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+# 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. +# +# WARNING +# This entire file has been adapted from the sync-client code in `src/huggingface_hub/inference/_client.py`. +# Any change in InferenceClient will be automatically reflected in AsyncInferenceClient. +# To re-generate the code, run `make style` or `python ./utils/generate_async_inference_client.py --update`. +# WARNING +import asyncio +import base64 +import logging +import re +import warnings +from typing import TYPE_CHECKING, Any, AsyncIterable, Dict, List, Literal, Optional, Set, Union, overload + +from huggingface_hub import constants +from huggingface_hub.errors import InferenceTimeoutError +from huggingface_hub.inference._common import ( + TASKS_EXPECTING_IMAGES, + ContentT, + ModelStatus, + RequestParameters, + _async_stream_chat_completion_response, + _async_stream_text_generation_response, + _b64_encode, + _b64_to_image, + _bytes_to_dict, + _bytes_to_image, + _bytes_to_list, + _get_unsupported_text_generation_kwargs, + _import_numpy, + _open_as_binary, + _set_unsupported_text_generation_kwargs, + raise_text_generation_error, +) +from huggingface_hub.inference._generated.types import ( + AudioClassificationOutputElement, + AudioClassificationOutputTransform, + AudioToAudioOutputElement, + AutomaticSpeechRecognitionOutput, + ChatCompletionInputGrammarType, + ChatCompletionInputStreamOptions, + ChatCompletionInputTool, + ChatCompletionInputToolChoiceClass, + ChatCompletionInputToolChoiceEnum, + ChatCompletionOutput, + ChatCompletionStreamOutput, + DocumentQuestionAnsweringOutputElement, + FillMaskOutputElement, + ImageClassificationOutputElement, + ImageClassificationOutputTransform, + ImageSegmentationOutputElement, + ImageSegmentationSubtask, + ImageToImageTargetSize, + ImageToTextOutput, + ObjectDetectionOutputElement, + Padding, + QuestionAnsweringOutputElement, + SummarizationOutput, + SummarizationTruncationStrategy, + TableQuestionAnsweringOutputElement, + TextClassificationOutputElement, + TextClassificationOutputTransform, + TextGenerationInputGrammarType, + TextGenerationOutput, + TextGenerationStreamOutput, + TextToSpeechEarlyStoppingEnum, + TokenClassificationAggregationStrategy, + TokenClassificationOutputElement, + TranslationOutput, + TranslationTruncationStrategy, + VisualQuestionAnsweringOutputElement, + ZeroShotClassificationOutputElement, + ZeroShotImageClassificationOutputElement, +) +from huggingface_hub.inference._providers import PROVIDER_T, HFInferenceTask, get_provider_helper +from huggingface_hub.utils import build_hf_headers, get_session, hf_raise_for_status +from huggingface_hub.utils._deprecation import _deprecate_arguments, _deprecate_method + +from .._common import _async_yield_from, _import_aiohttp + + +if TYPE_CHECKING: + import numpy as np + from aiohttp import ClientResponse, ClientSession + from PIL.Image import Image + +logger = logging.getLogger(__name__) + + +MODEL_KWARGS_NOT_USED_REGEX = re.compile(r"The following `model_kwargs` are not used by the model: \[(.*?)\]") + + +class AsyncInferenceClient: + """ + Initialize a new Inference Client. + + [`InferenceClient`] aims to provide a unified experience to perform inference. The client can be used + seamlessly with either the (free) Inference API, self-hosted Inference Endpoints, or third-party Inference Providers. + + Args: + model (`str`, `optional`): + The model to run inference with. Can be a model id hosted on the Hugging Face Hub, e.g. `meta-llama/Meta-Llama-3-8B-Instruct` + or a URL to a deployed Inference Endpoint. Defaults to None, in which case a recommended model is + automatically selected for the task. + Note: for better compatibility with OpenAI's client, `model` has been aliased as `base_url`. Those 2 + arguments are mutually exclusive. If using `base_url` for chat completion, the `/chat/completions` suffix + path will be appended to the base URL (see the [TGI Messages API](https://huggingface.co/docs/text-generation-inference/en/messages_api) + documentation for details). When passing a URL as `model`, the client will not append any suffix path to it. + provider (`str`, *optional*): + Name of the provider to use for inference. Can be `"black-forest-labs"`, `"fal-ai"`, `"fireworks-ai"`, `"hf-inference"`, `"hyperbolic"`, `"nebius"`, `"novita"`, `"replicate"`, "sambanova"` or `"together"`. + defaults to hf-inference (Hugging Face Serverless Inference API). + If model is a URL or `base_url` is passed, then `provider` is not used. + token (`str` or `bool`, *optional*): + Hugging Face token. Will default to the locally saved token if not provided. + Pass `token=False` if you don't want to send your token to the server. + Note: for better compatibility with OpenAI's client, `token` has been aliased as `api_key`. Those 2 + arguments are mutually exclusive and have the exact same behavior. + timeout (`float`, `optional`): + The maximum number of seconds to wait for a response from the server. Loading a new model in Inference + API can take up to several minutes. Defaults to None, meaning it will loop until the server is available. + headers (`Dict[str, str]`, `optional`): + Additional headers to send to the server. By default only the authorization and user-agent headers are sent. + Values in this dictionary will override the default values. + cookies (`Dict[str, str]`, `optional`): + Additional cookies to send to the server. + trust_env ('bool', 'optional'): + Trust environment settings for proxy configuration if the parameter is `True` (`False` by default). + proxies (`Any`, `optional`): + Proxies to use for the request. + base_url (`str`, `optional`): + Base URL to run inference. This is a duplicated argument from `model` to make [`InferenceClient`] + follow the same pattern as `openai.OpenAI` client. Cannot be used if `model` is set. Defaults to None. + api_key (`str`, `optional`): + Token to use for authentication. This is a duplicated argument from `token` to make [`InferenceClient`] + follow the same pattern as `openai.OpenAI` client. Cannot be used if `token` is set. Defaults to None. + """ + + def __init__( + self, + model: Optional[str] = None, + *, + provider: Optional[PROVIDER_T] = None, + token: Optional[str] = None, + timeout: Optional[float] = None, + headers: Optional[Dict[str, str]] = None, + cookies: Optional[Dict[str, str]] = None, + trust_env: bool = False, + proxies: Optional[Any] = None, + # OpenAI compatibility + base_url: Optional[str] = None, + api_key: Optional[str] = None, + ) -> None: + if model is not None and base_url is not None: + raise ValueError( + "Received both `model` and `base_url` arguments. Please provide only one of them." + " `base_url` is an alias for `model` to make the API compatible with OpenAI's client." + " If using `base_url` for chat completion, the `/chat/completions` suffix path will be appended to the base url." + " When passing a URL as `model`, the client will not append any suffix path to it." + ) + if token is not None and api_key is not None: + raise ValueError( + "Received both `token` and `api_key` arguments. Please provide only one of them." + " `api_key` is an alias for `token` to make the API compatible with OpenAI's client." + " It has the exact same behavior as `token`." + ) + + self.model: Optional[str] = base_url or model + self.token: Optional[str] = token if token is not None else api_key + self.headers = headers if headers is not None else {} + + # Configure provider + self.provider = provider if provider is not None else "hf-inference" + + self.cookies = cookies + self.timeout = timeout + self.trust_env = trust_env + self.proxies = proxies + + # Keep track of the sessions to close them properly + self._sessions: Dict["ClientSession", Set["ClientResponse"]] = dict() + + def __repr__(self): + return f"" + + @overload + async def post( # type: ignore[misc] + self, + *, + json: Optional[Union[str, Dict, List]] = None, + data: Optional[ContentT] = None, + model: Optional[str] = None, + task: Optional[str] = None, + stream: Literal[False] = ..., + ) -> bytes: ... + + @overload + async def post( # type: ignore[misc] + self, + *, + json: Optional[Union[str, Dict, List]] = None, + data: Optional[ContentT] = None, + model: Optional[str] = None, + task: Optional[str] = None, + stream: Literal[True] = ..., + ) -> AsyncIterable[bytes]: ... + + @overload + async def post( + self, + *, + json: Optional[Union[str, Dict, List]] = None, + data: Optional[ContentT] = None, + model: Optional[str] = None, + task: Optional[str] = None, + stream: bool = False, + ) -> Union[bytes, AsyncIterable[bytes]]: ... + + @_deprecate_method( + version="0.31.0", + message=( + "Making direct POST requests to the inference server is