """# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends
CONSTANT = None
def function(*args, **kwargs):
requires_backends(function, [\"torch\"])
class FakeClass(metaclass=DummyObject):
_backends = [\"torch\"]
def __init__(self, *args, **kwargs):
requires_backends(self, [\"torch\"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, [\"torch\"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, [\"torch\"])
"""
__lowerCAmelCase = create_dummy_files({"""torch""": ["""CONSTANT""", """function""", """FakeClass"""]} )
self.assertEqual(dummy_files["""torch"""],__SCREAMING_SNAKE_CASE )
| 689 |
'''simple docstring'''
def _lowerCAmelCase ( lowercase = 5000_0000 ) -> int:
__lowerCAmelCase = set()
__lowerCAmelCase = int((limit - 24) ** (1 / 2) )
__lowerCAmelCase = set(range(3 , prime_square_limit + 1 , 2 ) )
primes.add(2 )
for p in range(3 , prime_square_limit + 1 , 2 ):
if p not in primes:
continue
primes.difference_update(set(range(p * p , prime_square_limit + 1 , lowercase ) ) )
for primea in primes:
__lowerCAmelCase = primea * primea
for primea in primes:
__lowerCAmelCase = primea * primea * primea
if square + cube >= limit - 16:
break
for primea in primes:
__lowerCAmelCase = primea * primea * primea * primea
__lowerCAmelCase = square + cube + tetr
if total >= limit:
break
ret.add(lowercase )
return len(lowercase )
if __name__ == "__main__":
print(f'{solution() = }')
| 689 | 1 |
'''simple docstring'''
from math import ceil, sqrt
def _lowerCAmelCase ( lowercase = 100_0000 ) -> int:
__lowerCAmelCase = 0
for outer_width in range(3 , (limit // 4) + 2 ):
if outer_width**2 > limit:
__lowerCAmelCase = max(ceil(sqrt(outer_width**2 - limit ) ) , 1 )
else:
__lowerCAmelCase = 1
if (outer_width - hole_width_lower_bound) % 2:
hole_width_lower_bound += 1
answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1
return answer
if __name__ == "__main__":
print(f'{solution() = }')
| 689 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, load_numpy, skip_mps, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
@skip_mps
class _UpperCAmelCase ( lowerCAmelCase_ , unittest.TestCase ):
a : Optional[int] =TextToVideoSDPipeline
a : Optional[int] =TEXT_TO_IMAGE_PARAMS
a : Any =TEXT_TO_IMAGE_BATCH_PARAMS
# No `output_type`.
a : Union[str, Any] =frozenset(
[
"""num_inference_steps""",
"""generator""",
"""latents""",
"""return_dict""",
"""callback""",
"""callback_steps""",
] )
def lowerCamelCase__ ( self ):
'''simple docstring'''
torch.manual_seed(0 )
__lowerCAmelCase = UNetaDConditionModel(
block_out_channels=(32, 64, 64, 64),layers_per_block=2,sample_size=32,in_channels=4,out_channels=4,down_block_types=("""CrossAttnDownBlock3D""", """CrossAttnDownBlock3D""", """CrossAttnDownBlock3D""", """DownBlock3D"""),up_block_types=("""UpBlock3D""", """CrossAttnUpBlock3D""", """CrossAttnUpBlock3D""", """CrossAttnUpBlock3D"""),cross_attention_dim=32,attention_head_dim=4,)
__lowerCAmelCase = DDIMScheduler(
beta_start=0.0_0085,beta_end=0.012,beta_schedule="""scaled_linear""",clip_sample=__SCREAMING_SNAKE_CASE,set_alpha_to_one=__SCREAMING_SNAKE_CASE,)
torch.manual_seed(0 )
__lowerCAmelCase = AutoencoderKL(
block_out_channels=[32, 64],in_channels=3,out_channels=3,down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""],up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""],latent_channels=4,sample_size=1_28,)
torch.manual_seed(0 )
__lowerCAmelCase = CLIPTextConfig(
bos_token_id=0,eos_token_id=2,hidden_size=32,intermediate_size=37,layer_norm_eps=1e-05,num_attention_heads=4,num_hidden_layers=5,pad_token_id=1,vocab_size=10_00,hidden_act="""gelu""",projection_dim=5_12,)
__lowerCAmelCase = CLIPTextModel(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
__lowerCAmelCase = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
}
return components
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=0 ):
'''simple docstring'''
if str(__SCREAMING_SNAKE_CASE ).startswith("""mps""" ):
__lowerCAmelCase = torch.manual_seed(__SCREAMING_SNAKE_CASE )
else:
__lowerCAmelCase = torch.Generator(device=__SCREAMING_SNAKE_CASE ).manual_seed(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 6.0,
"""output_type""": """pt""",
}
return inputs
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = """cpu""" # ensure determinism for the device-dependent torch.Generator
__lowerCAmelCase = self.get_dummy_components()
__lowerCAmelCase = TextToVideoSDPipeline(**__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = sd_pipe.to(__SCREAMING_SNAKE_CASE )
sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = self.get_dummy_inputs(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = """np"""
__lowerCAmelCase = sd_pipe(**__SCREAMING_SNAKE_CASE ).frames
__lowerCAmelCase = frames[0][-3:, -3:, -1]
assert frames[0].shape == (64, 64, 3)
__lowerCAmelCase = np.array([158.0, 160.0, 153.0, 125.0, 100.0, 121.0, 111.0, 93.0, 113.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def lowerCamelCase__ ( self ):
'''simple docstring'''
self._test_attention_slicing_forward_pass(test_mean_pixel_difference=__SCREAMING_SNAKE_CASE,expected_max_diff=3e-3 )
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available(),reason="""XFormers attention is only available with CUDA and `xformers` installed""",)
def lowerCamelCase__ ( self ):
'''simple docstring'''
self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=__SCREAMING_SNAKE_CASE,expected_max_diff=1e-2 )
@unittest.skip(reason="""Batching needs to be properly figured out first for this pipeline.""" )
def lowerCamelCase__ ( self ):
'''simple docstring'''
pass
@unittest.skip(reason="""Batching needs to be properly figured out first for this pipeline.""" )
def lowerCamelCase__ ( self ):
'''simple docstring'''
pass
@unittest.skip(reason="""`num_images_per_prompt` argument is not supported for this pipeline.""" )
def lowerCamelCase__ ( self ):
'''simple docstring'''
pass
def lowerCamelCase__ ( self ):
'''simple docstring'''
return super().test_progress_bar()
@slow
@skip_mps
class _UpperCAmelCase ( unittest.TestCase ):
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video.npy""" )
__lowerCAmelCase = TextToVideoSDPipeline.from_pretrained("""damo-vilab/text-to-video-ms-1.7b""" )
__lowerCAmelCase = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
__lowerCAmelCase = pipe.to("""cuda""" )
__lowerCAmelCase = """Spiderman is surfing"""
__lowerCAmelCase = torch.Generator(device="""cpu""" ).manual_seed(0 )
__lowerCAmelCase = pipe(__SCREAMING_SNAKE_CASE,generator=__SCREAMING_SNAKE_CASE,num_inference_steps=25,output_type="""pt""" ).frames
__lowerCAmelCase = video_frames.cpu().numpy()
assert np.abs(expected_video - video ).mean() < 5e-2
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video_2step.npy""" )
__lowerCAmelCase = TextToVideoSDPipeline.from_pretrained("""damo-vilab/text-to-video-ms-1.7b""" )
__lowerCAmelCase = pipe.to("""cuda""" )
__lowerCAmelCase = """Spiderman is surfing"""
__lowerCAmelCase = torch.Generator(device="""cpu""" ).manual_seed(0 )
__lowerCAmelCase = pipe(__SCREAMING_SNAKE_CASE,generator=__SCREAMING_SNAKE_CASE,num_inference_steps=2,output_type="""pt""" ).frames
__lowerCAmelCase = video_frames.cpu().numpy()
assert np.abs(expected_video - video ).mean() < 5e-2
| 689 | 1 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
class _UpperCAmelCase ( lowerCAmelCase_ ):
a : Tuple ="""SpeechT5FeatureExtractor"""
a : str ="""SpeechT5Tokenizer"""
def __init__( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
super().__init__(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
def __call__( self,*__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase = kwargs.pop("""audio""",__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = kwargs.pop("""text""",__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = kwargs.pop("""text_target""",__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = kwargs.pop("""audio_target""",__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = kwargs.pop("""sampling_rate""",__SCREAMING_SNAKE_CASE )
if audio is not None and text is not None:
raise ValueError(
"""Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?""" )
if audio_target is not None and text_target is not None:
raise ValueError(
"""Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?""" )
if audio is None and audio_target is None and text is None and text_target is None:
raise ValueError(
"""You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process.""" )
if audio is not None:
__lowerCAmelCase = self.feature_extractor(__SCREAMING_SNAKE_CASE,*__SCREAMING_SNAKE_CASE,sampling_rate=__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE )
elif text is not None:
__lowerCAmelCase = self.tokenizer(__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE )
else:
__lowerCAmelCase = None
if audio_target is not None:
__lowerCAmelCase = self.feature_extractor(audio_target=__SCREAMING_SNAKE_CASE,*__SCREAMING_SNAKE_CASE,sampling_rate=__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = targets["""input_values"""]
elif text_target is not None:
__lowerCAmelCase = self.tokenizer(__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = targets["""input_ids"""]
else:
__lowerCAmelCase = None
if inputs is None:
return targets
if targets is not None:
__lowerCAmelCase = labels
__lowerCAmelCase = targets.get("""attention_mask""" )
if decoder_attention_mask is not None:
__lowerCAmelCase = decoder_attention_mask
return inputs
def lowerCamelCase__ ( self,*__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase = kwargs.pop("""input_values""",__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = kwargs.pop("""input_ids""",__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = kwargs.pop("""labels""",__SCREAMING_SNAKE_CASE )
if input_values is not None and input_ids is not None:
raise ValueError("""Cannot process both `input_values` and `input_ids` inputs.""" )
if input_values is None and input_ids is None and labels is None:
raise ValueError(
"""You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded.""" )
if input_values is not None:
__lowerCAmelCase = self.feature_extractor.pad(__SCREAMING_SNAKE_CASE,*__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE )
elif input_ids is not None:
__lowerCAmelCase = self.tokenizer.pad(__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE )
else:
__lowerCAmelCase = None
if labels is not None:
if "input_ids" in labels or (isinstance(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE ) and "input_ids" in labels[0]):
__lowerCAmelCase = self.tokenizer.pad(__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = targets["""input_ids"""]
else:
__lowerCAmelCase = self.feature_extractor.feature_size
__lowerCAmelCase = self.feature_extractor.num_mel_bins
__lowerCAmelCase = self.feature_extractor.pad(__SCREAMING_SNAKE_CASE,*__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = feature_size_hack
__lowerCAmelCase = targets["""input_values"""]
else:
__lowerCAmelCase = None
if inputs is None:
return targets
if targets is not None:
__lowerCAmelCase = labels
__lowerCAmelCase = targets.get("""attention_mask""" )
if decoder_attention_mask is not None:
__lowerCAmelCase = decoder_attention_mask
return inputs
def lowerCamelCase__ ( self,*__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
return self.tokenizer.batch_decode(*__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE )
def lowerCamelCase__ ( self,*__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
return self.tokenizer.decode(*__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE )
| 689 |
'''simple docstring'''
from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def _lowerCAmelCase ( lowercase ) -> Optional[int]:
if not is_accelerate_available():
return method
__lowerCAmelCase = version.parse(accelerate.__version__ ).base_version
if version.parse(lowercase ) < version.parse("""0.17.0""" ):
return method
def wrapper(self , *lowercase , **lowercase ):
if hasattr(self , """_hf_hook""" ) and hasattr(self._hf_hook , """pre_forward""" ):
self._hf_hook.pre_forward(self )
return method(self , *lowercase , **lowercase )
return wrapper
| 689 | 1 |
'''simple docstring'''
from argparse import ArgumentParser
from .env import EnvironmentCommand
def _lowerCAmelCase ( ) -> Union[str, Any]:
__lowerCAmelCase = ArgumentParser("""Diffusers CLI tool""" , usage="""diffusers-cli <command> [<args>]""" )
__lowerCAmelCase = parser.add_subparsers(help="""diffusers-cli command helpers""" )
# Register commands
EnvironmentCommand.register_subcommand(lowercase )
# Let's go
__lowerCAmelCase = parser.parse_args()
if not hasattr(lowercase , """func""" ):
parser.print_help()
exit(1 )
# Run
__lowerCAmelCase = args.func(lowercase )
service.run()
if __name__ == "__main__":
main()
| 689 |
'''simple docstring'''
import argparse
import torch
from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline
def _lowerCAmelCase ( lowercase , lowercase , lowercase , lowercase , lowercase ) -> Optional[int]:
# load base model
__lowerCAmelCase = StableDiffusionPipeline.from_pretrained(lowercase , torch_dtype=torch.floataa )
# load LoRA weight from .safetensors
__lowerCAmelCase = load_file(lowercase )
__lowerCAmelCase = []
# directly update weight in diffusers model
for key in state_dict:
# it is suggested to print out the key, it usually will be something like below
# "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"
# as we have set the alpha beforehand, so just skip
if ".alpha" in key or key in visited:
continue
if "text" in key:
__lowerCAmelCase = key.split(""".""" )[0].split(LORA_PREFIX_TEXT_ENCODER + """_""" )[-1].split("""_""" )
__lowerCAmelCase = pipeline.text_encoder
else:
__lowerCAmelCase = key.split(""".""" )[0].split(LORA_PREFIX_UNET + """_""" )[-1].split("""_""" )
__lowerCAmelCase = pipeline.unet
# find the target layer
__lowerCAmelCase = layer_infos.pop(0 )
while len(lowercase ) > -1:
try:
__lowerCAmelCase = curr_layer.__getattr__(lowercase )
if len(lowercase ) > 0:
__lowerCAmelCase = layer_infos.pop(0 )
elif len(lowercase ) == 0:
break
except Exception:
if len(lowercase ) > 0:
temp_name += "_" + layer_infos.pop(0 )
else:
__lowerCAmelCase = layer_infos.pop(0 )
__lowerCAmelCase = []
if "lora_down" in key:
pair_keys.append(key.replace("""lora_down""" , """lora_up""" ) )
pair_keys.append(lowercase )
else:
pair_keys.append(lowercase )
pair_keys.append(key.replace("""lora_up""" , """lora_down""" ) )
# update weight
if len(state_dict[pair_keys[0]].shape ) == 4:
__lowerCAmelCase = state_dict[pair_keys[0]].squeeze(3 ).squeeze(2 ).to(torch.floataa )
__lowerCAmelCase = state_dict[pair_keys[1]].squeeze(3 ).squeeze(2 ).to(torch.floataa )
curr_layer.weight.data += alpha * torch.mm(lowercase , lowercase ).unsqueeze(2 ).unsqueeze(3 )
else:
__lowerCAmelCase = state_dict[pair_keys[0]].to(torch.floataa )
__lowerCAmelCase = state_dict[pair_keys[1]].to(torch.floataa )
curr_layer.weight.data += alpha * torch.mm(lowercase , lowercase )
# update visited list
for item in pair_keys:
visited.append(lowercase )
return pipeline
if __name__ == "__main__":
_a : Union[str, Any] = argparse.ArgumentParser()
parser.add_argument(
"""--base_model_path""", default=None, type=str, required=True, help="""Path to the base model in diffusers format."""
)
parser.add_argument(
"""--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
)
parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")
parser.add_argument(
"""--lora_prefix_unet""", default="""lora_unet""", type=str, help="""The prefix of UNet weight in safetensors"""
)
parser.add_argument(
"""--lora_prefix_text_encoder""",
default="""lora_te""",
type=str,
help="""The prefix of text encoder weight in safetensors""",
)
parser.add_argument("""--alpha""", default=0.75, type=float, help="""The merging ratio in W = W0 + alpha * deltaW""")
parser.add_argument(
"""--to_safetensors""", action="""store_true""", help="""Whether to store pipeline in safetensors format or not."""
)
parser.add_argument("""--device""", type=str, help="""Device to use (e.g. cpu, cuda:0, cuda:1, etc.)""")
_a : Optional[int] = parser.parse_args()
_a : Dict = args.base_model_path
_a : Optional[Any] = args.checkpoint_path
_a : Union[str, Any] = args.dump_path
_a : Optional[int] = args.lora_prefix_unet
_a : int = args.lora_prefix_text_encoder
_a : str = args.alpha
_a : Any = convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)
_a : Tuple = pipe.to(args.device)
pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
| 689 | 1 |
'''simple docstring'''
from typing import List
import numpy as np
def _lowerCAmelCase ( lowercase ) -> int:
__lowerCAmelCase = {key: len(lowercase ) for key, value in gen_kwargs.items() if isinstance(lowercase , lowercase )}
if len(set(lists_lengths.values() ) ) > 1:
raise RuntimeError(
(
"""Sharding is ambiguous for this dataset: """
+ """we found several data sources lists of different lengths, and we don't know over which list we should parallelize:\n"""
+ """\n""".join(f'\t- key {key} has length {length}' for key, length in lists_lengths.items() )
+ """\nTo fix this, check the 'gen_kwargs' and make sure to use lists only for data sources, """
+ """and use tuples otherwise. In the end there should only be one single list, or several lists with the same length."""
) )
__lowerCAmelCase = max(lists_lengths.values() , default=0 )
return max(1 , lowercase )
def _lowerCAmelCase ( lowercase , lowercase ) -> List[range]:
__lowerCAmelCase = []
for group_idx in range(lowercase ):
__lowerCAmelCase = num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
if num_shards_to_add == 0:
break
__lowerCAmelCase = shards_indices_per_group[-1].stop if shards_indices_per_group else 0
__lowerCAmelCase = range(lowercase , start + num_shards_to_add )
shards_indices_per_group.append(lowercase )
return shards_indices_per_group
def _lowerCAmelCase ( lowercase , lowercase ) -> List[dict]:
__lowerCAmelCase = _number_of_shards_in_gen_kwargs(lowercase )
if num_shards == 1:
return [dict(lowercase )]
else:
__lowerCAmelCase = _distribute_shards(num_shards=lowercase , max_num_jobs=lowercase )
return [
{
key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
if isinstance(lowercase , lowercase )
else value
for key, value in gen_kwargs.items()
}
for group_idx in range(len(lowercase ) )
]
def _lowerCAmelCase ( lowercase ) -> dict:
return {
key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
if isinstance(gen_kwargs_list[0][key] , lowercase )
else gen_kwargs_list[0][key]
for key in gen_kwargs_list[0]
}
def _lowerCAmelCase ( lowercase , lowercase ) -> dict:
__lowerCAmelCase = {len(lowercase ) for value in gen_kwargs.values() if isinstance(lowercase , lowercase )}
__lowerCAmelCase = {}
for size in list_sizes:
__lowerCAmelCase = list(range(lowercase ) )
rng.shuffle(indices_per_size[size] )
# Now let's copy the gen_kwargs and shuffle the lists based on their sizes
__lowerCAmelCase = dict(lowercase )
for key, value in shuffled_kwargs.items():
if isinstance(lowercase , lowercase ):
__lowerCAmelCase = [value[i] for i in indices_per_size[len(lowercase )]]
return shuffled_kwargs
| 689 |
'''simple docstring'''
from collections import Counter
from timeit import timeit
def _lowerCAmelCase ( lowercase = "" , ) -> bool:
return sum(c % 2 for c in Counter(input_str.replace(""" """ , """""" ).lower() ).values() ) < 2
def _lowerCAmelCase ( lowercase = "" ) -> bool:
if len(lowercase ) == 0:
return True
__lowerCAmelCase = input_str.replace(""" """ , """""" ).lower()
# character_freq_dict: Stores the frequency of every character in the input string
__lowerCAmelCase = {}
for character in lower_case_input_str:
__lowerCAmelCase = character_freq_dict.get(lowercase , 0 ) + 1
__lowerCAmelCase = 0
for character_count in character_freq_dict.values():
if character_count % 2:
odd_char += 1
if odd_char > 1:
return False
return True
def _lowerCAmelCase ( lowercase = "" ) -> None:
print("""\nFor string = """ , lowercase , """:""" )
print(
"""> can_string_be_rearranged_as_palindrome_counter()""" , """\tans =""" , can_string_be_rearranged_as_palindrome_counter(lowercase ) , """\ttime =""" , timeit(
"""z.can_string_be_rearranged_as_palindrome_counter(z.check_str)""" , setup="""import __main__ as z""" , ) , """seconds""" , )
print(
"""> can_string_be_rearranged_as_palindrome()""" , """\tans =""" , can_string_be_rearranged_as_palindrome(lowercase ) , """\ttime =""" , timeit(
"""z.can_string_be_rearranged_as_palindrome(z.check_str)""" , setup="""import __main__ as z""" , ) , """seconds""" , )
if __name__ == "__main__":
_a : int = input(
"""Enter string to determine if it can be rearranged as a palindrome or not: """
).strip()
benchmark(check_str)
_a : Optional[int] = can_string_be_rearranged_as_palindrome_counter(check_str)
print(f'{check_str} can {"" if status else "not "}be rearranged as a palindrome')
| 689 | 1 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor
@require_tokenizers
@require_vision
class _UpperCAmelCase ( unittest.TestCase ):
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = tempfile.mkdtemp()
# fmt: off
__lowerCAmelCase = ["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing""", """,""", """low""", """lowest"""]
# fmt: on
__lowerCAmelCase = os.path.join(self.tmpdirname,VOCAB_FILES_NAMES["""vocab_file"""] )
with open(self.vocab_file,"""w""",encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens] ) )
__lowerCAmelCase = {
"""do_resize""": True,
"""size""": {"""height""": 18, """width""": 18},
"""do_normalize""": True,
"""image_mean""": [0.5, 0.5, 0.5],
"""image_std""": [0.5, 0.5, 0.5],
}
__lowerCAmelCase = os.path.join(self.tmpdirname,__SCREAMING_SNAKE_CASE )
with open(self.image_processor_file,"""w""",encoding="""utf-8""" ) as fp:
json.dump(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
def lowerCamelCase__ ( self,**__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
return BertTokenizer.from_pretrained(self.tmpdirname,**__SCREAMING_SNAKE_CASE )
def lowerCamelCase__ ( self,**__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
return ViTImageProcessor.from_pretrained(self.tmpdirname,**__SCREAMING_SNAKE_CASE )
def lowerCamelCase__ ( self ):
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = [np.random.randint(2_55,size=(3, 30, 4_00),dtype=np.uinta )]
__lowerCAmelCase = [Image.fromarray(np.moveaxis(__SCREAMING_SNAKE_CASE,0,-1 ) ) for x in image_inputs]
return image_inputs
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = self.get_tokenizer()
__lowerCAmelCase = self.get_image_processor()
__lowerCAmelCase = VisionTextDualEncoderProcessor(tokenizer=__SCREAMING_SNAKE_CASE,image_processor=__SCREAMING_SNAKE_CASE )
processor.save_pretrained(self.tmpdirname )
__lowerCAmelCase = VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab(),tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer,(BertTokenizer, BertTokenizerFast) )
self.assertEqual(processor.image_processor.to_json_string(),image_processor.to_json_string() )
self.assertIsInstance(processor.image_processor,__SCREAMING_SNAKE_CASE )
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = VisionTextDualEncoderProcessor(
tokenizer=self.get_tokenizer(),image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
__lowerCAmelCase = self.get_tokenizer(bos_token="""(BOS)""",eos_token="""(EOS)""" )
__lowerCAmelCase = self.get_image_processor(do_normalize=__SCREAMING_SNAKE_CASE,padding_value=1.0 )
__lowerCAmelCase = VisionTextDualEncoderProcessor.from_pretrained(
self.tmpdirname,bos_token="""(BOS)""",eos_token="""(EOS)""",do_normalize=__SCREAMING_SNAKE_CASE,padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab(),tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer,(BertTokenizer, BertTokenizerFast) )
self.assertEqual(processor.image_processor.to_json_string(),image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor,__SCREAMING_SNAKE_CASE )
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = self.get_image_processor()
__lowerCAmelCase = self.get_tokenizer()
__lowerCAmelCase = VisionTextDualEncoderProcessor(tokenizer=__SCREAMING_SNAKE_CASE,image_processor=__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = self.prepare_image_inputs()
__lowerCAmelCase = image_processor(__SCREAMING_SNAKE_CASE,return_tensors="""np""" )
__lowerCAmelCase = processor(images=__SCREAMING_SNAKE_CASE,return_tensors="""np""" )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum(),input_processor[key].sum(),delta=1e-2 )
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = self.get_image_processor()
__lowerCAmelCase = self.get_tokenizer()
__lowerCAmelCase = VisionTextDualEncoderProcessor(tokenizer=__SCREAMING_SNAKE_CASE,image_processor=__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = """lower newer"""
__lowerCAmelCase = processor(text=__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = tokenizer(__SCREAMING_SNAKE_CASE )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key],encoded_processor[key] )
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = self.get_image_processor()
__lowerCAmelCase = self.get_tokenizer()
__lowerCAmelCase = VisionTextDualEncoderProcessor(tokenizer=__SCREAMING_SNAKE_CASE,image_processor=__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = """lower newer"""
__lowerCAmelCase = self.prepare_image_inputs()
__lowerCAmelCase = processor(text=__SCREAMING_SNAKE_CASE,images=__SCREAMING_SNAKE_CASE )
self.assertListEqual(list(inputs.keys() ),["""input_ids""", """token_type_ids""", """attention_mask""", """pixel_values"""] )
# test if it raises when no input is passed
with self.assertRaises(__SCREAMING_SNAKE_CASE ):
processor()
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = self.get_image_processor()
__lowerCAmelCase = self.get_tokenizer()
__lowerCAmelCase = VisionTextDualEncoderProcessor(tokenizer=__SCREAMING_SNAKE_CASE,image_processor=__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
__lowerCAmelCase = processor.batch_decode(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = tokenizer.batch_decode(__SCREAMING_SNAKE_CASE )
self.assertListEqual(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = self.get_image_processor()
__lowerCAmelCase = self.get_tokenizer()
__lowerCAmelCase = VisionTextDualEncoderProcessor(tokenizer=__SCREAMING_SNAKE_CASE,image_processor=__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = """lower newer"""
__lowerCAmelCase = self.prepare_image_inputs()
__lowerCAmelCase = processor(text=__SCREAMING_SNAKE_CASE,images=__SCREAMING_SNAKE_CASE )
self.assertListEqual(list(inputs.keys() ),processor.model_input_names )
| 689 |
'''simple docstring'''
import argparse
import json
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
VideoMAEConfig,
VideoMAEForPreTraining,
VideoMAEForVideoClassification,
VideoMAEImageProcessor,
)
def _lowerCAmelCase ( lowercase ) -> List[Any]:
__lowerCAmelCase = VideoMAEConfig()
set_architecture_configs(lowercase , lowercase )
if "finetuned" not in model_name:
__lowerCAmelCase = False
if "finetuned" in model_name:
__lowerCAmelCase = """huggingface/label-files"""
if "kinetics" in model_name:
__lowerCAmelCase = 400
__lowerCAmelCase = """kinetics400-id2label.json"""
elif "ssv2" in model_name:
__lowerCAmelCase = 174
__lowerCAmelCase = """something-something-v2-id2label.json"""
else:
raise ValueError("""Model name should either contain 'kinetics' or 'ssv2' in case it's fine-tuned.""" )
__lowerCAmelCase = json.load(open(hf_hub_download(lowercase , lowercase , repo_type="""dataset""" ) , """r""" ) )
__lowerCAmelCase = {int(lowercase ): v for k, v in idalabel.items()}
__lowerCAmelCase = idalabel
__lowerCAmelCase = {v: k for k, v in idalabel.items()}
return config
def _lowerCAmelCase ( lowercase , lowercase ) -> Any:
if "small" in model_name:
__lowerCAmelCase = 384
__lowerCAmelCase = 1536
__lowerCAmelCase = 12
__lowerCAmelCase = 16
__lowerCAmelCase = 12
__lowerCAmelCase = 3
__lowerCAmelCase = 192
__lowerCAmelCase = 768
elif "large" in model_name:
__lowerCAmelCase = 1024
__lowerCAmelCase = 4096
__lowerCAmelCase = 24
__lowerCAmelCase = 16
__lowerCAmelCase = 12
__lowerCAmelCase = 8
__lowerCAmelCase = 512
__lowerCAmelCase = 2048
elif "huge" in model_name:
__lowerCAmelCase = 1280
__lowerCAmelCase = 5120
__lowerCAmelCase = 32
__lowerCAmelCase = 16
__lowerCAmelCase = 12
__lowerCAmelCase = 8
__lowerCAmelCase = 640
__lowerCAmelCase = 2560
elif "base" not in model_name:
raise ValueError("""Model name should include either \"small\", \"base\", \"large\", or \"huge\"""" )
def _lowerCAmelCase ( lowercase ) -> List[str]:
if "encoder." in name:
__lowerCAmelCase = name.replace("""encoder.""" , """""" )
if "cls_token" in name:
__lowerCAmelCase = name.replace("""cls_token""" , """videomae.embeddings.cls_token""" )
if "decoder_pos_embed" in name:
__lowerCAmelCase = name.replace("""decoder_pos_embed""" , """decoder.decoder_pos_embed""" )
if "pos_embed" in name and "decoder" not in name:
__lowerCAmelCase = name.replace("""pos_embed""" , """videomae.embeddings.position_embeddings""" )
if "patch_embed.proj" in name:
__lowerCAmelCase = name.replace("""patch_embed.proj""" , """videomae.embeddings.patch_embeddings.projection""" )
if "patch_embed.norm" in name:
__lowerCAmelCase = name.replace("""patch_embed.norm""" , """videomae.embeddings.norm""" )
if "decoder.blocks" in name:
__lowerCAmelCase = name.replace("""decoder.blocks""" , """decoder.decoder_layers""" )
if "blocks" in name:
__lowerCAmelCase = name.replace("""blocks""" , """videomae.encoder.layer""" )
if "attn.proj" in name:
__lowerCAmelCase = name.replace("""attn.proj""" , """attention.output.dense""" )
if "attn" in name and "bias" not in name:
__lowerCAmelCase = name.replace("""attn""" , """attention.self""" )
if "attn" in name:
__lowerCAmelCase = name.replace("""attn""" , """attention.attention""" )
if "norm1" in name:
__lowerCAmelCase = name.replace("""norm1""" , """layernorm_before""" )
if "norm2" in name:
__lowerCAmelCase = name.replace("""norm2""" , """layernorm_after""" )
if "mlp.fc1" in name:
__lowerCAmelCase = name.replace("""mlp.fc1""" , """intermediate.dense""" )
if "mlp.fc2" in name:
__lowerCAmelCase = name.replace("""mlp.fc2""" , """output.dense""" )
if "decoder_embed" in name:
__lowerCAmelCase = name.replace("""decoder_embed""" , """decoder.decoder_embed""" )
if "decoder_norm" in name:
__lowerCAmelCase = name.replace("""decoder_norm""" , """decoder.decoder_norm""" )
if "decoder_pred" in name:
__lowerCAmelCase = name.replace("""decoder_pred""" , """decoder.decoder_pred""" )
if "norm.weight" in name and "decoder" not in name and "fc" not in name:
__lowerCAmelCase = name.replace("""norm.weight""" , """videomae.layernorm.weight""" )
if "norm.bias" in name and "decoder" not in name and "fc" not in name:
__lowerCAmelCase = name.replace("""norm.bias""" , """videomae.layernorm.bias""" )
if "head" in name and "decoder" not in name:
__lowerCAmelCase = name.replace("""head""" , """classifier""" )
return name
def _lowerCAmelCase ( lowercase , lowercase ) -> List[Any]:
for key in orig_state_dict.copy().keys():
__lowerCAmelCase = orig_state_dict.pop(lowercase )
if key.startswith("""encoder.""" ):
__lowerCAmelCase = key.replace("""encoder.""" , """""" )
if "qkv" in key:
__lowerCAmelCase = key.split(""".""" )
if key.startswith("""decoder.blocks""" ):
__lowerCAmelCase = config.decoder_hidden_size
__lowerCAmelCase = int(key_split[2] )
__lowerCAmelCase = """decoder.decoder_layers."""
if "weight" in key:
__lowerCAmelCase = val[:dim, :]
__lowerCAmelCase = val[dim : dim * 2, :]
__lowerCAmelCase = val[-dim:, :]
else:
__lowerCAmelCase = config.hidden_size
__lowerCAmelCase = int(key_split[1] )
__lowerCAmelCase = """videomae.encoder.layer."""
if "weight" in key:
__lowerCAmelCase = val[:dim, :]
__lowerCAmelCase = val[dim : dim * 2, :]
__lowerCAmelCase = val[-dim:, :]
else:
__lowerCAmelCase = val
return orig_state_dict
def _lowerCAmelCase ( ) -> str:
__lowerCAmelCase = hf_hub_download(
repo_id="""hf-internal-testing/spaghetti-video""" , filename="""eating_spaghetti.npy""" , repo_type="""dataset""" )
__lowerCAmelCase = np.load(lowercase )
return list(lowercase )
def _lowerCAmelCase ( lowercase , lowercase , lowercase , lowercase ) -> Optional[Any]:
__lowerCAmelCase = get_videomae_config(lowercase )
if "finetuned" in model_name:
__lowerCAmelCase = VideoMAEForVideoClassification(lowercase )
else:
__lowerCAmelCase = VideoMAEForPreTraining(lowercase )
# download original checkpoint, hosted on Google Drive
__lowerCAmelCase = """pytorch_model.bin"""
gdown.cached_download(lowercase , lowercase , quiet=lowercase )
__lowerCAmelCase = torch.load(lowercase , map_location="""cpu""" )
if "model" in files:
__lowerCAmelCase = files["""model"""]
else:
__lowerCAmelCase = files["""module"""]
__lowerCAmelCase = convert_state_dict(lowercase , lowercase )
model.load_state_dict(lowercase )
model.eval()
# verify model on basic input
__lowerCAmelCase = VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5] , image_std=[0.5, 0.5, 0.5] )
__lowerCAmelCase = prepare_video()
__lowerCAmelCase = image_processor(lowercase , return_tensors="""pt""" )
if "finetuned" not in model_name:
__lowerCAmelCase = hf_hub_download(repo_id="""hf-internal-testing/bool-masked-pos""" , filename="""bool_masked_pos.pt""" )
__lowerCAmelCase = torch.load(lowercase )
__lowerCAmelCase = model(**lowercase )
__lowerCAmelCase = outputs.logits
__lowerCAmelCase = [
"""videomae-small-finetuned-kinetics""",
"""videomae-small-finetuned-ssv2""",
# Kinetics-400 checkpoints (short = pretrained only for 800 epochs instead of 1600)
"""videomae-base-short""",
"""videomae-base-short-finetuned-kinetics""",
"""videomae-base""",
"""videomae-base-finetuned-kinetics""",
"""videomae-large""",
"""videomae-large-finetuned-kinetics""",
"""videomae-huge-finetuned-kinetics""",
# Something-Something-v2 checkpoints (short = pretrained only for 800 epochs instead of 2400)
"""videomae-base-short-ssv2""",
"""videomae-base-short-finetuned-ssv2""",
"""videomae-base-ssv2""",
"""videomae-base-finetuned-ssv2""",
]
# NOTE: logits were tested with image_mean and image_std equal to [0.5, 0.5, 0.5] and [0.5, 0.5, 0.5]
if model_name == "videomae-small-finetuned-kinetics":
__lowerCAmelCase = torch.Size([1, 400] )
__lowerCAmelCase = torch.tensor([-0.92_91, -0.40_61, -0.93_07] )
elif model_name == "videomae-small-finetuned-ssv2":
__lowerCAmelCase = torch.Size([1, 174] )
__lowerCAmelCase = torch.tensor([0.26_71, -0.46_89, -0.82_35] )
elif model_name == "videomae-base":
__lowerCAmelCase = torch.Size([1, 1408, 1536] )
__lowerCAmelCase = torch.tensor([[0.77_39, 0.79_68, 0.70_89], [0.67_01, 0.74_87, 0.62_09], [0.42_87, 0.51_58, 0.47_73]] )
elif model_name == "videomae-base-short":
__lowerCAmelCase = torch.Size([1, 1408, 1536] )
__lowerCAmelCase = torch.tensor([[0.79_94, 0.96_12, 0.85_08], [0.74_01, 0.89_58, 0.83_02], [0.58_62, 0.74_68, 0.73_25]] )
# we verified the loss both for normalized and unnormalized targets for this one
__lowerCAmelCase = torch.tensor([0.51_42] ) if config.norm_pix_loss else torch.tensor([0.64_69] )
elif model_name == "videomae-large":
__lowerCAmelCase = torch.Size([1, 1408, 1536] )
__lowerCAmelCase = torch.tensor([[0.71_49, 0.79_97, 0.69_66], [0.67_68, 0.78_69, 0.69_48], [0.51_39, 0.62_21, 0.56_05]] )
elif model_name == "videomae-large-finetuned-kinetics":
__lowerCAmelCase = torch.Size([1, 400] )
__lowerCAmelCase = torch.tensor([0.07_71, 0.00_11, -0.36_25] )
elif model_name == "videomae-huge-finetuned-kinetics":
__lowerCAmelCase = torch.Size([1, 400] )
__lowerCAmelCase = torch.tensor([0.24_33, 0.16_32, -0.48_94] )
elif model_name == "videomae-base-short-finetuned-kinetics":
__lowerCAmelCase = torch.Size([1, 400] )
__lowerCAmelCase = torch.tensor([0.65_88, 0.09_90, -0.24_93] )
elif model_name == "videomae-base-finetuned-kinetics":
__lowerCAmelCase = torch.Size([1, 400] )
__lowerCAmelCase = torch.tensor([0.36_69, -0.06_88, -0.24_21] )
elif model_name == "videomae-base-short-ssv2":
__lowerCAmelCase = torch.Size([1, 1408, 1536] )
__lowerCAmelCase = torch.tensor([[0.47_12, 0.52_96, 0.57_86], [0.22_78, 0.27_29, 0.40_26], [0.03_52, 0.07_30, 0.25_06]] )
elif model_name == "videomae-base-short-finetuned-ssv2":
__lowerCAmelCase = torch.Size([1, 174] )
__lowerCAmelCase = torch.tensor([-0.05_37, -0.15_39, -0.32_66] )
elif model_name == "videomae-base-ssv2":
__lowerCAmelCase = torch.Size([1, 1408, 1536] )
__lowerCAmelCase = torch.tensor([[0.81_31, 0.87_27, 0.85_46], [0.73_66, 0.93_77, 0.88_70], [0.59_35, 0.88_74, 0.85_64]] )
elif model_name == "videomae-base-finetuned-ssv2":
__lowerCAmelCase = torch.Size([1, 174] )
__lowerCAmelCase = torch.tensor([0.19_61, -0.83_37, -0.63_89] )
else:
raise ValueError(f'Model name not supported. Should be one of {model_names}' )
# verify logits
assert logits.shape == expected_shape
if "finetuned" in model_name:
assert torch.allclose(logits[0, :3] , lowercase , atol=1e-4 )
else:
print("""Logits:""" , logits[0, :3, :3] )
assert torch.allclose(logits[0, :3, :3] , lowercase , atol=1e-4 )
print("""Logits ok!""" )
# verify loss, if applicable
if model_name == "videomae-base-short":
__lowerCAmelCase = outputs.loss
assert torch.allclose(lowercase , lowercase , atol=1e-4 )
print("""Loss ok!""" )
if pytorch_dump_folder_path is not None:
print(f'Saving model and image processor to {pytorch_dump_folder_path}' )
image_processor.save_pretrained(lowercase )
model.save_pretrained(lowercase )
if push_to_hub:
print("""Pushing to the hub...""" )
model.push_to_hub(lowercase , organization="""nielsr""" )
if __name__ == "__main__":
_a : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--checkpoint_url""",
default="""https://drive.google.com/u/1/uc?id=1tEhLyskjb755TJ65ptsrafUG2llSwQE1&export=download&confirm=t&uuid=aa3276eb-fb7e-482a-adec-dc7171df14c4""",
type=str,
help=(
"""URL of the original PyTorch checkpoint (on Google Drive) you'd like to convert. Should be a direct"""
""" download link."""
),
)
parser.add_argument(
"""--pytorch_dump_folder_path""",
default="""/Users/nielsrogge/Documents/VideoMAE/Test""",
type=str,
help="""Path to the output PyTorch model directory.""",
)
parser.add_argument("""--model_name""", default="""videomae-base""", type=str, help="""Name of the model.""")
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
)
_a : int = parser.parse_args()
convert_videomae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 689 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_a : Tuple = {
"""configuration_convnext""": ["""CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ConvNextConfig""", """ConvNextOnnxConfig"""]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_a : Optional[int] = ["""ConvNextFeatureExtractor"""]
_a : str = ["""ConvNextImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_a : List[Any] = [
"""CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""ConvNextForImageClassification""",
"""ConvNextModel""",
"""ConvNextPreTrainedModel""",
"""ConvNextBackbone""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_a : Union[str, Any] = [
"""TFConvNextForImageClassification""",
"""TFConvNextModel""",
"""TFConvNextPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig, ConvNextOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_convnext import ConvNextFeatureExtractor
from .image_processing_convnext import ConvNextImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_convnext import (
CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
ConvNextBackbone,
ConvNextForImageClassification,
ConvNextModel,
ConvNextPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel
else:
import sys
_a : Optional[int] = _LazyModule(__name__, globals()["""__file__"""], _import_structure)
| 689 |
'''simple docstring'''
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
_a : Tuple = """\
"""
_a : Tuple = """
Perplexity (PPL) is one of the most common metrics for evaluating language models.
It is defined as the exponentiated average negative log-likelihood of a sequence.
For more information, see https://huggingface.co/docs/transformers/perplexity
"""
_a : Optional[Any] = """
Args:
model_id (str): model used for calculating Perplexity
NOTE: Perplexity can only be calculated for causal language models.
This includes models such as gpt2, causal variations of bert,
causal versions of t5, and more (the full list can be found
in the AutoModelForCausalLM documentation here:
https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )
input_texts (list of str): input text, each separate text snippet
is one list entry.
batch_size (int): the batch size to run texts through the model. Defaults to 16.
add_start_token (bool): whether to add the start token to the texts,
so the perplexity can include the probability of the first word. Defaults to True.
device (str): device to run on, defaults to 'cuda' when available
Returns:
perplexity: dictionary containing the perplexity scores for the texts
in the input list, as well as the mean perplexity. If one of the input texts is
longer than the max input length of the model, then it is truncated to the
max length for the perplexity computation.
Examples:
Example 1:
>>> perplexity = datasets.load_metric(\"perplexity\")
>>> input_texts = [\"lorem ipsum\", \"Happy Birthday!\", \"Bienvenue\"]
>>> results = perplexity.compute(model_id='gpt2',
... add_start_token=False,
... input_texts=input_texts) # doctest:+ELLIPSIS
>>> print(list(results.keys()))
['perplexities', 'mean_perplexity']
>>> print(round(results[\"mean_perplexity\"], 2))
78.22
>>> print(round(results[\"perplexities\"][0], 2))
11.11
Example 2:
>>> perplexity = datasets.load_metric(\"perplexity\")
>>> input_texts = datasets.load_dataset(\"wikitext\",
... \"wikitext-2-raw-v1\",
... split=\"test\")[\"text\"][:50] # doctest:+ELLIPSIS
[...]
>>> input_texts = [s for s in input_texts if s!='']
>>> results = perplexity.compute(model_id='gpt2',
... input_texts=input_texts) # doctest:+ELLIPSIS
>>> print(list(results.keys()))
['perplexities', 'mean_perplexity']
>>> print(round(results[\"mean_perplexity\"], 2))
60.35
>>> print(round(results[\"perplexities\"][0], 2))
81.12
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _UpperCAmelCase ( datasets.Metric ):
def lowerCamelCase__ ( self ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION,citation=_CITATION,inputs_description=_KWARGS_DESCRIPTION,features=datasets.Features(
{
"""input_texts""": datasets.Value("""string""" ),
} ),reference_urls=["""https://huggingface.co/docs/transformers/perplexity"""],)
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE = 16,__SCREAMING_SNAKE_CASE = True,__SCREAMING_SNAKE_CASE=None ):
'''simple docstring'''
if device is not None:
assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
if device == "gpu":
__lowerCAmelCase = """cuda"""
else:
__lowerCAmelCase = """cuda""" if torch.cuda.is_available() else """cpu"""
__lowerCAmelCase = AutoModelForCausalLM.from_pretrained(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = model.to(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE )
# if batch_size > 1 (which generally leads to padding being required), and
# if there is not an already assigned pad_token, assign an existing
# special token to also be the padding token
if tokenizer.pad_token is None and batch_size > 1:
__lowerCAmelCase = list(tokenizer.special_tokens_map_extended.values() )
# check that the model already has at least one special token defined
assert (
len(__SCREAMING_SNAKE_CASE ) > 0
), "If batch_size > 1, model must have at least one special token to use for padding. Please use a different model or set batch_size=1."
# assign one of the special tokens to also be the pad token
tokenizer.add_special_tokens({"""pad_token""": existing_special_tokens[0]} )
if add_start_token:
# leave room for <BOS> token to be added:
assert (
tokenizer.bos_token is not None
), "Input model must already have a BOS token if using add_start_token=True. Please use a different model, or set add_start_token=False"
__lowerCAmelCase = model.config.max_length - 1
else:
__lowerCAmelCase = model.config.max_length
__lowerCAmelCase = tokenizer(
__SCREAMING_SNAKE_CASE,add_special_tokens=__SCREAMING_SNAKE_CASE,padding=__SCREAMING_SNAKE_CASE,truncation=__SCREAMING_SNAKE_CASE,max_length=__SCREAMING_SNAKE_CASE,return_tensors="""pt""",return_attention_mask=__SCREAMING_SNAKE_CASE,).to(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = encodings["""input_ids"""]
__lowerCAmelCase = encodings["""attention_mask"""]
# check that each input is long enough:
if add_start_token:
assert torch.all(torch.ge(attn_masks.sum(1 ),1 ) ), "Each input text must be at least one token long."
else:
assert torch.all(
torch.ge(attn_masks.sum(1 ),2 ) ), "When add_start_token=False, each input text must be at least two tokens long. Run with add_start_token=True if inputting strings of only one token, and remove all empty input strings."
