ai-bot-test
/
venv
/lib
/python3.11
/site-packages
/huggingface_hub
/inference
/_providers
/hyperbolic.py
import base64 | |
from typing import Any, Dict, Optional, Union | |
from huggingface_hub.hf_api import InferenceProviderMapping | |
from huggingface_hub.inference._common import RequestParameters, _as_dict | |
from huggingface_hub.inference._providers._common import BaseConversationalTask, TaskProviderHelper, filter_none | |
class HyperbolicTextToImageTask(TaskProviderHelper): | |
def __init__(self): | |
super().__init__(provider="hyperbolic", base_url="https://api.hyperbolic.xyz", task="text-to-image") | |
def _prepare_route(self, mapped_model: str, api_key: str) -> str: | |
return "/v1/images/generations" | |
def _prepare_payload_as_dict( | |
self, inputs: Any, parameters: Dict, provider_mapping_info: InferenceProviderMapping | |
) -> Optional[Dict]: | |
mapped_model = provider_mapping_info.provider_id | |
parameters = filter_none(parameters) | |
if "num_inference_steps" in parameters: | |
parameters["steps"] = parameters.pop("num_inference_steps") | |
if "guidance_scale" in parameters: | |
parameters["cfg_scale"] = parameters.pop("guidance_scale") | |
# For Hyperbolic, the width and height are required parameters | |
if "width" not in parameters: | |
parameters["width"] = 512 | |
if "height" not in parameters: | |
parameters["height"] = 512 | |
return {"prompt": inputs, "model_name": mapped_model, **parameters} | |
def get_response(self, response: Union[bytes, Dict], request_params: Optional[RequestParameters] = None) -> Any: | |
response_dict = _as_dict(response) | |
return base64.b64decode(response_dict["images"][0]["image"]) | |
class HyperbolicTextGenerationTask(BaseConversationalTask): | |
""" | |
Special case for Hyperbolic, where text-generation task is handled as a conversational task. | |
""" | |
def __init__(self, task: str): | |
super().__init__( | |
provider="hyperbolic", | |
base_url="https://api.hyperbolic.xyz", | |
) | |
self.task = task | |