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import datetime
import logging
import logging.handlers
import os
import sys
import numpy as np
import requests
from .constants import LOGDIR
server_error_msg = "**NETWORK ERROR DUE TO HIGH TRAFFIC. PLEASE REGENERATE OR REFRESH THIS PAGE.**"
moderation_msg = "I am sorry. Your input may violate our content moderation guidelines. Please avoid using harmful or offensive content."
handler = None
import torch.distributed as dist
try:
import av
except ImportError:
print("Please install pyav to use video processing functions.")
def process_video_with_pyav(video_file, data_args):
container = av.open(video_file)
stream = container.streams.video[0]
total_frame_num = stream.frames
avg_fps = round(stream.average_rate / data_args.video_fps)
frame_idx = [i for i in range(0, total_frame_num, avg_fps)]
if data_args.frames_upbound > 0:
if len(frame_idx) > data_args.frames_upbound:
uniform_sampled_frames = np.linspace(0, total_frame_num - 1, data_args.frames_upbound, dtype=int)
frame_idx = uniform_sampled_frames.tolist()
video_frames = []
for index, frame in enumerate(container.decode(video=0)):
if index in frame_idx:
video_frames.append(frame.to_rgb().to_ndarray())
if len(video_frames) == len(frame_idx): # Stop decoding once we have all needed frames
break
video = np.stack(video_frames)
return video
def rank0_print(*args):
if dist.is_initialized():
if dist.get_rank() == 0:
print(f"Rank {dist.get_rank()}: ", *args)
else:
print(*args)
def build_logger(logger_name, logger_filename):
global handler
formatter = logging.Formatter(
fmt="%(asctime)s | %(levelname)s | %(name)s | %(message)s",
datefmt="%Y-%m-%d %H:%M:%S",
)
# Set the format of root handlers
if not logging.getLogger().handlers:
logging.basicConfig(level=logging.INFO)
logging.getLogger().handlers[0].setFormatter(formatter)
# Redirect stdout and stderr to loggers
stdout_logger = logging.getLogger("stdout")
stdout_logger.setLevel(logging.INFO)
sl = StreamToLogger(stdout_logger, logging.INFO)
sys.stdout = sl
stderr_logger = logging.getLogger("stderr")
stderr_logger.setLevel(logging.ERROR)
sl = StreamToLogger(stderr_logger, logging.ERROR)
sys.stderr = sl
# Get logger
logger = logging.getLogger(logger_name)
logger.setLevel(logging.INFO)
# Add a file handler for all loggers
if handler is None:
os.makedirs(LOGDIR, exist_ok=True)
filename = os.path.join(LOGDIR, logger_filename)
handler = logging.handlers.TimedRotatingFileHandler(filename, when="D", utc=True)
handler.setFormatter(formatter)
for name, item in logging.root.manager.loggerDict.items():
if isinstance(item, logging.Logger):
item.addHandler(handler)
return logger
class StreamToLogger(object):
"""
Fake file-like stream object that redirects writes to a logger instance.
"""
def __init__(self, logger, log_level=logging.INFO):
self.terminal = sys.stdout
self.logger = logger
self.log_level = log_level
self.linebuf = ""
def __getattr__(self, attr):
return getattr(self.terminal, attr)
def write(self, buf):
temp_linebuf = self.linebuf + buf
self.linebuf = ""
for line in temp_linebuf.splitlines(True):
# From the io.TextIOWrapper docs:
# On output, if newline is None, any '\n' characters written
# are translated to the system default line separator.
# By default sys.stdout.write() expects '\n' newlines and then
# translates them so this is still cross platform.
if line[-1] == "\n":
self.logger.log(self.log_level, line.rstrip())
else:
self.linebuf += line
def flush(self):
if self.linebuf != "":
self.logger.log(self.log_level, self.linebuf.rstrip())
self.linebuf = ""
def disable_torch_init():
"""
Disable the redundant torch default initialization to accelerate model creation.
"""
import torch
setattr(torch.nn.Linear, "reset_parameters", lambda self: None)
setattr(torch.nn.LayerNorm, "reset_parameters", lambda self: None)
def violates_moderation(text):
"""
Check whether the text violates OpenAI moderation API.
"""
url = "https://api.openai.com/v1/moderations"
headers = {"Content-Type": "application/json", "Authorization": "Bearer " + os.environ["OPENAI_API_KEY"]}
text = text.replace("\n", "")
data = "{" + '"input": ' + f'"{text}"' + "}"
data = data.encode("utf-8")
try:
ret = requests.post(url, headers=headers, data=data, timeout=5)
flagged = ret.json()["results"][0]["flagged"]
except requests.exceptions.RequestException as e:
print(f"######################### Moderation Error: {e} #########################")
flagged = False
except KeyError as e:
print(f"######################### Moderation Error: {e} #########################")
flagged = False
return flagged
def pretty_print_semaphore(semaphore):
if semaphore is None:
return "None"
return f"Semaphore(value={semaphore._value}, locked={semaphore.locked()})"
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