__lowerCAmelCase = []
__lowerCAmelCase = CrossEntropyLoss(reduction="""none""" )
for start_index in logging.tqdm(range(0,len(__SCREAMING_SNAKE_CASE ),__SCREAMING_SNAKE_CASE ) ):
__lowerCAmelCase = min(start_index + batch_size,len(__SCREAMING_SNAKE_CASE ) )
__lowerCAmelCase = encoded_texts[start_index:end_index]
__lowerCAmelCase = attn_masks[start_index:end_index]
if add_start_token:
__lowerCAmelCase = torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0 ) ).to(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = torch.cat([bos_tokens_tensor, encoded_batch],dim=1 )
__lowerCAmelCase = torch.cat(
[torch.ones(bos_tokens_tensor.size(),dtype=torch.intaa ).to(__SCREAMING_SNAKE_CASE ), attn_mask],dim=1 )
__lowerCAmelCase = encoded_batch
with torch.no_grad():
__lowerCAmelCase = model(__SCREAMING_SNAKE_CASE,attention_mask=__SCREAMING_SNAKE_CASE ).logits
__lowerCAmelCase = out_logits[..., :-1, :].contiguous()
__lowerCAmelCase = labels[..., 1:].contiguous()
__lowerCAmelCase = attn_mask[..., 1:].contiguous()
__lowerCAmelCase = torch.expa(
(loss_fct(shift_logits.transpose(1,2 ),__SCREAMING_SNAKE_CASE ) * shift_attention_mask_batch).sum(1 )
/ shift_attention_mask_batch.sum(1 ) )
ppls += perplexity_batch.tolist()
return {"perplexities": ppls, "mean_perplexity": np.mean(__SCREAMING_SNAKE_CASE )}
| 689 | 1 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:
_a : Optional[Any] = None
_a : List[str] = logging.get_logger(__name__)
_a : str = """▁"""
_a : Union[str, Any] = {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}
_a : Union[str, Any] = {
"""vocab_file""": {"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"""},
"""tokenizer_file""": {
"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json"""
},
}
_a : Optional[Any] = {
"""google/pegasus-xsum""": 5_1_2,
}
class _UpperCAmelCase ( lowerCAmelCase_ ):
a : str =VOCAB_FILES_NAMES
a : Tuple =PRETRAINED_VOCAB_FILES_MAP
a : Optional[int] =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a : Union[str, Any] =PegasusTokenizer
a : List[Any] =["""input_ids""", """attention_mask"""]
def __init__( self,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE="<pad>",__SCREAMING_SNAKE_CASE="</s>",__SCREAMING_SNAKE_CASE="<unk>",__SCREAMING_SNAKE_CASE="<mask_2>",__SCREAMING_SNAKE_CASE="<mask_1>",__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=1_03,**__SCREAMING_SNAKE_CASE,):
'''simple docstring'''
__lowerCAmelCase = offset
if additional_special_tokens is not None:
if not isinstance(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE ):
raise TypeError(
f'additional_special_tokens should be of type {type(__SCREAMING_SNAKE_CASE )}, but is'
f' {type(__SCREAMING_SNAKE_CASE )}' )
__lowerCAmelCase = (
([mask_token_sent] + additional_special_tokens)
if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
else additional_special_tokens
)
# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
additional_special_tokens_extended += [
f'<unk_{i}>' for i in range(len(__SCREAMING_SNAKE_CASE ),self.offset - 1 )
]
if len(set(__SCREAMING_SNAKE_CASE ) ) != len(__SCREAMING_SNAKE_CASE ):
raise ValueError(
"""Please make sure that the provided additional_special_tokens do not contain an incorrectly"""
f' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.' )
__lowerCAmelCase = additional_special_tokens_extended
else:
__lowerCAmelCase = [mask_token_sent] if mask_token_sent is not None else []
additional_special_tokens += [f'<unk_{i}>' for i in range(2,self.offset )]
super().__init__(
__SCREAMING_SNAKE_CASE,tokenizer_file=__SCREAMING_SNAKE_CASE,pad_token=__SCREAMING_SNAKE_CASE,eos_token=__SCREAMING_SNAKE_CASE,unk_token=__SCREAMING_SNAKE_CASE,mask_token=__SCREAMING_SNAKE_CASE,mask_token_sent=__SCREAMING_SNAKE_CASE,offset=__SCREAMING_SNAKE_CASE,additional_special_tokens=__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE,)
__lowerCAmelCase = vocab_file
__lowerCAmelCase = False if not self.vocab_file else True
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase = set(self.all_special_ids ) # call it once instead of inside list comp
all_special_ids.remove(self.unk_token_id ) # <unk> is only sometimes special
if all_special_ids != set(range(len(self.additional_special_tokens ) + 3 ) ):
raise ValueError(
"""There should be 3 special tokens: mask_token, pad_token, and eos_token +"""
f' {len(self.additional_special_tokens )} additional_special_tokens, but got {all_special_ids}' )
return [1 if x in all_special_ids else 0 for x in seq]
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = False ):
'''simple docstring'''
if already_has_special_tokens:
return self._special_token_mask(__SCREAMING_SNAKE_CASE )
elif token_ids_a is None:
return self._special_token_mask(__SCREAMING_SNAKE_CASE ) + [1]
else:
return self._special_token_mask(token_ids_a + token_ids_a ) + [1]
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=None ):
'''simple docstring'''
if token_ids_a is None:
return token_ids_a + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic for API consistency
return token_ids_a + token_ids_a + [self.eos_token_id]
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE = None ):
'''simple docstring'''
if not self.can_save_slow_tokenizer:
raise ValueError(
"""Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
"""tokenizer.""" )
if not os.path.isdir(__SCREAMING_SNAKE_CASE ):
logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
return
__lowerCAmelCase = os.path.join(
__SCREAMING_SNAKE_CASE,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__SCREAMING_SNAKE_CASE ):
copyfile(self.vocab_file,__SCREAMING_SNAKE_CASE )
return (out_vocab_file,)
| 689 |
'''simple docstring'''
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
import torch
if is_tf_available():
import tensorflow as tf
class _UpperCAmelCase ( lowerCAmelCase_ ):
a : Union[str, Any] =["""image_processor"""]
a : Dict ="""SamImageProcessor"""
def __init__( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
super().__init__(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = self.image_processor
__lowerCAmelCase = -10
__lowerCAmelCase = self.image_processor.size["""longest_edge"""]
def __call__( self,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE = None,**__SCREAMING_SNAKE_CASE,):
'''simple docstring'''
__lowerCAmelCase = self.image_processor(
__SCREAMING_SNAKE_CASE,return_tensors=__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE,)
# pop arguments that are not used in the foward but used nevertheless
__lowerCAmelCase = encoding_image_processor["""original_sizes"""]
if hasattr(__SCREAMING_SNAKE_CASE,"""numpy""" ): # Checks if Torch or TF tensor
__lowerCAmelCase = original_sizes.numpy()
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = self._check_and_preprocess_points(
input_points=__SCREAMING_SNAKE_CASE,input_labels=__SCREAMING_SNAKE_CASE,input_boxes=__SCREAMING_SNAKE_CASE,)
__lowerCAmelCase = self._normalize_and_convert(
__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,input_points=__SCREAMING_SNAKE_CASE,input_labels=__SCREAMING_SNAKE_CASE,input_boxes=__SCREAMING_SNAKE_CASE,return_tensors=__SCREAMING_SNAKE_CASE,)
return encoding_image_processor
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE="pt",):
'''simple docstring'''
if input_points is not None:
if len(__SCREAMING_SNAKE_CASE ) != len(__SCREAMING_SNAKE_CASE ):
__lowerCAmelCase = [
self._normalize_coordinates(self.target_size,__SCREAMING_SNAKE_CASE,original_sizes[0] ) for point in input_points
]
else:
__lowerCAmelCase = [
self._normalize_coordinates(self.target_size,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
for point, original_size in zip(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
]
# check that all arrays have the same shape
if not all(point.shape == input_points[0].shape for point in input_points ):
if input_labels is not None:
__lowerCAmelCase , __lowerCAmelCase = self._pad_points_and_labels(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = np.array(__SCREAMING_SNAKE_CASE )
if input_labels is not None:
__lowerCAmelCase = np.array(__SCREAMING_SNAKE_CASE )
if input_boxes is not None:
if len(__SCREAMING_SNAKE_CASE ) != len(__SCREAMING_SNAKE_CASE ):
__lowerCAmelCase = [
self._normalize_coordinates(self.target_size,__SCREAMING_SNAKE_CASE,original_sizes[0],is_bounding_box=__SCREAMING_SNAKE_CASE )
for box in input_boxes
]
else:
__lowerCAmelCase = [
self._normalize_coordinates(self.target_size,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,is_bounding_box=__SCREAMING_SNAKE_CASE )
for box, original_size in zip(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
]
__lowerCAmelCase = np.array(__SCREAMING_SNAKE_CASE )
if input_boxes is not None:
if return_tensors == "pt":
__lowerCAmelCase = torch.from_numpy(__SCREAMING_SNAKE_CASE )
# boxes batch size of 1 by default
__lowerCAmelCase = input_boxes.unsqueeze(1 ) if len(input_boxes.shape ) != 3 else input_boxes
elif return_tensors == "tf":
__lowerCAmelCase = tf.convert_to_tensor(__SCREAMING_SNAKE_CASE )
# boxes batch size of 1 by default
__lowerCAmelCase = tf.expand_dims(__SCREAMING_SNAKE_CASE,1 ) if len(input_boxes.shape ) != 3 else input_boxes
encoding_image_processor.update({"""input_boxes""": input_boxes} )
if input_points is not None:
if return_tensors == "pt":
__lowerCAmelCase = torch.from_numpy(__SCREAMING_SNAKE_CASE )
# point batch size of 1 by default
__lowerCAmelCase = input_points.unsqueeze(1 ) if len(input_points.shape ) != 4 else input_points
elif return_tensors == "tf":
__lowerCAmelCase = tf.convert_to_tensor(__SCREAMING_SNAKE_CASE )
# point batch size of 1 by default
__lowerCAmelCase = tf.expand_dims(__SCREAMING_SNAKE_CASE,1 ) if len(input_points.shape ) != 4 else input_points
encoding_image_processor.update({"""input_points""": input_points} )
if input_labels is not None:
if return_tensors == "pt":
__lowerCAmelCase = torch.from_numpy(__SCREAMING_SNAKE_CASE )
# point batch size of 1 by default
__lowerCAmelCase = input_labels.unsqueeze(1 ) if len(input_labels.shape ) != 3 else input_labels
elif return_tensors == "tf":
__lowerCAmelCase = tf.convert_to_tensor(__SCREAMING_SNAKE_CASE )
# point batch size of 1 by default
__lowerCAmelCase = tf.expand_dims(__SCREAMING_SNAKE_CASE,1 ) if len(input_labels.shape ) != 3 else input_labels
encoding_image_processor.update({"""input_labels""": input_labels} )
return encoding_image_processor
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase = max([point.shape[0] for point in input_points] )
__lowerCAmelCase = []
for i, point in enumerate(__SCREAMING_SNAKE_CASE ):
if point.shape[0] != expected_nb_points:
__lowerCAmelCase = np.concatenate(
[point, np.zeros((expected_nb_points - point.shape[0], 2) ) + self.point_pad_value],axis=0 )
__lowerCAmelCase = np.append(input_labels[i],[self.point_pad_value] )
processed_input_points.append(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = processed_input_points
return input_points, input_labels
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=False ):
'''simple docstring'''
__lowerCAmelCase , __lowerCAmelCase = original_size
__lowerCAmelCase , __lowerCAmelCase = self.image_processor._get_preprocess_shape(__SCREAMING_SNAKE_CASE,longest_edge=__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = deepcopy(__SCREAMING_SNAKE_CASE ).astype(__SCREAMING_SNAKE_CASE )
if is_bounding_box:
__lowerCAmelCase = coords.reshape(-1,2,2 )
__lowerCAmelCase = coords[..., 0] * (new_w / old_w)
__lowerCAmelCase = coords[..., 1] * (new_h / old_h)
if is_bounding_box:
__lowerCAmelCase = coords.reshape(-1,4 )
return coords
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=None,):
'''simple docstring'''
if input_points is not None:
if hasattr(__SCREAMING_SNAKE_CASE,"""numpy""" ): # Checks for TF or Torch tensor
__lowerCAmelCase = input_points.numpy().tolist()
if not isinstance(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE ) or not isinstance(input_points[0],__SCREAMING_SNAKE_CASE ):
raise ValueError("""Input points must be a list of list of floating points.""" )
__lowerCAmelCase = [np.array(__SCREAMING_SNAKE_CASE ) for input_point in input_points]
else:
__lowerCAmelCase = None
if input_labels is not None:
if hasattr(__SCREAMING_SNAKE_CASE,"""numpy""" ):
__lowerCAmelCase = input_labels.numpy().tolist()
if not isinstance(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE ) or not isinstance(input_labels[0],__SCREAMING_SNAKE_CASE ):
raise ValueError("""Input labels must be a list of list integers.""" )
__lowerCAmelCase = [np.array(__SCREAMING_SNAKE_CASE ) for label in input_labels]
else:
__lowerCAmelCase = None
if input_boxes is not None:
if hasattr(__SCREAMING_SNAKE_CASE,"""numpy""" ):
__lowerCAmelCase = input_boxes.numpy().tolist()
if (
not isinstance(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
or not isinstance(input_boxes[0],__SCREAMING_SNAKE_CASE )
or not isinstance(input_boxes[0][0],__SCREAMING_SNAKE_CASE )
):
raise ValueError("""Input boxes must be a list of list of list of floating points.""" )
__lowerCAmelCase = [np.array(__SCREAMING_SNAKE_CASE ).astype(np.floataa ) for box in input_boxes]
else:
__lowerCAmelCase = None
return input_points, input_labels, input_boxes
@property
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = self.image_processor.model_input_names
return list(dict.fromkeys(__SCREAMING_SNAKE_CASE ) )
def lowerCamelCase__ ( self,*__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
return self.image_processor.post_process_masks(*__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE )
| 689 | 1 |
'''simple docstring'''
import string
import numpy
def _lowerCAmelCase ( lowercase , lowercase ) -> int:
return b if a == 0 else greatest_common_divisor(b % a , lowercase )
class _UpperCAmelCase :
a : Tuple =string.ascii_uppercase + string.digits
# This cipher takes alphanumerics into account
# i.e. a total of 36 characters
# take x and return x % len(key_string)
a : Union[str, Any] =numpy.vectorize(lambda lowerCAmelCase_ : x % 36 )
a : Optional[int] =numpy.vectorize(lowerCAmelCase_ )
def __init__( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase = self.modulus(__SCREAMING_SNAKE_CASE ) # mod36 calc's on the encrypt key
self.check_determinant() # validate the determinant of the encryption key
__lowerCAmelCase = encrypt_key.shape[0]
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
return self.key_string.index(__SCREAMING_SNAKE_CASE )
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
return self.key_string[round(__SCREAMING_SNAKE_CASE )]
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = round(numpy.linalg.det(self.encrypt_key ) )
if det < 0:
__lowerCAmelCase = det % len(self.key_string )
__lowerCAmelCase = len(self.key_string )
if greatest_common_divisor(__SCREAMING_SNAKE_CASE,len(self.key_string ) ) != 1:
__lowerCAmelCase = (
f'determinant modular {req_l} of encryption key({det}) '
f'is not co prime w.r.t {req_l}.\nTry another key.'
)
raise ValueError(__SCREAMING_SNAKE_CASE )
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase = [char for char in text.upper() if char in self.key_string]
__lowerCAmelCase = chars[-1]
while len(__SCREAMING_SNAKE_CASE ) % self.break_key != 0:
chars.append(__SCREAMING_SNAKE_CASE )
return "".join(__SCREAMING_SNAKE_CASE )
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase = self.process_text(text.upper() )
__lowerCAmelCase = """"""
for i in range(0,len(__SCREAMING_SNAKE_CASE ) - self.break_key + 1,self.break_key ):
__lowerCAmelCase = text[i : i + self.break_key]
__lowerCAmelCase = [self.replace_letters(__SCREAMING_SNAKE_CASE ) for char in batch]
__lowerCAmelCase = numpy.array([vec] ).T
__lowerCAmelCase = self.modulus(self.encrypt_key.dot(__SCREAMING_SNAKE_CASE ) ).T.tolist()[
0
]
__lowerCAmelCase = """""".join(
self.replace_digits(__SCREAMING_SNAKE_CASE ) for num in batch_encrypted )
encrypted += encrypted_batch
return encrypted
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = round(numpy.linalg.det(self.encrypt_key ) )
if det < 0:
__lowerCAmelCase = det % len(self.key_string )
__lowerCAmelCase = None
for i in range(len(self.key_string ) ):
if (det * i) % len(self.key_string ) == 1:
__lowerCAmelCase = i
break
__lowerCAmelCase = (
det_inv
* numpy.linalg.det(self.encrypt_key )
* numpy.linalg.inv(self.encrypt_key )
)
return self.to_int(self.modulus(__SCREAMING_SNAKE_CASE ) )
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase = self.make_decrypt_key()
__lowerCAmelCase = self.process_text(text.upper() )
__lowerCAmelCase = """"""
for i in range(0,len(__SCREAMING_SNAKE_CASE ) - self.break_key + 1,self.break_key ):
__lowerCAmelCase = text[i : i + self.break_key]
__lowerCAmelCase = [self.replace_letters(__SCREAMING_SNAKE_CASE ) for char in batch]
__lowerCAmelCase = numpy.array([vec] ).T
__lowerCAmelCase = self.modulus(decrypt_key.dot(__SCREAMING_SNAKE_CASE ) ).T.tolist()[0]
__lowerCAmelCase = """""".join(
self.replace_digits(__SCREAMING_SNAKE_CASE ) for num in batch_decrypted )
decrypted += decrypted_batch
return decrypted
def _lowerCAmelCase ( ) -> None:
__lowerCAmelCase = int(input("""Enter the order of the encryption key: """ ) )
__lowerCAmelCase = []
print("""Enter each row of the encryption key with space separated integers""" )
for _ in range(lowercase ):
__lowerCAmelCase = [int(lowercase ) for x in input().split()]
hill_matrix.append(lowercase )
__lowerCAmelCase = HillCipher(numpy.array(lowercase ) )
print("""Would you like to encrypt or decrypt some text? (1 or 2)""" )
__lowerCAmelCase = input("""\n1. Encrypt\n2. Decrypt\n""" )
if option == "1":
__lowerCAmelCase = input("""What text would you like to encrypt?: """ )
print("""Your encrypted text is:""" )
print(hc.encrypt(lowercase ) )
elif option == "2":
__lowerCAmelCase = input("""What text would you like to decrypt?: """ )
print("""Your decrypted text is:""" )
print(hc.decrypt(lowercase ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 689 |
'''simple docstring'''
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequenceClassification,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
TapexTokenizer,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version
from transformers.utils.versions import require_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("""4.17.0.dev0""")
require_version("""datasets>=1.8.0""", """To fix: pip install -r examples/pytorch/text-classification/requirements.txt""")
_a : int = logging.getLogger(__name__)
@dataclass
class _UpperCAmelCase :
a : Optional[str] =field(
default="""tab_fact""" , metadata={"""help""": """The name of the dataset to use (via the datasets library)."""} )
a : Optional[str] =field(
default="""tab_fact""" , metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""} , )
a : int =field(
default=10_24 , metadata={
"""help""": (
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
)
} , )
a : bool =field(
default=lowerCAmelCase_ , metadata={"""help""": """Overwrite the cached preprocessed datasets or not."""} )
a : bool =field(
default=lowerCAmelCase_ , metadata={
"""help""": (
"""Whether to pad all samples to `max_seq_length`. """
"""If False, will pad the samples dynamically when batching to the maximum length in the batch."""
)
} , )
a : Optional[int] =field(
default=lowerCAmelCase_ , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of training examples to this """
"""value if set."""
)
} , )
a : Optional[int] =field(
default=lowerCAmelCase_ , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of evaluation examples to this """
"""value if set."""
)
} , )
a : Optional[int] =field(
default=lowerCAmelCase_ , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of prediction examples to this """
"""value if set."""
)
} , )
a : Optional[str] =field(
default=lowerCAmelCase_ , metadata={"""help""": """A csv or a json file containing the training data."""} )
a : Optional[str] =field(
default=lowerCAmelCase_ , metadata={"""help""": """A csv or a json file containing the validation data."""} )
a : Optional[str] =field(default=lowerCAmelCase_ , metadata={"""help""": """A csv or a json file containing the test data."""} )
def lowerCamelCase__ ( self ):
'''simple docstring'''
if self.dataset_name is not None:
pass
elif self.train_file is None or self.validation_file is None:
raise ValueError("""Need either a GLUE task, a training/validation file or a dataset name.""" )
else:
__lowerCAmelCase = self.train_file.split(""".""" )[-1]
assert train_extension in ["csv", "json"], "`train_file` should be a csv or a json file."
__lowerCAmelCase = self.validation_file.split(""".""" )[-1]
assert (
validation_extension == train_extension
), "`validation_file` should have the same extension (csv or json) as `train_file`."
@dataclass
class _UpperCAmelCase :
a : str =field(
default=lowerCAmelCase_ , metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""} )
a : Optional[str] =field(
default=lowerCAmelCase_ , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""} )
a : Optional[str] =field(
default=lowerCAmelCase_ , metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""} )
a : Optional[str] =field(
default=lowerCAmelCase_ , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} , )
a : bool =field(
default=lowerCAmelCase_ , metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""} , )
a : str =field(
default="""main""" , metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""} , )
a : bool =field(
default=lowerCAmelCase_ , metadata={
"""help""": (
"""Will use the token generated when running `huggingface-cli login` (necessary to use this script """
"""with private models)."""
)
} , )
def _lowerCAmelCase ( ) -> Optional[Any]:
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
__lowerCAmelCase = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith(""".json""" ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = parser.parse_args_into_dataclasses()
# Setup logging
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , handlers=[logging.StreamHandler(sys.stdout )] , )
__lowerCAmelCase = training_args.get_process_log_level()
logger.setLevel(lowercase )
datasets.utils.logging.set_verbosity(lowercase )
transformers.utils.logging.set_verbosity(lowercase )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
f'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
+ f'distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}' )
logger.info(f'Training/evaluation parameters {training_args}' )
# Detecting last checkpoint.
__lowerCAmelCase = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
__lowerCAmelCase = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
f'Output directory ({training_args.output_dir}) already exists and is not empty. '
"""Use --overwrite_output_dir to overcome.""" )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
f'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
"""the `--output_dir` or add `--overwrite_output_dir` to train from scratch.""" )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
# or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub).
#
# For JSON files, this script will use the `question` column for the input question and `table` column for the corresponding table.
#
# If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this
# single column. You can easily tweak this behavior (see below)
#
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
# download the dataset.
if data_args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
__lowerCAmelCase = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , cache_dir=model_args.cache_dir )
else:
# Loading a dataset from your local files.
# CSV/JSON training and evaluation files are needed.
__lowerCAmelCase = {"""train""": data_args.train_file, """validation""": data_args.validation_file}
# Get the test dataset: you can provide your own CSV/JSON test file (see below)
# when you use `do_predict` without specifying a GLUE benchmark task.
if training_args.do_predict:
if data_args.test_file is not None:
__lowerCAmelCase = data_args.train_file.split(""".""" )[-1]
__lowerCAmelCase = data_args.test_file.split(""".""" )[-1]
assert (
test_extension == train_extension
), "`test_file` should have the same extension (csv or json) as `train_file`."
__lowerCAmelCase = data_args.test_file
else:
raise ValueError("""Need either a GLUE task or a test file for `do_predict`.""" )
for key in data_files.keys():
logger.info(f'load a local file for {key}: {data_files[key]}' )
if data_args.train_file.endswith(""".csv""" ):
# Loading a dataset from local csv files
__lowerCAmelCase = load_dataset("""csv""" , data_files=lowercase , cache_dir=model_args.cache_dir )
else:
# Loading a dataset from local json files
__lowerCAmelCase = load_dataset("""json""" , data_files=lowercase , cache_dir=model_args.cache_dir )
# See more about loading any type of standard or custom dataset at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Labels
__lowerCAmelCase = raw_datasets["""train"""].features["""label"""].names
__lowerCAmelCase = len(lowercase )
# Load pretrained model and tokenizer
#
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
__lowerCAmelCase = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=lowercase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# load tapex tokenizer
__lowerCAmelCase = TapexTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , add_prefix_space=lowercase , )
__lowerCAmelCase = BartForSequenceClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool(""".ckpt""" in model_args.model_name_or_path ) , config=lowercase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# Padding strategy
if data_args.pad_to_max_length:
__lowerCAmelCase = """max_length"""
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
__lowerCAmelCase = False
# Some models have set the order of the labels to use, so let's make sure we do use it.
__lowerCAmelCase = {"""Refused""": 0, """Entailed""": 1}
__lowerCAmelCase = {0: """Refused""", 1: """Entailed"""}
if data_args.max_seq_length > tokenizer.model_max_length:
logger.warning(
f'The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the'
f'model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.' )
__lowerCAmelCase = min(data_args.max_seq_length , tokenizer.model_max_length )
def preprocess_tabfact_function(lowercase ):
# Tokenize the texts
def _convert_table_text_to_pandas(lowercase ):
__lowerCAmelCase = [_table_row.split("""#""" ) for _table_row in _table_text.strip("""\n""" ).split("""\n""" )]
__lowerCAmelCase = pd.DataFrame.from_records(_table_content[1:] , columns=_table_content[0] )
return _table_pd
__lowerCAmelCase = examples["""statement"""]
__lowerCAmelCase = list(map(_convert_table_text_to_pandas , examples["""table_text"""] ) )
__lowerCAmelCase = tokenizer(lowercase , lowercase , padding=lowercase , max_length=lowercase , truncation=lowercase )
__lowerCAmelCase = examples["""label"""]
return result
with training_args.main_process_first(desc="""dataset map pre-processing""" ):
__lowerCAmelCase = raw_datasets.map(
lowercase , batched=lowercase , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on dataset""" , )
if training_args.do_train:
if "train" not in raw_datasets:
raise ValueError("""--do_train requires a train dataset""" )
__lowerCAmelCase = raw_datasets["""train"""]
if data_args.max_train_samples is not None:
__lowerCAmelCase = train_dataset.select(range(data_args.max_train_samples ) )
if training_args.do_eval:
if "validation" not in raw_datasets and "validation_matched" not in raw_datasets:
raise ValueError("""--do_eval requires a validation dataset""" )
__lowerCAmelCase = raw_datasets["""validation"""]
if data_args.max_eval_samples is not None:
__lowerCAmelCase = eval_dataset.select(range(data_args.max_eval_samples ) )
if training_args.do_predict or data_args.test_file is not None:
if "test" not in raw_datasets and "test_matched" not in raw_datasets:
raise ValueError("""--do_predict requires a test dataset""" )
__lowerCAmelCase = raw_datasets["""test"""]
if data_args.max_predict_samples is not None:
__lowerCAmelCase = predict_dataset.select(range(data_args.max_predict_samples ) )
# Log a few random samples from the training set:
if training_args.do_train:
for index in random.sample(range(len(lowercase ) ) , 3 ):
logger.info(f'Sample {index} of the training set: {train_dataset[index]}.' )
# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(lowercase ):
__lowerCAmelCase = p.predictions[0] if isinstance(p.predictions , lowercase ) else p.predictions
__lowerCAmelCase = np.argmax(lowercase , axis=1 )
return {"accuracy": (preds == p.label_ids).astype(np.floataa ).mean().item()}
# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
if data_args.pad_to_max_length:
__lowerCAmelCase = default_data_collator
elif training_args.fpaa:
__lowerCAmelCase = DataCollatorWithPadding(lowercase , pad_to_multiple_of=8 )
else:
__lowerCAmelCase = None
# Initialize our Trainer
__lowerCAmelCase = Trainer(
model=lowercase , args=lowercase , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , compute_metrics=lowercase , tokenizer=lowercase , data_collator=lowercase , )
# Training
if training_args.do_train:
__lowerCAmelCase = None
if training_args.resume_from_checkpoint is not None:
__lowerCAmelCase = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
__lowerCAmelCase = last_checkpoint
__lowerCAmelCase = trainer.train(resume_from_checkpoint=lowercase )
__lowerCAmelCase = train_result.metrics
__lowerCAmelCase = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(lowercase )
)
__lowerCAmelCase = min(lowercase , len(lowercase ) )
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics("""train""" , lowercase )
trainer.save_metrics("""train""" , lowercase )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info("""*** Evaluate ***""" )
__lowerCAmelCase = trainer.evaluate(eval_dataset=lowercase )
__lowerCAmelCase = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(lowercase )
__lowerCAmelCase = min(lowercase , len(lowercase ) )
trainer.log_metrics("""eval""" , lowercase )
trainer.save_metrics("""eval""" , lowercase )
if training_args.do_predict:
logger.info("""*** Predict ***""" )
# Removing the `label` columns because it contains -1 and Trainer won't like that.
__lowerCAmelCase = predict_dataset.remove_columns("""label""" )
__lowerCAmelCase = trainer.predict(lowercase , metric_key_prefix="""predict""" ).predictions
__lowerCAmelCase = np.argmax(lowercase , axis=1 )
__lowerCAmelCase = os.path.join(training_args.output_dir , """predict_results_tabfact.txt""" )
if trainer.is_world_process_zero():
with open(lowercase , """w""" ) as writer:
logger.info("""***** Predict Results *****""" )
writer.write("""index\tprediction\n""" )
for index, item in enumerate(lowercase ):
__lowerCAmelCase = label_list[item]
writer.write(f'{index}\t{item}\n' )
__lowerCAmelCase = {"""finetuned_from""": model_args.model_name_or_path, """tasks""": """text-classification"""}
if training_args.push_to_hub:
trainer.push_to_hub(**lowercase )
else:
trainer.create_model_card(**lowercase )
def _lowerCAmelCase ( lowercase ) -> str:
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 689 | 1 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
_a : Tuple = r"""
[`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and
can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.
Args:
title_sep (`str`, *optional*, defaults to `\" / \"`):
Separator inserted between the title and the text of the retrieved document when calling [`RagRetriever`].
doc_sep (`str`, *optional*, defaults to `\" // \"`):
Separator inserted between the text of the retrieved document and the original input when calling
[`RagRetriever`].
n_docs (`int`, *optional*, defaults to 5):
Number of documents to retrieve.
max_combined_length (`int`, *optional*, defaults to 300):
Max length of contextualized input returned by [`~RagRetriever.__call__`].
retrieval_vector_size (`int`, *optional*, defaults to 768):
Dimensionality of the document embeddings indexed by [`RagRetriever`].
retrieval_batch_size (`int`, *optional*, defaults to 8):
Retrieval batch size, defined as the number of queries issues concurrently to the faiss index encapsulated
[`RagRetriever`].
dataset (`str`, *optional*, defaults to `\"wiki_dpr\"`):
A dataset identifier of the indexed dataset in HuggingFace Datasets (list all available datasets and ids
using `datasets.list_datasets()`).
dataset_split (`str`, *optional*, defaults to `\"train\"`)
Which split of the `dataset` to load.
index_name (`str`, *optional*, defaults to `\"compressed\"`)
The index name of the index associated with the `dataset`. One can choose between `\"legacy\"`, `\"exact\"` and
`\"compressed\"`.
index_path (`str`, *optional*)
The path to the serialized faiss index on disk.
passages_path (`str`, *optional*):
A path to text passages compatible with the faiss index. Required if using
[`~models.rag.retrieval_rag.LegacyIndex`]
use_dummy_dataset (`bool`, *optional*, defaults to `False`)
Whether to load a \"dummy\" variant of the dataset specified by `dataset`.
label_smoothing (`float`, *optional*, defaults to 0.0):
Only relevant if `return_loss` is set to `True`. Controls the `epsilon` parameter value for label smoothing
in the loss calculation. If set to 0, no label smoothing is performed.
do_marginalize (`bool`, *optional*, defaults to `False`):
If `True`, the logits are marginalized over all documents by making use of
`torch.nn.functional.log_softmax`.
reduce_loss (`bool`, *optional*, defaults to `False`):
Whether or not to reduce the NLL loss using the `torch.Tensor.sum` operation.
do_deduplication (`bool`, *optional*, defaults to `True`):
Whether or not to deduplicate the generations from different context documents for a given input. Has to be
set to `False` if used while training with distributed backend.
exclude_bos_score (`bool`, *optional*, defaults to `False`):
Whether or not to disregard the BOS token when computing the loss.
output_retrieved(`bool`, *optional*, defaults to `False`):
If set to `True`, `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and
`context_attention_mask` are returned. See returned tensors for more detail.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models).
forced_eos_token_id (`int`, *optional*):
The id of the token to force as the last generated token when `max_length` is reached. Usually set to
`eos_token_id`.
"""
@add_start_docstrings(lowerCAmelCase_ )
class _UpperCAmelCase ( lowerCAmelCase_ ):
a : str ="""rag"""
a : Dict =True
def __init__( self,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=True,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=" / ",__SCREAMING_SNAKE_CASE=" // ",__SCREAMING_SNAKE_CASE=5,__SCREAMING_SNAKE_CASE=3_00,__SCREAMING_SNAKE_CASE=7_68,__SCREAMING_SNAKE_CASE=8,__SCREAMING_SNAKE_CASE="wiki_dpr",__SCREAMING_SNAKE_CASE="train",__SCREAMING_SNAKE_CASE="compressed",__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=False,__SCREAMING_SNAKE_CASE=False,__SCREAMING_SNAKE_CASE=0.0,__SCREAMING_SNAKE_CASE=True,__SCREAMING_SNAKE_CASE=False,__SCREAMING_SNAKE_CASE=False,__SCREAMING_SNAKE_CASE=False,__SCREAMING_SNAKE_CASE=True,__SCREAMING_SNAKE_CASE=None,**__SCREAMING_SNAKE_CASE,):
'''simple docstring'''
super().__init__(
bos_token_id=__SCREAMING_SNAKE_CASE,pad_token_id=__SCREAMING_SNAKE_CASE,eos_token_id=__SCREAMING_SNAKE_CASE,decoder_start_token_id=__SCREAMING_SNAKE_CASE,forced_eos_token_id=__SCREAMING_SNAKE_CASE,is_encoder_decoder=__SCREAMING_SNAKE_CASE,prefix=__SCREAMING_SNAKE_CASE,vocab_size=__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE,)
assert (
"question_encoder" in kwargs and "generator" in kwargs
), "Config has to be initialized with question_encoder and generator config"
__lowerCAmelCase = kwargs.pop("""question_encoder""" )
__lowerCAmelCase = question_encoder_config.pop("""model_type""" )
__lowerCAmelCase = kwargs.pop("""generator""" )
__lowerCAmelCase = decoder_config.pop("""model_type""" )
from ..auto.configuration_auto import AutoConfig
__lowerCAmelCase = AutoConfig.for_model(__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = AutoConfig.for_model(__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = reduce_loss
__lowerCAmelCase = label_smoothing
__lowerCAmelCase = exclude_bos_score
__lowerCAmelCase = do_marginalize
__lowerCAmelCase = title_sep
__lowerCAmelCase = doc_sep
__lowerCAmelCase = n_docs
__lowerCAmelCase = max_combined_length
__lowerCAmelCase = dataset
__lowerCAmelCase = dataset_split
__lowerCAmelCase = index_name
__lowerCAmelCase = retrieval_vector_size
__lowerCAmelCase = retrieval_batch_size
__lowerCAmelCase = passages_path
__lowerCAmelCase = index_path
__lowerCAmelCase = use_dummy_dataset
__lowerCAmelCase = output_retrieved
__lowerCAmelCase = do_deduplication
__lowerCAmelCase = use_cache
if self.forced_eos_token_id is None:
__lowerCAmelCase = getattr(self.generator,"""forced_eos_token_id""",__SCREAMING_SNAKE_CASE )
@classmethod
def lowerCamelCase__ ( cls,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
return cls(question_encoder=question_encoder_config.to_dict(),generator=generator_config.to_dict(),**__SCREAMING_SNAKE_CASE )
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = copy.deepcopy(self.__dict__ )
__lowerCAmelCase = self.question_encoder.to_dict()
__lowerCAmelCase = self.generator.to_dict()
__lowerCAmelCase = self.__class__.model_type
return output
| 689 |
'''simple docstring'''
import os
import sys
import unittest
_a : List[Any] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init # noqa: E402
# Align TRANSFORMERS_PATH in check_dummies with the current path
_a : Union[str, Any] = os.path.join(git_repo_path, """src""", """diffusers""")
class _UpperCAmelCase ( unittest.TestCase ):
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = find_backend(""" if not is_torch_available():""" )
self.assertEqual(__SCREAMING_SNAKE_CASE,"""torch""" )
# backend_with_underscore = find_backend(" if not is_tensorflow_text_available():")
# self.assertEqual(backend_with_underscore, "tensorflow_text")
__lowerCAmelCase = find_backend(""" if not (is_torch_available() and is_transformers_available()):""" )
self.assertEqual(__SCREAMING_SNAKE_CASE,"""torch_and_transformers""" )
# double_backend_with_underscore = find_backend(
# " if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
# )
# self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")
__lowerCAmelCase = find_backend(
""" if not (is_torch_available() and is_transformers_available() and is_onnx_available()):""" )
self.assertEqual(__SCREAMING_SNAKE_CASE,"""torch_and_transformers_and_onnx""" )
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = read_init()
# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
self.assertIn("""torch""",__SCREAMING_SNAKE_CASE )
self.assertIn("""torch_and_transformers""",__SCREAMING_SNAKE_CASE )
self.assertIn("""flax_and_transformers""",__SCREAMING_SNAKE_CASE )
self.assertIn("""torch_and_transformers_and_onnx""",__SCREAMING_SNAKE_CASE )
# Likewise, we can't assert on the exact content of a key
self.assertIn("""UNet2DModel""",objects["""torch"""] )
self.assertIn("""FlaxUNet2DConditionModel""",objects["""flax"""] )
self.assertIn("""StableDiffusionPipeline""",objects["""torch_and_transformers"""] )
self.assertIn("""FlaxStableDiffusionPipeline""",objects["""flax_and_transformers"""] )
self.assertIn("""LMSDiscreteScheduler""",objects["""torch_and_scipy"""] )
self.assertIn("""OnnxStableDiffusionPipeline""",objects["""torch_and_transformers_and_onnx"""] )
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = create_dummy_object("""CONSTANT""","""'torch'""" )
self.assertEqual(__SCREAMING_SNAKE_CASE,"""\nCONSTANT = None\n""" )
__lowerCAmelCase = create_dummy_object("""function""","""'torch'""" )
self.assertEqual(
__SCREAMING_SNAKE_CASE,"""\ndef function(*args, **kwargs):\n requires_backends(function, 'torch')\n""" )
__lowerCAmelCase = """
class FakeClass(metaclass=DummyObject):
_backends = 'torch'
def __init__(self, *args, **kwargs):
requires_backends(self, 'torch')
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, 'torch')
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, 'torch')
"""
__lowerCAmelCase = create_dummy_object("""FakeClass""","""'torch'""" )
self.assertEqual(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = """# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends
CONSTANT = None
def function(*args, **kwargs):
requires_backends(function, [\"torch\"])
class FakeClass(metaclass=DummyObject):
_backends = [\"torch\"]
def __init__(self, *args, **kwargs):
requires_backends(self, [\"torch\"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, [\"torch\"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, [\"torch\"])
"""
__lowerCAmelCase = create_dummy_files({"""torch""": ["""CONSTANT""", """function""", """FakeClass"""]} )
self.assertEqual(dummy_files["""torch"""],__SCREAMING_SNAKE_CASE )
| 689 | 1 |
'''simple docstring'''
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
_a : str = logging.get_logger("""transformers.models.speecht5""")
def _lowerCAmelCase ( lowercase , lowercase , lowercase ) -> Union[str, Any]:
hf_model.apply_weight_norm()
__lowerCAmelCase = checkpoint["""input_conv.weight_g"""]
__lowerCAmelCase = checkpoint["""input_conv.weight_v"""]
__lowerCAmelCase = checkpoint["""input_conv.bias"""]
for i in range(len(config.upsample_rates ) ):
__lowerCAmelCase = checkpoint[f'upsamples.{i}.1.weight_g']
__lowerCAmelCase = checkpoint[f'upsamples.{i}.1.weight_v']
__lowerCAmelCase = checkpoint[f'upsamples.{i}.1.bias']
for i in range(len(config.upsample_rates ) * len(config.resblock_kernel_sizes ) ):
for j in range(len(config.resblock_dilation_sizes ) ):
__lowerCAmelCase = checkpoint[f'blocks.{i}.convs1.{j}.1.weight_g']
__lowerCAmelCase = checkpoint[f'blocks.{i}.convs1.{j}.1.weight_v']
__lowerCAmelCase = checkpoint[f'blocks.{i}.convs1.{j}.1.bias']
__lowerCAmelCase = checkpoint[f'blocks.{i}.convs2.{j}.1.weight_g']
__lowerCAmelCase = checkpoint[f'blocks.{i}.convs2.{j}.1.weight_v']
__lowerCAmelCase = checkpoint[f'blocks.{i}.convs2.{j}.1.bias']
__lowerCAmelCase = checkpoint["""output_conv.1.weight_g"""]
__lowerCAmelCase = checkpoint["""output_conv.1.weight_v"""]
__lowerCAmelCase = checkpoint["""output_conv.1.bias"""]
hf_model.remove_weight_norm()
@torch.no_grad()
def _lowerCAmelCase ( lowercase , lowercase , lowercase , lowercase=None , lowercase=None , ) -> List[str]:
if config_path is not None:
__lowerCAmelCase = SpeechTaHifiGanConfig.from_pretrained(lowercase )
else:
__lowerCAmelCase = SpeechTaHifiGanConfig()
__lowerCAmelCase = SpeechTaHifiGan(lowercase )
__lowerCAmelCase = torch.load(lowercase )
load_weights(orig_checkpoint["""model"""]["""generator"""] , lowercase , lowercase )
__lowerCAmelCase = np.load(lowercase )
__lowerCAmelCase = stats[0].reshape(-1 )
__lowerCAmelCase = stats[1].reshape(-1 )
__lowerCAmelCase = torch.from_numpy(lowercase ).float()
__lowerCAmelCase = torch.from_numpy(lowercase ).float()
model.save_pretrained(lowercase )
if repo_id:
print("""Pushing to the hub...""" )
model.push_to_hub(lowercase )
if __name__ == "__main__":
_a : Tuple = argparse.ArgumentParser()
parser.add_argument("""--checkpoint_path""", required=True, default=None, type=str, help="""Path to original checkpoint""")
parser.add_argument("""--stats_path""", required=True, default=None, type=str, help="""Path to stats.npy file""")
parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
parser.add_argument(
"""--pytorch_dump_folder_path""", required=True, default=None, type=str, help="""Path to the output PyTorch model."""
)
parser.add_argument(
"""--push_to_hub""", default=None, type=str, help="""Where to upload the converted model on the 🤗 hub."""
)
_a : Dict = parser.parse_args()
convert_hifigan_checkpoint(
args.checkpoint_path,
args.stats_path,
args.pytorch_dump_folder_path,
args.config_path,
args.push_to_hub,
)
| 689 |
'''simple docstring'''
def _lowerCAmelCase ( lowercase ) -> tuple[int, int]:
try:
__lowerCAmelCase = float(lowercase )
except ValueError:
raise ValueError("""Please enter a valid number""" )
__lowerCAmelCase = decimal - int(lowercase )
if fractional_part == 0:
return int(lowercase ), 1
else:
__lowerCAmelCase = len(str(lowercase ).split(""".""" )[1] )
__lowerCAmelCase = int(decimal * (10**number_of_frac_digits) )
__lowerCAmelCase = 10**number_of_frac_digits
__lowerCAmelCase , __lowerCAmelCase = denominator, numerator
while True:
__lowerCAmelCase = dividend % divisor
if remainder == 0:
break
__lowerCAmelCase , __lowerCAmelCase = divisor, remainder
__lowerCAmelCase , __lowerCAmelCase = numerator / divisor, denominator / divisor
return int(lowercase ), int(lowercase )
if __name__ == "__main__":
print(f'{decimal_to_fraction(2) = }')
print(f'{decimal_to_fraction(89.0) = }')
print(f'{decimal_to_fraction("67") = }')
print(f'{decimal_to_fraction("45.0") = }')
print(f'{decimal_to_fraction(1.5) = }')
print(f'{decimal_to_fraction("6.25") = }')
print(f'{decimal_to_fraction("78td") = }')
| 689 | 1 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def _lowerCAmelCase ( lowercase ) -> Optional[Any]:
# vision encoder
if "img_encoder.pos_embed" in name:
__lowerCAmelCase = name.replace("""img_encoder.pos_embed""" , """vision_model.embeddings.position_embeddings""" )
if "img_encoder.patch_embed.proj" in name:
__lowerCAmelCase = name.replace("""img_encoder.patch_embed.proj""" , """vision_model.embeddings.patch_embeddings.projection""" )
if "img_encoder.patch_embed.norm" in name:
__lowerCAmelCase = name.replace("""img_encoder.patch_embed.norm""" , """vision_model.embeddings.layernorm""" )
if "img_encoder.layers" in name:
__lowerCAmelCase = name.replace("""img_encoder.layers""" , """vision_model.encoder.stages""" )
if "blocks" in name and "res" not in name:
__lowerCAmelCase = name.replace("""blocks""" , """layers""" )
if "attn" in name and "pre_assign" not in name:
__lowerCAmelCase = name.replace("""attn""" , """self_attn""" )
if "proj" in name and "self_attn" in name and "text" not in name:
__lowerCAmelCase = name.replace("""proj""" , """out_proj""" )
if "pre_assign_attn.attn.proj" in name:
__lowerCAmelCase = name.replace("""pre_assign_attn.attn.proj""" , """pre_assign_attn.attn.out_proj""" )
if "norm1" in name:
__lowerCAmelCase = name.replace("""norm1""" , """layer_norm1""" )
if "norm2" in name and "pre_assign" not in name:
__lowerCAmelCase = name.replace("""norm2""" , """layer_norm2""" )
if "img_encoder.norm" in name:
__lowerCAmelCase = name.replace("""img_encoder.norm""" , """vision_model.layernorm""" )
# text encoder
if "text_encoder.token_embedding" in name:
__lowerCAmelCase = name.replace("""text_encoder.token_embedding""" , """text_model.embeddings.token_embedding""" )
if "text_encoder.positional_embedding" in name:
__lowerCAmelCase = name.replace("""text_encoder.positional_embedding""" , """text_model.embeddings.position_embedding.weight""" )
if "text_encoder.transformer.resblocks." in name:
__lowerCAmelCase = name.replace("""text_encoder.transformer.resblocks.""" , """text_model.encoder.layers.""" )
if "ln_1" in name:
__lowerCAmelCase = name.replace("""ln_1""" , """layer_norm1""" )
if "ln_2" in name:
__lowerCAmelCase = name.replace("""ln_2""" , """layer_norm2""" )
if "c_fc" in name:
__lowerCAmelCase = name.replace("""c_fc""" , """fc1""" )
if "c_proj" in name:
__lowerCAmelCase = name.replace("""c_proj""" , """fc2""" )
if "text_encoder" in name:
__lowerCAmelCase = name.replace("""text_encoder""" , """text_model""" )
if "ln_final" in name:
__lowerCAmelCase = name.replace("""ln_final""" , """final_layer_norm""" )
# projection layers
if "img_projector.linear_hidden." in name:
__lowerCAmelCase = name.replace("""img_projector.linear_hidden.""" , """visual_projection.""" )
if "img_projector.linear_out." in name:
__lowerCAmelCase = name.replace("""img_projector.linear_out.""" , """visual_projection.3.""" )
if "text_projector.linear_hidden" in name:
__lowerCAmelCase = name.replace("""text_projector.linear_hidden""" , """text_projection""" )
if "text_projector.linear_out" in name:
__lowerCAmelCase = name.replace("""text_projector.linear_out""" , """text_projection.3""" )
return name
def _lowerCAmelCase ( lowercase , lowercase ) -> Dict:
for key in orig_state_dict.copy().keys():
__lowerCAmelCase = orig_state_dict.pop(lowercase )
if "qkv" in key:
# weights and biases of the key, value and query projections of vision encoder's attention layers require special treatment:
# we need to split them up into separate matrices/vectors
__lowerCAmelCase = key.split(""".""" )
__lowerCAmelCase , __lowerCAmelCase = int(key_split[2] ), int(key_split[4] )
__lowerCAmelCase = config.vision_config.hidden_size
if "weight" in key:
__lowerCAmelCase = val[:dim, :]
__lowerCAmelCase = val[dim : dim * 2, :]
__lowerCAmelCase = val[-dim:, :]
else:
__lowerCAmelCase = val[:dim]
__lowerCAmelCase = val[dim : dim * 2]
__lowerCAmelCase = val[-dim:]
elif "in_proj" in key:
# weights and biases of the key, value and query projections of text encoder's attention layers require special treatment:
# we need to split them up into separate matrices/vectors
__lowerCAmelCase = key.split(""".""" )
__lowerCAmelCase = int(key_split[3] )
__lowerCAmelCase = config.text_config.hidden_size
if "weight" in key:
__lowerCAmelCase = val[:dim, :]
__lowerCAmelCase = val[
dim : dim * 2, :
]
__lowerCAmelCase = val[-dim:, :]
else:
__lowerCAmelCase = val[:dim]
__lowerCAmelCase = val[dim : dim * 2]
__lowerCAmelCase = val[-dim:]
else:
__lowerCAmelCase = rename_key(lowercase )
# squeeze if necessary
if (
"text_projection.0" in new_name
or "text_projection.3" in new_name
or "visual_projection.0" in new_name
or "visual_projection.3" in new_name
):
__lowerCAmelCase = val.squeeze_()
else:
__lowerCAmelCase = val
return orig_state_dict
def _lowerCAmelCase ( ) -> str:
__lowerCAmelCase = """http://images.cocodataset.org/val2017/000000039769.jpg"""
__lowerCAmelCase = Image.open(requests.get(lowercase , stream=lowercase ).raw )
return im
@torch.no_grad()
def _lowerCAmelCase ( lowercase , lowercase , lowercase="groupvit-gcc-yfcc" , lowercase=False ) -> List[Any]:
__lowerCAmelCase = GroupViTConfig()
__lowerCAmelCase = GroupViTModel(lowercase ).eval()
__lowerCAmelCase = torch.load(lowercase , map_location="""cpu""" )["""model"""]
__lowerCAmelCase = convert_state_dict(lowercase , lowercase )
__lowerCAmelCase , __lowerCAmelCase = model.load_state_dict(lowercase , strict=lowercase )
assert missing_keys == ["text_model.embeddings.position_ids"]
assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(lowercase ) == 0)
# verify result
__lowerCAmelCase = CLIPProcessor.from_pretrained("""openai/clip-vit-base-patch32""" )
__lowerCAmelCase = prepare_img()
__lowerCAmelCase = processor(text=["""a photo of a cat""", """a photo of a dog"""] , images=lowercase , padding=lowercase , return_tensors="""pt""" )
with torch.no_grad():
__lowerCAmelCase = model(**lowercase )
if model_name == "groupvit-gcc-yfcc":
__lowerCAmelCase = torch.tensor([[13.35_23, 6.36_29]] )
elif model_name == "groupvit-gcc-redcaps":
__lowerCAmelCase = torch.tensor([[16.18_73, 8.62_30]] )
else:
raise ValueError(f'Model name {model_name} not supported.' )
assert torch.allclose(outputs.logits_per_image , lowercase , atol=1e-3 )
processor.save_pretrained(lowercase )
model.save_pretrained(lowercase )
print("""Successfully saved processor and model to""" , lowercase )
if push_to_hub:
print("""Pushing to the hub...""" )
processor.push_to_hub(lowercase , organization="""nielsr""" )
model.push_to_hub(lowercase , organization="""nielsr""" )
if __name__ == "__main__":
_a : int = argparse.ArgumentParser()
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to dump the processor and PyTorch model."""
)
parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to GroupViT checkpoint""")
parser.add_argument(
"""--model_name""",
default="""groupvit-gccy-fcc""",
type=str,
help="""Name of the model. Expecting either 'groupvit-gcc-yfcc' or 'groupvit-gcc-redcaps'""",
)
parser.add_argument(
"""--push_to_hub""",
action="""store_true""",
help="""Whether or not to push the converted model and processor to the 🤗 hub using the provided `model_name`.""",
)
_a : List[str] = parser.parse_args()
convert_groupvit_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 689 |
'''simple docstring'''
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
_a : Dict = _symbol_database.Default()
_a : Union[str, Any] = _descriptor_pool.Default().AddSerializedFile(
b"""\n\x19sentencepiece_model.proto\x12\rsentencepiece\"\x80\x0c\n\x0bTrainerSpec\x12\r\n\x05input\x18\x01 \x03(\t\x12\x14\n\x0cinput_format\x18\x07 \x01(\t\x12\x14\n\x0cmodel_prefix\x18\x02 \x01(\t\x12\x41\n\nmodel_type\x18\x03 \x01(\x0e\x32$.sentencepiece.TrainerSpec.ModelType:\x07UNIGRAM\x12\x18\n\nvocab_size\x18\x04 \x01(\x05:\x04\x38\x30\x30\x30\x12\x17\n\x0f\x61\x63\x63\x65pt_language\x18\x05 \x03(\t\x12 \n\x15self_test_sample_size\x18\x06 \x01(\x05:\x01\x30\x12*\n\x1b\x65nable_differential_privacy\x18\x32 \x01(\x08:\x05\x66\x61lse\x12+\n differential_privacy_noise_level\x18\x33 \x01(\x02:\x01\x30\x12\x32\n\'differential_privacy_clipping_threshold\x18\x34 \x01(\x04:\x01\x30\x12\"\n\x12\x63haracter_coverage\x18\n \x01(\x02:\x06\x30.9995\x12\x1e\n\x13input_sentence_size\x18\x0b \x01(\x04:\x01\x30\x12$\n\x16shuffle_input_sentence\x18\x13 \x01(\x08:\x04true\x12 \n\x14mining_sentence_size\x18\x0c \x01(\x05\x42\x02\x18\x01\x12\"\n\x16training_sentence_size\x18\r \x01(\x05\x42\x02\x18\x01\x12(\n\x17seed_sentencepiece_size\x18\x0e \x01(\x05:\x07\x31\x30\x30\x30\x30\x30\x30\x12\x1e\n\x10shrinking_factor\x18\x0f \x01(\x02:\x04\x30.75\x12!\n\x13max_sentence_length\x18\x12 \x01(\x05:\x04\x34\x31\x39\x32\x12\x17\n\x0bnum_threads\x18\x10 \x01(\x05:\x02\x31\x36\x12\x1d\n\x12num_sub_iterations\x18\x11 \x01(\x05:\x01\x32\x12$\n\x18max_sentencepiece_length\x18\x14 \x01(\x05:\x02\x31\x36\x12%\n\x17split_by_unicode_script\x18\x15 \x01(\x08:\x04true\x12\x1d\n\x0fsplit_by_number\x18\x17 \x01(\x08:\x04true\x12!\n\x13split_by_whitespace\x18\x16 \x01(\x08:\x04true\x12)\n\x1atreat_whitespace_as_suffix\x18\x18 \x01(\x08:\x05\x66\x61lse\x12+\n\x1c\x61llow_whitespace_only_pieces\x18\x1a \x01(\x08:\x05\x66\x61lse\x12\x1b\n\x0csplit_digits\x18\x19 \x01(\x08:\x05\x66\x61lse\x12#\n\x19pretokenization_delimiter\x18\x35 \x01(\t:\x00\x12\x17\n\x0f\x63ontrol_symbols\x18\x1e \x03(\t\x12\x1c\n\x14user_defined_symbols\x18\x1f \x03(\t\x12\x16\n\x0erequired_chars\x18$ \x01(\t\x12\x1c\n\rbyte_fallback\x18# \x01(\x08:\x05\x66\x61lse\x12+\n\x1dvocabulary_output_piece_score\x18 \x01(\x08:\x04true\x12\x1e\n\x10hard_vocab_limit\x18! \x01(\x08:\x04true\x12\x1c\n\ruse_all_vocab\x18\" \x01(\x08:\x05\x66\x61lse\x12\x11\n\x06unk_id\x18( \x01(\x05:\x01\x30\x12\x11\n\x06\x62os_id\x18) \x01(\x05:\x01\x31\x12\x11\n\x06\x65os_id\x18* \x01(\x05:\x01\x32\x12\x12\n\x06pad_id\x18+ \x01(\x05:\x02-1\x12\x18\n\tunk_piece\x18- \x01(\t:\x05<unk>\x12\x16\n\tbos_piece\x18. \x01(\t:\x03<s>\x12\x17\n\teos_piece\x18/ \x01(\t:\x04</s>\x12\x18\n\tpad_piece\x18\x30 \x01(\t:\x05<pad>\x12\x1a\n\x0bunk_surface\x18, \x01(\t:\x05 \xe2\x81\x87 \x12+\n\x1ctrain_extremely_large_corpus\x18\x31 \x01(\x08:\x05\x66\x61lse\"5\n\tModelType\x12\x0b\n\x07UNIGRAM\x10\x01\x12\x07\n\x03\x42PE\x10\x02\x12\x08\n\x04WORD\x10\x03\x12\x08\n\x04\x43HAR\x10\x04*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"\xd1\x01\n\x0eNormalizerSpec\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x1c\n\x14precompiled_charsmap\x18\x02 \x01(\x0c\x12\x1e\n\x10\x61\x64\x64_dummy_prefix\x18\x03 \x01(\x08:\x04true\x12&\n\x18remove_extra_whitespaces\x18\x04 \x01(\x08:\x04true\x12 \n\x12\x65scape_whitespaces\x18\x05 \x01(\x08:\x04true\x12\x1e\n\x16normalization_rule_tsv\x18\x06 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"y\n\x0cSelfTestData\x12\x33\n\x07samples\x18\x01 \x03(\x0b\x32\".sentencepiece.SelfTestData.Sample\x1a)\n\x06Sample\x12\r\n\x05input\x18\x01 \x01(\t\x12\x10\n\x08\x65xpected\x18\x02 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"\xfe\x03\n\nModelProto\x12\x37\n\x06pieces\x18\x01 \x03(\x0b\x32\'.sentencepiece.ModelProto.SentencePiece\x12\x30\n\x0ctrainer_spec\x18\x02 \x01(\x0b\x32\x1a.sentencepiece.TrainerSpec\x12\x36\n\x0fnormalizer_spec\x18\x03 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x12\x33\n\x0eself_test_data\x18\x04 \x01(\x0b\x32\x1b.sentencepiece.SelfTestData\x12\x38\n\x11\x64\x65normalizer_spec\x18\x05 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x1a\xd2\x01\n\rSentencePiece\x12\r\n\x05piece\x18\x01 \x01(\t\x12\r\n\x05score\x18\x02 \x01(\x02\x12\x42\n\x04type\x18\x03 \x01(\x0e\x32,.sentencepiece.ModelProto.SentencePiece.Type:\x06NORMAL\"T\n\x04Type\x12\n\n\x06NORMAL\x10\x01\x12\x0b\n\x07UNKNOWN\x10\x02\x12\x0b\n\x07\x43ONTROL\x10\x03\x12\x10\n\x0cUSER_DEFINED\x10\x04\x12\x08\n\x04\x42YTE\x10\x06\x12\n\n\x06UNUSED\x10\x05*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\x42\x02H\x03"""
)
_a : str = globals()
_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, """sentencepiece_model_pb2""", _globals)
if _descriptor._USE_C_DESCRIPTORS is False:
_a : str = None
_a : Union[str, Any] = b"""H\003"""
# (generated by protobuf compiler, but `_TRAINERSPEC` is not defined)
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._serialized_options = b"\030\001"
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._serialized_options = b"\030\001"
_a : Optional[int] = 4_5
_a : List[Any] = 1_5_8_1
_a : str = 1_5_1_7
_a : Optional[Any] = 1_5_7_0
_a : List[str] = 1_5_8_4
_a : List[Any] = 1_7_9_3
_a : Union[str, Any] = 1_7_9_5
_a : Tuple = 1_9_1_6
_a : List[Any] = 1_8_6_4
_a : Any = 1_9_0_5
_a : Optional[Any] = 1_9_1_9
_a : Optional[int] = 2_4_2_9
_a : Tuple = 2_2_0_8
_a : Optional[Any] = 2_4_1_8
_a : List[Any] = 2_3_2_3
_a : str = 2_4_0_7
# @@protoc_insertion_point(module_scope)
| 689 | 1 |
'''simple docstring'''
from . import (
albert,
align,
altclip,
audio_spectrogram_transformer,
auto,
autoformer,
bark,
bart,
barthez,
bartpho,
beit,
bert,
bert_generation,
bert_japanese,
bertweet,
big_bird,
bigbird_pegasus,
biogpt,
bit,
blenderbot,
blenderbot_small,
blip,
blip_a,
bloom,
bridgetower,
byta,
camembert,
canine,
chinese_clip,
clap,
clip,
clipseg,
codegen,
conditional_detr,
convbert,
convnext,
convnextva,
cpm,
cpmant,
ctrl,
cvt,
dataavec,
deberta,
deberta_va,
decision_transformer,
deformable_detr,
deit,
deprecated,
deta,
detr,
dialogpt,
dinat,
distilbert,
dit,
donut,
dpr,
dpt,
efficientformer,
efficientnet,
electra,
encodec,
encoder_decoder,
ernie,
ernie_m,
esm,
falcon,
flaubert,
flava,
fnet,
focalnet,
fsmt,
funnel,
git,
glpn,
gpta,
gpt_bigcode,
gpt_neo,
gpt_neox,
gpt_neox_japanese,
gpt_swa,
gptj,
gptsan_japanese,
graphormer,
groupvit,
herbert,
hubert,
ibert,
imagegpt,
informer,
instructblip,
jukebox,
layoutlm,
layoutlmva,
layoutlmva,
layoutxlm,
led,
levit,
lilt,
llama,
longformer,
longta,
luke,
lxmert,
mam_aaa,
marian,
markuplm,
maskaformer,
maskformer,
mbart,
mbartaa,
mega,
megatron_bert,
megatron_gpta,
mgp_str,
mluke,
mobilebert,
mobilenet_va,
mobilenet_va,
mobilevit,
mobilevitva,
mpnet,
mra,
mta,
musicgen,
mvp,
nat,
nezha,
nllb,
nllb_moe,
nystromformer,
oneformer,
open_llama,
openai,
opt,
owlvit,
pegasus,
pegasus_x,
perceiver,
phobert,
pixastruct,
plbart,
poolformer,
prophetnet,
qdqbert,
rag,
realm,
reformer,
regnet,
rembert,
resnet,
roberta,
roberta_prelayernorm,
roc_bert,
roformer,
rwkv,
sam,
segformer,
sew,
sew_d,
speech_encoder_decoder,
speech_to_text,
speech_to_text_a,
speechta,
splinter,
squeezebert,
swiftformer,
swin,
swinasr,
swinva,
switch_transformers,
ta,
table_transformer,
tapas,
time_series_transformer,
timesformer,
timm_backbone,
transfo_xl,
trocr,
tvlt,
umta,
unispeech,
unispeech_sat,
upernet,
videomae,
vilt,
vision_encoder_decoder,
vision_text_dual_encoder,
visual_bert,
vit,
vit_hybrid,
vit_mae,
vit_msn,
vivit,
wavaveca,
wavaveca_conformer,
wavaveca_phoneme,
wavaveca_with_lm,
wavlm,
whisper,
x_clip,
xglm,
xlm,
xlm_prophetnet,
xlm_roberta,
xlm_roberta_xl,
xlnet,
xmod,
yolos,
yoso,
)
| 689 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block
@dataclass
class _UpperCAmelCase ( lowerCAmelCase_ ):
a : torch.FloatTensor
class _UpperCAmelCase ( nn.Module ):
def __init__( self,__SCREAMING_SNAKE_CASE=3,__SCREAMING_SNAKE_CASE=3,__SCREAMING_SNAKE_CASE=("DownEncoderBlock2D",),__SCREAMING_SNAKE_CASE=(64,),__SCREAMING_SNAKE_CASE=2,__SCREAMING_SNAKE_CASE=32,__SCREAMING_SNAKE_CASE="silu",__SCREAMING_SNAKE_CASE=True,):
'''simple docstring'''
super().__init__()
__lowerCAmelCase = layers_per_block
__lowerCAmelCase = torch.nn.Convad(
__SCREAMING_SNAKE_CASE,block_out_channels[0],kernel_size=3,stride=1,padding=1,)
__lowerCAmelCase = None
__lowerCAmelCase = nn.ModuleList([] )
# down
__lowerCAmelCase = block_out_channels[0]
for i, down_block_type in enumerate(__SCREAMING_SNAKE_CASE ):
__lowerCAmelCase = output_channel
__lowerCAmelCase = block_out_channels[i]
__lowerCAmelCase = i == len(__SCREAMING_SNAKE_CASE ) - 1
__lowerCAmelCase = get_down_block(
__SCREAMING_SNAKE_CASE,num_layers=self.layers_per_block,in_channels=__SCREAMING_SNAKE_CASE,out_channels=__SCREAMING_SNAKE_CASE,add_downsample=not is_final_block,resnet_eps=1e-6,downsample_padding=0,resnet_act_fn=__SCREAMING_SNAKE_CASE,resnet_groups=__SCREAMING_SNAKE_CASE,attention_head_dim=__SCREAMING_SNAKE_CASE,temb_channels=__SCREAMING_SNAKE_CASE,)
self.down_blocks.append(__SCREAMING_SNAKE_CASE )
# mid
__lowerCAmelCase = UNetMidBlockaD(
in_channels=block_out_channels[-1],resnet_eps=1e-6,resnet_act_fn=__SCREAMING_SNAKE_CASE,output_scale_factor=1,resnet_time_scale_shift="""default""",attention_head_dim=block_out_channels[-1],resnet_groups=__SCREAMING_SNAKE_CASE,temb_channels=__SCREAMING_SNAKE_CASE,)
# out
__lowerCAmelCase = nn.GroupNorm(num_channels=block_out_channels[-1],num_groups=__SCREAMING_SNAKE_CASE,eps=1e-6 )
__lowerCAmelCase = nn.SiLU()
__lowerCAmelCase = 2 * out_channels if double_z else out_channels
__lowerCAmelCase = nn.Convad(block_out_channels[-1],__SCREAMING_SNAKE_CASE,3,padding=1 )
__lowerCAmelCase = False
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase = x
__lowerCAmelCase = self.conv_in(__SCREAMING_SNAKE_CASE )
if self.training and self.gradient_checkpointing:
def create_custom_forward(__SCREAMING_SNAKE_CASE ):
def custom_forward(*__SCREAMING_SNAKE_CASE ):
return module(*__SCREAMING_SNAKE_CASE )
return custom_forward
# down
if is_torch_version(""">=""","""1.11.0""" ):
for down_block in self.down_blocks:
__lowerCAmelCase = torch.utils.checkpoint.checkpoint(
create_custom_forward(__SCREAMING_SNAKE_CASE ),__SCREAMING_SNAKE_CASE,use_reentrant=__SCREAMING_SNAKE_CASE )
# middle
__lowerCAmelCase = torch.utils.checkpoint.checkpoint(
create_custom_forward(self.mid_block ),__SCREAMING_SNAKE_CASE,use_reentrant=__SCREAMING_SNAKE_CASE )
else:
for down_block in self.down_blocks:
__lowerCAmelCase = torch.utils.checkpoint.checkpoint(create_custom_forward(__SCREAMING_SNAKE_CASE ),__SCREAMING_SNAKE_CASE )
# middle
__lowerCAmelCase = torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block ),__SCREAMING_SNAKE_CASE )
else:
# down
for down_block in self.down_blocks:
__lowerCAmelCase = down_block(__SCREAMING_SNAKE_CASE )
# middle
__lowerCAmelCase = self.mid_block(__SCREAMING_SNAKE_CASE )
# post-process
__lowerCAmelCase = self.conv_norm_out(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = self.conv_act(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = self.conv_out(__SCREAMING_SNAKE_CASE )
return sample
class _UpperCAmelCase ( nn.Module ):
def __init__( self,__SCREAMING_SNAKE_CASE=3,__SCREAMING_SNAKE_CASE=3,__SCREAMING_SNAKE_CASE=("UpDecoderBlock2D",),__SCREAMING_SNAKE_CASE=(64,),__SCREAMING_SNAKE_CASE=2,__SCREAMING_SNAKE_CASE=32,__SCREAMING_SNAKE_CASE="silu",__SCREAMING_SNAKE_CASE="group",):
'''simple docstring'''
super().__init__()
__lowerCAmelCase = layers_per_block
__lowerCAmelCase = nn.Convad(
__SCREAMING_SNAKE_CASE,block_out_channels[-1],kernel_size=3,stride=1,padding=1,)
__lowerCAmelCase = None
__lowerCAmelCase = nn.ModuleList([] )
__lowerCAmelCase = in_channels if norm_type == """spatial""" else None
# mid
__lowerCAmelCase = UNetMidBlockaD(
in_channels=block_out_channels[-1],resnet_eps=1e-6,resnet_act_fn=__SCREAMING_SNAKE_CASE,output_scale_factor=1,resnet_time_scale_shift="""default""" if norm_type == """group""" else norm_type,attention_head_dim=block_out_channels[-1],resnet_groups=__SCREAMING_SNAKE_CASE,temb_channels=__SCREAMING_SNAKE_CASE,)
# up
__lowerCAmelCase = list(reversed(__SCREAMING_SNAKE_CASE ) )
__lowerCAmelCase = reversed_block_out_channels[0]
for i, up_block_type in enumerate(__SCREAMING_SNAKE_CASE ):
__lowerCAmelCase = output_channel
__lowerCAmelCase = reversed_block_out_channels[i]
__lowerCAmelCase = i == len(__SCREAMING_SNAKE_CASE ) - 1
__lowerCAmelCase = get_up_block(
__SCREAMING_SNAKE_CASE,num_layers=self.layers_per_block + 1,in_channels=__SCREAMING_SNAKE_CASE,out_channels=__SCREAMING_SNAKE_CASE,prev_output_channel=__SCREAMING_SNAKE_CASE,add_upsample=not is_final_block,resnet_eps=1e-6,resnet_act_fn=__SCREAMING_SNAKE_CASE,resnet_groups=__SCREAMING_SNAKE_CASE,attention_head_dim=__SCREAMING_SNAKE_CASE,temb_channels=__SCREAMING_SNAKE_CASE,resnet_time_scale_shift=__SCREAMING_SNAKE_CASE,)
self.up_blocks.append(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = output_channel
# out
if norm_type == "spatial":
__lowerCAmelCase = SpatialNorm(block_out_channels[0],__SCREAMING_SNAKE_CASE )
else:
__lowerCAmelCase = nn.GroupNorm(num_channels=block_out_channels[0],num_groups=__SCREAMING_SNAKE_CASE,eps=1e-6 )
__lowerCAmelCase = nn.SiLU()
__lowerCAmelCase = nn.Convad(block_out_channels[0],__SCREAMING_SNAKE_CASE,3,padding=1 )
__lowerCAmelCase = False
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=None ):
'''simple docstring'''
__lowerCAmelCase = z
__lowerCAmelCase = self.conv_in(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = next(iter(self.up_blocks.parameters() ) ).dtype
if self.training and self.gradient_checkpointing:
def create_custom_forward(__SCREAMING_SNAKE_CASE ):
def custom_forward(*__SCREAMING_SNAKE_CASE ):
return module(*__SCREAMING_SNAKE_CASE )
return custom_forward
if is_torch_version(""">=""","""1.11.0""" ):
# middle
__lowerCAmelCase = torch.utils.checkpoint.checkpoint(
create_custom_forward(self.mid_block ),__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,use_reentrant=__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = sample.to(__SCREAMING_SNAKE_CASE )
# up
for up_block in self.up_blocks:
__lowerCAmelCase = torch.utils.checkpoint.checkpoint(
create_custom_forward(__SCREAMING_SNAKE_CASE ),__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,use_reentrant=__SCREAMING_SNAKE_CASE )
else:
# middle
__lowerCAmelCase = torch.utils.checkpoint.checkpoint(
create_custom_forward(self.mid_block ),__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = sample.to(__SCREAMING_SNAKE_CASE )
# up
for up_block in self.up_blocks:
__lowerCAmelCase = torch.utils.checkpoint.checkpoint(create_custom_forward(__SCREAMING_SNAKE_CASE ),__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
else:
# middle
__lowerCAmelCase = self.mid_block(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = sample.to(__SCREAMING_SNAKE_CASE )
# up
for up_block in self.up_blocks:
__lowerCAmelCase = up_block(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
# post-process
if latent_embeds is None:
__lowerCAmelCase = self.conv_norm_out(__SCREAMING_SNAKE_CASE )
else:
__lowerCAmelCase = self.conv_norm_out(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = self.conv_act(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = self.conv_out(__SCREAMING_SNAKE_CASE )
return sample
class _UpperCAmelCase ( nn.Module ):
def __init__( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE="random",__SCREAMING_SNAKE_CASE=False,__SCREAMING_SNAKE_CASE=True ):
'''simple docstring'''
super().__init__()
__lowerCAmelCase = n_e
__lowerCAmelCase = vq_embed_dim
__lowerCAmelCase = beta
__lowerCAmelCase = legacy
__lowerCAmelCase = nn.Embedding(self.n_e,self.vq_embed_dim )
self.embedding.weight.data.uniform_(-1.0 / self.n_e,1.0 / self.n_e )
__lowerCAmelCase = remap
if self.remap is not None:
self.register_buffer("""used""",torch.tensor(np.load(self.remap ) ) )
__lowerCAmelCase = self.used.shape[0]
__lowerCAmelCase = unknown_index # "random" or "extra" or integer
if self.unknown_index == "extra":
__lowerCAmelCase = self.re_embed
__lowerCAmelCase = self.re_embed + 1
print(
f'Remapping {self.n_e} indices to {self.re_embed} indices. '
f'Using {self.unknown_index} for unknown indices.' )
else:
__lowerCAmelCase = n_e
__lowerCAmelCase = sane_index_shape
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase = inds.shape
assert len(__SCREAMING_SNAKE_CASE ) > 1
__lowerCAmelCase = inds.reshape(ishape[0],-1 )
__lowerCAmelCase = self.used.to(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = (inds[:, :, None] == used[None, None, ...]).long()
__lowerCAmelCase = match.argmax(-1 )
__lowerCAmelCase = match.sum(2 ) < 1
if self.unknown_index == "random":
__lowerCAmelCase = torch.randint(0,self.re_embed,size=new[unknown].shape ).to(device=new.device )
else:
__lowerCAmelCase = self.unknown_index
return new.reshape(__SCREAMING_SNAKE_CASE )
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase = inds.shape
assert len(__SCREAMING_SNAKE_CASE ) > 1
__lowerCAmelCase = inds.reshape(ishape[0],-1 )
__lowerCAmelCase = self.used.to(__SCREAMING_SNAKE_CASE )
if self.re_embed > self.used.shape[0]: # extra token
__lowerCAmelCase = 0 # simply set to zero
__lowerCAmelCase = torch.gather(used[None, :][inds.shape[0] * [0], :],1,__SCREAMING_SNAKE_CASE )
return back.reshape(__SCREAMING_SNAKE_CASE )
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase = z.permute(0,2,3,1 ).contiguous()
__lowerCAmelCase = z.view(-1,self.vq_embed_dim )
# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
__lowerCAmelCase = torch.argmin(torch.cdist(__SCREAMING_SNAKE_CASE,self.embedding.weight ),dim=1 )
__lowerCAmelCase = self.embedding(__SCREAMING_SNAKE_CASE ).view(z.shape )
__lowerCAmelCase = None
__lowerCAmelCase = None
# compute loss for embedding
if not self.legacy:
__lowerCAmelCase = self.beta * torch.mean((z_q.detach() - z) ** 2 ) + torch.mean((z_q - z.detach()) ** 2 )
else:
__lowerCAmelCase = torch.mean((z_q.detach() - z) ** 2 ) + self.beta * torch.mean((z_q - z.detach()) ** 2 )
# preserve gradients
__lowerCAmelCase = z + (z_q - z).detach()
# reshape back to match original input shape
__lowerCAmelCase = z_q.permute(0,3,1,2 ).contiguous()
if self.remap is not None:
__lowerCAmelCase = min_encoding_indices.reshape(z.shape[0],-1 ) # add batch axis
__lowerCAmelCase = self.remap_to_used(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = min_encoding_indices.reshape(-1,1 ) # flatten
if self.sane_index_shape:
__lowerCAmelCase = min_encoding_indices.reshape(z_q.shape[0],z_q.shape[2],z_q.shape[3] )
return z_q, loss, (perplexity, min_encodings, min_encoding_indices)
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
if self.remap is not None:
__lowerCAmelCase = indices.reshape(shape[0],-1 ) # add batch axis
__lowerCAmelCase = self.unmap_to_all(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = indices.reshape(-1 ) # flatten again
# get quantized latent vectors
__lowerCAmelCase = self.embedding(__SCREAMING_SNAKE_CASE )
if shape is not None:
__lowerCAmelCase = z_q.view(__SCREAMING_SNAKE_CASE )
# reshape back to match original input shape
__lowerCAmelCase = z_q.permute(0,3,1,2 ).contiguous()
return z_q
class _UpperCAmelCase ( lowerCAmelCase_ ):
def __init__( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=False ):
'''simple docstring'''
__lowerCAmelCase = parameters
__lowerCAmelCase , __lowerCAmelCase = torch.chunk(__SCREAMING_SNAKE_CASE,2,dim=1 )
__lowerCAmelCase = torch.clamp(self.logvar,-30.0,20.0 )
__lowerCAmelCase = deterministic
__lowerCAmelCase = torch.exp(0.5 * self.logvar )
__lowerCAmelCase = torch.exp(self.logvar )
if self.deterministic:
__lowerCAmelCase = __lowerCAmelCase = torch.zeros_like(
self.mean,device=self.parameters.device,dtype=self.parameters.dtype )
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE = None ):
'''simple docstring'''
__lowerCAmelCase = randn_tensor(
self.mean.shape,generator=__SCREAMING_SNAKE_CASE,device=self.parameters.device,dtype=self.parameters.dtype )
__lowerCAmelCase = self.mean + self.std * sample
return x
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE=None ):
'''simple docstring'''
if self.deterministic:
return torch.Tensor([0.0] )
else:
if other is None:
return 0.5 * torch.sum(torch.pow(self.mean,2 ) + self.var - 1.0 - self.logvar,dim=[1, 2, 3] )
else:
return 0.5 * torch.sum(
torch.pow(self.mean - other.mean,2 ) / other.var
+ self.var / other.var
- 1.0
- self.logvar
+ other.logvar,dim=[1, 2, 3],)
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=[1, 2, 3] ):
'''simple docstring'''
if self.deterministic:
return torch.Tensor([0.0] )
__lowerCAmelCase = np.log(2.0 * np.pi )
return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean,2 ) / self.var,dim=__SCREAMING_SNAKE_CASE )
def lowerCamelCase__ ( self ):
'''simple docstring'''
return self.mean
| 689 | 1 |
'''simple docstring'''
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def _lowerCAmelCase ( ) -> Tuple:
__lowerCAmelCase = ArgumentParser(
description=(
"""PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes"""
) )
# Optional arguments for the launch helper
parser.add_argument("""--num_cores""" , type=lowercase , default=1 , help="""Number of TPU cores to use (1 or 8).""" )
# positional
parser.add_argument(
"""training_script""" , type=lowercase , help=(
"""The full path to the single TPU training """
"""program/script to be launched in parallel, """
"""followed by all the arguments for the """
"""training script"""
) , )
# rest from the training program
parser.add_argument("""training_script_args""" , nargs=lowercase )
return parser.parse_args()
def _lowerCAmelCase ( ) -> str:
__lowerCAmelCase = parse_args()
# Import training_script as a module.
__lowerCAmelCase = Path(args.training_script )
sys.path.append(str(script_fpath.parent.resolve() ) )
__lowerCAmelCase = script_fpath.stem
__lowerCAmelCase = importlib.import_module(lowercase )
# Patch sys.argv
__lowerCAmelCase = [args.training_script] + args.training_script_args + ["""--tpu_num_cores""", str(args.num_cores )]
xmp.spawn(mod._mp_fn , args=() , nprocs=args.num_cores )
if __name__ == "__main__":
main()
| 689 |
'''simple docstring'''
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
_a : Optional[int] = logging.get_logger(__name__)
_a : int = list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
_a : str = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class _UpperCAmelCase :
a : str =field(
default=lowerCAmelCase_ , metadata={"""help""": """Model type selected in the list: """ + """, """.join(lowerCAmelCase_ )} )
a : str =field(
default=lowerCAmelCase_ , metadata={"""help""": """The input data dir. Should contain the .json files for the SQuAD task."""} )
a : int =field(
default=1_28 , metadata={
"""help""": (
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
)
} , )
a : int =field(
default=1_28 , metadata={"""help""": """When splitting up a long document into chunks, how much stride to take between chunks."""} , )
a : int =field(
default=64 , metadata={
"""help""": (
"""The maximum number of tokens for the question. Questions longer than this will """
"""be truncated to this length."""
)
} , )
a : int =field(
default=30 , metadata={
"""help""": (
"""The maximum length of an answer that can be generated. This is needed because the start """
"""and end predictions are not conditioned on one another."""
)
} , )
a : bool =field(
default=lowerCAmelCase_ , metadata={"""help""": """Overwrite the cached training and evaluation sets"""} )
a : bool =field(
default=lowerCAmelCase_ , metadata={"""help""": """If true, the SQuAD examples contain some that do not have an answer."""} )
a : float =field(
default=0.0 , metadata={"""help""": """If null_score - best_non_null is greater than the threshold predict null."""} )
a : int =field(
default=20 , metadata={"""help""": """If null_score - best_non_null is greater than the threshold predict null."""} )
a : int =field(
default=0 , metadata={
"""help""": (
"""language id of input for language-specific xlm models (see"""
""" tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)"""
)
} , )
a : int =field(default=1 , metadata={"""help""": """multiple threads for converting example to features"""} )
class _UpperCAmelCase ( lowerCAmelCase_ ):
a : Optional[Any] ="""train"""
a : Optional[int] ="""dev"""
class _UpperCAmelCase ( lowerCAmelCase_ ):
a : SquadDataTrainingArguments
a : List[SquadFeatures]
a : Split
a : bool
def __init__( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = Split.train,__SCREAMING_SNAKE_CASE = False,__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = "pt",):
'''simple docstring'''
__lowerCAmelCase = args
__lowerCAmelCase = is_language_sensitive
__lowerCAmelCase = SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
if isinstance(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE ):
try:
__lowerCAmelCase = Split[mode]
except KeyError:
raise KeyError("""mode is not a valid split name""" )
__lowerCAmelCase = mode
# Load data features from cache or dataset file
__lowerCAmelCase = """v2""" if args.version_2_with_negative else """v1"""
__lowerCAmelCase = os.path.join(
cache_dir if cache_dir is not None else args.data_dir,f'cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{version_tag}',)
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
__lowerCAmelCase = cached_features_file + """.lock"""
with FileLock(__SCREAMING_SNAKE_CASE ):
if os.path.exists(__SCREAMING_SNAKE_CASE ) and not args.overwrite_cache:
__lowerCAmelCase = time.time()
__lowerCAmelCase = torch.load(__SCREAMING_SNAKE_CASE )
# Legacy cache files have only features, while new cache files
# will have dataset and examples also.
__lowerCAmelCase = self.old_features["""features"""]
__lowerCAmelCase = self.old_features.get("""dataset""",__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = self.old_features.get("""examples""",__SCREAMING_SNAKE_CASE )
logger.info(
f'Loading features from cached file {cached_features_file} [took %.3f s]',time.time() - start )
if self.dataset is None or self.examples is None:
logger.warning(
f'Deleting cached file {cached_features_file} will allow dataset and examples to be cached in'
""" future run""" )
else:
if mode == Split.dev:
__lowerCAmelCase = self.processor.get_dev_examples(args.data_dir )
else:
__lowerCAmelCase = self.processor.get_train_examples(args.data_dir )
__lowerCAmelCase , __lowerCAmelCase = squad_convert_examples_to_features(
examples=self.examples,tokenizer=__SCREAMING_SNAKE_CASE,max_seq_length=args.max_seq_length,doc_stride=args.doc_stride,max_query_length=args.max_query_length,is_training=mode == Split.train,threads=args.threads,return_dataset=__SCREAMING_SNAKE_CASE,)
__lowerCAmelCase = time.time()
torch.save(
{"""features""": self.features, """dataset""": self.dataset, """examples""": self.examples},__SCREAMING_SNAKE_CASE,)
# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
logger.info(
f'Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]' )
def __len__( self ):
'''simple docstring'''
return len(self.features )
def __getitem__( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase = self.features[i]
__lowerCAmelCase = torch.tensor(feature.input_ids,dtype=torch.long )
__lowerCAmelCase = torch.tensor(feature.attention_mask,dtype=torch.long )
__lowerCAmelCase = torch.tensor(feature.token_type_ids,dtype=torch.long )
__lowerCAmelCase = torch.tensor(feature.cls_index,dtype=torch.long )
__lowerCAmelCase = torch.tensor(feature.p_mask,dtype=torch.float )
__lowerCAmelCase = torch.tensor(feature.is_impossible,dtype=torch.float )
__lowerCAmelCase = {
"""input_ids""": input_ids,
"""attention_mask""": attention_mask,
"""token_type_ids""": token_type_ids,
}
if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
del inputs["token_type_ids"]
if self.args.model_type in ["xlnet", "xlm"]:
inputs.update({"""cls_index""": cls_index, """p_mask""": p_mask} )
if self.args.version_2_with_negative:
inputs.update({"""is_impossible""": is_impossible} )
if self.is_language_sensitive:
inputs.update({"""langs""": (torch.ones(input_ids.shape,dtype=torch.intaa ) * self.args.lang_id)} )
if self.mode == Split.train:
__lowerCAmelCase = torch.tensor(feature.start_position,dtype=torch.long )
__lowerCAmelCase = torch.tensor(feature.end_position,dtype=torch.long )
inputs.update({"""start_positions""": start_positions, """end_positions""": end_positions} )
return inputs
| 689 | 1 |
'''simple docstring'''
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
_a : List[Any] = logging.get_logger(__name__)
_a : int = {
"""post_extract_proj""": """feature_projection.projection""",
"""encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
"""self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
"""self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
"""self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
"""self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
"""self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
"""fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
"""fc2""": """encoder.layers.*.feed_forward.output_dense""",
"""final_layer_norm""": """encoder.layers.*.final_layer_norm""",
"""encoder.layer_norm""": """encoder.layer_norm""",
"""encoder.layer_norm_for_extract""": """layer_norm_for_extract""",
"""w2v_model.layer_norm""": """feature_projection.layer_norm""",
"""quantizer.weight_proj""": """quantizer.weight_proj""",
"""quantizer.vars""": """quantizer.codevectors""",
"""project_q""": """project_q""",
"""final_proj""": """project_hid""",
"""w2v_encoder.proj""": """lm_head""",
"""label_embs_concat""": """label_embeddings_concat""",
"""mask_emb""": """masked_spec_embed""",
"""spk_proj""": """speaker_proj""",
}
_a : Any = [
"""lm_head""",
"""quantizer.weight_proj""",
"""quantizer.codevectors""",
"""project_q""",
"""project_hid""",
"""label_embeddings_concat""",
"""speaker_proj""",
"""layer_norm_for_extract""",
]
def _lowerCAmelCase ( lowercase , lowercase , lowercase , lowercase , lowercase ) -> str:
for attribute in key.split(""".""" ):
__lowerCAmelCase = getattr(lowercase , lowercase )
if weight_type is not None:
__lowerCAmelCase = getattr(lowercase , lowercase ).shape
else:
__lowerCAmelCase = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
f'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
f' {value.shape} for {full_name}' )
if weight_type == "weight":
__lowerCAmelCase = value
elif weight_type == "weight_g":
__lowerCAmelCase = value
elif weight_type == "weight_v":
__lowerCAmelCase = value
elif weight_type == "bias":
__lowerCAmelCase = value
else:
__lowerCAmelCase = value
logger.info(f'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.' )
def _lowerCAmelCase ( lowercase , lowercase ) -> List[Any]:
__lowerCAmelCase = []
__lowerCAmelCase = fairseq_model.state_dict()
__lowerCAmelCase = hf_model.unispeech_sat.feature_extractor
for name, value in fairseq_dict.items():
__lowerCAmelCase = False
if "conv_layers" in name:
load_conv_layer(
lowercase , lowercase , lowercase , lowercase , hf_model.config.feat_extract_norm == """group""" , )
__lowerCAmelCase = True
else:
for key, mapped_key in MAPPING.items():
__lowerCAmelCase = """unispeech_sat.""" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split("""w2v_model.""" )[-1] == name.split(""".""" )[0]:
if "layer_norm_for_extract" in name and (".".join(name.split(""".""" )[:-1] ) != key):
# special case since naming is very similar
continue
__lowerCAmelCase = True
if "*" in mapped_key:
__lowerCAmelCase = name.split(lowercase )[0].split(""".""" )[-2]
__lowerCAmelCase = mapped_key.replace("""*""" , lowercase )
if "weight_g" in name:
__lowerCAmelCase = """weight_g"""
elif "weight_v" in name:
__lowerCAmelCase = """weight_v"""
elif "bias" in name:
__lowerCAmelCase = """bias"""
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
__lowerCAmelCase = """weight"""
else:
__lowerCAmelCase = None
set_recursively(lowercase , lowercase , lowercase , lowercase , lowercase )
continue
if not is_used:
unused_weights.append(lowercase )
logger.warning(f'Unused weights: {unused_weights}' )
def _lowerCAmelCase ( lowercase , lowercase , lowercase , lowercase , lowercase ) -> Optional[Any]:
__lowerCAmelCase = full_name.split("""conv_layers.""" )[-1]
__lowerCAmelCase = name.split(""".""" )
__lowerCAmelCase = int(items[0] )
__lowerCAmelCase = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.' )
__lowerCAmelCase = value
logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.' )
__lowerCAmelCase = value
logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.' )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor[layer_id].layer_norm.bias.data.shape} was found.' )
__lowerCAmelCase = value
logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.' )
__lowerCAmelCase = value
logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
else:
unused_weights.append(lowercase )
@torch.no_grad()
def _lowerCAmelCase ( lowercase , lowercase , lowercase=None , lowercase=None , lowercase=True ) -> Dict:
if config_path is not None:
__lowerCAmelCase = UniSpeechSatConfig.from_pretrained(lowercase )
else:
__lowerCAmelCase = UniSpeechSatConfig()
__lowerCAmelCase = """"""
if is_finetuned:
__lowerCAmelCase = UniSpeechSatForCTC(lowercase )
else:
__lowerCAmelCase = UniSpeechSatForPreTraining(lowercase )
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={"""data""": """/""".join(dict_path.split("""/""" )[:-1] )} )
__lowerCAmelCase = model[0].eval()
recursively_load_weights(lowercase , lowercase )
hf_wavavec.save_pretrained(lowercase )
if __name__ == "__main__":
_a : List[str] = argparse.ArgumentParser()
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
parser.add_argument(
"""--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
)
_a : Union[str, Any] = parser.parse_args()
convert_unispeech_sat_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 689 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def _lowerCAmelCase ( lowercase ) -> Optional[Any]:
# vision encoder
if "img_encoder.pos_embed" in name:
__lowerCAmelCase = name.replace("""img_encoder.pos_embed""" , """vision_model.embeddings.position_embeddings""" )
if "img_encoder.patch_embed.proj" in name:
__lowerCAmelCase = name.replace("""img_encoder.patch_embed.proj""" , """vision_model.embeddings.patch_embeddings.projection""" )
if "img_encoder.patch_embed.norm" in name:
__lowerCAmelCase = name.replace("""img_encoder.patch_embed.norm""" , """vision_model.embeddings.layernorm""" )
if "img_encoder.layers" in name:
__lowerCAmelCase = name.replace("""img_encoder.layers""" , """vision_model.encoder.stages""" )
if "blocks" in name and "res" not in name:
__lowerCAmelCase = name.replace("""blocks""" , """layers""" )
if "attn" in name and "pre_assign" not in name:
__lowerCAmelCase = name.replace("""attn""" , """self_attn""" )
if "proj" in name and "self_attn" in name and "text" not in name:
__lowerCAmelCase = name.replace("""proj""" , """out_proj""" )
if "pre_assign_attn.attn.proj" in name:
__lowerCAmelCase = name.replace("""pre_assign_attn.attn.proj""" , """pre_assign_attn.attn.out_proj""" )
if "norm1" in name:
__lowerCAmelCase = name.replace("""norm1""" , """layer_norm1""" )
if "norm2" in name and "pre_assign" not in name:
__lowerCAmelCase = name.replace("""norm2""" , """layer_norm2""" )
if "img_encoder.norm" in name:
__lowerCAmelCase = name.replace("""img_encoder.norm""" , """vision_model.layernorm""" )
# text encoder
if "text_encoder.token_embedding" in name:
__lowerCAmelCase = name.replace("""text_encoder.token_embedding""" , """text_model.embeddings.token_embedding""" )
if "text_encoder.positional_embedding" in name:
__lowerCAmelCase = name.replace("""text_encoder.positional_embedding""" , """text_model.embeddings.position_embedding.weight""" )
if "text_encoder.transformer.resblocks." in name:
__lowerCAmelCase = name.replace("""text_encoder.transformer.resblocks.""" , """text_model.encoder.layers.""" )
if "ln_1" in name:
__lowerCAmelCase = name.replace("""ln_1""" , """layer_norm1""" )
if "ln_2" in name:
__lowerCAmelCase = name.replace("""ln_2""" , """layer_norm2""" )
if "c_fc" in name:
__lowerCAmelCase = name.replace("""c_fc""" , """fc1""" )
if "c_proj" in name:
__lowerCAmelCase = name.replace("""c_proj""" , """fc2""" )
if "text_encoder" in name:
__lowerCAmelCase = name.replace("""text_encoder""" , """text_model""" )
if "ln_final" in name:
__lowerCAmelCase = name.replace("""ln_final""" , """final_layer_norm""" )
# projection layers
if "img_projector.linear_hidden." in name:
__lowerCAmelCase = name.replace("""img_projector.linear_hidden.""" , """visual_projection.""" )
if "img_projector.linear_out." in name:
__lowerCAmelCase = name.replace("""img_projector.linear_out.""" , """visual_projection.3.""" )
if "text_projector.linear_hidden" in name:
__lowerCAmelCase = name.replace("""text_projector.linear_hidden""" , """text_projection""" )
if "text_projector.linear_out" in name:
__lowerCAmelCase = name.replace("""text_projector.linear_out""" , """text_projection.3""" )
return name
def _lowerCAmelCase ( lowercase , lowercase ) -> Dict:
for key in orig_state_dict.copy().keys():
__lowerCAmelCase = orig_state_dict.pop(lowercase )
if "qkv" in key:
# weights and biases of the key, value and query projections of vision encoder's attention layers require special treatment:
# we need to split them up into separate matrices/vectors
__lowerCAmelCase = key.split(""".""" )
__lowerCAmelCase , __lowerCAmelCase = int(key_split[2] ), int(key_split[4] )
__lowerCAmelCase = config.vision_config.hidden_size
if "weight" in key:
__lowerCAmelCase = val[:dim, :]
__lowerCAmelCase = val[dim : dim * 2, :]
__lowerCAmelCase = val[-dim:, :]
else:
__lowerCAmelCase = val[:dim]
__lowerCAmelCase = val[dim : dim * 2]
__lowerCAmelCase = val[-dim:]
elif "in_proj" in key:
# weights and biases of the key, value and query projections of text encoder's attention layers require special treatment:
# we need to split them up into separate matrices/vectors
__lowerCAmelCase = key.split(""".""" )
__lowerCAmelCase = int(key_split[3] )
__lowerCAmelCase = config.text_config.hidden_size
if "weight" in key:
__lowerCAmelCase = val[:dim, :]
__lowerCAmelCase = val[
dim : dim * 2, :
]
__lowerCAmelCase = val[-dim:, :]
else:
__lowerCAmelCase = val[:dim]
__lowerCAmelCase = val[dim : dim * 2]
__lowerCAmelCase = val[-dim:]
else:
__lowerCAmelCase = rename_key(lowercase )
# squeeze if necessary
if (
"text_projection.0" in new_name
or "text_projection.3" in new_name
or "visual_projection.0" in new_name
or "visual_projection.3" in new_name
):
__lowerCAmelCase = val.squeeze_()
else:
__lowerCAmelCase = val
return orig_state_dict
def _lowerCAmelCase ( ) -> str:
__lowerCAmelCase = """http://images.cocodataset.org/val2017/000000039769.jpg"""
__lowerCAmelCase = Image.open(requests.get(lowercase , stream=lowercase ).raw )
return im
@torch.no_grad()
def _lowerCAmelCase ( lowercase , lowercase , lowercase="groupvit-gcc-yfcc" , lowercase=False ) -> List[Any]:
__lowerCAmelCase = GroupViTConfig()
__lowerCAmelCase = GroupViTModel(lowercase ).eval()
__lowerCAmelCase = torch.load(lowercase , map_location="""cpu""" )["""model"""]
__lowerCAmelCase = convert_state_dict(lowercase , lowercase )
__lowerCAmelCase , __lowerCAmelCase = model.load_state_dict(lowercase , strict=lowercase )
assert missing_keys == ["text_model.embeddings.position_ids"]
assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(lowercase ) == 0)
# verify result
__lowerCAmelCase = CLIPProcessor.from_pretrained("""openai/clip-vit-base-patch32""" )
__lowerCAmelCase = prepare_img()
__lowerCAmelCase = processor(text=["""a photo of a cat""", """a photo of a dog"""] , images=lowercase , padding=lowercase , return_tensors="""pt""" )
with torch.no_grad():
__lowerCAmelCase = model(**lowercase )
if model_name == "groupvit-gcc-yfcc":
__lowerCAmelCase = torch.tensor([[13.35_23, 6.36_29]] )
elif model_name == "groupvit-gcc-redcaps":
__lowerCAmelCase = torch.tensor([[16.18_73, 8.62_30]] )
else:
raise ValueError(f'Model name {model_name} not supported.' )
assert torch.allclose(outputs.logits_per_image , lowercase , atol=1e-3 )
processor.save_pretrained(lowercase )
model.save_pretrained(lowercase )
print("""Successfully saved processor and model to""" , lowercase )
if push_to_hub:
print("""Pushing to the hub...""" )
processor.push_to_hub(lowercase , organization="""nielsr""" )
model.push_to_hub(lowercase , organization="""nielsr""" )
if __name__ == "__main__":
_a : int = argparse.ArgumentParser()
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to dump the processor and PyTorch model."""
)
parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to GroupViT checkpoint""")
parser.add_argument(
"""--model_name""",
default="""groupvit-gccy-fcc""",
type=str,
help="""Name of the model. Expecting either 'groupvit-gcc-yfcc' or 'groupvit-gcc-redcaps'""",
)
parser.add_argument(
"""--push_to_hub""",
action="""store_true""",
help="""Whether or not to push the converted model and processor to the 🤗 hub using the provided `model_name`.""",
)
_a : List[str] = parser.parse_args()
convert_groupvit_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 689 | 1 |
'''simple docstring'''
from . import __version__
# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
CLOUDFRONT_DISTRIB_PREFIX,
CONFIG_NAME,
DISABLE_TELEMETRY,
DUMMY_INPUTS,
DUMMY_MASK,
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_VARS_TRUE_VALUES,
FEATURE_EXTRACTOR_NAME,
FLAX_WEIGHTS_NAME,
HF_MODULES_CACHE,
HUGGINGFACE_CO_PREFIX,
HUGGINGFACE_CO_RESOLVE_ENDPOINT,
MODEL_CARD_NAME,
MULTIPLE_CHOICE_DUMMY_INPUTS,
PYTORCH_PRETRAINED_BERT_CACHE,
PYTORCH_TRANSFORMERS_CACHE,
S3_BUCKET_PREFIX,
SENTENCEPIECE_UNDERLINE,
SPIECE_UNDERLINE,
TF2_WEIGHTS_NAME,
TF_WEIGHTS_NAME,
TORCH_FX_REQUIRED_VERSION,
TRANSFORMERS_CACHE,
TRANSFORMERS_DYNAMIC_MODULE_NAME,
USE_JAX,
USE_TF,
USE_TORCH,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
ContextManagers,
DummyObject,
EntryNotFoundError,
ExplicitEnum,
ModelOutput,
PaddingStrategy,
PushToHubMixin,
RepositoryNotFoundError,
RevisionNotFoundError,
TensorType,
_LazyModule,
add_code_sample_docstrings,
add_end_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
cached_property,
copy_func,
default_cache_path,
define_sagemaker_information,
get_cached_models,
get_file_from_repo,
get_full_repo_name,
get_torch_version,
has_file,
http_user_agent,
is_apex_available,
is_bsa_available,
is_coloredlogs_available,
is_datasets_available,
is_detectrona_available,
is_faiss_available,
is_flax_available,
is_ftfy_available,
is_in_notebook,
is_ipex_available,
is_librosa_available,
is_offline_mode,
is_onnx_available,
is_pandas_available,
is_phonemizer_available,
is_protobuf_available,
is_psutil_available,
is_pyanvml_available,
is_pyctcdecode_available,
is_pytesseract_available,
is_pytorch_quantization_available,
is_rjieba_available,
is_sagemaker_dp_enabled,
is_sagemaker_mp_enabled,
is_scipy_available,
is_sentencepiece_available,
is_seqio_available,
is_sklearn_available,
is_soundfile_availble,
is_spacy_available,
is_speech_available,
is_tensor,
is_tensorflow_probability_available,
is_tfaonnx_available,
is_tf_available,
is_timm_available,
is_tokenizers_available,
is_torch_available,
is_torch_bfaa_available,
is_torch_cuda_available,
is_torch_fx_available,
is_torch_fx_proxy,
is_torch_mps_available,
is_torch_tfaa_available,
is_torch_tpu_available,
is_torchaudio_available,
is_training_run_on_sagemaker,
is_vision_available,
replace_return_docstrings,
requires_backends,
to_numpy,
to_py_obj,
torch_only_method,
)
| 689 |
'''simple docstring'''
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGeneration,
MusicgenProcessor,
TaEncoderModel,
)
from transformers.models.musicgen.modeling_musicgen import MusicgenForCausalLM
from transformers.utils import logging
logging.set_verbosity_info()
_a : Tuple = logging.get_logger(__name__)
_a : Optional[int] = ["""model.decoder.embed_positions.weights"""]
def _lowerCAmelCase ( lowercase ) -> Optional[Any]:
if "emb" in name:
__lowerCAmelCase = name.replace("""emb""" , """model.decoder.embed_tokens""" )
if "transformer" in name:
__lowerCAmelCase = name.replace("""transformer""" , """model.decoder""" )
if "cross_attention" in name:
__lowerCAmelCase = name.replace("""cross_attention""" , """encoder_attn""" )
if "linear1" in name:
__lowerCAmelCase = name.replace("""linear1""" , """fc1""" )
if "linear2" in name:
__lowerCAmelCase = name.replace("""linear2""" , """fc2""" )
if "norm1" in name:
__lowerCAmelCase = name.replace("""norm1""" , """self_attn_layer_norm""" )
if "norm_cross" in name:
__lowerCAmelCase = name.replace("""norm_cross""" , """encoder_attn_layer_norm""" )
if "norm2" in name:
__lowerCAmelCase = name.replace("""norm2""" , """final_layer_norm""" )
if "out_norm" in name:
__lowerCAmelCase = name.replace("""out_norm""" , """model.decoder.layer_norm""" )
if "linears" in name:
__lowerCAmelCase = name.replace("""linears""" , """lm_heads""" )
if "condition_provider.conditioners.description.output_proj" in name:
__lowerCAmelCase = name.replace("""condition_provider.conditioners.description.output_proj""" , """enc_to_dec_proj""" )
return name
def _lowerCAmelCase ( lowercase , lowercase ) -> Tuple[Dict, Dict]:
__lowerCAmelCase = list(state_dict.keys() )
__lowerCAmelCase = {}
for key in keys:
__lowerCAmelCase = state_dict.pop(lowercase )
__lowerCAmelCase = rename_keys(lowercase )
if "in_proj_weight" in key:
# split fused qkv proj
__lowerCAmelCase = val[:hidden_size, :]
__lowerCAmelCase = val[hidden_size : 2 * hidden_size, :]
__lowerCAmelCase = val[-hidden_size:, :]
elif "enc_to_dec_proj" in key:
__lowerCAmelCase = val
else:
__lowerCAmelCase = val
return state_dict, enc_dec_proj_state_dict
def _lowerCAmelCase ( lowercase ) -> MusicgenDecoderConfig:
if checkpoint == "small":
# default config values
__lowerCAmelCase = 1024
__lowerCAmelCase = 24
__lowerCAmelCase = 16
elif checkpoint == "medium":
__lowerCAmelCase = 1536
__lowerCAmelCase = 48
__lowerCAmelCase = 24
elif checkpoint == "large":
__lowerCAmelCase = 2048
__lowerCAmelCase = 48
__lowerCAmelCase = 32
else:
raise ValueError(f'Checkpoint should be one of `[\'small\', \'medium\', \'large\']`, got {checkpoint}.' )
__lowerCAmelCase = MusicgenDecoderConfig(
hidden_size=lowercase , ffn_dim=hidden_size * 4 , num_hidden_layers=lowercase , num_attention_heads=lowercase , )
return config
@torch.no_grad()
def _lowerCAmelCase ( lowercase , lowercase=None , lowercase=None , lowercase="cpu" ) -> Optional[Any]:
__lowerCAmelCase = MusicGen.get_pretrained(lowercase , device=lowercase )
__lowerCAmelCase = decoder_config_from_checkpoint(lowercase )
__lowerCAmelCase = fairseq_model.lm.state_dict()
__lowerCAmelCase , __lowerCAmelCase = rename_state_dict(
lowercase , hidden_size=decoder_config.hidden_size )
__lowerCAmelCase = TaEncoderModel.from_pretrained("""t5-base""" )
__lowerCAmelCase = EncodecModel.from_pretrained("""facebook/encodec_32khz""" )
__lowerCAmelCase = MusicgenForCausalLM(lowercase ).eval()
# load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
__lowerCAmelCase , __lowerCAmelCase = decoder.load_state_dict(lowercase , strict=lowercase )
for key in missing_keys.copy():
if key.startswith(("""text_encoder""", """audio_encoder""") ) or key in EXPECTED_MISSING_KEYS:
missing_keys.remove(lowercase )
if len(lowercase ) > 0:
raise ValueError(f'Missing key(s) in state_dict: {missing_keys}' )
if len(lowercase ) > 0:
raise ValueError(f'Unexpected key(s) in state_dict: {unexpected_keys}' )
# init the composite model
__lowerCAmelCase = MusicgenForConditionalGeneration(text_encoder=lowercase , audio_encoder=lowercase , decoder=lowercase )
# load the pre-trained enc-dec projection (from the decoder state dict)
model.enc_to_dec_proj.load_state_dict(lowercase )
# check we can do a forward pass
__lowerCAmelCase = torch.arange(0 , 8 , dtype=torch.long ).reshape(2 , -1 )
__lowerCAmelCase = input_ids.reshape(2 * 4 , -1 )
with torch.no_grad():
__lowerCAmelCase = model(input_ids=lowercase , decoder_input_ids=lowercase ).logits
if logits.shape != (8, 1, 2048):
raise ValueError("""Incorrect shape for logits""" )
# now construct the processor
__lowerCAmelCase = AutoTokenizer.from_pretrained("""t5-base""" )
__lowerCAmelCase = AutoFeatureExtractor.from_pretrained("""facebook/encodec_32khz""" , padding_side="""left""" )
__lowerCAmelCase = MusicgenProcessor(feature_extractor=lowercase , tokenizer=lowercase )
# set the appropriate bos/pad token ids
__lowerCAmelCase = 2048
__lowerCAmelCase = 2048
# set other default generation config params
__lowerCAmelCase = int(30 * audio_encoder.config.frame_rate )
__lowerCAmelCase = True
__lowerCAmelCase = 3.0
if pytorch_dump_folder is not None:
Path(lowercase ).mkdir(exist_ok=lowercase )
logger.info(f'Saving model {checkpoint} to {pytorch_dump_folder}' )
model.save_pretrained(lowercase )
processor.save_pretrained(lowercase )
if repo_id:
logger.info(f'Pushing model {checkpoint} to {repo_id}' )
model.push_to_hub(lowercase )
processor.push_to_hub(lowercase )
if __name__ == "__main__":
_a : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--checkpoint""",
default="""small""",
type=str,
help="""Checkpoint size of the MusicGen model you'd like to convert. Can be one of: `['small', 'medium', 'large']`.""",
)
parser.add_argument(
"""--pytorch_dump_folder""",
required=True,
default=None,
type=str,
help="""Path to the output PyTorch model directory.""",
)
parser.add_argument(
"""--push_to_hub""", default=None, type=str, help="""Where to upload the converted model on the 🤗 hub."""
)
parser.add_argument(
"""--device""", default="""cpu""", type=str, help="""Torch device to run the conversion, either cpu or cuda."""
)
_a : List[Any] = parser.parse_args()
convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)
| 689 | 1 |
'''simple docstring'''
from __future__ import annotations
def _lowerCAmelCase ( lowercase ) -> None:
create_state_space_tree(lowercase , [] , 0 , [0 for i in range(len(lowercase ) )] )
def _lowerCAmelCase ( lowercase , lowercase , lowercase , lowercase , ) -> None:
if index == len(lowercase ):
print(lowercase )
return
for i in range(len(lowercase ) ):
if not index_used[i]:
current_sequence.append(sequence[i] )
__lowerCAmelCase = True
create_state_space_tree(lowercase , lowercase , index + 1 , lowercase )
current_sequence.pop()
__lowerCAmelCase = False
_a : list[int | str] = [3, 1, 2, 4]
generate_all_permutations(sequence)
_a : list[int | str] = ["A", "B", "C"]
generate_all_permutations(sequence_a)
| 689 |
'''simple docstring'''
from collections import deque
def _lowerCAmelCase ( lowercase ) -> Dict:
__lowerCAmelCase = len(lowercase )
__lowerCAmelCase = deque()
__lowerCAmelCase = [False for _ in range(lowercase )]
__lowerCAmelCase = [-1 for _ in range(lowercase )]
__lowerCAmelCase = index_of[:]
def strong_connect(lowercase , lowercase , lowercase ):
__lowerCAmelCase = index # the number when this node is seen
__lowerCAmelCase = index # lowest rank node reachable from here
index += 1
stack.append(lowercase )
__lowerCAmelCase = True
for w in g[v]:
if index_of[w] == -1:
__lowerCAmelCase = strong_connect(lowercase , lowercase , lowercase )
__lowerCAmelCase = (
lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
)
elif on_stack[w]:
__lowerCAmelCase = (
lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
)
if lowlink_of[v] == index_of[v]:
__lowerCAmelCase = []
__lowerCAmelCase = stack.pop()
__lowerCAmelCase = False
component.append(lowercase )
while w != v:
__lowerCAmelCase = stack.pop()
__lowerCAmelCase = False
component.append(lowercase )
components.append(lowercase )
return index
__lowerCAmelCase = []
for v in range(lowercase ):
if index_of[v] == -1:
strong_connect(lowercase , 0 , lowercase )
return components
def _lowerCAmelCase ( lowercase , lowercase ) -> str:
__lowerCAmelCase = [[] for _ in range(lowercase )]
for u, v in edges:
g[u].append(lowercase )
return g
if __name__ == "__main__":
# Test
_a : Any = 7
_a : Tuple = [0, 0, 1, 2, 3, 3, 4, 4, 6]
_a : Optional[int] = [1, 3, 2, 0, 1, 4, 5, 6, 5]
_a : Optional[Any] = [(u, v) for u, v in zip(source, target)]
_a : Optional[int] = create_graph(n_vertices, edges)
assert [[5], [6], [4], [3, 2, 1, 0]] == tarjan(g)
| 689 | 1 |
'''simple docstring'''
import os
import pickle
import unittest
from transformers import AutoTokenizer
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.models.bert_japanese.tokenization_bert_japanese import (
VOCAB_FILES_NAMES,
BertJapaneseTokenizer,
CharacterTokenizer,
JumanppTokenizer,
MecabTokenizer,
SudachiTokenizer,
WordpieceTokenizer,
)
from transformers.testing_utils import custom_tokenizers, require_jumanpp, require_sudachi
from ...test_tokenization_common import TokenizerTesterMixin
@custom_tokenizers
class _UpperCAmelCase ( lowerCAmelCase_ , unittest.TestCase ):
a : Union[str, Any] =BertJapaneseTokenizer
a : Optional[Any] =False
a : str =True
def lowerCamelCase__ ( self ):
'''simple docstring'''
super().setUp()
__lowerCAmelCase = [
"""[UNK]""",
"""[CLS]""",
"""[SEP]""",
"""こんにちは""",
"""こん""",
"""にちは""",
"""ばんは""",
"""##こん""",
"""##にちは""",
"""##ばんは""",
"""世界""",
"""##世界""",
"""、""",
"""##、""",
"""。""",
"""##。""",
]
__lowerCAmelCase = os.path.join(self.tmpdirname,VOCAB_FILES_NAMES["""vocab_file"""] )
with open(self.vocab_file,"""w""",encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens] ) )
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase = """こんにちは、世界。 \nこんばんは、世界。"""
__lowerCAmelCase = """こんにちは 、 世界 。 こんばんは 、 世界 。"""
return input_text, output_text
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase , __lowerCAmelCase = self.get_input_output_texts(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = tokenizer.encode(__SCREAMING_SNAKE_CASE,add_special_tokens=__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = tokenizer.decode(__SCREAMING_SNAKE_CASE,clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE )
return text, ids
def lowerCamelCase__ ( self ):
'''simple docstring'''
pass # TODO add if relevant
def lowerCamelCase__ ( self ):
'''simple docstring'''
pass # TODO add if relevant
def lowerCamelCase__ ( self ):
'''simple docstring'''
pass # TODO add if relevant
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = self.tokenizer_class(self.vocab_file )
__lowerCAmelCase = tokenizer.tokenize("""こんにちは、世界。\nこんばんは、世界。""" )
self.assertListEqual(__SCREAMING_SNAKE_CASE,["""こんにちは""", """、""", """世界""", """。""", """こん""", """##ばんは""", """、""", """世界""", """。"""] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE ),[3, 12, 10, 14, 4, 9, 12, 10, 14] )
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = self.tokenizer_class(self.vocab_file,word_tokenizer_type="""mecab""" )
self.assertIsNotNone(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = """こんにちは、世界。\nこんばんは、世界。"""
__lowerCAmelCase = tokenizer.tokenize(__SCREAMING_SNAKE_CASE )
self.assertListEqual(__SCREAMING_SNAKE_CASE,["""こんにちは""", """、""", """世界""", """。""", """こん""", """##ばんは""", """、""", """世界""", """。"""] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE ),[3, 12, 10, 14, 4, 9, 12, 10, 14] )
__lowerCAmelCase = os.path.join(self.tmpdirname,"""tokenizer.bin""" )
with open(__SCREAMING_SNAKE_CASE,"""wb""" ) as handle:
pickle.dump(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
with open(__SCREAMING_SNAKE_CASE,"""rb""" ) as handle:
__lowerCAmelCase = pickle.load(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = tokenizer_new.tokenize(__SCREAMING_SNAKE_CASE )
self.assertListEqual(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = MecabTokenizer(mecab_dic="""ipadic""" )
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ),["""アップルストア""", """で""", """iPhone""", """8""", """が""", """発売""", """さ""", """れ""", """た""", """。"""],)
def lowerCamelCase__ ( self ):
'''simple docstring'''
try:
__lowerCAmelCase = MecabTokenizer(mecab_dic="""unidic_lite""" )
except ModuleNotFoundError:
return
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ),["""アップル""", """ストア""", """で""", """iPhone""", """8""", """が""", """発売""", """さ""", """れ""", """た""", """。"""],)
def lowerCamelCase__ ( self ):
'''simple docstring'''
try:
__lowerCAmelCase = MecabTokenizer(mecab_dic="""unidic""" )
except ModuleNotFoundError:
return
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ),["""アップル""", """ストア""", """で""", """iPhone""", """8""", """が""", """発売""", """さ""", """れ""", """た""", """。"""],)
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = MecabTokenizer(do_lower_case=__SCREAMING_SNAKE_CASE,mecab_dic="""ipadic""" )
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ),["""アップルストア""", """で""", """iphone""", """8""", """が""", """発売""", """さ""", """れ""", """た""", """。"""],)
def lowerCamelCase__ ( self ):
'''simple docstring'''
try:
__lowerCAmelCase = MecabTokenizer(
do_lower_case=__SCREAMING_SNAKE_CASE,normalize_text=__SCREAMING_SNAKE_CASE,mecab_option="""-d /usr/local/lib/mecab/dic/jumandic""" )
except RuntimeError:
# if dict doesn't exist in the system, previous code raises this error.
return
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ),["""ｱｯﾌﾟﾙストア""", """で""", """iPhone""", """８""", """が""", """発売""", """さ""", """れた""", """\u3000""", """。"""],)
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = MecabTokenizer(normalize_text=__SCREAMING_SNAKE_CASE,mecab_dic="""ipadic""" )
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ),["""ｱｯﾌﾟﾙストア""", """で""", """iPhone""", """８""", """が""", """発売""", """さ""", """れ""", """た""", """ """, """。"""],)
@require_sudachi
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = self.tokenizer_class(self.vocab_file,word_tokenizer_type="""sudachi""" )
self.assertIsNotNone(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = """こんにちは、世界。\nこんばんは、世界。"""
__lowerCAmelCase = tokenizer.tokenize(__SCREAMING_SNAKE_CASE )
self.assertListEqual(__SCREAMING_SNAKE_CASE,["""こんにちは""", """、""", """世界""", """。""", """こん""", """##ばんは""", """、""", """世界""", """。"""] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE ),[3, 12, 10, 14, 4, 9, 12, 10, 14] )
__lowerCAmelCase = os.path.join(self.tmpdirname,"""tokenizer.bin""" )
with open(__SCREAMING_SNAKE_CASE,"""wb""" ) as handle:
pickle.dump(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
with open(__SCREAMING_SNAKE_CASE,"""rb""" ) as handle:
__lowerCAmelCase = pickle.load(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = tokenizer_new.tokenize(__SCREAMING_SNAKE_CASE )
self.assertListEqual(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
@require_sudachi
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = SudachiTokenizer(sudachi_dict_type="""core""" )
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ),[""" """, """\t""", """アップル""", """ストア""", """で""", """iPhone""", """8""", """ """, """が""", """ """, """ """, """\n """, """発売""", """さ""", """れ""", """た""", """ """, """。""", """ """, """ """],)
@require_sudachi
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = SudachiTokenizer(sudachi_dict_type="""core""",sudachi_split_mode="""A""" )
self.assertListEqual(tokenizer.tokenize("""外国人参政権""" ),["""外国""", """人""", """参政""", """権"""] )
@require_sudachi
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = SudachiTokenizer(sudachi_dict_type="""core""",sudachi_split_mode="""B""" )
self.assertListEqual(tokenizer.tokenize("""外国人参政権""" ),["""外国人""", """参政権"""] )
@require_sudachi
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = SudachiTokenizer(sudachi_dict_type="""core""",sudachi_split_mode="""C""" )
self.assertListEqual(tokenizer.tokenize("""外国人参政権""" ),["""外国人参政権"""] )
@require_sudachi
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = SudachiTokenizer(do_lower_case=__SCREAMING_SNAKE_CASE,sudachi_dict_type="""core""" )
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ),[""" """, """\t""", """アップル""", """ストア""", """で""", """iphone""", """8""", """ """, """が""", """ """, """ """, """\n """, """発売""", """さ""", """れ""", """た""", """ """, """。""", """ """, """ """],)
@require_sudachi
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = SudachiTokenizer(normalize_text=__SCREAMING_SNAKE_CASE,sudachi_dict_type="""core""" )
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ),[""" """, """\t""", """ｱｯﾌﾟﾙ""", """ストア""", """で""", """iPhone""", """８""", """ """, """が""", """ """, """ """, """\n """, """発売""", """さ""", """れ""", """た""", """\u3000""", """。""", """ """, """ """],)
@require_sudachi
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = SudachiTokenizer(trim_whitespace=__SCREAMING_SNAKE_CASE,sudachi_dict_type="""core""" )
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ),["""アップル""", """ストア""", """で""", """iPhone""", """8""", """が""", """発売""", """さ""", """れ""", """た""", """。"""],)
@require_jumanpp
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = self.tokenizer_class(self.vocab_file,word_tokenizer_type="""jumanpp""" )
self.assertIsNotNone(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = """こんにちは、世界。\nこんばんは、世界。"""
__lowerCAmelCase = tokenizer.tokenize(__SCREAMING_SNAKE_CASE )
self.assertListEqual(__SCREAMING_SNAKE_CASE,["""こんにちは""", """、""", """世界""", """。""", """こん""", """##ばんは""", """、""", """世界""", """。"""] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE ),[3, 12, 10, 14, 4, 9, 12, 10, 14] )
__lowerCAmelCase = os.path.join(self.tmpdirname,"""tokenizer.bin""" )
with open(__SCREAMING_SNAKE_CASE,"""wb""" ) as handle:
pickle.dump(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
with open(__SCREAMING_SNAKE_CASE,"""rb""" ) as handle:
__lowerCAmelCase = pickle.load(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = tokenizer_new.tokenize(__SCREAMING_SNAKE_CASE )
self.assertListEqual(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
@require_jumanpp
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = JumanppTokenizer()
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ),["""アップル""", """ストア""", """で""", """iPhone""", """8""", """\u3000""", """が""", """\u3000""", """\u3000""", """\u3000""", """発売""", """さ""", """れた""", """\u3000""", """。"""],)
@require_jumanpp
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = JumanppTokenizer(do_lower_case=__SCREAMING_SNAKE_CASE )
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ),["""アップル""", """ストア""", """で""", """iphone""", """8""", """\u3000""", """が""", """\u3000""", """\u3000""", """\u3000""", """発売""", """さ""", """れた""", """\u3000""", """。"""],)
@require_jumanpp
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = JumanppTokenizer(normalize_text=__SCREAMING_SNAKE_CASE )
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ),["""ｱ""", """ｯ""", """ﾌ""", """ﾟ""", """ﾙ""", """ストア""", """で""", """iPhone""", """８""", """\u3000""", """が""", """\u3000""", """\u3000""", """\u3000""", """発売""", """さ""", """れた""", """\u3000""", """。"""],)
@require_jumanpp
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = JumanppTokenizer(trim_whitespace=__SCREAMING_SNAKE_CASE )
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ),["""アップル""", """ストア""", """で""", """iPhone""", """8""", """が""", """発売""", """さ""", """れた""", """。"""],)
@require_jumanpp
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = JumanppTokenizer()
self.assertListEqual(
tokenizer.tokenize("""ありがとうございますm(_ _)ｍ見つけるのが大変です。""" ),["""ありがとう""", """ございます""", """m(_ _)m""", """見つける""", """の""", """が""", """大変です""", """。"""],)
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = ["""[UNK]""", """[CLS]""", """[SEP]""", """こんにちは""", """こん""", """にちは""", """ばんは""", """##こん""", """##にちは""", """##ばんは"""]
__lowerCAmelCase = {}
for i, token in enumerate(__SCREAMING_SNAKE_CASE ):
__lowerCAmelCase = i
__lowerCAmelCase = WordpieceTokenizer(vocab=__SCREAMING_SNAKE_CASE,unk_token="""[UNK]""" )
self.assertListEqual(tokenizer.tokenize("""""" ),[] )
self.assertListEqual(tokenizer.tokenize("""こんにちは""" ),["""こんにちは"""] )
self.assertListEqual(tokenizer.tokenize("""こんばんは""" ),["""こん""", """##ばんは"""] )
self.assertListEqual(tokenizer.tokenize("""こんばんは こんばんにちは こんにちは""" ),["""こん""", """##ばんは""", """[UNK]""", """こんにちは"""] )
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = BertJapaneseTokenizer.from_pretrained("""nlp-waseda/roberta-base-japanese-with-auto-jumanpp""" )
__lowerCAmelCase = tokenizer.subword_tokenizer
__lowerCAmelCase = subword_tokenizer.tokenize("""国境 の 長い トンネル を 抜ける と 雪国 であった 。""" )
self.assertListEqual(__SCREAMING_SNAKE_CASE,["""▁国境""", """▁の""", """▁長い""", """▁トンネル""", """▁を""", """▁抜ける""", """▁と""", """▁雪""", """国""", """▁であった""", """▁。"""] )
__lowerCAmelCase = subword_tokenizer.tokenize("""こんばんは こんばん にち は こんにちは""" )
self.assertListEqual(__SCREAMING_SNAKE_CASE,["""▁こん""", """ばん""", """は""", """▁こん""", """ばん""", """▁に""", """ち""", """▁は""", """▁こんにちは"""] )
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = self.tokenizer_class.from_pretrained("""cl-tohoku/bert-base-japanese""" )
__lowerCAmelCase = tokenizer.encode("""ありがとう。""",add_special_tokens=__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = tokenizer.encode("""どういたしまして。""",add_special_tokens=__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
# 2 is for "[CLS]", 3 is for "[SEP]"
assert encoded_sentence == [2] + text + [3]
assert encoded_pair == [2] + text + [3] + text_a + [3]
@custom_tokenizers
class _UpperCAmelCase ( lowerCAmelCase_ , unittest.TestCase ):
a : Tuple =BertJapaneseTokenizer
a : List[Any] =False
def lowerCamelCase__ ( self ):
'''simple docstring'''
super().setUp()
__lowerCAmelCase = ["""[UNK]""", """[CLS]""", """[SEP]""", """こ""", """ん""", """に""", """ち""", """は""", """ば""", """世""", """界""", """、""", """。"""]
__lowerCAmelCase = os.path.join(self.tmpdirname,VOCAB_FILES_NAMES["""vocab_file"""] )
with open(self.vocab_file,"""w""",encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens] ) )
def lowerCamelCase__ ( self,**__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
return BertJapaneseTokenizer.from_pretrained(self.tmpdirname,subword_tokenizer_type="""character""",**__SCREAMING_SNAKE_CASE )
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase = """こんにちは、世界。 \nこんばんは、世界。"""
__lowerCAmelCase = """こ ん に ち は 、 世 界 。 こ ん ば ん は 、 世 界 。"""
return input_text, output_text
def lowerCamelCase__ ( self ):
'''simple docstring'''
pass # TODO add if relevant
def lowerCamelCase__ ( self ):
'''simple docstring'''
pass # TODO add if relevant
def lowerCamelCase__ ( self ):
'''simple docstring'''
pass # TODO add if relevant
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = self.tokenizer_class(self.vocab_file,subword_tokenizer_type="""character""" )
__lowerCAmelCase = tokenizer.tokenize("""こんにちは、世界。 \nこんばんは、世界。""" )
self.assertListEqual(
__SCREAMING_SNAKE_CASE,["""こ""", """ん""", """に""", """ち""", """は""", """、""", """世""", """界""", """。""", """こ""", """ん""", """ば""", """ん""", """は""", """、""", """世""", """界""", """。"""] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE ),[3, 4, 5, 6, 7, 11, 9, 10, 12, 3, 4, 8, 4, 7, 11, 9, 10, 12] )
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = ["""[UNK]""", """[CLS]""", """[SEP]""", """こ""", """ん""", """に""", """ち""", """は""", """ば""", """世""", """界""", """、""", """。"""]
__lowerCAmelCase = {}
for i, token in enumerate(__SCREAMING_SNAKE_CASE ):
__lowerCAmelCase = i
__lowerCAmelCase = CharacterTokenizer(vocab=__SCREAMING_SNAKE_CASE,unk_token="""[UNK]""" )
self.assertListEqual(tokenizer.tokenize("""""" ),[] )
self.assertListEqual(tokenizer.tokenize("""こんにちは""" ),["""こ""", """ん""", """に""", """ち""", """は"""] )
self.assertListEqual(tokenizer.tokenize("""こんにちほ""" ),["""こ""", """ん""", """に""", """ち""", """[UNK]"""] )
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = self.tokenizer_class.from_pretrained("""cl-tohoku/bert-base-japanese-char""" )
__lowerCAmelCase = tokenizer.encode("""ありがとう。""",add_special_tokens=__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = tokenizer.encode("""どういたしまして。""",add_special_tokens=__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
# 2 is for "[CLS]", 3 is for "[SEP]"
assert encoded_sentence == [2] + text + [3]
assert encoded_pair == [2] + text + [3] + text_a + [3]
@custom_tokenizers
class _UpperCAmelCase ( unittest.TestCase ):
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = """cl-tohoku/bert-base-japanese"""
__lowerCAmelCase = AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE )
self.assertIsInstance(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
class _UpperCAmelCase ( unittest.TestCase ):
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = """cl-tohoku/bert-base-japanese"""
with self.assertLogs("""transformers""",level="""WARNING""" ) as cm:
BertTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE )
self.assertTrue(
cm.records[0].message.startswith(
"""The tokenizer class you load from this checkpoint is not the same type as the class this function"""
""" is called from.""" ) )
__lowerCAmelCase = """bert-base-cased"""
with self.assertLogs("""transformers""",level="""WARNING""" ) as cm:
BertJapaneseTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE )
self.assertTrue(
cm.records[0].message.startswith(
"""The tokenizer class you load from this checkpoint is not the same type as the class this function"""
""" is called from.""" ) )
| 689 |
'''simple docstring'''
from argparse import ArgumentParser
from .env import EnvironmentCommand
def _lowerCAmelCase ( ) -> Union[str, Any]:
__lowerCAmelCase = ArgumentParser("""Diffusers CLI tool""" , usage="""diffusers-cli <command> [<args>]""" )
__lowerCAmelCase = parser.add_subparsers(help="""diffusers-cli command helpers""" )
# Register commands
EnvironmentCommand.register_subcommand(lowercase )
# Let's go
__lowerCAmelCase = parser.parse_args()
if not hasattr(lowercase , """func""" ):
parser.print_help()
exit(1 )
# Run
__lowerCAmelCase = args.func(lowercase )
service.run()
if __name__ == "__main__":
main()
| 689 | 1 |
'''simple docstring'''
from scipy.stats import spearmanr
import datasets
_a : str = """
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlations imply that as data in dataset x increases, so
does data in dataset y. Negative correlations imply that as x increases,
y decreases. Correlations of -1 or +1 imply an exact monotonic relationship.
Unlike the Pearson correlation, the Spearman correlation does not
assume that both datasets are normally distributed.
The p-value roughly indicates the probability of an uncorrelated system
producing datasets that have a Spearman correlation at least as extreme
as the one computed from these datasets. The p-values are not entirely
reliable but are probably reasonable for datasets larger than 500 or so.
"""
_a : Dict = """
Args:
predictions (`List[float]`): Predicted labels, as returned by a model.
references (`List[float]`): Ground truth labels.
return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns
only the spearmanr score. Defaults to `False`.
Returns:
spearmanr (`float`): Spearman correlation coefficient.
p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.
Examples:
Example 1:
>>> spearmanr_metric = datasets.load_metric(\"spearmanr\")
>>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])
>>> print(results)
{'spearmanr': -0.7}
Example 2:
>>> spearmanr_metric = datasets.load_metric(\"spearmanr\")
>>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],
... predictions=[10, 9, 2.5, 6, 4],
... return_pvalue=True)
>>> print(results['spearmanr'])
-0.7
>>> print(round(results['spearmanr_pvalue'], 2))
0.19
"""
_a : List[str] = r"""\
@book{kokoska2000crc,
title={CRC standard probability and statistics tables and formulae},
author={Kokoska, Stephen and Zwillinger, Daniel},
year={2000},
publisher={Crc Press}
}
@article{2020SciPy-NMeth,
author = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
Haberland, Matt and Reddy, Tyler and Cournapeau, David and
Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
Kern, Robert and Larson, Eric and Carey, C J and
Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and
{VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
Harris, Charles R. and Archibald, Anne M. and
Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and
{van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
title = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
Computing in Python}},
journal = {Nature Methods},
year = {2020},
volume = {17},
pages = {261--272},
adsurl = {https://rdcu.be/b08Wh},
doi = {10.1038/s41592-019-0686-2},
}
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _UpperCAmelCase ( datasets.Metric ):
def lowerCamelCase__ ( self ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION,citation=_CITATION,inputs_description=_KWARGS_DESCRIPTION,features=datasets.Features(
{
"""predictions""": datasets.Value("""float""" ),
"""references""": datasets.Value("""float""" ),
} ),reference_urls=["""https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.spearmanr.html"""],)
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=False ):
'''simple docstring'''
__lowerCAmelCase = spearmanr(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
if return_pvalue:
return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
else:
return {"spearmanr": results[0]}
| 689 |
'''simple docstring'''
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
_a : List[Any] = logging.get_logger(__name__)
_a : int = {
"""post_extract_proj""": """feature_projection.projection""",
"""encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
"""self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
"""self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
"""self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
"""self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
"""self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
"""fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
"""fc2""": """encoder.layers.*.feed_forward.output_dense""",
"""final_layer_norm""": """encoder.layers.*.final_layer_norm""",
"""encoder.layer_norm""": """encoder.layer_norm""",
"""encoder.layer_norm_for_extract""": """layer_norm_for_extract""",
"""w2v_model.layer_norm""": """feature_projection.layer_norm""",
"""quantizer.weight_proj""": """quantizer.weight_proj""",
"""quantizer.vars""": """quantizer.codevectors""",
"""project_q""": """project_q""",
"""final_proj""": """project_hid""",
"""w2v_encoder.proj""": """lm_head""",
"""label_embs_concat""": """label_embeddings_concat""",
"""mask_emb""": """masked_spec_embed""",
"""spk_proj""": """speaker_proj""",
}
_a : Any = [
"""lm_head""",
"""quantizer.weight_proj""",
"""quantizer.codevectors""",
"""project_q""",
"""project_hid""",
"""label_embeddings_concat""",
"""speaker_proj""",
"""layer_norm_for_extract""",
]
def _lowerCAmelCase ( lowercase , lowercase , lowercase , lowercase , lowercase ) -> str:
for attribute in key.split(""".""" ):
__lowerCAmelCase = getattr(lowercase , lowercase )
if weight_type is not None:
__lowerCAmelCase = getattr(lowercase , lowercase ).shape
else:
__lowerCAmelCase = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
f'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
f' {value.shape} for {full_name}' )
if weight_type == "weight":
__lowerCAmelCase = value
elif weight_type == "weight_g":
__lowerCAmelCase = value
elif weight_type == "weight_v":
__lowerCAmelCase = value
elif weight_type == "bias":
__lowerCAmelCase = value
else:
__lowerCAmelCase = value
logger.info(f'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.' )
def _lowerCAmelCase ( lowercase , lowercase ) -> List[Any]:
__lowerCAmelCase = []
__lowerCAmelCase = fairseq_model.state_dict()
__lowerCAmelCase = hf_model.unispeech_sat.feature_extractor
for name, value in fairseq_dict.items():
__lowerCAmelCase = False
if "conv_layers" in name:
load_conv_layer(
lowercase , lowercase , lowercase , lowercase , hf_model.config.feat_extract_norm == """group""" , )
__lowerCAmelCase = True
else:
for key, mapped_key in MAPPING.items():
__lowerCAmelCase = """unispeech_sat.""" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split("""w2v_model.""" )[-1] == name.split(""".""" )[0]:
if "layer_norm_for_extract" in name and (".".join(name.split(""".""" )[:-1] ) != key):
# special case since naming is very similar
continue
__lowerCAmelCase = True
if "*" in mapped_key:
__lowerCAmelCase = name.split(lowercase )[0].split(""".""" )[-2]
__lowerCAmelCase = mapped_key.replace("""*""" , lowercase )
if "weight_g" in name:
__lowerCAmelCase = """weight_g"""
elif "weight_v" in name:
__lowerCAmelCase = """weight_v"""
elif "bias" in name:
__lowerCAmelCase = """bias"""
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
__lowerCAmelCase = """weight"""
else:
__lowerCAmelCase = None
set_recursively(lowercase , lowercase , lowercase , lowercase , lowercase )
continue
if not is_used:
unused_weights.append(lowercase )
logger.warning(f'Unused weights: {unused_weights}' )
def _lowerCAmelCase ( lowercase , lowercase , lowercase , lowercase , lowercase ) -> Optional[Any]:
__lowerCAmelCase = full_name.split("""conv_layers.""" )[-1]
__lowerCAmelCase = name.split(""".""" )
__lowerCAmelCase = int(items[0] )
__lowerCAmelCase = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.' )
__lowerCAmelCase = value
logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.' )
__lowerCAmelCase = value
logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.' )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor[layer_id].layer_norm.bias.data.shape} was found.' )
__lowerCAmelCase = value
logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
f'{full_name} has size {value.shape}, but'
f' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.' )
__lowerCAmelCase = value
logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
else:
unused_weights.append(lowercase )
@torch.no_grad()
def _lowerCAmelCase ( lowercase , lowercase , lowercase=None , lowercase=None , lowercase=True ) -> Dict:
if config_path is not None:
__lowerCAmelCase = UniSpeechSatConfig.from_pretrained(lowercase )
else:
__lowerCAmelCase = UniSpeechSatConfig()
__lowerCAmelCase = """"""
if is_finetuned:
__lowerCAmelCase = UniSpeechSatForCTC(lowercase )
else:
__lowerCAmelCase = UniSpeechSatForPreTraining(lowercase )
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={"""data""": """/""".join(dict_path.split("""/""" )[:-1] )} )
__lowerCAmelCase = model[0].eval()
recursively_load_weights(lowercase , lowercase )
hf_wavavec.save_pretrained(lowercase )
if __name__ == "__main__":
_a : List[str] = argparse.ArgumentParser()
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
parser.add_argument(
"""--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
)
_a : Union[str, Any] = parser.parse_args()
convert_unispeech_sat_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 689 | 1 |
import inspect
import unittest
from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
class lowerCamelCase_ :
def __init__( self , __lowerCAmelCase , __lowerCAmelCase=3 , __lowerCAmelCase=3_2 , __lowerCAmelCase=3 , __lowerCAmelCase=1_0 , __lowerCAmelCase=[8, 1_6, 3_2, 6_4] , __lowerCAmelCase=[1, 1, 2, 1] , __lowerCAmelCase=True , __lowerCAmelCase=True , __lowerCAmelCase="relu" , __lowerCAmelCase=3 , __lowerCAmelCase=None , __lowerCAmelCase=["stage2", "stage3", "stage4"] , __lowerCAmelCase=[2, 3, 4] , __lowerCAmelCase=1 , ):
"""simple docstring"""
__magic_name__ :Optional[int] = parent
__magic_name__ :str = batch_size
__magic_name__ :List[Any] = image_size
__magic_name__ :Any = num_channels
__magic_name__ :Optional[Any] = embeddings_size
__magic_name__ :Any = hidden_sizes
__magic_name__ :Tuple = depths
__magic_name__ :Any = is_training
__magic_name__ :Union[str, Any] = use_labels
__magic_name__ :List[str] = hidden_act
__magic_name__ :Optional[int] = num_labels
__magic_name__ :Any = scope
__magic_name__ :Dict = len(__lowerCAmelCase )
__magic_name__ :Any = out_features
__magic_name__ :str = out_indices
__magic_name__ :Tuple = num_groups
def A ( self ):
"""simple docstring"""
__magic_name__ :Optional[int] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__magic_name__ :Any = None
if self.use_labels:
__magic_name__ :str = ids_tensor([self.batch_size] , self.num_labels )
__magic_name__ :Optional[int] = self.get_config()
return config, pixel_values, labels
def A ( self ):
"""simple docstring"""
return BitConfig(
num_channels=self.num_channels , embeddings_size=self.embeddings_size , hidden_sizes=self.hidden_sizes , depths=self.depths , hidden_act=self.hidden_act , num_labels=self.num_labels , out_features=self.out_features , out_indices=self.out_indices , num_groups=self.num_groups , )
def A ( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
"""simple docstring"""
__magic_name__ :Dict = BitModel(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
__magic_name__ :Optional[Any] = model(__lowerCAmelCase )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2) , )
def A ( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
"""simple docstring"""
__magic_name__ :Optional[int] = self.num_labels
__magic_name__ :int = BitForImageClassification(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
__magic_name__ :str = model(__lowerCAmelCase , labels=__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def A ( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
"""simple docstring"""
__magic_name__ :List[Any] = BitBackbone(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
__magic_name__ :Optional[int] = model(__lowerCAmelCase )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[1], 4, 4] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , config.hidden_sizes[1:] )
# verify backbone works with out_features=None
__magic_name__ :Any = None
__magic_name__ :Union[str, Any] = BitBackbone(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
__magic_name__ :Optional[Any] = model(__lowerCAmelCase )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , 1 )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[-1], 1, 1] )
# verify channels
self.parent.assertEqual(len(model.channels ) , 1 )
self.parent.assertListEqual(model.channels , [config.hidden_sizes[-1]] )
def A ( self ):
"""simple docstring"""
__magic_name__ :Tuple = self.prepare_config_and_inputs()
__magic_name__ , __magic_name__ , __magic_name__ :int = config_and_inputs
__magic_name__ :str = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class lowerCamelCase_ ( lowerCamelCase , lowerCamelCase , unittest.TestCase ):
a__ = (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
a__ = (
{'''feature-extraction''': BitModel, '''image-classification''': BitForImageClassification}
if is_torch_available()
else {}
)
a__ = False
a__ = False
a__ = False
a__ = False
a__ = False
def A ( self ):
"""simple docstring"""
__magic_name__ :Dict = BitModelTester(self )
__magic_name__ :List[str] = ConfigTester(self , config_class=__lowerCAmelCase , has_text_modality=__lowerCAmelCase )
def A ( self ):
"""simple docstring"""
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def A ( self ):
"""simple docstring"""
return
@unittest.skip(reason='''Bit does not output attentions''' )
def A ( self ):
"""simple docstring"""
pass
@unittest.skip(reason='''Bit does not use inputs_embeds''' )
def A ( self ):
"""simple docstring"""
pass
@unittest.skip(reason='''Bit does not support input and output embeddings''' )
def A ( self ):
"""simple docstring"""
pass
def A ( self ):
"""simple docstring"""
__magic_name__ , __magic_name__ :int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__magic_name__ :int = model_class(__lowerCAmelCase )
__magic_name__ :List[str] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__magic_name__ :int = [*signature.parameters.keys()]
__magic_name__ :Optional[int] = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , __lowerCAmelCase )
def A ( self ):
"""simple docstring"""
__magic_name__ :Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__lowerCAmelCase )
def A ( self ):
"""simple docstring"""
__magic_name__ :str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*__lowerCAmelCase )
def A ( self ):
"""simple docstring"""
__magic_name__ , __magic_name__ :Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__magic_name__ :Tuple = model_class(config=__lowerCAmelCase )
for name, module in model.named_modules():
if isinstance(__lowerCAmelCase , (nn.BatchNormad, nn.GroupNorm) ):
self.assertTrue(
torch.all(module.weight == 1 ) , msg=F'''Parameter {name} of model {model_class} seems not properly initialized''' , )
self.assertTrue(
torch.all(module.bias == 0 ) , msg=F'''Parameter {name} of model {model_class} seems not properly initialized''' , )
def A ( self ):
"""simple docstring"""
def check_hidden_states_output(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
__magic_name__ :Optional[int] = model_class(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
with torch.no_grad():
__magic_name__ :Tuple = model(**self._prepare_for_class(__lowerCAmelCase , __lowerCAmelCase ) )
__magic_name__ :List[str] = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
__magic_name__ :Dict = self.model_tester.num_stages
self.assertEqual(len(__lowerCAmelCase ) , expected_num_stages + 1 )
# Bit's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )
__magic_name__ , __magic_name__ :Tuple = self.model_tester.prepare_config_and_inputs_for_common()
__magic_name__ :Tuple = ['''preactivation''', '''bottleneck''']
for model_class in self.all_model_classes:
for layer_type in layers_type:
__magic_name__ :int = layer_type
__magic_name__ :Tuple = True
check_hidden_states_output(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__magic_name__ :Tuple = True
check_hidden_states_output(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
@unittest.skip(reason='''Bit does not use feedforward chunking''' )
def A ( self ):
"""simple docstring"""
pass
def A ( self ):
"""simple docstring"""
__magic_name__ :List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__lowerCAmelCase )
@slow
def A ( self ):
"""simple docstring"""
for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__magic_name__ :List[str] = BitModel.from_pretrained(__lowerCAmelCase )
self.assertIsNotNone(__lowerCAmelCase )
def __lowercase ( ):
"""simple docstring"""
__magic_name__ :Union[str, Any] = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class lowerCamelCase_ ( unittest.TestCase ):
@cached_property
def A ( self ):
"""simple docstring"""
return (
BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ) if is_vision_available() else None
)
@slow
def A ( self ):
"""simple docstring"""
__magic_name__ :Any = BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(__lowerCAmelCase )
__magic_name__ :Union[str, Any] = self.default_image_processor
__magic_name__ :int = prepare_img()
__magic_name__ :Tuple = image_processor(images=__lowerCAmelCase , return_tensors='''pt''' ).to(__lowerCAmelCase )
# forward pass
with torch.no_grad():
__magic_name__ :Tuple = model(**__lowerCAmelCase )
# verify the logits
__magic_name__ :Optional[Any] = torch.Size((1, 1_0_0_0) )
self.assertEqual(outputs.logits.shape , __lowerCAmelCase )
__magic_name__ :int = torch.tensor([[-0.6526, -0.5263, -1.4398]] ).to(__lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __lowerCAmelCase , atol=1E-4 ) )
@require_torch
class lowerCamelCase_ ( lowerCamelCase , unittest.TestCase ):
a__ = (BitBackbone,) if is_torch_available() else ()
a__ = BitConfig
a__ = False
def A ( self ):
"""simple docstring"""
__magic_name__ :Dict = BitModelTester(self )
| 0 |
'''simple docstring'''
from scipy.stats import spearmanr
import datasets
_a : str = """
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlations imply that as data in dataset x increases, so
does data in dataset y. Negative correlations imply that as x increases,
y decreases. Correlations of -1 or +1 imply an exact monotonic relationship.
Unlike the Pearson correlation, the Spearman correlation does not
assume that both datasets are normally distributed.
The p-value roughly indicates the probability of an uncorrelated system
producing datasets that have a Spearman correlation at least as extreme
as the one computed from these datasets. The p-values are not entirely
reliable but are probably reasonable for datasets larger than 500 or so.
"""
_a : Dict = """
Args:
predictions (`List[float]`): Predicted labels, as returned by a model.
references (`List[float]`): Ground truth labels.
return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns
only the spearmanr score. Defaults to `False`.
Returns:
spearmanr (`float`): Spearman correlation coefficient.
p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.
Examples:
Example 1:
>>> spearmanr_metric = datasets.load_metric(\"spearmanr\")
>>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])
>>> print(results)
{'spearmanr': -0.7}
Example 2:
>>> spearmanr_metric = datasets.load_metric(\"spearmanr\")
>>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],
... predictions=[10, 9, 2.5, 6, 4],
... return_pvalue=True)
>>> print(results['spearmanr'])
-0.7
>>> print(round(results['spearmanr_pvalue'], 2))
0.19
"""
_a : List[str] = r"""\
@book{kokoska2000crc,
title={CRC standard probability and statistics tables and formulae},
author={Kokoska, Stephen and Zwillinger, Daniel},
year={2000},
publisher={Crc Press}
}
@article{2020SciPy-NMeth,
author = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
Haberland, Matt and Reddy, Tyler and Cournapeau, David and
Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
Kern, Robert and Larson, Eric and Carey, C J and
Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and
{VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
Harris, Charles R. and Archibald, Anne M. and
Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and
{van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
title = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
Computing in Python}},
journal = {Nature Methods},
year = {2020},
volume = {17},
pages = {261--272},
adsurl = {https://rdcu.be/b08Wh},
doi = {10.1038/s41592-019-0686-2},
}
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _UpperCAmelCase ( datasets.Metric ):
def lowerCamelCase__ ( self ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION,citation=_CITATION,inputs_description=_KWARGS_DESCRIPTION,features=datasets.Features(
{
"""predictions""": datasets.Value("""float""" ),
"""references""": datasets.Value("""float""" ),
} ),reference_urls=["""https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.spearmanr.html"""],)
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=False ):
'''simple docstring'''
__lowerCAmelCase = spearmanr(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
if return_pvalue:
return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
else:
return {"spearmanr": results[0]}
| 689 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
'''junnyu/roformer_chinese_small''': '''https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json''',
'''junnyu/roformer_chinese_base''': '''https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json''',
'''junnyu/roformer_chinese_char_small''': (
'''https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json'''
),
'''junnyu/roformer_chinese_char_base''': (
'''https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json'''
),
'''junnyu/roformer_small_discriminator''': (
'''https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json'''
),
'''junnyu/roformer_small_generator''': (
'''https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json'''
),
# See all RoFormer models at https://huggingface.co/models?filter=roformer
}
class __lowerCamelCase (_a ):
_lowercase = """roformer"""
def __init__( self: List[Any],A_: Union[str, Any]=5_0000,A_: List[str]=None,A_: List[Any]=768,A_: List[Any]=12,A_: List[str]=12,A_: Any=3072,A_: Tuple="gelu",A_: List[str]=0.1,A_: int=0.1,A_: str=1536,A_: Dict=2,A_: List[str]=0.0_2,A_: int=1E-12,A_: List[str]=0,A_: Optional[Any]=False,A_: str=True,**A_: Optional[int],):
'''simple docstring'''
super().__init__(pad_token_id=A_,**A_ )
__UpperCamelCase = vocab_size
__UpperCamelCase = hidden_size if embedding_size is None else embedding_size
__UpperCamelCase = hidden_size
__UpperCamelCase = num_hidden_layers
__UpperCamelCase = num_attention_heads
__UpperCamelCase = hidden_act
__UpperCamelCase = intermediate_size
__UpperCamelCase = hidden_dropout_prob
__UpperCamelCase = attention_probs_dropout_prob
__UpperCamelCase = max_position_embeddings
__UpperCamelCase = type_vocab_size
__UpperCamelCase = initializer_range
__UpperCamelCase = layer_norm_eps
__UpperCamelCase = rotary_value
__UpperCamelCase = use_cache
class __lowerCamelCase (_a ):
@property
def snake_case_ ( self: str ):
'''simple docstring'''
if self.task == "multiple-choice":
__UpperCamelCase = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
__UpperCamelCase = {0: 'batch', 1: 'sequence'}
__UpperCamelCase = {0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
('token_type_ids', dynamic_axis),
] )
| 1 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _UpperCAmelCase ( metaclass=lowerCAmelCase_ ):
a : List[str] =["""onnx"""]
def __init__( self,*__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
requires_backends(self,["""onnx"""] )
@classmethod
def lowerCamelCase__ ( cls,*__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
requires_backends(cls,["""onnx"""] )
@classmethod
def lowerCamelCase__ ( cls,*__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
requires_backends(cls,["""onnx"""] )
| 689 | 0 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
UpperCAmelCase_ = TypeVar("""T""")
class lowerCamelCase__ ( Generic[T]):
"""simple docstring"""
def __init__( self : Tuple , __lowerCAmelCase : T ) -> Optional[Any]:
_A = data
_A = None
def __str__( self : Optional[Any] ) -> str:
return f'''{self.data}'''
class lowerCamelCase__ ( Generic[T]):
"""simple docstring"""
def __init__( self : Any ) -> None:
_A = None
def __iter__( self : int ) -> Iterator[T]:
_A = self.top
while node:
yield node.data
_A = node.next
def __str__( self : Any ) -> str:
return "->".join([str(__lowerCAmelCase ) for item in self] )
def __len__( self : List[str] ) -> int:
return len(tuple(iter(self ) ) )
def snake_case_ ( self : Optional[Any] ) -> bool:
return self.top is None
def snake_case_ ( self : int , __lowerCAmelCase : T ) -> None:
_A = Node(__lowerCAmelCase )
if not self.is_empty():
_A = self.top
_A = node
def snake_case_ ( self : List[Any] ) -> T:
if self.is_empty():
raise IndexError('''pop from empty stack''' )
assert isinstance(self.top , __lowerCAmelCase )
_A = self.top
_A = self.top.next
return pop_node.data
def snake_case_ ( self : Optional[int] ) -> T:
if self.is_empty():
raise IndexError('''peek from empty stack''' )
assert self.top is not None
return self.top.data
def snake_case_ ( self : Tuple ) -> None:
_A = None
if __name__ == "__main__":
from doctest import testmod
testmod()
| 2 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
_a : int = logging.get_logger(__name__)
_a : Optional[int] = {
"""EleutherAI/gpt-j-6B""": """https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json""",
# See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}
class _UpperCAmelCase ( lowerCAmelCase_ ):
a : List[str] ="""gptj"""
a : Optional[int] ={
"""max_position_embeddings""": """n_positions""",
"""hidden_size""": """n_embd""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self,__SCREAMING_SNAKE_CASE=5_04_00,__SCREAMING_SNAKE_CASE=20_48,__SCREAMING_SNAKE_CASE=40_96,__SCREAMING_SNAKE_CASE=28,__SCREAMING_SNAKE_CASE=16,__SCREAMING_SNAKE_CASE=64,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE="gelu_new",__SCREAMING_SNAKE_CASE=0.0,__SCREAMING_SNAKE_CASE=0.0,__SCREAMING_SNAKE_CASE=0.0,__SCREAMING_SNAKE_CASE=1e-5,__SCREAMING_SNAKE_CASE=0.02,__SCREAMING_SNAKE_CASE=True,__SCREAMING_SNAKE_CASE=5_02_56,__SCREAMING_SNAKE_CASE=5_02_56,__SCREAMING_SNAKE_CASE=False,**__SCREAMING_SNAKE_CASE,):
'''simple docstring'''
__lowerCAmelCase = vocab_size
__lowerCAmelCase = n_positions
__lowerCAmelCase = n_embd
__lowerCAmelCase = n_layer
__lowerCAmelCase = n_head
__lowerCAmelCase = n_inner
__lowerCAmelCase = rotary_dim
__lowerCAmelCase = activation_function
__lowerCAmelCase = resid_pdrop
__lowerCAmelCase = embd_pdrop
__lowerCAmelCase = attn_pdrop
__lowerCAmelCase = layer_norm_epsilon
__lowerCAmelCase = initializer_range
__lowerCAmelCase = use_cache
__lowerCAmelCase = bos_token_id
__lowerCAmelCase = eos_token_id
super().__init__(
bos_token_id=__SCREAMING_SNAKE_CASE,eos_token_id=__SCREAMING_SNAKE_CASE,tie_word_embeddings=__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE )
class _UpperCAmelCase ( lowerCAmelCase_ ):
def __init__( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE = "default",__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = False,):
'''simple docstring'''
super().__init__(__SCREAMING_SNAKE_CASE,task=__SCREAMING_SNAKE_CASE,patching_specs=__SCREAMING_SNAKE_CASE,use_past=__SCREAMING_SNAKE_CASE )
if not getattr(self._config,"""pad_token_id""",__SCREAMING_SNAKE_CASE ):
# TODO: how to do that better?
__lowerCAmelCase = 0
@property
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}} )
if self.use_past:
self.fill_with_past_key_values_(__SCREAMING_SNAKE_CASE,direction="""inputs""" )
__lowerCAmelCase = {0: """batch""", 1: """past_sequence + sequence"""}
else:
__lowerCAmelCase = {0: """batch""", 1: """sequence"""}
return common_inputs
@property
def lowerCamelCase__ ( self ):
'''simple docstring'''
return self._config.n_layer
@property
def lowerCamelCase__ ( self ):
'''simple docstring'''
return self._config.n_head
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE = -1,__SCREAMING_SNAKE_CASE = -1,__SCREAMING_SNAKE_CASE = False,__SCREAMING_SNAKE_CASE = None,):
'''simple docstring'''
__lowerCAmelCase = super(__SCREAMING_SNAKE_CASE,self ).generate_dummy_inputs(
__SCREAMING_SNAKE_CASE,batch_size=__SCREAMING_SNAKE_CASE,seq_length=__SCREAMING_SNAKE_CASE,is_pair=__SCREAMING_SNAKE_CASE,framework=__SCREAMING_SNAKE_CASE )
# We need to order the input in the way they appears in the forward()
__lowerCAmelCase = OrderedDict({"""input_ids""": common_inputs["""input_ids"""]} )
# Need to add the past_keys
if self.use_past:
if not is_torch_available():
raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed.""" )
else:
import torch
__lowerCAmelCase , __lowerCAmelCase = common_inputs["""input_ids"""].shape
# Not using the same length for past_key_values
__lowerCAmelCase = seqlen + 2
__lowerCAmelCase = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
__lowerCAmelCase = [
(torch.zeros(__SCREAMING_SNAKE_CASE ), torch.zeros(__SCREAMING_SNAKE_CASE )) for _ in range(self.num_layers )
]
__lowerCAmelCase = common_inputs["""attention_mask"""]
if self.use_past:
__lowerCAmelCase = ordered_inputs["""attention_mask"""].dtype
__lowerCAmelCase = torch.cat(
[ordered_inputs["""attention_mask"""], torch.ones(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,dtype=__SCREAMING_SNAKE_CASE )],dim=1 )
return ordered_inputs
@property
def lowerCamelCase__ ( self ):
'''simple docstring'''
return 13
| 689 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCAmelCase : List[Any] = {
'configuration_altclip': [
'ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'AltCLIPConfig',
'AltCLIPTextConfig',
'AltCLIPVisionConfig',
],
'processing_altclip': ['AltCLIPProcessor'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase : int = [
'ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
'AltCLIPPreTrainedModel',
'AltCLIPModel',
'AltCLIPTextModel',
'AltCLIPVisionModel',
]
if TYPE_CHECKING:
from .configuration_altclip import (
ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
AltCLIPConfig,
AltCLIPTextConfig,
AltCLIPVisionConfig,
)
from .processing_altclip import AltCLIPProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_altclip import (
ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
AltCLIPModel,
AltCLIPPreTrainedModel,
AltCLIPTextModel,
AltCLIPVisionModel,
)
else:
import sys
lowerCAmelCase : Optional[Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 3 |
'''simple docstring'''
def _lowerCAmelCase ( lowercase = 5000_0000 ) -> int:
__lowerCAmelCase = set()
__lowerCAmelCase = int((limit - 24) ** (1 / 2) )
__lowerCAmelCase = set(range(3 , prime_square_limit + 1 , 2 ) )
primes.add(2 )
for p in range(3 , prime_square_limit + 1 , 2 ):
if p not in primes:
continue
primes.difference_update(set(range(p * p , prime_square_limit + 1 , lowercase ) ) )
for primea in primes:
__lowerCAmelCase = primea * primea
for primea in primes:
__lowerCAmelCase = primea * primea * primea
if square + cube >= limit - 16:
break
for primea in primes:
__lowerCAmelCase = primea * primea * primea * primea
__lowerCAmelCase = square + cube + tetr
if total >= limit:
break
ret.add(lowercase )
return len(lowercase )
if __name__ == "__main__":
print(f'{solution() = }')
| 689 | 0 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : int ):
return str(_UpperCAmelCase ) == str(_UpperCAmelCase )[::-1]
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : int ):
return int(_UpperCAmelCase ) + int(str(_UpperCAmelCase )[::-1] )
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : int = 1_0000 ):
lowerCAmelCase = []
for num in range(1 , _UpperCAmelCase ):
lowerCAmelCase = 0
lowerCAmelCase = num
while iterations < 50:
lowerCAmelCase = sum_reverse(_UpperCAmelCase )
iterations += 1
if is_palindrome(_UpperCAmelCase ):
break
else:
lychrel_nums.append(_UpperCAmelCase )
return len(_UpperCAmelCase )
if __name__ == "__main__":
print(f'''{solution() = }''')
| 4 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, load_numpy, skip_mps, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
@skip_mps
class _UpperCAmelCase ( lowerCAmelCase_ , unittest.TestCase ):
a : Optional[int] =TextToVideoSDPipeline
a : Optional[int] =TEXT_TO_IMAGE_PARAMS
a : Any =TEXT_TO_IMAGE_BATCH_PARAMS
# No `output_type`.
a : Union[str, Any] =frozenset(
[
"""num_inference_steps""",
"""generator""",
"""latents""",
"""return_dict""",
"""callback""",
"""callback_steps""",
] )
def lowerCamelCase__ ( self ):
'''simple docstring'''
torch.manual_seed(0 )
__lowerCAmelCase = UNetaDConditionModel(
block_out_channels=(32, 64, 64, 64),layers_per_block=2,sample_size=32,in_channels=4,out_channels=4,down_block_types=("""CrossAttnDownBlock3D""", """CrossAttnDownBlock3D""", """CrossAttnDownBlock3D""", """DownBlock3D"""),up_block_types=("""UpBlock3D""", """CrossAttnUpBlock3D""", """CrossAttnUpBlock3D""", """CrossAttnUpBlock3D"""),cross_attention_dim=32,attention_head_dim=4,)
__lowerCAmelCase = DDIMScheduler(
beta_start=0.0_0085,beta_end=0.012,beta_schedule="""scaled_linear""",clip_sample=__SCREAMING_SNAKE_CASE,set_alpha_to_one=__SCREAMING_SNAKE_CASE,)
torch.manual_seed(0 )
__lowerCAmelCase = AutoencoderKL(
block_out_channels=[32, 64],in_channels=3,out_channels=3,down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""],up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""],latent_channels=4,sample_size=1_28,)
torch.manual_seed(0 )
__lowerCAmelCase = CLIPTextConfig(
bos_token_id=0,eos_token_id=2,hidden_size=32,intermediate_size=37,layer_norm_eps=1e-05,num_attention_heads=4,num_hidden_layers=5,pad_token_id=1,vocab_size=10_00,hidden_act="""gelu""",projection_dim=5_12,)
__lowerCAmelCase = CLIPTextModel(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
__lowerCAmelCase = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
}
return components
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=0 ):
'''simple docstring'''
if str(__SCREAMING_SNAKE_CASE ).startswith("""mps""" ):
__lowerCAmelCase = torch.manual_seed(__SCREAMING_SNAKE_CASE )
else:
__lowerCAmelCase = torch.Generator(device=__SCREAMING_SNAKE_CASE ).manual_seed(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 6.0,
"""output_type""": """pt""",
}
return inputs
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = """cpu""" # ensure determinism for the device-dependent torch.Generator
__lowerCAmelCase = self.get_dummy_components()
__lowerCAmelCase = TextToVideoSDPipeline(**__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = sd_pipe.to(__SCREAMING_SNAKE_CASE )
sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = self.get_dummy_inputs(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = """np"""
__lowerCAmelCase = sd_pipe(**__SCREAMING_SNAKE_CASE ).frames
__lowerCAmelCase = frames[0][-3:, -3:, -1]
assert frames[0].shape == (64, 64, 3)
__lowerCAmelCase = np.array([158.0, 160.0, 153.0, 125.0, 100.0, 121.0, 111.0, 93.0, 113.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def lowerCamelCase__ ( self ):
'''simple docstring'''
self._test_attention_slicing_forward_pass(test_mean_pixel_difference=__SCREAMING_SNAKE_CASE,expected_max_diff=3e-3 )
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available(),reason="""XFormers attention is only available with CUDA and `xformers` installed""",)
def lowerCamelCase__ ( self ):
'''simple docstring'''
self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=__SCREAMING_SNAKE_CASE,expected_max_diff=1e-2 )
@unittest.skip(reason="""Batching needs to be properly figured out first for this pipeline.""" )
def lowerCamelCase__ ( self ):
'''simple docstring'''
pass
@unittest.skip(reason="""Batching needs to be properly figured out first for this pipeline.""" )
def lowerCamelCase__ ( self ):
'''simple docstring'''
pass
@unittest.skip(reason="""`num_images_per_prompt` argument is not supported for this pipeline.""" )
def lowerCamelCase__ ( self ):
'''simple docstring'''
pass
def lowerCamelCase__ ( self ):
'''simple docstring'''
return super().test_progress_bar()
@slow
@skip_mps
class _UpperCAmelCase ( unittest.TestCase ):
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video.npy""" )
__lowerCAmelCase = TextToVideoSDPipeline.from_pretrained("""damo-vilab/text-to-video-ms-1.7b""" )
__lowerCAmelCase = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
__lowerCAmelCase = pipe.to("""cuda""" )
__lowerCAmelCase = """Spiderman is surfing"""
__lowerCAmelCase = torch.Generator(device="""cpu""" ).manual_seed(0 )
__lowerCAmelCase = pipe(__SCREAMING_SNAKE_CASE,generator=__SCREAMING_SNAKE_CASE,num_inference_steps=25,output_type="""pt""" ).frames
__lowerCAmelCase = video_frames.cpu().numpy()
assert np.abs(expected_video - video ).mean() < 5e-2
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video_2step.npy""" )
__lowerCAmelCase = TextToVideoSDPipeline.from_pretrained("""damo-vilab/text-to-video-ms-1.7b""" )
__lowerCAmelCase = pipe.to("""cuda""" )
__lowerCAmelCase = """Spiderman is surfing"""
__lowerCAmelCase = torch.Generator(device="""cpu""" ).manual_seed(0 )
__lowerCAmelCase = pipe(__SCREAMING_SNAKE_CASE,generator=__SCREAMING_SNAKE_CASE,num_inference_steps=2,output_type="""pt""" ).frames
__lowerCAmelCase = video_frames.cpu().numpy()
assert np.abs(expected_video - video ).mean() < 5e-2
| 689 | 0 |
'''simple docstring'''
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
_lowercase = logging.get_logger(__name__)
def A (__lowerCamelCase :List[str] ):
_lowerCAmelCase = r"""\w+[.]\d+"""
_lowerCAmelCase = re.findall(__lowerCamelCase , __lowerCamelCase )
for pat in pats:
_lowerCAmelCase = key.replace(__lowerCamelCase , """_""".join(pat.split(""".""" ) ) )
return key
def A (__lowerCamelCase :Union[str, Any] , __lowerCamelCase :Any , __lowerCamelCase :int ):
_lowerCAmelCase = pt_tuple_key[:-1] + ("""scale""",)
if (
any("""norm""" in str_ for str_ in pt_tuple_key )
and (pt_tuple_key[-1] == "bias")
and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
):
_lowerCAmelCase = pt_tuple_key[:-1] + ("""scale""",)
return renamed_pt_tuple_key, pt_tensor
elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
_lowerCAmelCase = pt_tuple_key[:-1] + ("""scale""",)
return renamed_pt_tuple_key, pt_tensor
# embedding
if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
_lowerCAmelCase = pt_tuple_key[:-1] + ("""embedding""",)
return renamed_pt_tuple_key, pt_tensor
# conv layer
_lowerCAmelCase = pt_tuple_key[:-1] + ("""kernel""",)
if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
_lowerCAmelCase = pt_tensor.transpose(2 , 3 , 1 , 0 )
return renamed_pt_tuple_key, pt_tensor
# linear layer
_lowerCAmelCase = pt_tuple_key[:-1] + ("""kernel""",)
if pt_tuple_key[-1] == "weight":
_lowerCAmelCase = pt_tensor.T
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm weight
_lowerCAmelCase = pt_tuple_key[:-1] + ("""weight""",)
if pt_tuple_key[-1] == "gamma":
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm bias
_lowerCAmelCase = pt_tuple_key[:-1] + ("""bias""",)
if pt_tuple_key[-1] == "beta":
return renamed_pt_tuple_key, pt_tensor
return pt_tuple_key, pt_tensor
def A (__lowerCamelCase :Any , __lowerCamelCase :int , __lowerCamelCase :Dict=42 ):
# Step 1: Convert pytorch tensor to numpy
_lowerCAmelCase = {k: v.numpy() for k, v in pt_state_dict.items()}
# Step 2: Since the model is stateless, get random Flax params
_lowerCAmelCase = flax_model.init_weights(PRNGKey(__lowerCamelCase ) )
_lowerCAmelCase = flatten_dict(__lowerCamelCase )
_lowerCAmelCase = {}
# Need to change some parameters name to match Flax names
for pt_key, pt_tensor in pt_state_dict.items():
_lowerCAmelCase = rename_key(__lowerCamelCase )
_lowerCAmelCase = tuple(renamed_pt_key.split(""".""" ) )
# Correctly rename weight parameters
_lowerCAmelCase , _lowerCAmelCase = rename_key_and_reshape_tensor(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
if flax_key in random_flax_state_dict:
if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
raise ValueError(
f'PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '
f'{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.' )
# also add unexpected weight so that warning is thrown
_lowerCAmelCase = jnp.asarray(__lowerCamelCase )
return unflatten_dict(__lowerCamelCase )
| 5 |
'''simple docstring'''
from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def _lowerCAmelCase ( lowercase ) -> Optional[int]:
if not is_accelerate_available():
return method
__lowerCAmelCase = version.parse(accelerate.__version__ ).base_version
if version.parse(lowercase ) < version.parse("""0.17.0""" ):
return method
def wrapper(self , *lowercase , **lowercase ):
if hasattr(self , """_hf_hook""" ) and hasattr(self._hf_hook , """pre_forward""" ):
self._hf_hook.pre_forward(self )
return method(self , *lowercase , **lowercase )
return wrapper
| 689 | 0 |
import torch
from diffusers import DDIMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class UpperCamelCase_ ( UpperCamelCase__ ):
lowerCamelCase_ = (DDIMParallelScheduler,)
lowerCamelCase_ = (("eta", 0.0), ("num_inference_steps", 50))
def _snake_case ( self :List[Any] , **__A :List[str] ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = {
"""num_train_timesteps""": 1000,
"""beta_start""": 0.0_0_0_1,
"""beta_end""": 0.0_2,
"""beta_schedule""": """linear""",
"""clip_sample""": True,
}
config.update(**__A )
return config
def _snake_case ( self :int , **__A :List[str] ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ = self.get_scheduler_config(**__A )
SCREAMING_SNAKE_CASE__ = scheduler_class(**__A )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = 10, 0.0
SCREAMING_SNAKE_CASE__ = self.dummy_model()
SCREAMING_SNAKE_CASE__ = self.dummy_sample_deter
scheduler.set_timesteps(__A )
for t in scheduler.timesteps:
SCREAMING_SNAKE_CASE__ = model(__A , __A )
SCREAMING_SNAKE_CASE__ = scheduler.step(__A , __A , __A , __A ).prev_sample
return sample
def _snake_case ( self :Tuple ) -> Optional[int]:
"""simple docstring"""
for timesteps in [100, 500, 1000]:
self.check_over_configs(num_train_timesteps=__A )
def _snake_case ( self :Union[str, Any] ) -> Tuple:
"""simple docstring"""
for steps_offset in [0, 1]:
self.check_over_configs(steps_offset=__A )
SCREAMING_SNAKE_CASE__ = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ = self.get_scheduler_config(steps_offset=1 )
SCREAMING_SNAKE_CASE__ = scheduler_class(**__A )
scheduler.set_timesteps(5 )
assert torch.equal(scheduler.timesteps , torch.LongTensor([801, 601, 401, 201, 1] ) )
def _snake_case ( self :List[str] ) -> Any:
"""simple docstring"""
for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1] , [0.0_0_2, 0.0_2, 0.2, 2] ):
self.check_over_configs(beta_start=__A , beta_end=__A )
def _snake_case ( self :Dict ) -> Optional[Any]:
"""simple docstring"""
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=__A )
def _snake_case ( self :int ) -> List[Any]:
"""simple docstring"""
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=__A )
def _snake_case ( self :Optional[Any] ) -> List[Any]:
"""simple docstring"""
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=__A )
def _snake_case ( self :Any ) -> Any:
"""simple docstring"""
for timestep_spacing in ["trailing", "leading"]:
self.check_over_configs(timestep_spacing=__A )
def _snake_case ( self :List[Any] ) -> Tuple:
"""simple docstring"""
for rescale_betas_zero_snr in [True, False]:
self.check_over_configs(rescale_betas_zero_snr=__A )
def _snake_case ( self :Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
self.check_over_configs(thresholding=__A )
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(
thresholding=__A , prediction_type=__A , sample_max_value=__A , )
def _snake_case ( self :Optional[int] ) -> List[Any]:
"""simple docstring"""
for t in [1, 10, 49]:
self.check_over_forward(time_step=__A )
def _snake_case ( self :str ) -> Tuple:
"""simple docstring"""
for t, num_inference_steps in zip([1, 10, 50] , [10, 50, 500] ):
self.check_over_forward(time_step=__A , num_inference_steps=__A )
def _snake_case ( self :Tuple ) -> str:
"""simple docstring"""
for t, eta in zip([1, 10, 49] , [0.0, 0.5, 1.0] ):
self.check_over_forward(time_step=__A , eta=__A )
def _snake_case ( self :List[str] ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ = scheduler_class(**__A )
assert torch.sum(torch.abs(scheduler._get_variance(0 , 0 ) - 0.0 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(420 , 400 ) - 0.1_4_7_7_1 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(980 , 960 ) - 0.3_2_4_6_0 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(0 , 0 ) - 0.0 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(487 , 486 ) - 0.0_0_9_7_9 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(999 , 998 ) - 0.0_2 ) ) < 1E-5
def _snake_case ( self :int ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE__ = self.get_scheduler_config()
SCREAMING_SNAKE_CASE__ = scheduler_class(**__A )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = 10, 0.0
scheduler.set_timesteps(__A )
SCREAMING_SNAKE_CASE__ = self.dummy_model()
SCREAMING_SNAKE_CASE__ = self.dummy_sample_deter
SCREAMING_SNAKE_CASE__ = self.dummy_sample_deter + 0.1
SCREAMING_SNAKE_CASE__ = self.dummy_sample_deter - 0.1
SCREAMING_SNAKE_CASE__ = samplea.shape[0]
SCREAMING_SNAKE_CASE__ = torch.stack([samplea, samplea, samplea] , dim=0 )
SCREAMING_SNAKE_CASE__ = torch.arange(__A )[0:3, None].repeat(1 , __A )
SCREAMING_SNAKE_CASE__ = model(samples.flatten(0 , 1 ) , timesteps.flatten(0 , 1 ) )
SCREAMING_SNAKE_CASE__ = scheduler.batch_step_no_noise(__A , timesteps.flatten(0 , 1 ) , samples.flatten(0 , 1 ) , __A )
SCREAMING_SNAKE_CASE__ = torch.sum(torch.abs(__A ) )
SCREAMING_SNAKE_CASE__ = torch.mean(torch.abs(__A ) )
assert abs(result_sum.item() - 1_1_4_7.7_9_0_4 ) < 1E-2
assert abs(result_mean.item() - 0.4_9_8_2 ) < 1E-3
def _snake_case ( self :Tuple ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = self.full_loop()
SCREAMING_SNAKE_CASE__ = torch.sum(torch.abs(__A ) )
SCREAMING_SNAKE_CASE__ = torch.mean(torch.abs(__A ) )
assert abs(result_sum.item() - 1_7_2.0_0_6_7 ) < 1E-2
assert abs(result_mean.item() - 0.2_2_3_9_6_7 ) < 1E-3
def _snake_case ( self :Optional[Any] ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = self.full_loop(prediction_type="""v_prediction""" )
SCREAMING_SNAKE_CASE__ = torch.sum(torch.abs(__A ) )
SCREAMING_SNAKE_CASE__ = torch.mean(torch.abs(__A ) )
assert abs(result_sum.item() - 5_2.5_3_0_2 ) < 1E-2
assert abs(result_mean.item() - 0.0_6_8_4 ) < 1E-3
def _snake_case ( self :Any ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = self.full_loop(set_alpha_to_one=__A , beta_start=0.0_1 )
SCREAMING_SNAKE_CASE__ = torch.sum(torch.abs(__A ) )
SCREAMING_SNAKE_CASE__ = torch.mean(torch.abs(__A ) )
assert abs(result_sum.item() - 1_4_9.8_2_9_5 ) < 1E-2
assert abs(result_mean.item() - 0.1_9_5_1 ) < 1E-3
def _snake_case ( self :Tuple ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = self.full_loop(set_alpha_to_one=__A , beta_start=0.0_1 )
SCREAMING_SNAKE_CASE__ = torch.sum(torch.abs(__A ) )
SCREAMING_SNAKE_CASE__ = torch.mean(torch.abs(__A ) )
assert abs(result_sum.item() - 1_4_9.0_7_8_4 ) < 1E-2
assert abs(result_mean.item() - 0.1_9_4_1 ) < 1E-3 | 6 |
'''simple docstring'''
import argparse
import torch
from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline
def _lowerCAmelCase ( lowercase , lowercase , lowercase , lowercase , lowercase ) -> Optional[int]:
# load base model
__lowerCAmelCase = StableDiffusionPipeline.from_pretrained(lowercase , torch_dtype=torch.floataa )
# load LoRA weight from .safetensors
__lowerCAmelCase = load_file(lowercase )
__lowerCAmelCase = []
# directly update weight in diffusers model
for key in state_dict:
# it is suggested to print out the key, it usually will be something like below
# "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"
# as we have set the alpha beforehand, so just skip
if ".alpha" in key or key in visited:
continue
if "text" in key:
__lowerCAmelCase = key.split(""".""" )[0].split(LORA_PREFIX_TEXT_ENCODER + """_""" )[-1].split("""_""" )
__lowerCAmelCase = pipeline.text_encoder
else:
__lowerCAmelCase = key.split(""".""" )[0].split(LORA_PREFIX_UNET + """_""" )[-1].split("""_""" )
__lowerCAmelCase = pipeline.unet
# find the target layer
__lowerCAmelCase = layer_infos.pop(0 )
while len(lowercase ) > -1:
try:
__lowerCAmelCase = curr_layer.__getattr__(lowercase )
if len(lowercase ) > 0:
__lowerCAmelCase = layer_infos.pop(0 )
elif len(lowercase ) == 0:
break
except Exception:
if len(lowercase ) > 0:
temp_name += "_" + layer_infos.pop(0 )
else:
__lowerCAmelCase = layer_infos.pop(0 )
__lowerCAmelCase = []
if "lora_down" in key:
pair_keys.append(key.replace("""lora_down""" , """lora_up""" ) )
pair_keys.append(lowercase )
else:
pair_keys.append(lowercase )
pair_keys.append(key.replace("""lora_up""" , """lora_down""" ) )
# update weight
if len(state_dict[pair_keys[0]].shape ) == 4:
__lowerCAmelCase = state_dict[pair_keys[0]].squeeze(3 ).squeeze(2 ).to(torch.floataa )
__lowerCAmelCase = state_dict[pair_keys[1]].squeeze(3 ).squeeze(2 ).to(torch.floataa )
curr_layer.weight.data += alpha * torch.mm(lowercase , lowercase ).unsqueeze(2 ).unsqueeze(3 )
else:
__lowerCAmelCase = state_dict[pair_keys[0]].to(torch.floataa )
__lowerCAmelCase = state_dict[pair_keys[1]].to(torch.floataa )
curr_layer.weight.data += alpha * torch.mm(lowercase , lowercase )
# update visited list
for item in pair_keys:
visited.append(lowercase )
return pipeline
if __name__ == "__main__":
_a : Union[str, Any] = argparse.ArgumentParser()
parser.add_argument(
"""--base_model_path""", default=None, type=str, required=True, help="""Path to the base model in diffusers format."""
)
parser.add_argument(
"""--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
)
parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")
parser.add_argument(
"""--lora_prefix_unet""", default="""lora_unet""", type=str, help="""The prefix of UNet weight in safetensors"""
)
parser.add_argument(
"""--lora_prefix_text_encoder""",
default="""lora_te""",
type=str,
help="""The prefix of text encoder weight in safetensors""",
)
parser.add_argument("""--alpha""", default=0.75, type=float, help="""The merging ratio in W = W0 + alpha * deltaW""")
parser.add_argument(
"""--to_safetensors""", action="""store_true""", help="""Whether to store pipeline in safetensors format or not."""
)
parser.add_argument("""--device""", type=str, help="""Device to use (e.g. cpu, cuda:0, cuda:1, etc.)""")
_a : Optional[int] = parser.parse_args()
_a : Dict = args.base_model_path
_a : Optional[Any] = args.checkpoint_path
_a : Union[str, Any] = args.dump_path
_a : Optional[int] = args.lora_prefix_unet
_a : int = args.lora_prefix_text_encoder
_a : str = args.alpha
_a : Any = convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)
_a : Tuple = pipe.to(args.device)
pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
| 689 | 0 |
"""simple docstring"""
import os
import numpy
import onnx
def _snake_case ( _snake_case : Optional[Any] , _snake_case : Any ) -> Dict:
'''simple docstring'''
_A = a.name
_A = b.name
_A = ''
_A = ''
_A = a == b
_A = name_a
_A = name_b
return res
def _snake_case ( _snake_case : Optional[int] , _snake_case : Tuple , _snake_case : List[Any] ) -> Tuple:
'''simple docstring'''
for i, input_name in enumerate(node_proto.input ):
if input_name == name:
node_proto.input.insert(_snake_case , _snake_case )
node_proto.input.pop(i + 1 )
if node_proto.op_type == "If":
_graph_replace_input_with(node_proto.attribute[0].g , _snake_case , _snake_case )
_graph_replace_input_with(node_proto.attribute[1].g , _snake_case , _snake_case )
if node_proto.op_type == "Loop":
_graph_replace_input_with(node_proto.attribute[0].g , _snake_case , _snake_case )
def _snake_case ( _snake_case : Any , _snake_case : Optional[Any] , _snake_case : Any ) -> int:
'''simple docstring'''
for n in graph_proto.node:
_node_replace_input_with(_snake_case , _snake_case , _snake_case )
def _snake_case ( _snake_case : List[Any] , _snake_case : List[str] , _snake_case : Any ) -> Optional[Any]:
'''simple docstring'''
_A = list(model.graph.initializer )
_A = list(model_without_ext.graph.initializer )
for i, ref_i in ind_to_replace:
assert inits_with_data[i].name == inits[i].name
assert inits_with_data[ref_i].name == inits[ref_i].name
assert i > ref_i
_A = inits[i].name
_A = inits[ref_i].name
model_without_ext.graph.initializer.remove(inits[i] )
# for n in model.graph.node:
_graph_replace_input_with(model_without_ext.graph , _snake_case , _snake_case )
def _snake_case ( _snake_case : int ) -> int:
'''simple docstring'''
_A = os.path.dirname(_snake_case )
_A = os.path.basename(_snake_case )
_A = onnx.load(os.path.join(_snake_case , _snake_case ) )
_A = list(model.graph.initializer )
_A = set()
_A = {}
_A = []
_A = 0
for i in range(len(_snake_case ) ):
if i in dup_set:
continue
for j in range(i + 1 , len(_snake_case ) ):
if j in dup_set:
continue
if _is_equal_tensor_proto(inits[i] , inits[j] ):
dup_set.add(_snake_case )
dup_set.add(_snake_case )
_A = inits[j].data_type
_A = numpy.prod(inits[j].dims )
if dtype == 1:
mem_size *= 4
elif dtype == 6:
mem_size *= 4
elif dtype == 7 or dtype == 11:
mem_size *= 8
else:
print('unexpected data type: ' , _snake_case )
total_reduced_size += mem_size
_A = inits[i].name
_A = inits[j].name
if name_i in dup_map:
dup_map[name_i].append(_snake_case )
else:
_A = [name_j]
ind_to_replace.append((j, i) )
print('total reduced size: ' , total_reduced_size / 10_24 / 10_24 / 10_24 , 'GB' )
_A = sorted(_snake_case )
_remove_dup_initializers_from_model(_snake_case , _snake_case , _snake_case )
_A = 'optimized_' + model_file_name
_A = os.path.join(_snake_case , _snake_case )
onnx.save(_snake_case , _snake_case )
return new_model
| 7 |
'''simple docstring'''
from collections import Counter
from timeit import timeit
def _lowerCAmelCase ( lowercase = "" , ) -> bool:
return sum(c % 2 for c in Counter(input_str.replace(""" """ , """""" ).lower() ).values() ) < 2
def _lowerCAmelCase ( lowercase = "" ) -> bool:
if len(lowercase ) == 0:
return True
__lowerCAmelCase = input_str.replace(""" """ , """""" ).lower()
# character_freq_dict: Stores the frequency of every character in the input string
__lowerCAmelCase = {}
for character in lower_case_input_str:
__lowerCAmelCase = character_freq_dict.get(lowercase , 0 ) + 1
__lowerCAmelCase = 0
for character_count in character_freq_dict.values():
if character_count % 2:
odd_char += 1
if odd_char > 1:
return False
return True
def _lowerCAmelCase ( lowercase = "" ) -> None:
print("""\nFor string = """ , lowercase , """:""" )
print(
"""> can_string_be_rearranged_as_palindrome_counter()""" , """\tans =""" , can_string_be_rearranged_as_palindrome_counter(lowercase ) , """\ttime =""" , timeit(
"""z.can_string_be_rearranged_as_palindrome_counter(z.check_str)""" , setup="""import __main__ as z""" , ) , """seconds""" , )
print(
"""> can_string_be_rearranged_as_palindrome()""" , """\tans =""" , can_string_be_rearranged_as_palindrome(lowercase ) , """\ttime =""" , timeit(
"""z.can_string_be_rearranged_as_palindrome(z.check_str)""" , setup="""import __main__ as z""" , ) , """seconds""" , )
if __name__ == "__main__":
_a : int = input(
"""Enter string to determine if it can be rearranged as a palindrome or not: """
).strip()
benchmark(check_str)
_a : Optional[int] = can_string_be_rearranged_as_palindrome_counter(check_str)
print(f'{check_str} can {"" if status else "not "}be rearranged as a palindrome')
| 689 | 0 |
'''simple docstring'''
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
if is_torch_available():
lowercase__ : Any = '''pt'''
elif is_tf_available():
lowercase__ : Tuple = '''tf'''
else:
lowercase__ : Union[str, Any] = '''jax'''
class SCREAMING_SNAKE_CASE (a__ , unittest.TestCase ):
lowerCAmelCase = ByTaTokenizer
lowerCAmelCase = False
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
super().setUp()
__A : Any = ByTaTokenizer()
tokenizer.save_pretrained(self.tmpdirname)
@cached_property
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
return ByTaTokenizer.from_pretrained('google/byt5-small')
def SCREAMING_SNAKE_CASE ( self , **_UpperCAmelCase):
'''simple docstring'''
return self.tokenizer_class.from_pretrained(self.tmpdirname , **_UpperCAmelCase)
def SCREAMING_SNAKE_CASE ( self , _UpperCAmelCase , _UpperCAmelCase=False , _UpperCAmelCase=20 , _UpperCAmelCase=5):
'''simple docstring'''
__A : Optional[Any] = []
for i in range(len(_UpperCAmelCase)):
try:
__A : Optional[Any] = tokenizer.decode([i] , clean_up_tokenization_spaces=_UpperCAmelCase)
except UnicodeDecodeError:
pass
toks.append((i, tok))
__A : List[str] = list(filter(lambda _UpperCAmelCase: re.match(R'^[ a-zA-Z]+$' , t[1]) , _UpperCAmelCase))
__A : Optional[Any] = list(filter(lambda _UpperCAmelCase: [t[0]] == tokenizer.encode(t[1] , add_special_tokens=_UpperCAmelCase) , _UpperCAmelCase))
if max_length is not None and len(_UpperCAmelCase) > max_length:
__A : Optional[int] = toks[:max_length]
if min_length is not None and len(_UpperCAmelCase) < min_length and len(_UpperCAmelCase) > 0:
while len(_UpperCAmelCase) < min_length:
__A : Tuple = toks + toks
# toks_str = [t[1] for t in toks]
__A : Tuple = [t[0] for t in toks]
# Ensure consistency
__A : Optional[Any] = tokenizer.decode(_UpperCAmelCase , clean_up_tokenization_spaces=_UpperCAmelCase)
if " " not in output_txt and len(_UpperCAmelCase) > 1:
__A : Union[str, Any] = (
tokenizer.decode([toks_ids[0]] , clean_up_tokenization_spaces=_UpperCAmelCase)
+ ' '
+ tokenizer.decode(toks_ids[1:] , clean_up_tokenization_spaces=_UpperCAmelCase)
)
if with_prefix_space:
__A : Dict = ' ' + output_txt
__A : int = tokenizer.encode(_UpperCAmelCase , add_special_tokens=_UpperCAmelCase)
return output_txt, output_ids
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
__A : List[Any] = self.ta_base_tokenizer
__A : Tuple = tokenizer(['hi</s>', 'I went to the gym</s>', '</s>'])
__A : List[Any] = tokenizer(['hi', 'I went to the gym', ''])
self.assertListEqual(batch_with_eos_added['input_ids'] , batch_without_eos_added['input_ids'])
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
__A : Optional[int] = self.ta_base_tokenizer
__A : Any = 'Unicode €.'
__A : Union[str, Any] = tokenizer(_UpperCAmelCase)
__A : Optional[Any] = [88, 113, 108, 102, 114, 103, 104, 35, 229, 133, 175, 49, 1]
self.assertEqual(encoded['input_ids'] , _UpperCAmelCase)
# decoding
__A : List[str] = tokenizer.decode(_UpperCAmelCase)
self.assertEqual(_UpperCAmelCase , 'Unicode €.</s>')
__A : Any = tokenizer('e è é ê ë')
__A : List[str] = [104, 35, 198, 171, 35, 198, 172, 35, 198, 173, 35, 198, 174, 1]
self.assertEqual(encoded['input_ids'] , _UpperCAmelCase)
# decoding
__A : Optional[int] = tokenizer.decode(_UpperCAmelCase)
self.assertEqual(_UpperCAmelCase , 'e è é ê ë</s>')
# encode/decode, but with `encode` instead of `__call__`
self.assertEqual(tokenizer.decode(tokenizer.encode('e è é ê ë')) , 'e è é ê ë</s>')
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
__A : Union[str, Any] = self.ta_base_tokenizer
__A : Optional[Any] = ['A long paragraph for summarization.', 'Another paragraph for summarization.']
# fmt: off
__A : Tuple = [68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 1, 0]
# fmt: on
__A : str = tokenizer(_UpperCAmelCase , padding=_UpperCAmelCase , return_tensors=_UpperCAmelCase)
self.assertIsInstance(_UpperCAmelCase , _UpperCAmelCase)
if FRAMEWORK != "jax":
__A : Optional[Any] = list(batch.input_ids.numpy()[0])
else:
__A : str = list(batch.input_ids.tolist()[0])
self.assertListEqual(_UpperCAmelCase , _UpperCAmelCase)
self.assertEqual((2, 37) , batch.input_ids.shape)
self.assertEqual((2, 37) , batch.attention_mask.shape)
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
__A : List[Any] = self.ta_base_tokenizer
__A : str = ['A long paragraph for summarization.', 'Another paragraph for summarization.']
__A : Tuple = tokenizer(_UpperCAmelCase , padding=_UpperCAmelCase , return_tensors=_UpperCAmelCase)
# check if input_ids are returned and no decoder_input_ids
self.assertIn('input_ids' , _UpperCAmelCase)
self.assertIn('attention_mask' , _UpperCAmelCase)
self.assertNotIn('decoder_input_ids' , _UpperCAmelCase)
self.assertNotIn('decoder_attention_mask' , _UpperCAmelCase)
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
__A : Tuple = self.ta_base_tokenizer
__A : Any = [
'Summary of the text.',
'Another summary.',
]
__A : Optional[Any] = tokenizer(
text_target=_UpperCAmelCase , max_length=32 , padding='max_length' , truncation=_UpperCAmelCase , return_tensors=_UpperCAmelCase)
self.assertEqual(32 , targets['input_ids'].shape[1])
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
__A : str = self.ta_base_tokenizer
__A : Optional[Any] = ['A long paragraph for summarization. </s>']
__A : Dict = ['Summary of the text. </s>']
# fmt: off
__A : str = [68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 35, 1]
__A : List[str] = [86, 120, 112, 112, 100, 117, 124, 35, 114, 105, 35, 119, 107, 104, 35, 119, 104, 123, 119, 49, 35, 1]
# fmt: on
__A : Any = tokenizer(_UpperCAmelCase , text_target=_UpperCAmelCase)
self.assertEqual(_UpperCAmelCase , batch['input_ids'][0])
self.assertEqual(_UpperCAmelCase , batch['labels'][0])
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
__A : List[Any] = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(F'{tokenizer.__class__.__name__}'):
self.assertNotEqual(tokenizer.model_max_length , 42)
# Now let's start the test
__A : List[Any] = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(F'{tokenizer.__class__.__name__}'):
# Isolate this from the other tests because we save additional tokens/etc
__A : Dict = tempfile.mkdtemp()
__A : Dict = ' He is very happy, UNwant\u00E9d,running'
__A : List[str] = tokenizer.encode(_UpperCAmelCase , add_special_tokens=_UpperCAmelCase)
tokenizer.save_pretrained(_UpperCAmelCase)
__A : Optional[Any] = tokenizer.__class__.from_pretrained(_UpperCAmelCase)
__A : Dict = after_tokenizer.encode(_UpperCAmelCase , add_special_tokens=_UpperCAmelCase)
self.assertListEqual(_UpperCAmelCase , _UpperCAmelCase)
shutil.rmtree(_UpperCAmelCase)
__A : List[Any] = self.get_tokenizers(model_max_length=42)
for tokenizer in tokenizers:
with self.subTest(F'{tokenizer.__class__.__name__}'):
# Isolate this from the other tests because we save additional tokens/etc
__A : List[str] = tempfile.mkdtemp()
__A : str = ' He is very happy, UNwant\u00E9d,running'
tokenizer.add_tokens(['bim', 'bambam'])
__A : List[str] = tokenizer.additional_special_tokens
additional_special_tokens.append('new_additional_special_token')
tokenizer.add_special_tokens({'additional_special_tokens': additional_special_tokens})
__A : Union[str, Any] = tokenizer.encode(_UpperCAmelCase , add_special_tokens=_UpperCAmelCase)
tokenizer.save_pretrained(_UpperCAmelCase)
__A : Dict = tokenizer.__class__.from_pretrained(_UpperCAmelCase)
__A : Optional[Any] = after_tokenizer.encode(_UpperCAmelCase , add_special_tokens=_UpperCAmelCase)
self.assertListEqual(_UpperCAmelCase , _UpperCAmelCase)
self.assertIn('new_additional_special_token' , after_tokenizer.additional_special_tokens)
self.assertEqual(after_tokenizer.model_max_length , 42)
__A : str = tokenizer.__class__.from_pretrained(_UpperCAmelCase , model_max_length=43)
self.assertEqual(tokenizer.model_max_length , 43)
shutil.rmtree(_UpperCAmelCase)
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
__A : List[Any] = []
if self.test_slow_tokenizer:
tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()))
if self.test_rust_tokenizer:
tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()))
for tokenizer_class, tokenizer_utils in tokenizer_list:
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer_utils.save_pretrained(_UpperCAmelCase)
with open(os.path.join(_UpperCAmelCase , 'special_tokens_map.json') , encoding='utf-8') as json_file:
__A : Tuple = json.load(_UpperCAmelCase)
with open(os.path.join(_UpperCAmelCase , 'tokenizer_config.json') , encoding='utf-8') as json_file:
__A : List[Any] = json.load(_UpperCAmelCase)
__A : str = [F'<extra_id_{i}>' for i in range(125)]
__A : Union[str, Any] = added_tokens_extra_ids + [
'an_additional_special_token'
]
__A : List[Any] = added_tokens_extra_ids + [
'an_additional_special_token'
]
with open(os.path.join(_UpperCAmelCase , 'special_tokens_map.json') , 'w' , encoding='utf-8') as outfile:
json.dump(_UpperCAmelCase , _UpperCAmelCase)
with open(os.path.join(_UpperCAmelCase , 'tokenizer_config.json') , 'w' , encoding='utf-8') as outfile:
json.dump(_UpperCAmelCase , _UpperCAmelCase)
# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
# "special_tokens_map.json" files
__A : Any = tokenizer_class.from_pretrained(
_UpperCAmelCase , )
self.assertIn(
'an_additional_special_token' , tokenizer_without_change_in_init.additional_special_tokens)
# self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
self.assertEqual(
['an_additional_special_token'] , tokenizer_without_change_in_init.convert_ids_to_tokens(
tokenizer_without_change_in_init.convert_tokens_to_ids(['an_additional_special_token'])) , )
# Now we test that we can change the value of additional_special_tokens in the from_pretrained
__A : Union[str, Any] = added_tokens_extra_ids + [AddedToken('a_new_additional_special_token' , lstrip=_UpperCAmelCase)]
__A : int = tokenizer_class.from_pretrained(
_UpperCAmelCase , additional_special_tokens=_UpperCAmelCase , )
self.assertIn('a_new_additional_special_token' , tokenizer.additional_special_tokens)
self.assertEqual(
['a_new_additional_special_token'] , tokenizer.convert_ids_to_tokens(
tokenizer.convert_tokens_to_ids(['a_new_additional_special_token'])) , )
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
__A : Optional[int] = []
if self.test_slow_tokenizer:
tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()))
if self.test_rust_tokenizer:
tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()))
for tokenizer_class, tokenizer_utils in tokenizer_list:
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer_utils.save_pretrained(_UpperCAmelCase)
__A : Optional[Any] = tokenizer_class.from_pretrained(_UpperCAmelCase)
self.assertTrue(tokenizer.decode([255]) == '')
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
pass
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
pass
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
pass
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
pass
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
__A : Dict = self.get_tokenizers(fast=_UpperCAmelCase , do_lower_case=_UpperCAmelCase)
for tokenizer in tokenizers:
with self.subTest(F'{tokenizer.__class__.__name__}'):
__A : List[str] = ['t', 'h', 'i', 's', ' ', 'i', 's', ' ', 'a', ' ', 't', 'e', 'x', 't', '</s>']
__A : Tuple = tokenizer.convert_tokens_to_string(_UpperCAmelCase)
self.assertIsInstance(_UpperCAmelCase , _UpperCAmelCase)
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
__A : Optional[Any] = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(F'{tokenizer.__class__.__name__}'):
__A : Tuple = [
'bos_token',
'eos_token',
'unk_token',
'sep_token',
'pad_token',
'cls_token',
'mask_token',
]
__A : Optional[int] = 0
__A : List[Any] = tokenizer.convert_ids_to_tokens(
_UpperCAmelCase , skip_special_tokens=_UpperCAmelCase)
for attr in attributes_list:
setattr(_UpperCAmelCase , attr + '_id' , _UpperCAmelCase)
self.assertEqual(getattr(_UpperCAmelCase , _UpperCAmelCase) , _UpperCAmelCase)
self.assertEqual(getattr(_UpperCAmelCase , attr + '_id') , _UpperCAmelCase)
setattr(_UpperCAmelCase , attr + '_id' , _UpperCAmelCase)
self.assertEqual(getattr(_UpperCAmelCase , _UpperCAmelCase) , _UpperCAmelCase)
self.assertEqual(getattr(_UpperCAmelCase , attr + '_id') , _UpperCAmelCase)
setattr(_UpperCAmelCase , 'additional_special_tokens_ids' , [])
self.assertListEqual(getattr(_UpperCAmelCase , 'additional_special_tokens') , [])
self.assertListEqual(getattr(_UpperCAmelCase , 'additional_special_tokens_ids') , [])
setattr(_UpperCAmelCase , 'additional_special_tokens_ids' , [token_id_to_test_setters])
self.assertListEqual(getattr(_UpperCAmelCase , 'additional_special_tokens') , [token_to_test_setters])
self.assertListEqual(getattr(_UpperCAmelCase , 'additional_special_tokens_ids') , [token_id_to_test_setters]) | 8 |
'''simple docstring'''
import argparse
import json
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
VideoMAEConfig,
VideoMAEForPreTraining,
VideoMAEForVideoClassification,
VideoMAEImageProcessor,
)
def _lowerCAmelCase ( lowercase ) -> List[Any]:
__lowerCAmelCase = VideoMAEConfig()
set_architecture_configs(lowercase , lowercase )
if "finetuned" not in model_name:
__lowerCAmelCase = False
if "finetuned" in model_name:
__lowerCAmelCase = """huggingface/label-files"""
if "kinetics" in model_name:
__lowerCAmelCase = 400
__lowerCAmelCase = """kinetics400-id2label.json"""
elif "ssv2" in model_name:
__lowerCAmelCase = 174
__lowerCAmelCase = """something-something-v2-id2label.json"""
else:
raise ValueError("""Model name should either contain 'kinetics' or 'ssv2' in case it's fine-tuned.""" )
__lowerCAmelCase = json.load(open(hf_hub_download(lowercase , lowercase , repo_type="""dataset""" ) , """r""" ) )
__lowerCAmelCase = {int(lowercase ): v for k, v in idalabel.items()}
__lowerCAmelCase = idalabel
__lowerCAmelCase = {v: k for k, v in idalabel.items()}
return config
def _lowerCAmelCase ( lowercase , lowercase ) -> Any:
if "small" in model_name:
__lowerCAmelCase = 384
__lowerCAmelCase = 1536
__lowerCAmelCase = 12
__lowerCAmelCase = 16
__lowerCAmelCase = 12
__lowerCAmelCase = 3
__lowerCAmelCase = 192
__lowerCAmelCase = 768
elif "large" in model_name:
__lowerCAmelCase = 1024
__lowerCAmelCase = 4096
__lowerCAmelCase = 24
__lowerCAmelCase = 16
__lowerCAmelCase = 12
__lowerCAmelCase = 8
__lowerCAmelCase = 512
__lowerCAmelCase = 2048
elif "huge" in model_name:
__lowerCAmelCase = 1280
__lowerCAmelCase = 5120
__lowerCAmelCase = 32
__lowerCAmelCase = 16
__lowerCAmelCase = 12
__lowerCAmelCase = 8
__lowerCAmelCase = 640
__lowerCAmelCase = 2560
elif "base" not in model_name:
raise ValueError("""Model name should include either \"small\", \"base\", \"large\", or \"huge\"""" )
def _lowerCAmelCase ( lowercase ) -> List[str]:
if "encoder." in name:
__lowerCAmelCase = name.replace("""encoder.""" , """""" )
if "cls_token" in name:
__lowerCAmelCase = name.replace("""cls_token""" , """videomae.embeddings.cls_token""" )
if "decoder_pos_embed" in name:
__lowerCAmelCase = name.replace("""decoder_pos_embed""" , """decoder.decoder_pos_embed""" )
if "pos_embed" in name and "decoder" not in name:
__lowerCAmelCase = name.replace("""pos_embed""" , """videomae.embeddings.position_embeddings""" )
if "patch_embed.proj" in name:
__lowerCAmelCase = name.replace("""patch_embed.proj""" , """videomae.embeddings.patch_embeddings.projection""" )
if "patch_embed.norm" in name:
__lowerCAmelCase = name.replace("""patch_embed.norm""" , """videomae.embeddings.norm""" )
if "decoder.blocks" in name:
__lowerCAmelCase = name.replace("""decoder.blocks""" , """decoder.decoder_layers""" )
if "blocks" in name:
__lowerCAmelCase = name.replace("""blocks""" , """videomae.encoder.layer""" )
if "attn.proj" in name:
__lowerCAmelCase = name.replace("""attn.proj""" , """attention.output.dense""" )
if "attn" in name and "bias" not in name:
__lowerCAmelCase = name.replace("""attn""" , """attention.self""" )
if "attn" in name:
__lowerCAmelCase = name.replace("""attn""" , """attention.attention""" )
if "norm1" in name:
__lowerCAmelCase = name.replace("""norm1""" , """layernorm_before""" )
if "norm2" in name:
__lowerCAmelCase = name.replace("""norm2""" , """layernorm_after""" )
if "mlp.fc1" in name:
__lowerCAmelCase = name.replace("""mlp.fc1""" , """intermediate.dense""" )
if "mlp.fc2" in name:
__lowerCAmelCase = name.replace("""mlp.fc2""" , """output.dense""" )
if "decoder_embed" in name:
__lowerCAmelCase = name.replace("""decoder_embed""" , """decoder.decoder_embed""" )
if "decoder_norm" in name:
__lowerCAmelCase = name.replace("""decoder_norm""" , """decoder.decoder_norm""" )
if "decoder_pred" in name:
__lowerCAmelCase = name.replace("""decoder_pred""" , """decoder.decoder_pred""" )
if "norm.weight" in name and "decoder" not in name and "fc" not in name:
__lowerCAmelCase = name.replace("""norm.weight""" , """videomae.layernorm.weight""" )
if "norm.bias" in name and "decoder" not in name and "fc" not in name:
__lowerCAmelCase = name.replace("""norm.bias""" , """videomae.layernorm.bias""" )
if "head" in name and "decoder" not in name:
__lowerCAmelCase = name.replace("""head""" , """classifier""" )
return name
def _lowerCAmelCase ( lowercase , lowercase ) -> List[Any]:
for key in orig_state_dict.copy().keys():
__lowerCAmelCase = orig_state_dict.pop(lowercase )
if key.startswith("""encoder.""" ):
__lowerCAmelCase = key.replace("""encoder.""" , """""" )
if "qkv" in key:
__lowerCAmelCase = key.split(""".""" )
if key.startswith("""decoder.blocks""" ):
__lowerCAmelCase = config.decoder_hidden_size
__lowerCAmelCase = int(key_split[2] )
__lowerCAmelCase = """decoder.decoder_layers."""
if "weight" in key:
__lowerCAmelCase = val[:dim, :]
__lowerCAmelCase = val[dim : dim * 2, :]
__lowerCAmelCase = val[-dim:, :]
else:
__lowerCAmelCase = config.hidden_size
__lowerCAmelCase = int(key_split[1] )
__lowerCAmelCase = """videomae.encoder.layer."""
if "weight" in key:
__lowerCAmelCase = val[:dim, :]
__lowerCAmelCase = val[dim : dim * 2, :]
__lowerCAmelCase = val[-dim:, :]
else:
__lowerCAmelCase = val
return orig_state_dict
def _lowerCAmelCase ( ) -> str:
__lowerCAmelCase = hf_hub_download(
repo_id="""hf-internal-testing/spaghetti-video""" , filename="""eating_spaghetti.npy""" , repo_type="""dataset""" )
__lowerCAmelCase = np.load(lowercase )
return list(lowercase )
def _lowerCAmelCase ( lowercase , lowercase , lowercase , lowercase ) -> Optional[Any]:
__lowerCAmelCase = get_videomae_config(lowercase )
if "finetuned" in model_name:
__lowerCAmelCase = VideoMAEForVideoClassification(lowercase )
else:
__lowerCAmelCase = VideoMAEForPreTraining(lowercase )
# download original checkpoint, hosted on Google Drive
__lowerCAmelCase = """pytorch_model.bin"""
gdown.cached_download(lowercase , lowercase , quiet=lowercase )
__lowerCAmelCase = torch.load(lowercase , map_location="""cpu""" )
if "model" in files:
__lowerCAmelCase = files["""model"""]
else:
__lowerCAmelCase = files["""module"""]
__lowerCAmelCase = convert_state_dict(lowercase , lowercase )
model.load_state_dict(lowercase )
model.eval()
# verify model on basic input
__lowerCAmelCase = VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5] , image_std=[0.5, 0.5, 0.5] )
__lowerCAmelCase = prepare_video()
__lowerCAmelCase = image_processor(lowercase , return_tensors="""pt""" )
if "finetuned" not in model_name:
__lowerCAmelCase = hf_hub_download(repo_id="""hf-internal-testing/bool-masked-pos""" , filename="""bool_masked_pos.pt""" )
__lowerCAmelCase = torch.load(lowercase )
__lowerCAmelCase = model(**lowercase )
__lowerCAmelCase = outputs.logits
__lowerCAmelCase = [
"""videomae-small-finetuned-kinetics""",
"""videomae-small-finetuned-ssv2""",
# Kinetics-400 checkpoints (short = pretrained only for 800 epochs instead of 1600)
"""videomae-base-short""",
"""videomae-base-short-finetuned-kinetics""",
"""videomae-base""",
"""videomae-base-finetuned-kinetics""",
"""videomae-large""",
"""videomae-large-finetuned-kinetics""",
"""videomae-huge-finetuned-kinetics""",
# Something-Something-v2 checkpoints (short = pretrained only for 800 epochs instead of 2400)
"""videomae-base-short-ssv2""",
"""videomae-base-short-finetuned-ssv2""",
"""videomae-base-ssv2""",
"""videomae-base-finetuned-ssv2""",
]
# NOTE: logits were tested with image_mean and image_std equal to [0.5, 0.5, 0.5] and [0.5, 0.5, 0.5]
if model_name == "videomae-small-finetuned-kinetics":
__lowerCAmelCase = torch.Size([1, 400] )
__lowerCAmelCase = torch.tensor([-0.92_91, -0.40_61, -0.93_07] )
elif model_name == "videomae-small-finetuned-ssv2":
__lowerCAmelCase = torch.Size([1, 174] )
__lowerCAmelCase = torch.tensor([0.26_71, -0.46_89, -0.82_35] )
elif model_name == "videomae-base":
__lowerCAmelCase = torch.Size([1, 1408, 1536] )
__lowerCAmelCase = torch.tensor([[0.77_39, 0.79_68, 0.70_89], [0.67_01, 0.74_87, 0.62_09], [0.42_87, 0.51_58, 0.47_73]] )
elif model_name == "videomae-base-short":
__lowerCAmelCase = torch.Size([1, 1408, 1536] )
__lowerCAmelCase = torch.tensor([[0.79_94, 0.96_12, 0.85_08], [0.74_01, 0.89_58, 0.83_02], [0.58_62, 0.74_68, 0.73_25]] )
# we verified the loss both for normalized and unnormalized targets for this one
__lowerCAmelCase = torch.tensor([0.51_42] ) if config.norm_pix_loss else torch.tensor([0.64_69] )
elif model_name == "videomae-large":
__lowerCAmelCase = torch.Size([1, 1408, 1536] )
__lowerCAmelCase = torch.tensor([[0.71_49, 0.79_97, 0.69_66], [0.67_68, 0.78_69, 0.69_48], [0.51_39, 0.62_21, 0.56_05]] )
elif model_name == "videomae-large-finetuned-kinetics":
__lowerCAmelCase = torch.Size([1, 400] )
__lowerCAmelCase = torch.tensor([0.07_71, 0.00_11, -0.36_25] )
elif model_name == "videomae-huge-finetuned-kinetics":
__lowerCAmelCase = torch.Size([1, 400] )
__lowerCAmelCase = torch.tensor([0.24_33, 0.16_32, -0.48_94] )
elif model_name == "videomae-base-short-finetuned-kinetics":
__lowerCAmelCase = torch.Size([1, 400] )
__lowerCAmelCase = torch.tensor([0.65_88, 0.09_90, -0.24_93] )
elif model_name == "videomae-base-finetuned-kinetics":
__lowerCAmelCase = torch.Size([1, 400] )
__lowerCAmelCase = torch.tensor([0.36_69, -0.06_88, -0.24_21] )
elif model_name == "videomae-base-short-ssv2":
__lowerCAmelCase = torch.Size([1, 1408, 1536] )
__lowerCAmelCase = torch.tensor([[0.47_12, 0.52_96, 0.57_86], [0.22_78, 0.27_29, 0.40_26], [0.03_52, 0.07_30, 0.25_06]] )
elif model_name == "videomae-base-short-finetuned-ssv2":
__lowerCAmelCase = torch.Size([1, 174] )
__lowerCAmelCase = torch.tensor([-0.05_37, -0.15_39, -0.32_66] )
elif model_name == "videomae-base-ssv2":
__lowerCAmelCase = torch.Size([1, 1408, 1536] )
__lowerCAmelCase = torch.tensor([[0.81_31, 0.87_27, 0.85_46], [0.73_66, 0.93_77, 0.88_70], [0.59_35, 0.88_74, 0.85_64]] )
elif model_name == "videomae-base-finetuned-ssv2":
__lowerCAmelCase = torch.Size([1, 174] )
__lowerCAmelCase = torch.tensor([0.19_61, -0.83_37, -0.63_89] )
else:
raise ValueError(f'Model name not supported. Should be one of {model_names}' )
# verify logits
assert logits.shape == expected_shape
if "finetuned" in model_name:
assert torch.allclose(logits[0, :3] , lowercase , atol=1e-4 )
else:
print("""Logits:""" , logits[0, :3, :3] )
assert torch.allclose(logits[0, :3, :3] , lowercase , atol=1e-4 )
print("""Logits ok!""" )
# verify loss, if applicable
if model_name == "videomae-base-short":
__lowerCAmelCase = outputs.loss
assert torch.allclose(lowercase , lowercase , atol=1e-4 )
print("""Loss ok!""" )
if pytorch_dump_folder_path is not None:
print(f'Saving model and image processor to {pytorch_dump_folder_path}' )
image_processor.save_pretrained(lowercase )
model.save_pretrained(lowercase )
if push_to_hub:
print("""Pushing to the hub...""" )
model.push_to_hub(lowercase , organization="""nielsr""" )
if __name__ == "__main__":
_a : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--checkpoint_url""",
default="""https://drive.google.com/u/1/uc?id=1tEhLyskjb755TJ65ptsrafUG2llSwQE1&export=download&confirm=t&uuid=aa3276eb-fb7e-482a-adec-dc7171df14c4""",
type=str,
help=(
"""URL of the original PyTorch checkpoint (on Google Drive) you'd like to convert. Should be a direct"""
""" download link."""
),
)
parser.add_argument(
"""--pytorch_dump_folder_path""",
default="""/Users/nielsrogge/Documents/VideoMAE/Test""",
type=str,
help="""Path to the output PyTorch model directory.""",
)
parser.add_argument("""--model_name""", default="""videomae-base""", type=str, help="""Name of the model.""")
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
)
_a : int = parser.parse_args()
convert_videomae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 689 | 0 |
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
def A ( __UpperCamelCase ) -> Union[str, Any]:
A__ = SwinConfig.from_pretrained(
'microsoft/swin-tiny-patch4-window7-224' , out_features=['stage1', 'stage2', 'stage3', 'stage4'] )
A__ = MaskFormerConfig(backbone_config=__UpperCamelCase )
A__ = 'huggingface/label-files'
if "ade20k-full" in model_name:
# this should be ok
A__ = 847
A__ = 'maskformer-ade20k-full-id2label.json'
elif "ade" in model_name:
# this should be ok
A__ = 150
A__ = 'ade20k-id2label.json'
elif "coco-stuff" in model_name:
# this should be ok
A__ = 171
A__ = 'maskformer-coco-stuff-id2label.json'
elif "coco" in model_name:
# TODO
A__ = 133
A__ = 'coco-panoptic-id2label.json'
elif "cityscapes" in model_name:
# this should be ok
A__ = 19
A__ = 'cityscapes-id2label.json'
elif "vistas" in model_name:
# this should be ok
A__ = 65
A__ = 'mapillary-vistas-id2label.json'
A__ = json.load(open(hf_hub_download(__UpperCamelCase , __UpperCamelCase , repo_type='dataset' ) , 'r' ) )
A__ = {int(__UpperCamelCase ): v for k, v in idalabel.items()}
return config
def A ( __UpperCamelCase ) -> Dict:
A__ = []
# stem
# fmt: off
rename_keys.append(('backbone.patch_embed.proj.weight', 'model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight') )
rename_keys.append(('backbone.patch_embed.proj.bias', 'model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias') )
rename_keys.append(('backbone.patch_embed.norm.weight', 'model.pixel_level_module.encoder.model.embeddings.norm.weight') )
rename_keys.append(('backbone.patch_embed.norm.bias', 'model.pixel_level_module.encoder.model.embeddings.norm.bias') )
# stages
for i in range(len(config.backbone_config.depths ) ):
for j in range(config.backbone_config.depths[i] ):
rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.norm1.weight''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight''') )
rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.norm1.bias''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias''') )
rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table''') )
rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.attn.relative_position_index''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index''') )
rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.attn.proj.weight''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight''') )
rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.attn.proj.bias''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias''') )
rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.norm2.weight''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight''') )
rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.norm2.bias''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias''') )
rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.mlp.fc1.weight''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight''') )
rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.mlp.fc1.bias''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias''') )
rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.mlp.fc2.weight''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight''') )
rename_keys.append((f'''backbone.layers.{i}.blocks.{j}.mlp.fc2.bias''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias''') )
if i < 3:
rename_keys.append((f'''backbone.layers.{i}.downsample.reduction.weight''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight''') )
rename_keys.append((f'''backbone.layers.{i}.downsample.norm.weight''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight''') )
rename_keys.append((f'''backbone.layers.{i}.downsample.norm.bias''', f'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias''') )
rename_keys.append((f'''backbone.norm{i}.weight''', f'''model.pixel_level_module.encoder.hidden_states_norms.{i}.weight''') )
rename_keys.append((f'''backbone.norm{i}.bias''', f'''model.pixel_level_module.encoder.hidden_states_norms.{i}.bias''') )
# FPN
rename_keys.append(('sem_seg_head.layer_4.weight', 'model.pixel_level_module.decoder.fpn.stem.0.weight') )
rename_keys.append(('sem_seg_head.layer_4.norm.weight', 'model.pixel_level_module.decoder.fpn.stem.1.weight') )
rename_keys.append(('sem_seg_head.layer_4.norm.bias', 'model.pixel_level_module.decoder.fpn.stem.1.bias') )
for source_index, target_index in zip(range(3 , 0 , -1 ) , range(0 , 3 ) ):
rename_keys.append((f'''sem_seg_head.adapter_{source_index}.weight''', f'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight''') )
rename_keys.append((f'''sem_seg_head.adapter_{source_index}.norm.weight''', f'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight''') )
rename_keys.append((f'''sem_seg_head.adapter_{source_index}.norm.bias''', f'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias''') )
rename_keys.append((f'''sem_seg_head.layer_{source_index}.weight''', f'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight''') )
rename_keys.append((f'''sem_seg_head.layer_{source_index}.norm.weight''', f'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight''') )
rename_keys.append((f'''sem_seg_head.layer_{source_index}.norm.bias''', f'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias''') )
rename_keys.append(('sem_seg_head.mask_features.weight', 'model.pixel_level_module.decoder.mask_projection.weight') )
rename_keys.append(('sem_seg_head.mask_features.bias', 'model.pixel_level_module.decoder.mask_projection.bias') )
# Transformer decoder
for idx in range(config.decoder_config.decoder_layers ):
# self-attention out projection
rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight''', f'''model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight''') )
rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias''', f'''model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias''') )
# cross-attention out projection
rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight''', f'''model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight''') )
rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias''', f'''model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias''') )
# MLP 1
rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight''', f'''model.transformer_module.decoder.layers.{idx}.fc1.weight''') )
rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias''', f'''model.transformer_module.decoder.layers.{idx}.fc1.bias''') )
# MLP 2
rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight''', f'''model.transformer_module.decoder.layers.{idx}.fc2.weight''') )
rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias''', f'''model.transformer_module.decoder.layers.{idx}.fc2.bias''') )
# layernorm 1 (self-attention layernorm)
rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight''', f'''model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight''') )
rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias''', f'''model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias''') )
# layernorm 2 (cross-attention layernorm)
rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight''', f'''model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight''') )
rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias''', f'''model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias''') )
# layernorm 3 (final layernorm)
rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight''', f'''model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight''') )
rename_keys.append((f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias''', f'''model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias''') )
rename_keys.append(('sem_seg_head.predictor.transformer.decoder.norm.weight', 'model.transformer_module.decoder.layernorm.weight') )
rename_keys.append(('sem_seg_head.predictor.transformer.decoder.norm.bias', 'model.transformer_module.decoder.layernorm.bias') )
# heads on top
rename_keys.append(('sem_seg_head.predictor.query_embed.weight', 'model.transformer_module.queries_embedder.weight') )
rename_keys.append(('sem_seg_head.predictor.input_proj.weight', 'model.transformer_module.input_projection.weight') )
rename_keys.append(('sem_seg_head.predictor.input_proj.bias', 'model.transformer_module.input_projection.bias') )
rename_keys.append(('sem_seg_head.predictor.class_embed.weight', 'class_predictor.weight') )
rename_keys.append(('sem_seg_head.predictor.class_embed.bias', 'class_predictor.bias') )
for i in range(3 ):
rename_keys.append((f'''sem_seg_head.predictor.mask_embed.layers.{i}.weight''', f'''mask_embedder.{i}.0.weight''') )
rename_keys.append((f'''sem_seg_head.predictor.mask_embed.layers.{i}.bias''', f'''mask_embedder.{i}.0.bias''') )
# fmt: on
return rename_keys
def A ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ) -> List[str]:
A__ = dct.pop(__UpperCamelCase )
A__ = val
def A ( __UpperCamelCase , __UpperCamelCase ) -> str:
A__ = [int(backbone_config.embed_dim * 2**i ) for i in range(len(backbone_config.depths ) )]
for i in range(len(backbone_config.depths ) ):
A__ = num_features[i]
for j in range(backbone_config.depths[i] ):
# fmt: off
# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
A__ = state_dict.pop(f'''backbone.layers.{i}.blocks.{j}.attn.qkv.weight''' )
A__ = state_dict.pop(f'''backbone.layers.{i}.blocks.{j}.attn.qkv.bias''' )
# next, add query, keys and values (in that order) to the state dict
A__ = in_proj_weight[:dim, :]
A__ = in_proj_bias[: dim]
A__ = in_proj_weight[
dim : dim * 2, :
]
A__ = in_proj_bias[
dim : dim * 2
]
A__ = in_proj_weight[
-dim :, :
]
A__ = in_proj_bias[-dim :]
# fmt: on
def A ( __UpperCamelCase , __UpperCamelCase ) -> Optional[int]:
# fmt: off
A__ = config.decoder_config.hidden_size
for idx in range(config.decoder_config.decoder_layers ):
# read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
A__ = state_dict.pop(f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight''' )
A__ = state_dict.pop(f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) to the state dict
A__ = in_proj_weight[: hidden_size, :]
A__ = in_proj_bias[:config.hidden_size]
A__ = in_proj_weight[hidden_size : hidden_size * 2, :]
A__ = in_proj_bias[hidden_size : hidden_size * 2]
A__ = in_proj_weight[-hidden_size :, :]
A__ = in_proj_bias[-hidden_size :]
# read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
A__ = state_dict.pop(f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight''' )
A__ = state_dict.pop(f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) to the state dict
A__ = in_proj_weight[: hidden_size, :]
A__ = in_proj_bias[:config.hidden_size]
A__ = in_proj_weight[hidden_size : hidden_size * 2, :]
A__ = in_proj_bias[hidden_size : hidden_size * 2]
A__ = in_proj_weight[-hidden_size :, :]
A__ = in_proj_bias[-hidden_size :]
# fmt: on
def A ( ) -> torch.Tensor:
A__ = 'http://images.cocodataset.org/val2017/000000039769.jpg'
A__ = Image.open(requests.get(__UpperCamelCase , stream=__UpperCamelCase ).raw )
return im
@torch.no_grad()
def A ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = False ) -> List[Any]:
A__ = get_maskformer_config(__UpperCamelCase )
# load original state_dict
with open(__UpperCamelCase , 'rb' ) as f:
A__ = pickle.load(__UpperCamelCase )
A__ = data['model']
# for name, param in state_dict.items():
# print(name, param.shape)
# rename keys
A__ = create_rename_keys(__UpperCamelCase )
for src, dest in rename_keys:
rename_key(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
read_in_swin_q_k_v(__UpperCamelCase , config.backbone_config )
read_in_decoder_q_k_v(__UpperCamelCase , __UpperCamelCase )
# update to torch tensors
for key, value in state_dict.items():
A__ = torch.from_numpy(__UpperCamelCase )
# load 🤗 model
A__ = MaskFormerForInstanceSegmentation(__UpperCamelCase )
model.eval()
for name, param in model.named_parameters():
print(__UpperCamelCase , param.shape )
A__ , A__ = model.load_state_dict(__UpperCamelCase , strict=__UpperCamelCase )
assert missing_keys == [
"model.pixel_level_module.encoder.model.layernorm.weight",
"model.pixel_level_module.encoder.model.layernorm.bias",
]
assert len(__UpperCamelCase ) == 0, f'''Unexpected keys: {unexpected_keys}'''
# verify results
A__ = prepare_img()
if "vistas" in model_name:
A__ = 65
elif "cityscapes" in model_name:
A__ = 65_535
else:
A__ = 255
A__ = True if 'ade' in model_name else False
A__ = MaskFormerImageProcessor(ignore_index=__UpperCamelCase , reduce_labels=__UpperCamelCase )
A__ = image_processor(__UpperCamelCase , return_tensors='pt' )
A__ = model(**__UpperCamelCase )
print('Logits:' , outputs.class_queries_logits[0, :3, :3] )
if model_name == "maskformer-swin-tiny-ade":
A__ = torch.tensor(
[[3.6353, -4.4770, -2.6065], [0.5081, -4.2394, -3.5343], [2.1909, -5.0353, -1.9323]] )
assert torch.allclose(outputs.class_queries_logits[0, :3, :3] , __UpperCamelCase , atol=1E-4 )
print('Looks ok!' )
if pytorch_dump_folder_path is not None:
print(f'''Saving model and image processor to {pytorch_dump_folder_path}''' )
Path(__UpperCamelCase ).mkdir(exist_ok=__UpperCamelCase )
model.save_pretrained(__UpperCamelCase )
image_processor.save_pretrained(__UpperCamelCase )
if push_to_hub:
print('Pushing model and image processor to the hub...' )
model.push_to_hub(f'''nielsr/{model_name}''' )
image_processor.push_to_hub(f'''nielsr/{model_name}''' )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--model_name''',
default='''maskformer-swin-tiny-ade''',
type=str,
help=('''Name of the MaskFormer model you\'d like to convert''',),
)
parser.add_argument(
'''--checkpoint_path''',
default='''/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl''',
type=str,
help='''Path to the original state dict (.pth file).''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
parser.add_argument(
'''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
)
SCREAMING_SNAKE_CASE__ = parser.parse_args()
convert_maskformer_checkpoint(
args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
)
| 9 |
'''simple docstring'''
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
_a : Tuple = """\
"""
_a : Tuple = """
Perplexity (PPL) is one of the most common metrics for evaluating language models.
It is defined as the exponentiated average negative log-likelihood of a sequence.
For more information, see https://huggingface.co/docs/transformers/perplexity
"""
_a : Optional[Any] = """
Args:
model_id (str): model used for calculating Perplexity
NOTE: Perplexity can only be calculated for causal language models.
This includes models such as gpt2, causal variations of bert,
causal versions of t5, and more (the full list can be found
in the AutoModelForCausalLM documentation here:
https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )
input_texts (list of str): input text, each separate text snippet
is one list entry.
batch_size (int): the batch size to run texts through the model. Defaults to 16.
add_start_token (bool): whether to add the start token to the texts,
so the perplexity can include the probability of the first word. Defaults to True.
device (str): device to run on, defaults to 'cuda' when available
Returns:
perplexity: dictionary containing the perplexity scores for the texts
in the input list, as well as the mean perplexity. If one of the input texts is
longer than the max input length of the model, then it is truncated to the
max length for the perplexity computation.
Examples:
Example 1:
>>> perplexity = datasets.load_metric(\"perplexity\")
>>> input_texts = [\"lorem ipsum\", \"Happy Birthday!\", \"Bienvenue\"]
>>> results = perplexity.compute(model_id='gpt2',
... add_start_token=False,
... input_texts=input_texts) # doctest:+ELLIPSIS
>>> print(list(results.keys()))
['perplexities', 'mean_perplexity']
>>> print(round(results[\"mean_perplexity\"], 2))
78.22
>>> print(round(results[\"perplexities\"][0], 2))
11.11
Example 2:
>>> perplexity = datasets.load_metric(\"perplexity\")
>>> input_texts = datasets.load_dataset(\"wikitext\",
... \"wikitext-2-raw-v1\",
... split=\"test\")[\"text\"][:50] # doctest:+ELLIPSIS
[...]
>>> input_texts = [s for s in input_texts if s!='']
>>> results = perplexity.compute(model_id='gpt2',
... input_texts=input_texts) # doctest:+ELLIPSIS
>>> print(list(results.keys()))
['perplexities', 'mean_perplexity']
>>> print(round(results[\"mean_perplexity\"], 2))
60.35
>>> print(round(results[\"perplexities\"][0], 2))
81.12
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _UpperCAmelCase ( datasets.Metric ):
def lowerCamelCase__ ( self ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION,citation=_CITATION,inputs_description=_KWARGS_DESCRIPTION,features=datasets.Features(
{
"""input_texts""": datasets.Value("""string""" ),
} ),reference_urls=["""https://huggingface.co/docs/transformers/perplexity"""],)
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE = 16,__SCREAMING_SNAKE_CASE = True,__SCREAMING_SNAKE_CASE=None ):
'''simple docstring'''
if device is not None:
assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
if device == "gpu":
__lowerCAmelCase = """cuda"""
else:
__lowerCAmelCase = """cuda""" if torch.cuda.is_available() else """cpu"""
__lowerCAmelCase = AutoModelForCausalLM.from_pretrained(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = model.to(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE )
# if batch_size > 1 (which generally leads to padding being required), and
# if there is not an already assigned pad_token, assign an existing
# special token to also be the padding token
if tokenizer.pad_token is None and batch_size > 1:
__lowerCAmelCase = list(tokenizer.special_tokens_map_extended.values() )
# check that the model already has at least one special token defined
assert (
len(__SCREAMING_SNAKE_CASE ) > 0
), "If batch_size > 1, model must have at least one special token to use for padding. Please use a different model or set batch_size=1."
# assign one of the special tokens to also be the pad token
tokenizer.add_special_tokens({"""pad_token""": existing_special_tokens[0]} )
if add_start_token:
# leave room for <BOS> token to be added:
assert (
tokenizer.bos_token is not None
), "Input model must already have a BOS token if using add_start_token=True. Please use a different model, or set add_start_token=False"
__lowerCAmelCase = model.config.max_length - 1
else:
__lowerCAmelCase = model.config.max_length
__lowerCAmelCase = tokenizer(
__SCREAMING_SNAKE_CASE,add_special_tokens=__SCREAMING_SNAKE_CASE,padding=__SCREAMING_SNAKE_CASE,truncation=__SCREAMING_SNAKE_CASE,max_length=__SCREAMING_SNAKE_CASE,return_tensors="""pt""",return_attention_mask=__SCREAMING_SNAKE_CASE,).to(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = encodings["""input_ids"""]
__lowerCAmelCase = encodings["""attention_mask"""]
# check that each input is long enough:
if add_start_token:
assert torch.all(torch.ge(attn_masks.sum(1 ),1 ) ), "Each input text must be at least one token long."
else:
assert torch.all(
torch.ge(attn_masks.sum(1 ),2 ) ), "When add_start_token=False, each input text must be at least two tokens long. Run with add_start_token=True if inputting strings of only one token, and remove all empty input strings."
__lowerCAmelCase = []
__lowerCAmelCase = CrossEntropyLoss(reduction="""none""" )
for start_index in logging.tqdm(range(0,len(__SCREAMING_SNAKE_CASE ),__SCREAMING_SNAKE_CASE ) ):
__lowerCAmelCase = min(start_index + batch_size,len(__SCREAMING_SNAKE_CASE ) )
__lowerCAmelCase = encoded_texts[start_index:end_index]
__lowerCAmelCase = attn_masks[start_index:end_index]
if add_start_token:
__lowerCAmelCase = torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0 ) ).to(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = torch.cat([bos_tokens_tensor, encoded_batch],dim=1 )
__lowerCAmelCase = torch.cat(
[torch.ones(bos_tokens_tensor.size(),dtype=torch.intaa ).to(__SCREAMING_SNAKE_CASE ), attn_mask],dim=1 )
__lowerCAmelCase = encoded_batch
with torch.no_grad():
__lowerCAmelCase = model(__SCREAMING_SNAKE_CASE,attention_mask=__SCREAMING_SNAKE_CASE ).logits
__lowerCAmelCase = out_logits[..., :-1, :].contiguous()
__lowerCAmelCase = labels[..., 1:].contiguous()
__lowerCAmelCase = attn_mask[..., 1:].contiguous()
__lowerCAmelCase = torch.expa(
(loss_fct(shift_logits.transpose(1,2 ),__SCREAMING_SNAKE_CASE ) * shift_attention_mask_batch).sum(1 )
/ shift_attention_mask_batch.sum(1 ) )
ppls += perplexity_batch.tolist()
return {"perplexities": ppls, "mean_perplexity": np.mean(__SCREAMING_SNAKE_CASE )}
| 689 | 0 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
_lowerCAmelCase = logging.get_logger(__name__)
class lowerCAmelCase_ ( __lowercase ):
UpperCAmelCase = "upernet"
def __init__( self : List[str] , _A : Union[str, Any]=None , _A : List[Any]=512 , _A : Dict=0.02 , _A : str=[1, 2, 3, 6] , _A : Tuple=True , _A : List[Any]=0.4 , _A : Optional[Any]=384 , _A : List[Any]=256 , _A : Optional[int]=1 , _A : str=False , _A : str=255 , **_A : int , ):
super().__init__(**_A )
if backbone_config is None:
logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.''' )
_UpperCamelCase = CONFIG_MAPPING['''resnet'''](out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4'''] )
elif isinstance(_A , _A ):
_UpperCamelCase = backbone_config.get('''model_type''' )
_UpperCamelCase = CONFIG_MAPPING[backbone_model_type]
_UpperCamelCase = config_class.from_dict(_A )
_UpperCamelCase = backbone_config
_UpperCamelCase = hidden_size
_UpperCamelCase = initializer_range
_UpperCamelCase = pool_scales
_UpperCamelCase = use_auxiliary_head
_UpperCamelCase = auxiliary_loss_weight
_UpperCamelCase = auxiliary_in_channels
_UpperCamelCase = auxiliary_channels
_UpperCamelCase = auxiliary_num_convs
_UpperCamelCase = auxiliary_concat_input
_UpperCamelCase = loss_ignore_index
def UpperCamelCase_ ( self : str ):
_UpperCamelCase = copy.deepcopy(self.__dict__ )
_UpperCamelCase = self.backbone_config.to_dict()
_UpperCamelCase = self.__class__.model_type
return output
| 10 |
'''simple docstring'''
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
import torch
if is_tf_available():
import tensorflow as tf
class _UpperCAmelCase ( lowerCAmelCase_ ):
a : Union[str, Any] =["""image_processor"""]
a : Dict ="""SamImageProcessor"""
def __init__( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
super().__init__(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = self.image_processor
__lowerCAmelCase = -10
__lowerCAmelCase = self.image_processor.size["""longest_edge"""]
def __call__( self,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE = None,**__SCREAMING_SNAKE_CASE,):
'''simple docstring'''
__lowerCAmelCase = self.image_processor(
__SCREAMING_SNAKE_CASE,return_tensors=__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE,)
# pop arguments that are not used in the foward but used nevertheless
__lowerCAmelCase = encoding_image_processor["""original_sizes"""]
if hasattr(__SCREAMING_SNAKE_CASE,"""numpy""" ): # Checks if Torch or TF tensor
__lowerCAmelCase = original_sizes.numpy()
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = self._check_and_preprocess_points(
input_points=__SCREAMING_SNAKE_CASE,input_labels=__SCREAMING_SNAKE_CASE,input_boxes=__SCREAMING_SNAKE_CASE,)
__lowerCAmelCase = self._normalize_and_convert(
__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,input_points=__SCREAMING_SNAKE_CASE,input_labels=__SCREAMING_SNAKE_CASE,input_boxes=__SCREAMING_SNAKE_CASE,return_tensors=__SCREAMING_SNAKE_CASE,)
return encoding_image_processor
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE="pt",):
'''simple docstring'''
if input_points is not None:
if len(__SCREAMING_SNAKE_CASE ) != len(__SCREAMING_SNAKE_CASE ):
__lowerCAmelCase = [
self._normalize_coordinates(self.target_size,__SCREAMING_SNAKE_CASE,original_sizes[0] ) for point in input_points
]
else:
__lowerCAmelCase = [
self._normalize_coordinates(self.target_size,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
for point, original_size in zip(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
]
# check that all arrays have the same shape
if not all(point.shape == input_points[0].shape for point in input_points ):
if input_labels is not None:
__lowerCAmelCase , __lowerCAmelCase = self._pad_points_and_labels(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = np.array(__SCREAMING_SNAKE_CASE )
if input_labels is not None:
__lowerCAmelCase = np.array(__SCREAMING_SNAKE_CASE )
if input_boxes is not None:
if len(__SCREAMING_SNAKE_CASE ) != len(__SCREAMING_SNAKE_CASE ):
__lowerCAmelCase = [
self._normalize_coordinates(self.target_size,__SCREAMING_SNAKE_CASE,original_sizes[0],is_bounding_box=__SCREAMING_SNAKE_CASE )
for box in input_boxes
]
else:
__lowerCAmelCase = [
self._normalize_coordinates(self.target_size,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,is_bounding_box=__SCREAMING_SNAKE_CASE )
for box, original_size in zip(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
]
__lowerCAmelCase = np.array(__SCREAMING_SNAKE_CASE )
if input_boxes is not None:
if return_tensors == "pt":
__lowerCAmelCase = torch.from_numpy(__SCREAMING_SNAKE_CASE )
# boxes batch size of 1 by default
__lowerCAmelCase = input_boxes.unsqueeze(1 ) if len(input_boxes.shape ) != 3 else input_boxes
elif return_tensors == "tf":
__lowerCAmelCase = tf.convert_to_tensor(__SCREAMING_SNAKE_CASE )
# boxes batch size of 1 by default
__lowerCAmelCase = tf.expand_dims(__SCREAMING_SNAKE_CASE,1 ) if len(input_boxes.shape ) != 3 else input_boxes
encoding_image_processor.update({"""input_boxes""": input_boxes} )
if input_points is not None:
if return_tensors == "pt":
__lowerCAmelCase = torch.from_numpy(__SCREAMING_SNAKE_CASE )
# point batch size of 1 by default
__lowerCAmelCase = input_points.unsqueeze(1 ) if len(input_points.shape ) != 4 else input_points
elif return_tensors == "tf":
__lowerCAmelCase = tf.convert_to_tensor(__SCREAMING_SNAKE_CASE )
# point batch size of 1 by default
__lowerCAmelCase = tf.expand_dims(__SCREAMING_SNAKE_CASE,1 ) if len(input_points.shape ) != 4 else input_points
encoding_image_processor.update({"""input_points""": input_points} )
if input_labels is not None:
if return_tensors == "pt":
__lowerCAmelCase = torch.from_numpy(__SCREAMING_SNAKE_CASE )
# point batch size of 1 by default
__lowerCAmelCase = input_labels.unsqueeze(1 ) if len(input_labels.shape ) != 3 else input_labels
elif return_tensors == "tf":
__lowerCAmelCase = tf.convert_to_tensor(__SCREAMING_SNAKE_CASE )
# point batch size of 1 by default
__lowerCAmelCase = tf.expand_dims(__SCREAMING_SNAKE_CASE,1 ) if len(input_labels.shape ) != 3 else input_labels
encoding_image_processor.update({"""input_labels""": input_labels} )
return encoding_image_processor
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase = max([point.shape[0] for point in input_points] )
__lowerCAmelCase = []
for i, point in enumerate(__SCREAMING_SNAKE_CASE ):
if point.shape[0] != expected_nb_points:
__lowerCAmelCase = np.concatenate(
[point, np.zeros((expected_nb_points - point.shape[0], 2) ) + self.point_pad_value],axis=0 )
__lowerCAmelCase = np.append(input_labels[i],[self.point_pad_value] )
processed_input_points.append(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = processed_input_points
return input_points, input_labels
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=False ):
'''simple docstring'''
__lowerCAmelCase , __lowerCAmelCase = original_size
__lowerCAmelCase , __lowerCAmelCase = self.image_processor._get_preprocess_shape(__SCREAMING_SNAKE_CASE,longest_edge=__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = deepcopy(__SCREAMING_SNAKE_CASE ).astype(__SCREAMING_SNAKE_CASE )
if is_bounding_box:
__lowerCAmelCase = coords.reshape(-1,2,2 )
__lowerCAmelCase = coords[..., 0] * (new_w / old_w)
__lowerCAmelCase = coords[..., 1] * (new_h / old_h)
if is_bounding_box:
__lowerCAmelCase = coords.reshape(-1,4 )
return coords
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=None,):
'''simple docstring'''
if input_points is not None:
if hasattr(__SCREAMING_SNAKE_CASE,"""numpy""" ): # Checks for TF or Torch tensor
__lowerCAmelCase = input_points.numpy().tolist()
if not isinstance(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE ) or not isinstance(input_points[0],__SCREAMING_SNAKE_CASE ):
raise ValueError("""Input points must be a list of list of floating points.""" )
__lowerCAmelCase = [np.array(__SCREAMING_SNAKE_CASE ) for input_point in input_points]
else:
__lowerCAmelCase = None
if input_labels is not None:
if hasattr(__SCREAMING_SNAKE_CASE,"""numpy""" ):
__lowerCAmelCase = input_labels.numpy().tolist()
if not isinstance(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE ) or not isinstance(input_labels[0],__SCREAMING_SNAKE_CASE ):
raise ValueError("""Input labels must be a list of list integers.""" )
__lowerCAmelCase = [np.array(__SCREAMING_SNAKE_CASE ) for label in input_labels]
else:
__lowerCAmelCase = None
if input_boxes is not None:
if hasattr(__SCREAMING_SNAKE_CASE,"""numpy""" ):
__lowerCAmelCase = input_boxes.numpy().tolist()
if (
not isinstance(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
or not isinstance(input_boxes[0],__SCREAMING_SNAKE_CASE )
or not isinstance(input_boxes[0][0],__SCREAMING_SNAKE_CASE )
):
raise ValueError("""Input boxes must be a list of list of list of floating points.""" )
__lowerCAmelCase = [np.array(__SCREAMING_SNAKE_CASE ).astype(np.floataa ) for box in input_boxes]
else:
__lowerCAmelCase = None
return input_points, input_labels, input_boxes
@property
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = self.image_processor.model_input_names
return list(dict.fromkeys(__SCREAMING_SNAKE_CASE ) )
def lowerCamelCase__ ( self,*__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
return self.image_processor.post_process_masks(*__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE )
| 689 | 0 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transformers.models.roberta.modeling_flax_roberta import (
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaModel,
)
class __A ( unittest.TestCase ):
'''simple docstring'''
def __init__(self , A , A=13 , A=7 , A=True , A=True , A=True , A=True , A=99 , A=32 , A=5 , A=4 , A=37 , A="gelu" , A=0.1 , A=0.1 , A=512 , A=16 , A=2 , A=0.02 , A=4 , ) -> Optional[int]:
"""simple docstring"""
_a = parent
_a = batch_size
_a = seq_length
_a = is_training
_a = use_attention_mask
_a = use_token_type_ids
_a = use_labels
_a = vocab_size
_a = hidden_size
_a = num_hidden_layers
_a = num_attention_heads
_a = intermediate_size
_a = hidden_act
_a = hidden_dropout_prob
_a = attention_probs_dropout_prob
_a = max_position_embeddings
_a = type_vocab_size
_a = type_sequence_label_size
_a = initializer_range
_a = num_choices
def a__ (self ) -> Tuple:
"""simple docstring"""
_a = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_a = None
if self.use_attention_mask:
_a = random_attention_mask([self.batch_size, self.seq_length] )
_a = None
if self.use_token_type_ids:
_a = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_a = RobertaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=A , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def a__ (self ) -> Tuple:
"""simple docstring"""
_a = self.prepare_config_and_inputs()
_a , _a , _a , _a = config_and_inputs
_a = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
return config, inputs_dict
def a__ (self ) -> Optional[int]:
"""simple docstring"""
_a = self.prepare_config_and_inputs()
_a , _a , _a , _a = config_and_inputs
_a = True
_a = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
_a = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
encoder_hidden_states,
encoder_attention_mask,
)
@require_flax
class __A ( A , unittest.TestCase ):
'''simple docstring'''
__lowerCamelCase : str = True
__lowerCamelCase : int = (
(
FlaxRobertaModel,
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
)
if is_flax_available()
else ()
)
def a__ (self ) -> Union[str, Any]:
"""simple docstring"""
_a = FlaxRobertaModelTester(self )
@slow
def a__ (self ) -> Tuple:
"""simple docstring"""
for model_class_name in self.all_model_classes:
_a = model_class_name.from_pretrained('''roberta-base''' , from_pt=A )
_a = model(np.ones((1, 1) ) )
self.assertIsNotNone(A )
| 11 |
'''simple docstring'''
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequenceClassification,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
TapexTokenizer,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version
from transformers.utils.versions import require_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("""4.17.0.dev0""")
require_version("""datasets>=1.8.0""", """To fix: pip install -r examples/pytorch/text-classification/requirements.txt""")
_a : int = logging.getLogger(__name__)
@dataclass
class _UpperCAmelCase :
a : Optional[str] =field(
default="""tab_fact""" , metadata={"""help""": """The name of the dataset to use (via the datasets library)."""} )
a : Optional[str] =field(
default="""tab_fact""" , metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""} , )
a : int =field(
default=10_24 , metadata={
"""help""": (
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
)
} , )
a : bool =field(
default=lowerCAmelCase_ , metadata={"""help""": """Overwrite the cached preprocessed datasets or not."""} )
a : bool =field(
default=lowerCAmelCase_ , metadata={
"""help""": (
"""Whether to pad all samples to `max_seq_length`. """
"""If False, will pad the samples dynamically when batching to the maximum length in the batch."""
)
} , )
a : Optional[int] =field(
default=lowerCAmelCase_ , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of training examples to this """
"""value if set."""
)
} , )
a : Optional[int] =field(
default=lowerCAmelCase_ , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of evaluation examples to this """
"""value if set."""
)
} , )
a : Optional[int] =field(
default=lowerCAmelCase_ , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of prediction examples to this """
"""value if set."""
)
} , )
a : Optional[str] =field(
default=lowerCAmelCase_ , metadata={"""help""": """A csv or a json file containing the training data."""} )
a : Optional[str] =field(
default=lowerCAmelCase_ , metadata={"""help""": """A csv or a json file containing the validation data."""} )
a : Optional[str] =field(default=lowerCAmelCase_ , metadata={"""help""": """A csv or a json file containing the test data."""} )
def lowerCamelCase__ ( self ):
'''simple docstring'''
if self.dataset_name is not None:
pass
elif self.train_file is None or self.validation_file is None:
raise ValueError("""Need either a GLUE task, a training/validation file or a dataset name.""" )
else:
__lowerCAmelCase = self.train_file.split(""".""" )[-1]
assert train_extension in ["csv", "json"], "`train_file` should be a csv or a json file."
__lowerCAmelCase = self.validation_file.split(""".""" )[-1]
assert (
validation_extension == train_extension
), "`validation_file` should have the same extension (csv or json) as `train_file`."
@dataclass
class _UpperCAmelCase :
a : str =field(
default=lowerCAmelCase_ , metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""} )
a : Optional[str] =field(
default=lowerCAmelCase_ , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""} )
a : Optional[str] =field(
default=lowerCAmelCase_ , metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""} )
a : Optional[str] =field(
default=lowerCAmelCase_ , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} , )
a : bool =field(
default=lowerCAmelCase_ , metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""} , )
a : str =field(
default="""main""" , metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""} , )
a : bool =field(
default=lowerCAmelCase_ , metadata={
"""help""": (
"""Will use the token generated when running `huggingface-cli login` (necessary to use this script """
"""with private models)."""
)
} , )
def _lowerCAmelCase ( ) -> Optional[Any]:
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
__lowerCAmelCase = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith(""".json""" ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = parser.parse_args_into_dataclasses()
# Setup logging
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , handlers=[logging.StreamHandler(sys.stdout )] , )
__lowerCAmelCase = training_args.get_process_log_level()
logger.setLevel(lowercase )
datasets.utils.logging.set_verbosity(lowercase )
transformers.utils.logging.set_verbosity(lowercase )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
f'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
+ f'distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}' )
logger.info(f'Training/evaluation parameters {training_args}' )
# Detecting last checkpoint.
__lowerCAmelCase = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
__lowerCAmelCase = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
f'Output directory ({training_args.output_dir}) already exists and is not empty. '
"""Use --overwrite_output_dir to overcome.""" )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
f'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
"""the `--output_dir` or add `--overwrite_output_dir` to train from scratch.""" )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
# or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub).
#
# For JSON files, this script will use the `question` column for the input question and `table` column for the corresponding table.
#
# If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this
# single column. You can easily tweak this behavior (see below)
#
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
# download the dataset.
if data_args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
__lowerCAmelCase = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , cache_dir=model_args.cache_dir )
else:
# Loading a dataset from your local files.
# CSV/JSON training and evaluation files are needed.
__lowerCAmelCase = {"""train""": data_args.train_file, """validation""": data_args.validation_file}
# Get the test dataset: you can provide your own CSV/JSON test file (see below)
# when you use `do_predict` without specifying a GLUE benchmark task.
if training_args.do_predict:
if data_args.test_file is not None:
__lowerCAmelCase = data_args.train_file.split(""".""" )[-1]
__lowerCAmelCase = data_args.test_file.split(""".""" )[-1]
assert (
test_extension == train_extension
), "`test_file` should have the same extension (csv or json) as `train_file`."
__lowerCAmelCase = data_args.test_file
else:
raise ValueError("""Need either a GLUE task or a test file for `do_predict`.""" )
for key in data_files.keys():
logger.info(f'load a local file for {key}: {data_files[key]}' )
if data_args.train_file.endswith(""".csv""" ):
# Loading a dataset from local csv files
__lowerCAmelCase = load_dataset("""csv""" , data_files=lowercase , cache_dir=model_args.cache_dir )
else:
# Loading a dataset from local json files
__lowerCAmelCase = load_dataset("""json""" , data_files=lowercase , cache_dir=model_args.cache_dir )
# See more about loading any type of standard or custom dataset at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Labels
__lowerCAmelCase = raw_datasets["""train"""].features["""label"""].names
__lowerCAmelCase = len(lowercase )
# Load pretrained model and tokenizer
#
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
__lowerCAmelCase = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=lowercase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# load tapex tokenizer
__lowerCAmelCase = TapexTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , add_prefix_space=lowercase , )
__lowerCAmelCase = BartForSequenceClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool(""".ckpt""" in model_args.model_name_or_path ) , config=lowercase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# Padding strategy
if data_args.pad_to_max_length:
__lowerCAmelCase = """max_length"""
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
__lowerCAmelCase = False
# Some models have set the order of the labels to use, so let's make sure we do use it.
__lowerCAmelCase = {"""Refused""": 0, """Entailed""": 1}
__lowerCAmelCase = {0: """Refused""", 1: """Entailed"""}
if data_args.max_seq_length > tokenizer.model_max_length:
logger.warning(
f'The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the'
f'model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.' )
__lowerCAmelCase = min(data_args.max_seq_length , tokenizer.model_max_length )
def preprocess_tabfact_function(lowercase ):
# Tokenize the texts
def _convert_table_text_to_pandas(lowercase ):
__lowerCAmelCase = [_table_row.split("""#""" ) for _table_row in _table_text.strip("""\n""" ).split("""\n""" )]
__lowerCAmelCase = pd.DataFrame.from_records(_table_content[1:] , columns=_table_content[0] )
return _table_pd
__lowerCAmelCase = examples["""statement"""]
__lowerCAmelCase = list(map(_convert_table_text_to_pandas , examples["""table_text"""] ) )
__lowerCAmelCase = tokenizer(lowercase , lowercase , padding=lowercase , max_length=lowercase , truncation=lowercase )
__lowerCAmelCase = examples["""label"""]
return result
with training_args.main_process_first(desc="""dataset map pre-processing""" ):
__lowerCAmelCase = raw_datasets.map(
lowercase , batched=lowercase , load_from_cache_file=not data_args.overwrite_cache , desc="""Running tokenizer on dataset""" , )
if training_args.do_train:
if "train" not in raw_datasets:
raise ValueError("""--do_train requires a train dataset""" )
__lowerCAmelCase = raw_datasets["""train"""]
if data_args.max_train_samples is not None:
__lowerCAmelCase = train_dataset.select(range(data_args.max_train_samples ) )
if training_args.do_eval:
if "validation" not in raw_datasets and "validation_matched" not in raw_datasets:
raise ValueError("""--do_eval requires a validation dataset""" )
__lowerCAmelCase = raw_datasets["""validation"""]
if data_args.max_eval_samples is not None:
__lowerCAmelCase = eval_dataset.select(range(data_args.max_eval_samples ) )
if training_args.do_predict or data_args.test_file is not None:
if "test" not in raw_datasets and "test_matched" not in raw_datasets:
raise ValueError("""--do_predict requires a test dataset""" )
__lowerCAmelCase = raw_datasets["""test"""]
if data_args.max_predict_samples is not None:
__lowerCAmelCase = predict_dataset.select(range(data_args.max_predict_samples ) )
# Log a few random samples from the training set:
if training_args.do_train:
for index in random.sample(range(len(lowercase ) ) , 3 ):
logger.info(f'Sample {index} of the training set: {train_dataset[index]}.' )
# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(lowercase ):
__lowerCAmelCase = p.predictions[0] if isinstance(p.predictions , lowercase ) else p.predictions
__lowerCAmelCase = np.argmax(lowercase , axis=1 )
return {"accuracy": (preds == p.label_ids).astype(np.floataa ).mean().item()}
# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
if data_args.pad_to_max_length:
__lowerCAmelCase = default_data_collator
elif training_args.fpaa:
__lowerCAmelCase = DataCollatorWithPadding(lowercase , pad_to_multiple_of=8 )
else:
__lowerCAmelCase = None
# Initialize our Trainer
__lowerCAmelCase = Trainer(
model=lowercase , args=lowercase , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , compute_metrics=lowercase , tokenizer=lowercase , data_collator=lowercase , )
# Training
if training_args.do_train:
__lowerCAmelCase = None
if training_args.resume_from_checkpoint is not None:
__lowerCAmelCase = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
__lowerCAmelCase = last_checkpoint
__lowerCAmelCase = trainer.train(resume_from_checkpoint=lowercase )
__lowerCAmelCase = train_result.metrics
__lowerCAmelCase = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(lowercase )
)
__lowerCAmelCase = min(lowercase , len(lowercase ) )
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics("""train""" , lowercase )
trainer.save_metrics("""train""" , lowercase )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info("""*** Evaluate ***""" )
__lowerCAmelCase = trainer.evaluate(eval_dataset=lowercase )
__lowerCAmelCase = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(lowercase )
__lowerCAmelCase = min(lowercase , len(lowercase ) )
trainer.log_metrics("""eval""" , lowercase )
trainer.save_metrics("""eval""" , lowercase )
if training_args.do_predict:
logger.info("""*** Predict ***""" )
# Removing the `label` columns because it contains -1 and Trainer won't like that.
__lowerCAmelCase = predict_dataset.remove_columns("""label""" )
__lowerCAmelCase = trainer.predict(lowercase , metric_key_prefix="""predict""" ).predictions
__lowerCAmelCase = np.argmax(lowercase , axis=1 )
__lowerCAmelCase = os.path.join(training_args.output_dir , """predict_results_tabfact.txt""" )
if trainer.is_world_process_zero():
with open(lowercase , """w""" ) as writer:
logger.info("""***** Predict Results *****""" )
writer.write("""index\tprediction\n""" )
for index, item in enumerate(lowercase ):
__lowerCAmelCase = label_list[item]
writer.write(f'{index}\t{item}\n' )
__lowerCAmelCase = {"""finetuned_from""": model_args.model_name_or_path, """tasks""": """text-classification"""}
if training_args.push_to_hub:
trainer.push_to_hub(**lowercase )
else:
trainer.create_model_card(**lowercase )
def _lowerCAmelCase ( lowercase ) -> str:
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 689 | 0 |
import contextlib
import os
import sqlitea
import pytest
from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy
def UpperCamelCase ( lowercase_ , lowercase_ ) -> Union[str, Any]:
'''simple docstring'''
assert isinstance(lowercase_ , lowercase_ )
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@require_sqlalchemy
@pytest.mark.parametrize("""keep_in_memory""" , [False, True] )
def UpperCamelCase ( lowercase_ , lowercase_ , lowercase_ , lowercase_ ) -> List[Any]:
'''simple docstring'''
lowercase__ : Tuple = tmp_path / """cache"""
lowercase__ : Union[str, Any] = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
lowercase__ : Optional[int] = SqlDatasetReader(
"""dataset""" , """sqlite:///""" + sqlite_path , cache_dir=lowercase_ , keep_in_memory=lowercase_ ).read()
_check_sql_dataset(lowercase_ , lowercase_ )
@require_sqlalchemy
@pytest.mark.parametrize(
"""features""" , [
None,
{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
{"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
{"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
{"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
] , )
def UpperCamelCase ( lowercase_ , lowercase_ , lowercase_ , lowercase_ ) -> Dict:
'''simple docstring'''
lowercase__ : Optional[int] = tmp_path / """cache"""
lowercase__ : List[Any] = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
lowercase__ : Optional[int] = features.copy() if features else default_expected_features
lowercase__ : str = (
Features({feature: Value(lowercase_ ) for feature, dtype in features.items()} ) if features is not None else None
)
lowercase__ : Tuple = SqlDatasetReader("""dataset""" , """sqlite:///""" + sqlite_path , features=lowercase_ , cache_dir=lowercase_ ).read()
_check_sql_dataset(lowercase_ , lowercase_ )
def UpperCamelCase ( lowercase_ ) -> int:
'''simple docstring'''
with contextlib.closing(sqlitea.connect(lowercase_ ) ) as con:
lowercase__ : str = con.cursor()
cur.execute("""SELECT * FROM dataset""" )
for row in cur:
yield row
@require_sqlalchemy
def UpperCamelCase ( lowercase_ , lowercase_ , lowercase_ ) -> Optional[int]:
'''simple docstring'''
lowercase__ : str = tmp_path / """cache"""
lowercase__ : Optional[Any] = os.path.join(lowercase_ , """tmp.sql""" )
lowercase__ : Union[str, Any] = SqlDatasetReader("""dataset""" , """sqlite:///""" + sqlite_path , cache_dir=lowercase_ ).read()
SqlDatasetWriter(lowercase_ , """dataset""" , """sqlite:///""" + output_sqlite_path , num_proc=1 ).write()
lowercase__ : Optional[Any] = iter_sql_file(lowercase_ )
lowercase__ : int = iter_sql_file(lowercase_ )
for rowa, rowa in zip(lowercase_ , lowercase_ ):
assert rowa == rowa
@require_sqlalchemy
def UpperCamelCase ( lowercase_ , lowercase_ , lowercase_ ) -> str:
'''simple docstring'''
lowercase__ : List[str] = tmp_path / """cache"""
lowercase__ : str = os.path.join(lowercase_ , """tmp.sql""" )
lowercase__ : List[str] = SqlDatasetReader("""dataset""" , """sqlite:///""" + sqlite_path , cache_dir=lowercase_ ).read()
SqlDatasetWriter(lowercase_ , """dataset""" , """sqlite:///""" + output_sqlite_path , num_proc=2 ).write()
lowercase__ : List[Any] = iter_sql_file(lowercase_ )
lowercase__ : Tuple = iter_sql_file(lowercase_ )
for rowa, rowa in zip(lowercase_ , lowercase_ ):
assert rowa == rowa
@require_sqlalchemy
def UpperCamelCase ( lowercase_ , lowercase_ , lowercase_ ) -> Optional[Any]:
'''simple docstring'''
lowercase__ : Tuple = tmp_path / """cache"""
lowercase__ : Union[str, Any] = os.path.join(lowercase_ , """tmp.sql""" )
lowercase__ : Dict = SqlDatasetReader("""dataset""" , """sqlite:///""" + sqlite_path , cache_dir=lowercase_ ).read()
with pytest.raises(lowercase_ ):
SqlDatasetWriter(lowercase_ , """dataset""" , """sqlite:///""" + output_sqlite_path , num_proc=0 ).write()
| 12 |
'''simple docstring'''
import os
import sys
import unittest
_a : List[Any] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init # noqa: E402
# Align TRANSFORMERS_PATH in check_dummies with the current path
_a : Union[str, Any] = os.path.join(git_repo_path, """src""", """diffusers""")
class _UpperCAmelCase ( unittest.TestCase ):
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = find_backend(""" if not is_torch_available():""" )
self.assertEqual(__SCREAMING_SNAKE_CASE,"""torch""" )
# backend_with_underscore = find_backend(" if not is_tensorflow_text_available():")
# self.assertEqual(backend_with_underscore, "tensorflow_text")
__lowerCAmelCase = find_backend(""" if not (is_torch_available() and is_transformers_available()):""" )
self.assertEqual(__SCREAMING_SNAKE_CASE,"""torch_and_transformers""" )
# double_backend_with_underscore = find_backend(
# " if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
# )
# self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")
__lowerCAmelCase = find_backend(
""" if not (is_torch_available() and is_transformers_available() and is_onnx_available()):""" )
self.assertEqual(__SCREAMING_SNAKE_CASE,"""torch_and_transformers_and_onnx""" )
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = read_init()
# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
self.assertIn("""torch""",__SCREAMING_SNAKE_CASE )
self.assertIn("""torch_and_transformers""",__SCREAMING_SNAKE_CASE )
self.assertIn("""flax_and_transformers""",__SCREAMING_SNAKE_CASE )
self.assertIn("""torch_and_transformers_and_onnx""",__SCREAMING_SNAKE_CASE )
# Likewise, we can't assert on the exact content of a key
self.assertIn("""UNet2DModel""",objects["""torch"""] )
self.assertIn("""FlaxUNet2DConditionModel""",objects["""flax"""] )
self.assertIn("""StableDiffusionPipeline""",objects["""torch_and_transformers"""] )
self.assertIn("""FlaxStableDiffusionPipeline""",objects["""flax_and_transformers"""] )
self.assertIn("""LMSDiscreteScheduler""",objects["""torch_and_scipy"""] )
self.assertIn("""OnnxStableDiffusionPipeline""",objects["""torch_and_transformers_and_onnx"""] )
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = create_dummy_object("""CONSTANT""","""'torch'""" )
self.assertEqual(__SCREAMING_SNAKE_CASE,"""\nCONSTANT = None\n""" )
__lowerCAmelCase = create_dummy_object("""function""","""'torch'""" )
self.assertEqual(
__SCREAMING_SNAKE_CASE,"""\ndef function(*args, **kwargs):\n requires_backends(function, 'torch')\n""" )
__lowerCAmelCase = """
class FakeClass(metaclass=DummyObject):
_backends = 'torch'
def __init__(self, *args, **kwargs):
requires_backends(self, 'torch')
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, 'torch')
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, 'torch')
"""
__lowerCAmelCase = create_dummy_object("""FakeClass""","""'torch'""" )
self.assertEqual(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = """# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends
CONSTANT = None
def function(*args, **kwargs):
requires_backends(function, [\"torch\"])
class FakeClass(metaclass=DummyObject):
_backends = [\"torch\"]
def __init__(self, *args, **kwargs):
requires_backends(self, [\"torch\"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, [\"torch\"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, [\"torch\"])
"""
__lowerCAmelCase = create_dummy_files({"""torch""": ["""CONSTANT""", """function""", """FakeClass"""]} )
self.assertEqual(dummy_files["""torch"""],__SCREAMING_SNAKE_CASE )
| 689 | 0 |
'''simple docstring'''
import shutil
import tempfile
import unittest
from transformers import (
SPIECE_UNDERLINE,
AddedToken,
BatchEncoding,
NllbTokenizer,
NllbTokenizerFast,
is_torch_available,
)
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common import TokenizerTesterMixin
A__ : Union[str, Any] = get_tests_dir("""fixtures/test_sentencepiece.model""")
if is_torch_available():
from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right
A__ : str = 256047
A__ : int = 256145
@require_sentencepiece
@require_tokenizers
class UpperCAmelCase_ (_UpperCAmelCase , unittest.TestCase ):
"""simple docstring"""
lowerCamelCase : List[Any] = NllbTokenizer
lowerCamelCase : Optional[int] = NllbTokenizerFast
lowerCamelCase : List[Any] = True
lowerCamelCase : Dict = True
lowerCamelCase : int = {}
def lowercase_ ( self ) -> str:
super().setUp()
# We have a SentencePiece fixture for testing
__lowerCamelCase : Union[str, Any] = NllbTokenizer(SCREAMING_SNAKE_CASE_ , keep_accents=SCREAMING_SNAKE_CASE_ )
tokenizer.save_pretrained(self.tmpdirname )
def lowercase_ ( self ) -> Tuple:
__lowerCamelCase : str = NllbTokenizer(SCREAMING_SNAKE_CASE_ , keep_accents=SCREAMING_SNAKE_CASE_ )
__lowerCamelCase : str = tokenizer.tokenize('This is a test' )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , ['▁This', '▁is', '▁a', '▁t', 'est'] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ ) , [value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]] , )
__lowerCamelCase : Dict = tokenizer.tokenize('I was born in 92000, and this is falsé.' )
self.assertListEqual(
SCREAMING_SNAKE_CASE_ , [
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'9',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
's',
'é',
'.',
] , )
__lowerCamelCase : int = tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
SCREAMING_SNAKE_CASE_ , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
] , )
__lowerCamelCase : Any = tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(
SCREAMING_SNAKE_CASE_ , [
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'<unk>',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
's',
'<unk>',
'.',
] , )
def lowercase_ ( self ) -> Tuple:
__lowerCamelCase : Union[str, Any] = (self.rust_tokenizer_class, 'hf-internal-testing/tiny-random-nllb', {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})' ):
__lowerCamelCase : Optional[Any] = self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
__lowerCamelCase : Tuple = self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
__lowerCamelCase : Optional[int] = tempfile.mkdtemp()
__lowerCamelCase : Any = tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE_ )
__lowerCamelCase : Any = tokenizer_p.save_pretrained(SCREAMING_SNAKE_CASE_ )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files ) )
__lowerCamelCase : List[str] = tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f )
self.assertSequenceEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Checks everything loads correctly in the same way
__lowerCamelCase : Tuple = tokenizer_r.from_pretrained(SCREAMING_SNAKE_CASE_ )
__lowerCamelCase : int = tokenizer_p.from_pretrained(SCREAMING_SNAKE_CASE_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
shutil.rmtree(SCREAMING_SNAKE_CASE_ )
# Save tokenizer rust, legacy_format=True
__lowerCamelCase : Any = tempfile.mkdtemp()
__lowerCamelCase : int = tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE_ , legacy_format=SCREAMING_SNAKE_CASE_ )
__lowerCamelCase : Any = tokenizer_p.save_pretrained(SCREAMING_SNAKE_CASE_ )
# Checks it save with the same files
self.assertSequenceEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# Checks everything loads correctly in the same way
__lowerCamelCase : Optional[Any] = tokenizer_r.from_pretrained(SCREAMING_SNAKE_CASE_ )
__lowerCamelCase : Any = tokenizer_p.from_pretrained(SCREAMING_SNAKE_CASE_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
shutil.rmtree(SCREAMING_SNAKE_CASE_ )
# Save tokenizer rust, legacy_format=False
__lowerCamelCase : Any = tempfile.mkdtemp()
__lowerCamelCase : int = tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE_ , legacy_format=SCREAMING_SNAKE_CASE_ )
__lowerCamelCase : Optional[int] = tokenizer_p.save_pretrained(SCREAMING_SNAKE_CASE_ )
# Checks it saved the tokenizer.json file
self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
__lowerCamelCase : str = tokenizer_r.from_pretrained(SCREAMING_SNAKE_CASE_ )
__lowerCamelCase : Tuple = tokenizer_p.from_pretrained(SCREAMING_SNAKE_CASE_ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
shutil.rmtree(SCREAMING_SNAKE_CASE_ )
@require_torch
def lowercase_ ( self ) -> Optional[int]:
if not self.test_seqaseq:
return
__lowerCamelCase : Optional[int] = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f'{tokenizer.__class__.__name__}' ):
# Longer text that will definitely require truncation.
__lowerCamelCase : Union[str, Any] = [
' UN Chief Says There Is No Military Solution in Syria',
' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for'
' Syria is that \'there is no military solution\' to the nearly five-year conflict and more weapons'
' will only worsen the violence and misery for millions of people.',
]
__lowerCamelCase : List[str] = [
'Şeful ONU declară că nu există o soluţie militară în Siria',
'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al'
' Rusiei pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi'
' că noi arme nu vor face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.',
]
try:
__lowerCamelCase : Optional[Any] = tokenizer.prepare_seqaseq_batch(
src_texts=SCREAMING_SNAKE_CASE_ , tgt_texts=SCREAMING_SNAKE_CASE_ , max_length=3 , max_target_length=10 , return_tensors='pt' , src_lang='eng_Latn' , tgt_lang='ron_Latn' , )
except NotImplementedError:
return
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.labels.shape[1] , 10 )
# max_target_length will default to max_length if not specified
__lowerCamelCase : int = tokenizer.prepare_seqaseq_batch(
SCREAMING_SNAKE_CASE_ , tgt_texts=SCREAMING_SNAKE_CASE_ , max_length=3 , return_tensors='pt' )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.labels.shape[1] , 3 )
__lowerCamelCase : List[str] = tokenizer.prepare_seqaseq_batch(
src_texts=SCREAMING_SNAKE_CASE_ , max_length=3 , max_target_length=10 , return_tensors='pt' )
self.assertEqual(batch_encoder_only.input_ids.shape[1] , 3 )
self.assertEqual(batch_encoder_only.attention_mask.shape[1] , 3 )
self.assertNotIn('decoder_input_ids' , SCREAMING_SNAKE_CASE_ )
@unittest.skip('Unfortunately way too slow to build a BPE with SentencePiece.' )
def lowercase_ ( self ) -> Optional[Any]:
pass
def lowercase_ ( self ) -> Tuple:
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})' ):
__lowerCamelCase : Dict = [AddedToken('<special>' , lstrip=SCREAMING_SNAKE_CASE_ )]
__lowerCamelCase : Dict = self.rust_tokenizer_class.from_pretrained(
SCREAMING_SNAKE_CASE_ , additional_special_tokens=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
__lowerCamelCase : List[Any] = tokenizer_r.encode('Hey this is a <special> token' )
__lowerCamelCase : Union[str, Any] = tokenizer_r.encode('<special>' , add_special_tokens=SCREAMING_SNAKE_CASE_ )[0]
self.assertTrue(special_token_id in r_output )
if self.test_slow_tokenizer:
__lowerCamelCase : Optional[int] = self.rust_tokenizer_class.from_pretrained(
SCREAMING_SNAKE_CASE_ , additional_special_tokens=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
__lowerCamelCase : Dict = self.tokenizer_class.from_pretrained(
SCREAMING_SNAKE_CASE_ , additional_special_tokens=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
__lowerCamelCase : str = tokenizer_p.encode('Hey this is a <special> token' )
__lowerCamelCase : Union[str, Any] = tokenizer_cr.encode('Hey this is a <special> token' )
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertTrue(special_token_id in p_output )
self.assertTrue(special_token_id in cr_output )
@require_torch
@require_sentencepiece
@require_tokenizers
class UpperCAmelCase_ (unittest.TestCase ):
"""simple docstring"""
lowerCamelCase : Optional[int] = 'facebook/nllb-200-distilled-600M'
lowerCamelCase : List[str] = [
' UN Chief Says There Is No Military Solution in Syria',
' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.',
]
lowerCamelCase : List[str] = [
'Şeful ONU declară că nu există o soluţie militară în Siria',
'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'
' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'
' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.',
]
lowerCamelCase : Tuple = [
2_5_6_0_4_7,
1_6_2_9_7,
1_3_4_4_0_8,
8_1_6_5,
2_4_8_0_6_6,
1_4_7_3_4,
9_5_0,
1_1_3_5,
1_0_5_7_2_1,
3_5_7_3,
8_3,
2_7_3_5_2,
1_0_8,
4_9_4_8_6,
2,
]
@classmethod
def lowercase_ ( cls ) -> Union[str, Any]:
__lowerCamelCase : NllbTokenizer = NllbTokenizer.from_pretrained(
cls.checkpoint_name , src_lang='eng_Latn' , tgt_lang='ron_Latn' )
__lowerCamelCase : Dict = 1
return cls
def lowercase_ ( self ) -> List[Any]:
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ace_Arab'] , 25_60_01 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ace_Latn'] , 25_60_02 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['fra_Latn'] , 25_60_57 )
def lowercase_ ( self ) -> Any:
__lowerCamelCase : Optional[Any] = self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , SCREAMING_SNAKE_CASE_ )
def lowercase_ ( self ) -> List[Any]:
self.assertIn(SCREAMING_SNAKE_CASE_ , self.tokenizer.all_special_ids )
# fmt: off
__lowerCamelCase : List[Any] = [RO_CODE, 42_54, 9_80_68, 11_29_23, 3_90_72, 39_09, 7_13, 10_27_67, 26, 1_73_14, 3_56_42, 1_46_83, 3_31_18, 20_22, 6_69_87, 2, 25_60_47]
# fmt: on
__lowerCamelCase : Optional[int] = self.tokenizer.decode(SCREAMING_SNAKE_CASE_ , skip_special_tokens=SCREAMING_SNAKE_CASE_ )
__lowerCamelCase : Union[str, Any] = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=SCREAMING_SNAKE_CASE_ )
self.assertEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertNotIn(self.tokenizer.eos_token , SCREAMING_SNAKE_CASE_ )
def lowercase_ ( self ) -> int:
__lowerCamelCase : Tuple = ['this is gunna be a long sentence ' * 20]
assert isinstance(src_text[0] , SCREAMING_SNAKE_CASE_ )
__lowerCamelCase : str = 10
__lowerCamelCase : List[Any] = self.tokenizer(SCREAMING_SNAKE_CASE_ , max_length=SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ ).input_ids[0]
self.assertEqual(ids[-1] , 2 )
self.assertEqual(ids[0] , SCREAMING_SNAKE_CASE_ )
self.assertEqual(len(SCREAMING_SNAKE_CASE_ ) , SCREAMING_SNAKE_CASE_ )
def lowercase_ ( self ) -> Union[str, Any]:
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR'] ) , [25_62_03, 3] )
def lowercase_ ( self ) -> str:
__lowerCamelCase : Dict = tempfile.mkdtemp()
__lowerCamelCase : List[Any] = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(SCREAMING_SNAKE_CASE_ )
__lowerCamelCase : Any = NllbTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , SCREAMING_SNAKE_CASE_ )
@require_torch
def lowercase_ ( self ) -> Any:
__lowerCamelCase : Tuple = self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ , max_length=len(self.expected_src_tokens ) , return_tensors='pt' , )
__lowerCamelCase : Dict = shift_tokens_right(
batch['labels'] , self.tokenizer.pad_token_id , self.tokenizer.lang_code_to_id['ron_Latn'] )
self.assertIsInstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
self.assertEqual((2, 15) , batch.input_ids.shape )
self.assertEqual((2, 15) , batch.attention_mask.shape )
__lowerCamelCase : List[Any] = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , SCREAMING_SNAKE_CASE_ )
self.assertEqual(SCREAMING_SNAKE_CASE_ , batch.decoder_input_ids[0, 0] ) # EOS
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens , [EN_CODE] )
self.assertEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
def lowercase_ ( self ) -> Dict:
__lowerCamelCase : int = self.tokenizer(self.src_text , padding=SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ , max_length=3 , return_tensors='pt' )
__lowerCamelCase : Tuple = self.tokenizer(
text_target=self.tgt_text , padding=SCREAMING_SNAKE_CASE_ , truncation=SCREAMING_SNAKE_CASE_ , max_length=10 , return_tensors='pt' )
__lowerCamelCase : Optional[int] = targets['input_ids']
__lowerCamelCase : List[Any] = shift_tokens_right(
SCREAMING_SNAKE_CASE_ , self.tokenizer.pad_token_id , decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang] , )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.decoder_input_ids.shape[1] , 10 )
@require_torch
def lowercase_ ( self ) -> Optional[int]:
__lowerCamelCase : Tuple = self.tokenizer._build_translation_inputs(
'A test' , return_tensors='pt' , src_lang='eng_Latn' , tgt_lang='fra_Latn' )
self.assertEqual(
nested_simplify(SCREAMING_SNAKE_CASE_ ) , {
# A, test, EOS, en_XX
'input_ids': [[25_60_47, 70, 73_56, 2]],
'attention_mask': [[1, 1, 1, 1]],
# ar_AR
'forced_bos_token_id': 25_60_57,
} , )
@require_torch
def lowercase_ ( self ) -> List[Any]:
__lowerCamelCase : List[Any] = True
__lowerCamelCase : Any = self.tokenizer(
'UN Chief says there is no military solution in Syria' , src_lang='eng_Latn' , tgt_lang='fra_Latn' )
self.assertEqual(
inputs.input_ids , [1_62_97, 13_44_08, 2_56_53, 63_70, 2_48, 2_54, 10_39_29, 9_49_95, 1_08, 4_94_86, 2, 25_60_47] )
__lowerCamelCase : List[Any] = False
__lowerCamelCase : Union[str, Any] = self.tokenizer(
'UN Chief says there is no military solution in Syria' , src_lang='eng_Latn' , tgt_lang='fra_Latn' )
self.assertEqual(
inputs.input_ids , [25_60_47, 1_62_97, 13_44_08, 2_56_53, 63_70, 2_48, 2_54, 10_39_29, 9_49_95, 1_08, 4_94_86, 2] )
| 13 |
'''simple docstring'''
def _lowerCAmelCase ( lowercase ) -> tuple[int, int]:
try:
__lowerCAmelCase = float(lowercase )
except ValueError:
raise ValueError("""Please enter a valid number""" )
__lowerCAmelCase = decimal - int(lowercase )
if fractional_part == 0:
return int(lowercase ), 1
else:
__lowerCAmelCase = len(str(lowercase ).split(""".""" )[1] )
__lowerCAmelCase = int(decimal * (10**number_of_frac_digits) )
__lowerCAmelCase = 10**number_of_frac_digits
__lowerCAmelCase , __lowerCAmelCase = denominator, numerator
while True:
__lowerCAmelCase = dividend % divisor
if remainder == 0:
break
__lowerCAmelCase , __lowerCAmelCase = divisor, remainder
__lowerCAmelCase , __lowerCAmelCase = numerator / divisor, denominator / divisor
return int(lowercase ), int(lowercase )
if __name__ == "__main__":
print(f'{decimal_to_fraction(2) = }')
print(f'{decimal_to_fraction(89.0) = }')
print(f'{decimal_to_fraction("67") = }')
print(f'{decimal_to_fraction("45.0") = }')
print(f'{decimal_to_fraction(1.5) = }')
print(f'{decimal_to_fraction("6.25") = }')
print(f'{decimal_to_fraction("78td") = }')
| 689 | 0 |
from . import (
albert,
align,
altclip,
audio_spectrogram_transformer,
auto,
autoformer,
bark,
bart,
barthez,
bartpho,
beit,
bert,
bert_generation,
bert_japanese,
bertweet,
big_bird,
bigbird_pegasus,
biogpt,
bit,
blenderbot,
blenderbot_small,
blip,
blip_a,
bloom,
bridgetower,
byta,
camembert,
canine,
chinese_clip,
clap,
clip,
clipseg,
codegen,
conditional_detr,
convbert,
convnext,
convnextva,
cpm,
cpmant,
ctrl,
cvt,
dataavec,
deberta,
deberta_va,
decision_transformer,
deformable_detr,
deit,
deprecated,
deta,
detr,
dialogpt,
dinat,
distilbert,
dit,
donut,
dpr,
dpt,
efficientformer,
efficientnet,
electra,
encodec,
encoder_decoder,
ernie,
ernie_m,
esm,
falcon,
flaubert,
flava,
fnet,
focalnet,
fsmt,
funnel,
git,
glpn,
gpta,
gpt_bigcode,
gpt_neo,
gpt_neox,
gpt_neox_japanese,
gpt_swa,
gptj,
gptsan_japanese,
graphormer,
groupvit,
herbert,
hubert,
ibert,
imagegpt,
informer,
instructblip,
jukebox,
layoutlm,
layoutlmva,
layoutlmva,
layoutxlm,
led,
levit,
lilt,
llama,
longformer,
longta,
luke,
lxmert,
mam_aaa,
marian,
markuplm,
maskaformer,
maskformer,
mbart,
mbartaa,
mega,
megatron_bert,
megatron_gpta,
mgp_str,
mluke,
mobilebert,
mobilenet_va,
mobilenet_va,
mobilevit,
mobilevitva,
mpnet,
mra,
mta,
musicgen,
mvp,
nat,
nezha,
nllb,
nllb_moe,
nystromformer,
oneformer,
open_llama,
openai,
opt,
owlvit,
pegasus,
pegasus_x,
perceiver,
phobert,
pixastruct,
plbart,
poolformer,
prophetnet,
qdqbert,
rag,
realm,
reformer,
regnet,
rembert,
resnet,
roberta,
roberta_prelayernorm,
roc_bert,
roformer,
rwkv,
sam,
segformer,
sew,
sew_d,
speech_encoder_decoder,
speech_to_text,
speech_to_text_a,
speechta,
splinter,
squeezebert,
swiftformer,
swin,
swinasr,
swinva,
switch_transformers,
ta,
table_transformer,
tapas,
time_series_transformer,
timesformer,
timm_backbone,
transfo_xl,
trocr,
tvlt,
umta,
unispeech,
unispeech_sat,
upernet,
videomae,
vilt,
vision_encoder_decoder,
vision_text_dual_encoder,
visual_bert,
vit,
vit_hybrid,
vit_mae,
vit_msn,
vivit,
wavaveca,
wavaveca_conformer,
wavaveca_phoneme,
wavaveca_with_lm,
wavlm,
whisper,
x_clip,
xglm,
xlm,
xlm_prophetnet,
xlm_roberta,
xlm_roberta_xl,
xlnet,
xmod,
yolos,
yoso,
)
| 14 |
'''simple docstring'''
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
_a : Dict = _symbol_database.Default()
_a : Union[str, Any] = _descriptor_pool.Default().AddSerializedFile(
b"""\n\x19sentencepiece_model.proto\x12\rsentencepiece\"\x80\x0c\n\x0bTrainerSpec\x12\r\n\x05input\x18\x01 \x03(\t\x12\x14\n\x0cinput_format\x18\x07 \x01(\t\x12\x14\n\x0cmodel_prefix\x18\x02 \x01(\t\x12\x41\n\nmodel_type\x18\x03 \x01(\x0e\x32$.sentencepiece.TrainerSpec.ModelType:\x07UNIGRAM\x12\x18\n\nvocab_size\x18\x04 \x01(\x05:\x04\x38\x30\x30\x30\x12\x17\n\x0f\x61\x63\x63\x65pt_language\x18\x05 \x03(\t\x12 \n\x15self_test_sample_size\x18\x06 \x01(\x05:\x01\x30\x12*\n\x1b\x65nable_differential_privacy\x18\x32 \x01(\x08:\x05\x66\x61lse\x12+\n differential_privacy_noise_level\x18\x33 \x01(\x02:\x01\x30\x12\x32\n\'differential_privacy_clipping_threshold\x18\x34 \x01(\x04:\x01\x30\x12\"\n\x12\x63haracter_coverage\x18\n \x01(\x02:\x06\x30.9995\x12\x1e\n\x13input_sentence_size\x18\x0b \x01(\x04:\x01\x30\x12$\n\x16shuffle_input_sentence\x18\x13 \x01(\x08:\x04true\x12 \n\x14mining_sentence_size\x18\x0c \x01(\x05\x42\x02\x18\x01\x12\"\n\x16training_sentence_size\x18\r \x01(\x05\x42\x02\x18\x01\x12(\n\x17seed_sentencepiece_size\x18\x0e \x01(\x05:\x07\x31\x30\x30\x30\x30\x30\x30\x12\x1e\n\x10shrinking_factor\x18\x0f \x01(\x02:\x04\x30.75\x12!\n\x13max_sentence_length\x18\x12 \x01(\x05:\x04\x34\x31\x39\x32\x12\x17\n\x0bnum_threads\x18\x10 \x01(\x05:\x02\x31\x36\x12\x1d\n\x12num_sub_iterations\x18\x11 \x01(\x05:\x01\x32\x12$\n\x18max_sentencepiece_length\x18\x14 \x01(\x05:\x02\x31\x36\x12%\n\x17split_by_unicode_script\x18\x15 \x01(\x08:\x04true\x12\x1d\n\x0fsplit_by_number\x18\x17 \x01(\x08:\x04true\x12!\n\x13split_by_whitespace\x18\x16 \x01(\x08:\x04true\x12)\n\x1atreat_whitespace_as_suffix\x18\x18 \x01(\x08:\x05\x66\x61lse\x12+\n\x1c\x61llow_whitespace_only_pieces\x18\x1a \x01(\x08:\x05\x66\x61lse\x12\x1b\n\x0csplit_digits\x18\x19 \x01(\x08:\x05\x66\x61lse\x12#\n\x19pretokenization_delimiter\x18\x35 \x01(\t:\x00\x12\x17\n\x0f\x63ontrol_symbols\x18\x1e \x03(\t\x12\x1c\n\x14user_defined_symbols\x18\x1f \x03(\t\x12\x16\n\x0erequired_chars\x18$ \x01(\t\x12\x1c\n\rbyte_fallback\x18# \x01(\x08:\x05\x66\x61lse\x12+\n\x1dvocabulary_output_piece_score\x18 \x01(\x08:\x04true\x12\x1e\n\x10hard_vocab_limit\x18! \x01(\x08:\x04true\x12\x1c\n\ruse_all_vocab\x18\" \x01(\x08:\x05\x66\x61lse\x12\x11\n\x06unk_id\x18( \x01(\x05:\x01\x30\x12\x11\n\x06\x62os_id\x18) \x01(\x05:\x01\x31\x12\x11\n\x06\x65os_id\x18* \x01(\x05:\x01\x32\x12\x12\n\x06pad_id\x18+ \x01(\x05:\x02-1\x12\x18\n\tunk_piece\x18- \x01(\t:\x05<unk>\x12\x16\n\tbos_piece\x18. \x01(\t:\x03<s>\x12\x17\n\teos_piece\x18/ \x01(\t:\x04</s>\x12\x18\n\tpad_piece\x18\x30 \x01(\t:\x05<pad>\x12\x1a\n\x0bunk_surface\x18, \x01(\t:\x05 \xe2\x81\x87 \x12+\n\x1ctrain_extremely_large_corpus\x18\x31 \x01(\x08:\x05\x66\x61lse\"5\n\tModelType\x12\x0b\n\x07UNIGRAM\x10\x01\x12\x07\n\x03\x42PE\x10\x02\x12\x08\n\x04WORD\x10\x03\x12\x08\n\x04\x43HAR\x10\x04*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"\xd1\x01\n\x0eNormalizerSpec\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x1c\n\x14precompiled_charsmap\x18\x02 \x01(\x0c\x12\x1e\n\x10\x61\x64\x64_dummy_prefix\x18\x03 \x01(\x08:\x04true\x12&\n\x18remove_extra_whitespaces\x18\x04 \x01(\x08:\x04true\x12 \n\x12\x65scape_whitespaces\x18\x05 \x01(\x08:\x04true\x12\x1e\n\x16normalization_rule_tsv\x18\x06 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"y\n\x0cSelfTestData\x12\x33\n\x07samples\x18\x01 \x03(\x0b\x32\".sentencepiece.SelfTestData.Sample\x1a)\n\x06Sample\x12\r\n\x05input\x18\x01 \x01(\t\x12\x10\n\x08\x65xpected\x18\x02 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"\xfe\x03\n\nModelProto\x12\x37\n\x06pieces\x18\x01 \x03(\x0b\x32\'.sentencepiece.ModelProto.SentencePiece\x12\x30\n\x0ctrainer_spec\x18\x02 \x01(\x0b\x32\x1a.sentencepiece.TrainerSpec\x12\x36\n\x0fnormalizer_spec\x18\x03 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x12\x33\n\x0eself_test_data\x18\x04 \x01(\x0b\x32\x1b.sentencepiece.SelfTestData\x12\x38\n\x11\x64\x65normalizer_spec\x18\x05 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x1a\xd2\x01\n\rSentencePiece\x12\r\n\x05piece\x18\x01 \x01(\t\x12\r\n\x05score\x18\x02 \x01(\x02\x12\x42\n\x04type\x18\x03 \x01(\x0e\x32,.sentencepiece.ModelProto.SentencePiece.Type:\x06NORMAL\"T\n\x04Type\x12\n\n\x06NORMAL\x10\x01\x12\x0b\n\x07UNKNOWN\x10\x02\x12\x0b\n\x07\x43ONTROL\x10\x03\x12\x10\n\x0cUSER_DEFINED\x10\x04\x12\x08\n\x04\x42YTE\x10\x06\x12\n\n\x06UNUSED\x10\x05*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\x42\x02H\x03"""
)
_a : str = globals()
_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, """sentencepiece_model_pb2""", _globals)
if _descriptor._USE_C_DESCRIPTORS is False:
_a : str = None
_a : Union[str, Any] = b"""H\003"""
# (generated by protobuf compiler, but `_TRAINERSPEC` is not defined)
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._serialized_options = b"\030\001"
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._serialized_options = b"\030\001"
_a : Optional[int] = 4_5
_a : List[Any] = 1_5_8_1
_a : str = 1_5_1_7
_a : Optional[Any] = 1_5_7_0
_a : List[str] = 1_5_8_4
_a : List[Any] = 1_7_9_3
_a : Union[str, Any] = 1_7_9_5
_a : Tuple = 1_9_1_6
_a : List[Any] = 1_8_6_4
_a : Any = 1_9_0_5
_a : Optional[Any] = 1_9_1_9
_a : Optional[int] = 2_4_2_9
_a : Tuple = 2_2_0_8
_a : Optional[Any] = 2_4_1_8
_a : List[Any] = 2_3_2_3
_a : str = 2_4_0_7
# @@protoc_insertion_point(module_scope)
| 689 | 0 |
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobertaTokenizerFast,
)
def UpperCamelCase ( __magic_name__ : Any ) -> int:
"""simple docstring"""
lowercase__ = model.config
lowercase__ = DonutSwinConfig(
image_size=original_config.input_size , patch_size=4 , depths=original_config.encoder_layer , num_heads=[4, 8, 16, 32] , window_size=original_config.window_size , embed_dim=128 , )
lowercase__ = MBartConfig(
is_decoder=__magic_name__ , is_encoder_decoder=__magic_name__ , add_cross_attention=__magic_name__ , decoder_layers=original_config.decoder_layer , max_position_embeddings=original_config.max_position_embeddings , vocab_size=len(
model.decoder.tokenizer ) , scale_embedding=__magic_name__ , add_final_layer_norm=__magic_name__ , )
return encoder_config, decoder_config
def UpperCamelCase ( __magic_name__ : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
if "encoder.model" in name:
lowercase__ = name.replace("""encoder.model""" , """encoder""" )
if "decoder.model" in name:
lowercase__ = name.replace("""decoder.model""" , """decoder""" )
if "patch_embed.proj" in name:
lowercase__ = name.replace("""patch_embed.proj""" , """embeddings.patch_embeddings.projection""" )
if "patch_embed.norm" in name:
lowercase__ = name.replace("""patch_embed.norm""" , """embeddings.norm""" )
if name.startswith("""encoder""" ):
if "layers" in name:
lowercase__ = """encoder.""" + name
if "attn.proj" in name:
lowercase__ = name.replace("""attn.proj""" , """attention.output.dense""" )
if "attn" in name and "mask" not in name:
lowercase__ = name.replace("""attn""" , """attention.self""" )
if "norm1" in name:
lowercase__ = name.replace("""norm1""" , """layernorm_before""" )
if "norm2" in name:
lowercase__ = name.replace("""norm2""" , """layernorm_after""" )
if "mlp.fc1" in name:
lowercase__ = name.replace("""mlp.fc1""" , """intermediate.dense""" )
if "mlp.fc2" in name:
lowercase__ = name.replace("""mlp.fc2""" , """output.dense""" )
if name == "encoder.norm.weight":
lowercase__ = """encoder.layernorm.weight"""
if name == "encoder.norm.bias":
lowercase__ = """encoder.layernorm.bias"""
return name
def UpperCamelCase ( __magic_name__ : Any , __magic_name__ : str ) -> Optional[int]:
"""simple docstring"""
for key in orig_state_dict.copy().keys():
lowercase__ = orig_state_dict.pop(__magic_name__ )
if "qkv" in key:
lowercase__ = key.split(""".""" )
lowercase__ = int(key_split[3] )
lowercase__ = int(key_split[5] )
lowercase__ = model.encoder.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size
if "weight" in key:
lowercase__ = val[:dim, :]
lowercase__ = val[dim : dim * 2, :]
lowercase__ = val[-dim:, :]
else:
lowercase__ = val[:dim]
lowercase__ = val[dim : dim * 2]
lowercase__ = val[-dim:]
elif "attn_mask" in key or key in ["encoder.model.norm.weight", "encoder.model.norm.bias"]:
# HuggingFace implementation doesn't use attn_mask buffer
# and model doesn't use final LayerNorms for the encoder
pass
else:
lowercase__ = val
return orig_state_dict
def UpperCamelCase ( __magic_name__ : Union[str, Any] , __magic_name__ : List[Any]=None , __magic_name__ : Dict=False ) -> int:
"""simple docstring"""
lowercase__ = DonutModel.from_pretrained(__magic_name__ ).eval()
# load HuggingFace model
lowercase__ , lowercase__ = get_configs(__magic_name__ )
lowercase__ = DonutSwinModel(__magic_name__ )
lowercase__ = MBartForCausalLM(__magic_name__ )
lowercase__ = VisionEncoderDecoderModel(encoder=__magic_name__ , decoder=__magic_name__ )
model.eval()
lowercase__ = original_model.state_dict()
lowercase__ = convert_state_dict(__magic_name__ , __magic_name__ )
model.load_state_dict(__magic_name__ )
# verify results on scanned document
lowercase__ = load_dataset("""hf-internal-testing/example-documents""" )
lowercase__ = dataset["""test"""][0]["""image"""].convert("""RGB""" )
lowercase__ = XLMRobertaTokenizerFast.from_pretrained(__magic_name__ , from_slow=__magic_name__ )
lowercase__ = DonutImageProcessor(
do_align_long_axis=original_model.config.align_long_axis , size=original_model.config.input_size[::-1] )
lowercase__ = DonutProcessor(__magic_name__ , __magic_name__ )
lowercase__ = processor(__magic_name__ , return_tensors="""pt""" ).pixel_values
if model_name == "naver-clova-ix/donut-base-finetuned-docvqa":
lowercase__ = """<s_docvqa><s_question>{user_input}</s_question><s_answer>"""
lowercase__ = """When is the coffee break?"""
lowercase__ = task_prompt.replace("""{user_input}""" , __magic_name__ )
elif model_name == "naver-clova-ix/donut-base-finetuned-rvlcdip":
lowercase__ = """<s_rvlcdip>"""
elif model_name in [
"naver-clova-ix/donut-base-finetuned-cord-v1",
"naver-clova-ix/donut-base-finetuned-cord-v1-2560",
]:
lowercase__ = """<s_cord>"""
elif model_name == "naver-clova-ix/donut-base-finetuned-cord-v2":
lowercase__ = """s_cord-v2>"""
elif model_name == "naver-clova-ix/donut-base-finetuned-zhtrainticket":
lowercase__ = """<s_zhtrainticket>"""
elif model_name in ["naver-clova-ix/donut-proto", "naver-clova-ix/donut-base"]:
# use a random prompt
lowercase__ = """hello world"""
else:
raise ValueError("""Model name not supported""" )
lowercase__ = original_model.decoder.tokenizer(__magic_name__ , add_special_tokens=__magic_name__ , return_tensors="""pt""" )[
"""input_ids"""
]
lowercase__ = original_model.encoder.model.patch_embed(__magic_name__ )
lowercase__ , lowercase__ = model.encoder.embeddings(__magic_name__ )
assert torch.allclose(__magic_name__ , __magic_name__ , atol=1E-3 )
# verify encoder hidden states
lowercase__ = original_model.encoder(__magic_name__ )
lowercase__ = model.encoder(__magic_name__ ).last_hidden_state
assert torch.allclose(__magic_name__ , __magic_name__ , atol=1E-2 )
# verify decoder hidden states
lowercase__ = original_model(__magic_name__ , __magic_name__ , __magic_name__ ).logits
lowercase__ = model(__magic_name__ , decoder_input_ids=__magic_name__ ).logits
assert torch.allclose(__magic_name__ , __magic_name__ , atol=1E-3 )
print("""Looks ok!""" )
if pytorch_dump_folder_path is not None:
print(f'''Saving model and processor to {pytorch_dump_folder_path}''' )
model.save_pretrained(__magic_name__ )
processor.save_pretrained(__magic_name__ )
if push_to_hub:
model.push_to_hub("""nielsr/""" + model_name.split("""/""" )[-1] , commit_message="""Update model""" )
processor.push_to_hub("""nielsr/""" + model_name.split("""/""" )[-1] , commit_message="""Update model""" )
if __name__ == "__main__":
A : Dict = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='naver-clova-ix/donut-base-finetuned-docvqa',
required=False,
type=str,
help='Name of the original model you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path',
default=None,
required=False,
type=str,
help='Path to the output PyTorch model directory.',
)
parser.add_argument(
'--push_to_hub',
action='store_true',
help='Whether or not to push the converted model and processor to the 🤗 hub.',
)
A : Optional[int] = parser.parse_args()
convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 15 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block
@dataclass
class _UpperCAmelCase ( lowerCAmelCase_ ):
a : torch.FloatTensor
class _UpperCAmelCase ( nn.Module ):
def __init__( self,__SCREAMING_SNAKE_CASE=3,__SCREAMING_SNAKE_CASE=3,__SCREAMING_SNAKE_CASE=("DownEncoderBlock2D",),__SCREAMING_SNAKE_CASE=(64,),__SCREAMING_SNAKE_CASE=2,__SCREAMING_SNAKE_CASE=32,__SCREAMING_SNAKE_CASE="silu",__SCREAMING_SNAKE_CASE=True,):
'''simple docstring'''
super().__init__()
__lowerCAmelCase = layers_per_block
__lowerCAmelCase = torch.nn.Convad(
__SCREAMING_SNAKE_CASE,block_out_channels[0],kernel_size=3,stride=1,padding=1,)
__lowerCAmelCase = None
__lowerCAmelCase = nn.ModuleList([] )
# down
__lowerCAmelCase = block_out_channels[0]
for i, down_block_type in enumerate(__SCREAMING_SNAKE_CASE ):
__lowerCAmelCase = output_channel
__lowerCAmelCase = block_out_channels[i]
__lowerCAmelCase = i == len(__SCREAMING_SNAKE_CASE ) - 1
__lowerCAmelCase = get_down_block(
__SCREAMING_SNAKE_CASE,num_layers=self.layers_per_block,in_channels=__SCREAMING_SNAKE_CASE,out_channels=__SCREAMING_SNAKE_CASE,add_downsample=not is_final_block,resnet_eps=1e-6,downsample_padding=0,resnet_act_fn=__SCREAMING_SNAKE_CASE,resnet_groups=__SCREAMING_SNAKE_CASE,attention_head_dim=__SCREAMING_SNAKE_CASE,temb_channels=__SCREAMING_SNAKE_CASE,)
self.down_blocks.append(__SCREAMING_SNAKE_CASE )
# mid
__lowerCAmelCase = UNetMidBlockaD(
in_channels=block_out_channels[-1],resnet_eps=1e-6,resnet_act_fn=__SCREAMING_SNAKE_CASE,output_scale_factor=1,resnet_time_scale_shift="""default""",attention_head_dim=block_out_channels[-1],resnet_groups=__SCREAMING_SNAKE_CASE,temb_channels=__SCREAMING_SNAKE_CASE,)
# out
__lowerCAmelCase = nn.GroupNorm(num_channels=block_out_channels[-1],num_groups=__SCREAMING_SNAKE_CASE,eps=1e-6 )
__lowerCAmelCase = nn.SiLU()
__lowerCAmelCase = 2 * out_channels if double_z else out_channels
__lowerCAmelCase = nn.Convad(block_out_channels[-1],__SCREAMING_SNAKE_CASE,3,padding=1 )
__lowerCAmelCase = False
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase = x
__lowerCAmelCase = self.conv_in(__SCREAMING_SNAKE_CASE )
if self.training and self.gradient_checkpointing:
def create_custom_forward(__SCREAMING_SNAKE_CASE ):
def custom_forward(*__SCREAMING_SNAKE_CASE ):
return module(*__SCREAMING_SNAKE_CASE )
return custom_forward
# down
if is_torch_version(""">=""","""1.11.0""" ):
for down_block in self.down_blocks:
__lowerCAmelCase = torch.utils.checkpoint.checkpoint(
create_custom_forward(__SCREAMING_SNAKE_CASE ),__SCREAMING_SNAKE_CASE,use_reentrant=__SCREAMING_SNAKE_CASE )
# middle
__lowerCAmelCase = torch.utils.checkpoint.checkpoint(
create_custom_forward(self.mid_block ),__SCREAMING_SNAKE_CASE,use_reentrant=__SCREAMING_SNAKE_CASE )
else:
for down_block in self.down_blocks:
__lowerCAmelCase = torch.utils.checkpoint.checkpoint(create_custom_forward(__SCREAMING_SNAKE_CASE ),__SCREAMING_SNAKE_CASE )
# middle
__lowerCAmelCase = torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block ),__SCREAMING_SNAKE_CASE )
else:
# down
for down_block in self.down_blocks:
__lowerCAmelCase = down_block(__SCREAMING_SNAKE_CASE )
# middle
__lowerCAmelCase = self.mid_block(__SCREAMING_SNAKE_CASE )
# post-process
__lowerCAmelCase = self.conv_norm_out(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = self.conv_act(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = self.conv_out(__SCREAMING_SNAKE_CASE )
return sample
class _UpperCAmelCase ( nn.Module ):
def __init__( self,__SCREAMING_SNAKE_CASE=3,__SCREAMING_SNAKE_CASE=3,__SCREAMING_SNAKE_CASE=("UpDecoderBlock2D",),__SCREAMING_SNAKE_CASE=(64,),__SCREAMING_SNAKE_CASE=2,__SCREAMING_SNAKE_CASE=32,__SCREAMING_SNAKE_CASE="silu",__SCREAMING_SNAKE_CASE="group",):
'''simple docstring'''
super().__init__()
__lowerCAmelCase = layers_per_block
__lowerCAmelCase = nn.Convad(
__SCREAMING_SNAKE_CASE,block_out_channels[-1],kernel_size=3,stride=1,padding=1,)
__lowerCAmelCase = None
__lowerCAmelCase = nn.ModuleList([] )
__lowerCAmelCase = in_channels if norm_type == """spatial""" else None
# mid
__lowerCAmelCase = UNetMidBlockaD(
in_channels=block_out_channels[-1],resnet_eps=1e-6,resnet_act_fn=__SCREAMING_SNAKE_CASE,output_scale_factor=1,resnet_time_scale_shift="""default""" if norm_type == """group""" else norm_type,attention_head_dim=block_out_channels[-1],resnet_groups=__SCREAMING_SNAKE_CASE,temb_channels=__SCREAMING_SNAKE_CASE,)
# up
__lowerCAmelCase = list(reversed(__SCREAMING_SNAKE_CASE ) )
__lowerCAmelCase = reversed_block_out_channels[0]
for i, up_block_type in enumerate(__SCREAMING_SNAKE_CASE ):
__lowerCAmelCase = output_channel
__lowerCAmelCase = reversed_block_out_channels[i]
__lowerCAmelCase = i == len(__SCREAMING_SNAKE_CASE ) - 1
__lowerCAmelCase = get_up_block(
__SCREAMING_SNAKE_CASE,num_layers=self.layers_per_block + 1,in_channels=__SCREAMING_SNAKE_CASE,out_channels=__SCREAMING_SNAKE_CASE,prev_output_channel=__SCREAMING_SNAKE_CASE,add_upsample=not is_final_block,resnet_eps=1e-6,resnet_act_fn=__SCREAMING_SNAKE_CASE,resnet_groups=__SCREAMING_SNAKE_CASE,attention_head_dim=__SCREAMING_SNAKE_CASE,temb_channels=__SCREAMING_SNAKE_CASE,resnet_time_scale_shift=__SCREAMING_SNAKE_CASE,)
self.up_blocks.append(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = output_channel
# out
if norm_type == "spatial":
__lowerCAmelCase = SpatialNorm(block_out_channels[0],__SCREAMING_SNAKE_CASE )
else:
__lowerCAmelCase = nn.GroupNorm(num_channels=block_out_channels[0],num_groups=__SCREAMING_SNAKE_CASE,eps=1e-6 )
__lowerCAmelCase = nn.SiLU()
__lowerCAmelCase = nn.Convad(block_out_channels[0],__SCREAMING_SNAKE_CASE,3,padding=1 )
__lowerCAmelCase = False
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=None ):
'''simple docstring'''
__lowerCAmelCase = z
__lowerCAmelCase = self.conv_in(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = next(iter(self.up_blocks.parameters() ) ).dtype
if self.training and self.gradient_checkpointing:
def create_custom_forward(__SCREAMING_SNAKE_CASE ):
def custom_forward(*__SCREAMING_SNAKE_CASE ):
return module(*__SCREAMING_SNAKE_CASE )
return custom_forward
if is_torch_version(""">=""","""1.11.0""" ):
# middle
__lowerCAmelCase = torch.utils.checkpoint.checkpoint(
create_custom_forward(self.mid_block ),__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,use_reentrant=__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = sample.to(__SCREAMING_SNAKE_CASE )
# up
for up_block in self.up_blocks:
__lowerCAmelCase = torch.utils.checkpoint.checkpoint(
create_custom_forward(__SCREAMING_SNAKE_CASE ),__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,use_reentrant=__SCREAMING_SNAKE_CASE )
else:
# middle
__lowerCAmelCase = torch.utils.checkpoint.checkpoint(
create_custom_forward(self.mid_block ),__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = sample.to(__SCREAMING_SNAKE_CASE )
# up
for up_block in self.up_blocks:
__lowerCAmelCase = torch.utils.checkpoint.checkpoint(create_custom_forward(__SCREAMING_SNAKE_CASE ),__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
else:
# middle
__lowerCAmelCase = self.mid_block(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = sample.to(__SCREAMING_SNAKE_CASE )
# up
for up_block in self.up_blocks:
__lowerCAmelCase = up_block(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
# post-process
if latent_embeds is None:
__lowerCAmelCase = self.conv_norm_out(__SCREAMING_SNAKE_CASE )
else:
__lowerCAmelCase = self.conv_norm_out(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = self.conv_act(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = self.conv_out(__SCREAMING_SNAKE_CASE )
return sample
class _UpperCAmelCase ( nn.Module ):
def __init__( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE="random",__SCREAMING_SNAKE_CASE=False,__SCREAMING_SNAKE_CASE=True ):
'''simple docstring'''
super().__init__()
__lowerCAmelCase = n_e
__lowerCAmelCase = vq_embed_dim
__lowerCAmelCase = beta
__lowerCAmelCase = legacy
__lowerCAmelCase = nn.Embedding(self.n_e,self.vq_embed_dim )
self.embedding.weight.data.uniform_(-1.0 / self.n_e,1.0 / self.n_e )
__lowerCAmelCase = remap
if self.remap is not None:
self.register_buffer("""used""",torch.tensor(np.load(self.remap ) ) )
__lowerCAmelCase = self.used.shape[0]
__lowerCAmelCase = unknown_index # "random" or "extra" or integer
if self.unknown_index == "extra":
__lowerCAmelCase = self.re_embed
__lowerCAmelCase = self.re_embed + 1
print(
f'Remapping {self.n_e} indices to {self.re_embed} indices. '
f'Using {self.unknown_index} for unknown indices.' )
else:
__lowerCAmelCase = n_e
__lowerCAmelCase = sane_index_shape
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase = inds.shape
assert len(__SCREAMING_SNAKE_CASE ) > 1
__lowerCAmelCase = inds.reshape(ishape[0],-1 )
__lowerCAmelCase = self.used.to(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = (inds[:, :, None] == used[None, None, ...]).long()
__lowerCAmelCase = match.argmax(-1 )
__lowerCAmelCase = match.sum(2 ) < 1
if self.unknown_index == "random":
__lowerCAmelCase = torch.randint(0,self.re_embed,size=new[unknown].shape ).to(device=new.device )
else:
__lowerCAmelCase = self.unknown_index
return new.reshape(__SCREAMING_SNAKE_CASE )
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase = inds.shape
assert len(__SCREAMING_SNAKE_CASE ) > 1
__lowerCAmelCase = inds.reshape(ishape[0],-1 )
__lowerCAmelCase = self.used.to(__SCREAMING_SNAKE_CASE )
if self.re_embed > self.used.shape[0]: # extra token
__lowerCAmelCase = 0 # simply set to zero
__lowerCAmelCase = torch.gather(used[None, :][inds.shape[0] * [0], :],1,__SCREAMING_SNAKE_CASE )
return back.reshape(__SCREAMING_SNAKE_CASE )
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase = z.permute(0,2,3,1 ).contiguous()
__lowerCAmelCase = z.view(-1,self.vq_embed_dim )
# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
__lowerCAmelCase = torch.argmin(torch.cdist(__SCREAMING_SNAKE_CASE,self.embedding.weight ),dim=1 )
__lowerCAmelCase = self.embedding(__SCREAMING_SNAKE_CASE ).view(z.shape )
__lowerCAmelCase = None
__lowerCAmelCase = None
# compute loss for embedding
if not self.legacy:
__lowerCAmelCase = self.beta * torch.mean((z_q.detach() - z) ** 2 ) + torch.mean((z_q - z.detach()) ** 2 )
else:
__lowerCAmelCase = torch.mean((z_q.detach() - z) ** 2 ) + self.beta * torch.mean((z_q - z.detach()) ** 2 )
# preserve gradients
__lowerCAmelCase = z + (z_q - z).detach()
# reshape back to match original input shape
__lowerCAmelCase = z_q.permute(0,3,1,2 ).contiguous()
if self.remap is not None:
__lowerCAmelCase = min_encoding_indices.reshape(z.shape[0],-1 ) # add batch axis
__lowerCAmelCase = self.remap_to_used(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = min_encoding_indices.reshape(-1,1 ) # flatten
if self.sane_index_shape:
__lowerCAmelCase = min_encoding_indices.reshape(z_q.shape[0],z_q.shape[2],z_q.shape[3] )
return z_q, loss, (perplexity, min_encodings, min_encoding_indices)
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
if self.remap is not None:
__lowerCAmelCase = indices.reshape(shape[0],-1 ) # add batch axis
__lowerCAmelCase = self.unmap_to_all(__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = indices.reshape(-1 ) # flatten again
# get quantized latent vectors
__lowerCAmelCase = self.embedding(__SCREAMING_SNAKE_CASE )
if shape is not None:
__lowerCAmelCase = z_q.view(__SCREAMING_SNAKE_CASE )
# reshape back to match original input shape
__lowerCAmelCase = z_q.permute(0,3,1,2 ).contiguous()
return z_q
class _UpperCAmelCase ( lowerCAmelCase_ ):
def __init__( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=False ):
'''simple docstring'''
__lowerCAmelCase = parameters
__lowerCAmelCase , __lowerCAmelCase = torch.chunk(__SCREAMING_SNAKE_CASE,2,dim=1 )
__lowerCAmelCase = torch.clamp(self.logvar,-30.0,20.0 )
__lowerCAmelCase = deterministic
__lowerCAmelCase = torch.exp(0.5 * self.logvar )
__lowerCAmelCase = torch.exp(self.logvar )
if self.deterministic:
__lowerCAmelCase = __lowerCAmelCase = torch.zeros_like(
self.mean,device=self.parameters.device,dtype=self.parameters.dtype )
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE = None ):
'''simple docstring'''
__lowerCAmelCase = randn_tensor(
self.mean.shape,generator=__SCREAMING_SNAKE_CASE,device=self.parameters.device,dtype=self.parameters.dtype )
__lowerCAmelCase = self.mean + self.std * sample
return x
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE=None ):
'''simple docstring'''
if self.deterministic:
return torch.Tensor([0.0] )
else:
if other is None:
return 0.5 * torch.sum(torch.pow(self.mean,2 ) + self.var - 1.0 - self.logvar,dim=[1, 2, 3] )
else:
return 0.5 * torch.sum(
torch.pow(self.mean - other.mean,2 ) / other.var
+ self.var / other.var
- 1.0
- self.logvar
+ other.logvar,dim=[1, 2, 3],)
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=[1, 2, 3] ):
'''simple docstring'''
if self.deterministic:
return torch.Tensor([0.0] )
__lowerCAmelCase = np.log(2.0 * np.pi )
return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean,2 ) / self.var,dim=__SCREAMING_SNAKE_CASE )
def lowerCamelCase__ ( self ):
'''simple docstring'''
return self.mean
| 689 | 0 |
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def __a ( A__ : List[str] , A__ : Optional[Any] , A__ : Optional[int] ):
# Initialise PyTorch model
SCREAMING_SNAKE_CASE = BertConfig.from_json_file(A__ )
print(F"Building PyTorch model from configuration: {config}" )
SCREAMING_SNAKE_CASE = BertForPreTraining(A__ )
# Load weights from tf checkpoint
load_tf_weights_in_bert(A__ , A__ , A__ )
# Save pytorch-model
print(F"Save PyTorch model to {pytorch_dump_path}" )
torch.save(model.state_dict() , A__ )
if __name__ == "__main__":
__A : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
)
parser.add_argument(
'--bert_config_file',
default=None,
type=str,
required=True,
help=(
'The config json file corresponding to the pre-trained BERT model. \n'
'This specifies the model architecture.'
),
)
parser.add_argument(
'--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
__A : Optional[Any] = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path) | 16 |
'''simple docstring'''
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
_a : Optional[int] = logging.get_logger(__name__)
_a : int = list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
_a : str = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class _UpperCAmelCase :
a : str =field(
default=lowerCAmelCase_ , metadata={"""help""": """Model type selected in the list: """ + """, """.join(lowerCAmelCase_ )} )
a : str =field(
default=lowerCAmelCase_ , metadata={"""help""": """The input data dir. Should contain the .json files for the SQuAD task."""} )
a : int =field(
default=1_28 , metadata={
"""help""": (
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
)
} , )
a : int =field(
default=1_28 , metadata={"""help""": """When splitting up a long document into chunks, how much stride to take between chunks."""} , )
a : int =field(
default=64 , metadata={
"""help""": (
"""The maximum number of tokens for the question. Questions longer than this will """
"""be truncated to this length."""
)
} , )
a : int =field(
default=30 , metadata={
"""help""": (
"""The maximum length of an answer that can be generated. This is needed because the start """
"""and end predictions are not conditioned on one another."""
)
} , )
a : bool =field(
default=lowerCAmelCase_ , metadata={"""help""": """Overwrite the cached training and evaluation sets"""} )
a : bool =field(
default=lowerCAmelCase_ , metadata={"""help""": """If true, the SQuAD examples contain some that do not have an answer."""} )
a : float =field(
default=0.0 , metadata={"""help""": """If null_score - best_non_null is greater than the threshold predict null."""} )
a : int =field(
default=20 , metadata={"""help""": """If null_score - best_non_null is greater than the threshold predict null."""} )
a : int =field(
default=0 , metadata={
"""help""": (
"""language id of input for language-specific xlm models (see"""
""" tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)"""
)
} , )
a : int =field(default=1 , metadata={"""help""": """multiple threads for converting example to features"""} )
class _UpperCAmelCase ( lowerCAmelCase_ ):
a : Optional[Any] ="""train"""
a : Optional[int] ="""dev"""
class _UpperCAmelCase ( lowerCAmelCase_ ):
a : SquadDataTrainingArguments
a : List[SquadFeatures]
a : Split
a : bool
def __init__( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = Split.train,__SCREAMING_SNAKE_CASE = False,__SCREAMING_SNAKE_CASE = None,__SCREAMING_SNAKE_CASE = "pt",):
'''simple docstring'''
__lowerCAmelCase = args
__lowerCAmelCase = is_language_sensitive
__lowerCAmelCase = SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
if isinstance(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE ):
try:
__lowerCAmelCase = Split[mode]
except KeyError:
raise KeyError("""mode is not a valid split name""" )
__lowerCAmelCase = mode
# Load data features from cache or dataset file
__lowerCAmelCase = """v2""" if args.version_2_with_negative else """v1"""
__lowerCAmelCase = os.path.join(
cache_dir if cache_dir is not None else args.data_dir,f'cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{version_tag}',)
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
__lowerCAmelCase = cached_features_file + """.lock"""
with FileLock(__SCREAMING_SNAKE_CASE ):
if os.path.exists(__SCREAMING_SNAKE_CASE ) and not args.overwrite_cache:
__lowerCAmelCase = time.time()
__lowerCAmelCase = torch.load(__SCREAMING_SNAKE_CASE )
# Legacy cache files have only features, while new cache files
# will have dataset and examples also.
__lowerCAmelCase = self.old_features["""features"""]
__lowerCAmelCase = self.old_features.get("""dataset""",__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = self.old_features.get("""examples""",__SCREAMING_SNAKE_CASE )
logger.info(
f'Loading features from cached file {cached_features_file} [took %.3f s]',time.time() - start )
if self.dataset is None or self.examples is None:
logger.warning(
f'Deleting cached file {cached_features_file} will allow dataset and examples to be cached in'
""" future run""" )
else:
if mode == Split.dev:
__lowerCAmelCase = self.processor.get_dev_examples(args.data_dir )
else:
__lowerCAmelCase = self.processor.get_train_examples(args.data_dir )
__lowerCAmelCase , __lowerCAmelCase = squad_convert_examples_to_features(
examples=self.examples,tokenizer=__SCREAMING_SNAKE_CASE,max_seq_length=args.max_seq_length,doc_stride=args.doc_stride,max_query_length=args.max_query_length,is_training=mode == Split.train,threads=args.threads,return_dataset=__SCREAMING_SNAKE_CASE,)
__lowerCAmelCase = time.time()
torch.save(
{"""features""": self.features, """dataset""": self.dataset, """examples""": self.examples},__SCREAMING_SNAKE_CASE,)
# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
logger.info(
f'Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]' )
def __len__( self ):
'''simple docstring'''
return len(self.features )
def __getitem__( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase = self.features[i]
__lowerCAmelCase = torch.tensor(feature.input_ids,dtype=torch.long )
__lowerCAmelCase = torch.tensor(feature.attention_mask,dtype=torch.long )
__lowerCAmelCase = torch.tensor(feature.token_type_ids,dtype=torch.long )
__lowerCAmelCase = torch.tensor(feature.cls_index,dtype=torch.long )
__lowerCAmelCase = torch.tensor(feature.p_mask,dtype=torch.float )
__lowerCAmelCase = torch.tensor(feature.is_impossible,dtype=torch.float )
__lowerCAmelCase = {
"""input_ids""": input_ids,
"""attention_mask""": attention_mask,
"""token_type_ids""": token_type_ids,
}
if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
del inputs["token_type_ids"]
if self.args.model_type in ["xlnet", "xlm"]:
inputs.update({"""cls_index""": cls_index, """p_mask""": p_mask} )
if self.args.version_2_with_negative:
inputs.update({"""is_impossible""": is_impossible} )
if self.is_language_sensitive:
inputs.update({"""langs""": (torch.ones(input_ids.shape,dtype=torch.intaa ) * self.args.lang_id)} )
if self.mode == Split.train:
__lowerCAmelCase = torch.tensor(feature.start_position,dtype=torch.long )
__lowerCAmelCase = torch.tensor(feature.end_position,dtype=torch.long )
inputs.update({"""start_positions""": start_positions, """end_positions""": end_positions} )
return inputs
| 689 | 0 |
from __future__ import annotations
import typing
from collections import Counter
def __SCREAMING_SNAKE_CASE ( a__ : int ) -> typing.Counter[int]:
__A : typing.Counter[int] = Counter()
for base in range(1 ,max_perimeter + 1 ):
for perpendicular in range(a__ ,max_perimeter + 1 ):
__A : Any = (base * base + perpendicular * perpendicular) ** 0.5
if hypotenuse == int(a__ ):
__A : int = int(base + perpendicular + hypotenuse )
if perimeter > max_perimeter:
continue
triplets[perimeter] += 1
return triplets
def __SCREAMING_SNAKE_CASE ( a__ : int = 1000 ) -> int:
__A : str = pythagorean_triple(a__ )
return triplets.most_common(1 )[0][0]
if __name__ == "__main__":
print(f"""Perimeter {solution()} has maximum solutions""")
| 17 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def _lowerCAmelCase ( lowercase ) -> Optional[Any]:
# vision encoder
if "img_encoder.pos_embed" in name:
__lowerCAmelCase = name.replace("""img_encoder.pos_embed""" , """vision_model.embeddings.position_embeddings""" )
if "img_encoder.patch_embed.proj" in name:
__lowerCAmelCase = name.replace("""img_encoder.patch_embed.proj""" , """vision_model.embeddings.patch_embeddings.projection""" )
if "img_encoder.patch_embed.norm" in name:
__lowerCAmelCase = name.replace("""img_encoder.patch_embed.norm""" , """vision_model.embeddings.layernorm""" )
if "img_encoder.layers" in name:
__lowerCAmelCase = name.replace("""img_encoder.layers""" , """vision_model.encoder.stages""" )
if "blocks" in name and "res" not in name:
__lowerCAmelCase = name.replace("""blocks""" , """layers""" )
if "attn" in name and "pre_assign" not in name:
__lowerCAmelCase = name.replace("""attn""" , """self_attn""" )
if "proj" in name and "self_attn" in name and "text" not in name:
__lowerCAmelCase = name.replace("""proj""" , """out_proj""" )
if "pre_assign_attn.attn.proj" in name:
__lowerCAmelCase = name.replace("""pre_assign_attn.attn.proj""" , """pre_assign_attn.attn.out_proj""" )
if "norm1" in name:
__lowerCAmelCase = name.replace("""norm1""" , """layer_norm1""" )
if "norm2" in name and "pre_assign" not in name:
__lowerCAmelCase = name.replace("""norm2""" , """layer_norm2""" )
if "img_encoder.norm" in name:
__lowerCAmelCase = name.replace("""img_encoder.norm""" , """vision_model.layernorm""" )
# text encoder
if "text_encoder.token_embedding" in name:
__lowerCAmelCase = name.replace("""text_encoder.token_embedding""" , """text_model.embeddings.token_embedding""" )
if "text_encoder.positional_embedding" in name:
__lowerCAmelCase = name.replace("""text_encoder.positional_embedding""" , """text_model.embeddings.position_embedding.weight""" )
if "text_encoder.transformer.resblocks." in name:
__lowerCAmelCase = name.replace("""text_encoder.transformer.resblocks.""" , """text_model.encoder.layers.""" )
if "ln_1" in name:
__lowerCAmelCase = name.replace("""ln_1""" , """layer_norm1""" )
if "ln_2" in name:
__lowerCAmelCase = name.replace("""ln_2""" , """layer_norm2""" )
if "c_fc" in name:
__lowerCAmelCase = name.replace("""c_fc""" , """fc1""" )
if "c_proj" in name:
__lowerCAmelCase = name.replace("""c_proj""" , """fc2""" )
if "text_encoder" in name:
__lowerCAmelCase = name.replace("""text_encoder""" , """text_model""" )
if "ln_final" in name:
__lowerCAmelCase = name.replace("""ln_final""" , """final_layer_norm""" )
# projection layers
if "img_projector.linear_hidden." in name:
__lowerCAmelCase = name.replace("""img_projector.linear_hidden.""" , """visual_projection.""" )
if "img_projector.linear_out." in name:
__lowerCAmelCase = name.replace("""img_projector.linear_out.""" , """visual_projection.3.""" )
if "text_projector.linear_hidden" in name:
__lowerCAmelCase = name.replace("""text_projector.linear_hidden""" , """text_projection""" )
if "text_projector.linear_out" in name:
__lowerCAmelCase = name.replace("""text_projector.linear_out""" , """text_projection.3""" )
return name
def _lowerCAmelCase ( lowercase , lowercase ) -> Dict:
for key in orig_state_dict.copy().keys():
__lowerCAmelCase = orig_state_dict.pop(lowercase )
if "qkv" in key:
# weights and biases of the key, value and query projections of vision encoder's attention layers require special treatment:
# we need to split them up into separate matrices/vectors
__lowerCAmelCase = key.split(""".""" )
__lowerCAmelCase , __lowerCAmelCase = int(key_split[2] ), int(key_split[4] )
__lowerCAmelCase = config.vision_config.hidden_size
if "weight" in key:
__lowerCAmelCase = val[:dim, :]
__lowerCAmelCase = val[dim : dim * 2, :]
__lowerCAmelCase = val[-dim:, :]
else:
__lowerCAmelCase = val[:dim]
__lowerCAmelCase = val[dim : dim * 2]
__lowerCAmelCase = val[-dim:]
elif "in_proj" in key:
# weights and biases of the key, value and query projections of text encoder's attention layers require special treatment:
# we need to split them up into separate matrices/vectors
__lowerCAmelCase = key.split(""".""" )
__lowerCAmelCase = int(key_split[3] )
__lowerCAmelCase = config.text_config.hidden_size
if "weight" in key:
__lowerCAmelCase = val[:dim, :]
__lowerCAmelCase = val[
dim : dim * 2, :
]
__lowerCAmelCase = val[-dim:, :]
else:
__lowerCAmelCase = val[:dim]
__lowerCAmelCase = val[dim : dim * 2]
__lowerCAmelCase = val[-dim:]
else:
__lowerCAmelCase = rename_key(lowercase )
# squeeze if necessary
if (
"text_projection.0" in new_name
or "text_projection.3" in new_name
or "visual_projection.0" in new_name
or "visual_projection.3" in new_name
):
__lowerCAmelCase = val.squeeze_()
else:
__lowerCAmelCase = val
return orig_state_dict
def _lowerCAmelCase ( ) -> str:
__lowerCAmelCase = """http://images.cocodataset.org/val2017/000000039769.jpg"""
__lowerCAmelCase = Image.open(requests.get(lowercase , stream=lowercase ).raw )
return im
@torch.no_grad()
def _lowerCAmelCase ( lowercase , lowercase , lowercase="groupvit-gcc-yfcc" , lowercase=False ) -> List[Any]:
__lowerCAmelCase = GroupViTConfig()
__lowerCAmelCase = GroupViTModel(lowercase ).eval()
__lowerCAmelCase = torch.load(lowercase , map_location="""cpu""" )["""model"""]
__lowerCAmelCase = convert_state_dict(lowercase , lowercase )
__lowerCAmelCase , __lowerCAmelCase = model.load_state_dict(lowercase , strict=lowercase )
assert missing_keys == ["text_model.embeddings.position_ids"]
assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(lowercase ) == 0)
# verify result
__lowerCAmelCase = CLIPProcessor.from_pretrained("""openai/clip-vit-base-patch32""" )
__lowerCAmelCase = prepare_img()
__lowerCAmelCase = processor(text=["""a photo of a cat""", """a photo of a dog"""] , images=lowercase , padding=lowercase , return_tensors="""pt""" )
with torch.no_grad():
__lowerCAmelCase = model(**lowercase )
if model_name == "groupvit-gcc-yfcc":
__lowerCAmelCase = torch.tensor([[13.35_23, 6.36_29]] )
elif model_name == "groupvit-gcc-redcaps":
__lowerCAmelCase = torch.tensor([[16.18_73, 8.62_30]] )
else:
raise ValueError(f'Model name {model_name} not supported.' )
assert torch.allclose(outputs.logits_per_image , lowercase , atol=1e-3 )
processor.save_pretrained(lowercase )
model.save_pretrained(lowercase )
print("""Successfully saved processor and model to""" , lowercase )
if push_to_hub:
print("""Pushing to the hub...""" )
processor.push_to_hub(lowercase , organization="""nielsr""" )
model.push_to_hub(lowercase , organization="""nielsr""" )
if __name__ == "__main__":
_a : int = argparse.ArgumentParser()
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to dump the processor and PyTorch model."""
)
parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to GroupViT checkpoint""")
parser.add_argument(
"""--model_name""",
default="""groupvit-gccy-fcc""",
type=str,
help="""Name of the model. Expecting either 'groupvit-gcc-yfcc' or 'groupvit-gcc-redcaps'""",
)
parser.add_argument(
"""--push_to_hub""",
action="""store_true""",
help="""Whether or not to push the converted model and processor to the 🤗 hub using the provided `model_name`.""",
)
_a : List[str] = parser.parse_args()
convert_groupvit_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 689 | 0 |
'''simple docstring'''
def __a(SCREAMING_SNAKE_CASE_ : Any ):
'''simple docstring'''
_lowerCAmelCase = [0] * len(SCREAMING_SNAKE_CASE_ )
_lowerCAmelCase = []
_lowerCAmelCase = []
_lowerCAmelCase = 0
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(SCREAMING_SNAKE_CASE_ ) ):
if indegree[i] == 0:
queue.append(SCREAMING_SNAKE_CASE_ )
while queue:
_lowerCAmelCase = queue.pop(0 )
cnt += 1
topo.append(SCREAMING_SNAKE_CASE_ )
for x in graph[vertex]:
indegree[x] -= 1
if indegree[x] == 0:
queue.append(SCREAMING_SNAKE_CASE_ )
if cnt != len(SCREAMING_SNAKE_CASE_ ):
print("Cycle exists" )
else:
print(SCREAMING_SNAKE_CASE_ )
# Adjacency List of Graph
_SCREAMING_SNAKE_CASE = {0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}
topological_sort(graph)
| 18 |
'''simple docstring'''
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGeneration,
MusicgenProcessor,
TaEncoderModel,
)
from transformers.models.musicgen.modeling_musicgen import MusicgenForCausalLM
from transformers.utils import logging
logging.set_verbosity_info()
_a : Tuple = logging.get_logger(__name__)
_a : Optional[int] = ["""model.decoder.embed_positions.weights"""]
def _lowerCAmelCase ( lowercase ) -> Optional[Any]:
if "emb" in name:
__lowerCAmelCase = name.replace("""emb""" , """model.decoder.embed_tokens""" )
if "transformer" in name:
__lowerCAmelCase = name.replace("""transformer""" , """model.decoder""" )
if "cross_attention" in name:
__lowerCAmelCase = name.replace("""cross_attention""" , """encoder_attn""" )
if "linear1" in name:
__lowerCAmelCase = name.replace("""linear1""" , """fc1""" )
if "linear2" in name:
__lowerCAmelCase = name.replace("""linear2""" , """fc2""" )
if "norm1" in name:
__lowerCAmelCase = name.replace("""norm1""" , """self_attn_layer_norm""" )
if "norm_cross" in name:
__lowerCAmelCase = name.replace("""norm_cross""" , """encoder_attn_layer_norm""" )
if "norm2" in name:
__lowerCAmelCase = name.replace("""norm2""" , """final_layer_norm""" )
if "out_norm" in name:
__lowerCAmelCase = name.replace("""out_norm""" , """model.decoder.layer_norm""" )
if "linears" in name:
__lowerCAmelCase = name.replace("""linears""" , """lm_heads""" )
if "condition_provider.conditioners.description.output_proj" in name:
__lowerCAmelCase = name.replace("""condition_provider.conditioners.description.output_proj""" , """enc_to_dec_proj""" )
return name
def _lowerCAmelCase ( lowercase , lowercase ) -> Tuple[Dict, Dict]:
__lowerCAmelCase = list(state_dict.keys() )
__lowerCAmelCase = {}
for key in keys:
__lowerCAmelCase = state_dict.pop(lowercase )
__lowerCAmelCase = rename_keys(lowercase )
if "in_proj_weight" in key:
# split fused qkv proj
__lowerCAmelCase = val[:hidden_size, :]
__lowerCAmelCase = val[hidden_size : 2 * hidden_size, :]
__lowerCAmelCase = val[-hidden_size:, :]
elif "enc_to_dec_proj" in key:
__lowerCAmelCase = val
else:
__lowerCAmelCase = val
return state_dict, enc_dec_proj_state_dict
def _lowerCAmelCase ( lowercase ) -> MusicgenDecoderConfig:
if checkpoint == "small":
# default config values
__lowerCAmelCase = 1024
__lowerCAmelCase = 24
__lowerCAmelCase = 16
elif checkpoint == "medium":
__lowerCAmelCase = 1536
__lowerCAmelCase = 48
__lowerCAmelCase = 24
elif checkpoint == "large":
__lowerCAmelCase = 2048
__lowerCAmelCase = 48
__lowerCAmelCase = 32
else:
raise ValueError(f'Checkpoint should be one of `[\'small\', \'medium\', \'large\']`, got {checkpoint}.' )
__lowerCAmelCase = MusicgenDecoderConfig(
hidden_size=lowercase , ffn_dim=hidden_size * 4 , num_hidden_layers=lowercase , num_attention_heads=lowercase , )
return config
@torch.no_grad()
def _lowerCAmelCase ( lowercase , lowercase=None , lowercase=None , lowercase="cpu" ) -> Optional[Any]:
__lowerCAmelCase = MusicGen.get_pretrained(lowercase , device=lowercase )
__lowerCAmelCase = decoder_config_from_checkpoint(lowercase )
__lowerCAmelCase = fairseq_model.lm.state_dict()
__lowerCAmelCase , __lowerCAmelCase = rename_state_dict(
lowercase , hidden_size=decoder_config.hidden_size )
__lowerCAmelCase = TaEncoderModel.from_pretrained("""t5-base""" )
__lowerCAmelCase = EncodecModel.from_pretrained("""facebook/encodec_32khz""" )
__lowerCAmelCase = MusicgenForCausalLM(lowercase ).eval()
# load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
__lowerCAmelCase , __lowerCAmelCase = decoder.load_state_dict(lowercase , strict=lowercase )
for key in missing_keys.copy():
if key.startswith(("""text_encoder""", """audio_encoder""") ) or key in EXPECTED_MISSING_KEYS:
missing_keys.remove(lowercase )
if len(lowercase ) > 0:
raise ValueError(f'Missing key(s) in state_dict: {missing_keys}' )
if len(lowercase ) > 0:
raise ValueError(f'Unexpected key(s) in state_dict: {unexpected_keys}' )
# init the composite model
__lowerCAmelCase = MusicgenForConditionalGeneration(text_encoder=lowercase , audio_encoder=lowercase , decoder=lowercase )
# load the pre-trained enc-dec projection (from the decoder state dict)
model.enc_to_dec_proj.load_state_dict(lowercase )
# check we can do a forward pass
__lowerCAmelCase = torch.arange(0 , 8 , dtype=torch.long ).reshape(2 , -1 )
__lowerCAmelCase = input_ids.reshape(2 * 4 , -1 )
with torch.no_grad():
__lowerCAmelCase = model(input_ids=lowercase , decoder_input_ids=lowercase ).logits
if logits.shape != (8, 1, 2048):
raise ValueError("""Incorrect shape for logits""" )
# now construct the processor
__lowerCAmelCase = AutoTokenizer.from_pretrained("""t5-base""" )
__lowerCAmelCase = AutoFeatureExtractor.from_pretrained("""facebook/encodec_32khz""" , padding_side="""left""" )
__lowerCAmelCase = MusicgenProcessor(feature_extractor=lowercase , tokenizer=lowercase )
# set the appropriate bos/pad token ids
__lowerCAmelCase = 2048
__lowerCAmelCase = 2048
# set other default generation config params
__lowerCAmelCase = int(30 * audio_encoder.config.frame_rate )
__lowerCAmelCase = True
__lowerCAmelCase = 3.0
if pytorch_dump_folder is not None:
Path(lowercase ).mkdir(exist_ok=lowercase )
logger.info(f'Saving model {checkpoint} to {pytorch_dump_folder}' )
model.save_pretrained(lowercase )
processor.save_pretrained(lowercase )
if repo_id:
logger.info(f'Pushing model {checkpoint} to {repo_id}' )
model.push_to_hub(lowercase )
processor.push_to_hub(lowercase )
if __name__ == "__main__":
_a : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--checkpoint""",
default="""small""",
type=str,
help="""Checkpoint size of the MusicGen model you'd like to convert. Can be one of: `['small', 'medium', 'large']`.""",
)
parser.add_argument(
"""--pytorch_dump_folder""",
required=True,
default=None,
type=str,
help="""Path to the output PyTorch model directory.""",
)
parser.add_argument(
"""--push_to_hub""", default=None, type=str, help="""Where to upload the converted model on the 🤗 hub."""
)
parser.add_argument(
"""--device""", default="""cpu""", type=str, help="""Torch device to run the conversion, either cpu or cuda."""
)
_a : List[Any] = parser.parse_args()
convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)
| 689 | 0 |
"""simple docstring"""
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class _UpperCAmelCase( lowerCamelCase ):
lowercase__ = ['image_processor', 'tokenizer']
lowercase__ = 'AutoImageProcessor'
lowercase__ = 'AutoTokenizer'
def __init__( self , __a=None , __a=None , **__a) -> Optional[Any]:
'''simple docstring'''
_UpperCamelCase = None
if "feature_extractor" in kwargs:
warnings.warn(
'''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
''' instead.''' , __a , )
_UpperCamelCase = kwargs.pop('''feature_extractor''')
_UpperCamelCase = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('''You need to specify an `image_processor`.''')
if tokenizer is None:
raise ValueError('''You need to specify a `tokenizer`.''')
super().__init__(__a , __a)
_UpperCamelCase = self.image_processor
_UpperCamelCase = False
def __call__( self , *__a , **__a) -> Optional[int]:
'''simple docstring'''
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor(*__a , **__a)
_UpperCamelCase = kwargs.pop('''images''' , __a)
_UpperCamelCase = kwargs.pop('''text''' , __a)
if len(__a) > 0:
_UpperCamelCase = args[0]
_UpperCamelCase = args[1:]
if images is None and text is None:
raise ValueError('''You need to specify either an `images` or `text` input to process.''')
if images is not None:
_UpperCamelCase = self.image_processor(__a , *__a , **__a)
if text is not None:
_UpperCamelCase = self.tokenizer(__a , **__a)
if text is None:
return inputs
elif images is None:
return encodings
else:
_UpperCamelCase = encodings['''input_ids''']
return inputs
def UpperCAmelCase ( self , *__a , **__a) -> List[Any]:
'''simple docstring'''
return self.tokenizer.batch_decode(*__a , **__a)
def UpperCAmelCase ( self , *__a , **__a) -> List[str]:
'''simple docstring'''
return self.tokenizer.decode(*__a , **__a)
@contextmanager
def UpperCAmelCase ( self) -> Any:
'''simple docstring'''
warnings.warn(
'''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
'''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
'''your images inputs, or in a separate call.''')
_UpperCamelCase = True
_UpperCamelCase = self.tokenizer
yield
_UpperCamelCase = self.image_processor
_UpperCamelCase = False
def UpperCAmelCase ( self , __a , __a=False , __a=None) -> Any:
'''simple docstring'''
if added_vocab is None:
_UpperCamelCase = self.tokenizer.get_added_vocab()
_UpperCamelCase = {}
while tokens:
_UpperCamelCase = re.search(R'''<s_(.*?)>''' , __a , re.IGNORECASE)
if start_token is None:
break
_UpperCamelCase = start_token.group(1)
_UpperCamelCase = re.search(RF'''</s_{key}>''' , __a , re.IGNORECASE)
_UpperCamelCase = start_token.group()
if end_token is None:
_UpperCamelCase = tokens.replace(__a , '''''')
else:
_UpperCamelCase = end_token.group()
_UpperCamelCase = re.escape(__a)
_UpperCamelCase = re.escape(__a)
_UpperCamelCase = re.search(F'''{start_token_escaped}(.*?){end_token_escaped}''' , __a , re.IGNORECASE)
if content is not None:
_UpperCamelCase = content.group(1).strip()
if r"<s_" in content and r"</s_" in content: # non-leaf node
_UpperCamelCase = self.tokenajson(__a , is_inner_value=__a , added_vocab=__a)
if value:
if len(__a) == 1:
_UpperCamelCase = value[0]
_UpperCamelCase = value
else: # leaf nodes
_UpperCamelCase = []
for leaf in content.split(R'''<sep/>'''):
_UpperCamelCase = leaf.strip()
if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
_UpperCamelCase = leaf[1:-2] # for categorical special tokens
output[key].append(__a)
if len(output[key]) == 1:
_UpperCamelCase = output[key][0]
_UpperCamelCase = tokens[tokens.find(__a) + len(__a) :].strip()
if tokens[:6] == r"<sep/>": # non-leaf nodes
return [output] + self.tokenajson(tokens[6:] , is_inner_value=__a , added_vocab=__a)
if len(__a):
return [output] if is_inner_value else output
else:
return [] if is_inner_value else {"text_sequence": tokens}
@property
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
warnings.warn(
'''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' , __a , )
return self.image_processor_class
@property
def UpperCAmelCase ( self) -> Tuple:
'''simple docstring'''
warnings.warn(
'''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' , __a , )
return self.image_processor
| 19 |
'''simple docstring'''
from collections import deque
def _lowerCAmelCase ( lowercase ) -> Dict:
__lowerCAmelCase = len(lowercase )
__lowerCAmelCase = deque()
__lowerCAmelCase = [False for _ in range(lowercase )]
__lowerCAmelCase = [-1 for _ in range(lowercase )]
__lowerCAmelCase = index_of[:]
def strong_connect(lowercase , lowercase , lowercase ):
__lowerCAmelCase = index # the number when this node is seen
__lowerCAmelCase = index # lowest rank node reachable from here
index += 1
stack.append(lowercase )
__lowerCAmelCase = True
for w in g[v]:
if index_of[w] == -1:
__lowerCAmelCase = strong_connect(lowercase , lowercase , lowercase )
__lowerCAmelCase = (
lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
)
elif on_stack[w]:
__lowerCAmelCase = (
lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
)
if lowlink_of[v] == index_of[v]:
__lowerCAmelCase = []
__lowerCAmelCase = stack.pop()
__lowerCAmelCase = False
component.append(lowercase )
while w != v:
__lowerCAmelCase = stack.pop()
__lowerCAmelCase = False
component.append(lowercase )
components.append(lowercase )
return index
__lowerCAmelCase = []
for v in range(lowercase ):
if index_of[v] == -1:
strong_connect(lowercase , 0 , lowercase )
return components
def _lowerCAmelCase ( lowercase , lowercase ) -> str:
__lowerCAmelCase = [[] for _ in range(lowercase )]
for u, v in edges:
g[u].append(lowercase )
return g
if __name__ == "__main__":
# Test
_a : Any = 7
_a : Tuple = [0, 0, 1, 2, 3, 3, 4, 4, 6]
_a : Optional[int] = [1, 3, 2, 0, 1, 4, 5, 6, 5]
_a : Optional[Any] = [(u, v) for u, v in zip(source, target)]
_a : Optional[int] = create_graph(n_vertices, edges)
assert [[5], [6], [4], [3, 2, 1, 0]] == tarjan(g)
| 689 | 0 |
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