not supported anymore. " + "Please use task methods instead (e.g. `InferenceClient.chat_completion`). " + "If your use case is not supported, please open an issue in https://github.com/huggingface/huggingface_hub." + ), + ) + async def post( + self, + *, + json: Optional[Union[str, Dict, List]] = None, + data: Optional[ContentT] = None, + model: Optional[str] = None, + task: Optional[str] = None, + stream: bool = False, + ) -> Union[bytes, AsyncIterable[bytes]]: + """ + Make a POST request to the inference server. + + This method is deprecated and will be removed in the future. + Please use task methods instead (e.g. `InferenceClient.chat_completion`). + """ + if self.provider != "hf-inference": + raise ValueError( + "Cannot use `post` with another provider than `hf-inference`. " + "`InferenceClient.post` is deprecated and should not be used directly anymore." + ) + provider_helper = HFInferenceTask(task or "unknown") + mapped_model = provider_helper._prepare_mapped_model(model or self.model) + url = provider_helper._prepare_url(self.token, mapped_model) # type: ignore[arg-type] + headers = provider_helper._prepare_headers(self.headers, self.token) # type: ignore[arg-type] + return await self._inner_post( + request_parameters=RequestParameters( + url=url, + task=task or "unknown", + model=model or "unknown", + json=json, + data=data, + headers=headers, + ), + stream=stream, + ) + + @overload + async def _inner_post( # type: ignore[misc] + self, request_parameters: RequestParameters, *, stream: Literal[False] = ... + ) -> bytes: ... + + @overload + async def _inner_post( # type: ignore[misc] + self, request_parameters: RequestParameters, *, stream: Literal[True] = ... + ) -> AsyncIterable[bytes]: ... + + @overload + async def _inner_post( + self, request_parameters: RequestParameters, *, stream: bool = False + ) -> Union[bytes, AsyncIterable[bytes]]: ... + + async def _inner_post( + self, request_parameters: RequestParameters, *, stream: bool = False + ) -> Union[bytes, AsyncIterable[bytes]]: + """Make a request to the inference server.""" + + aiohttp = _import_aiohttp() + + # TODO: this should be handled in provider helpers directly + if request_parameters.task in TASKS_EXPECTING_IMAGES and "Accept" not in request_parameters.headers: + request_parameters.headers["Accept"] = "image/png" + + while True: + with _open_as_binary(request_parameters.data) as data_as_binary: + # Do not use context manager as we don't want to close the connection immediately when returning + # a stream + session = self._get_client_session(headers=request_parameters.headers) + + try: + response = await session.post( + request_parameters.url, json=request_parameters.json, data=data_as_binary, proxy=self.proxies + ) + response_error_payload = None + if response.status != 200: + try: + response_error_payload = await response.json() # get payload before connection closed + except Exception: + pass + response.raise_for_status() + if stream: + return _async_yield_from(session, response) + else: + content = await response.read() + await session.close() + return content + except asyncio.TimeoutError as error: + await session.close() + # Convert any `TimeoutError` to a `InferenceTimeoutError` + raise InferenceTimeoutError(f"Inference call timed out: {request_parameters.url}") from error # type: ignore + except aiohttp.ClientResponseError as error: + error.response_error_payload = response_error_payload + await session.close() + raise error + except Exception: + await session.close() + raise + + async def __aenter__(self): + return self + + async def __aexit__(self, exc_type, exc_value, traceback): + await self.close() + + def __del__(self): + if len(self._sessions) > 0: + warnings.warn( + "Deleting 'AsyncInferenceClient' client but some sessions are still open. " + "This can happen if you've stopped streaming data from the server before the stream was complete. " + "To close the client properly, you must call `await client.close()` " + "or use an async context (e.g. `async with AsyncInferenceClient(): ...`." + ) + + async def close(self): + """Close all open sessions. + + By default, 'aiohttp.ClientSession' objects are closed automatically when a call is completed. However, if you + are streaming data from the server and you stop before the stream is complete, you must call this method to + close the session properly. + + Another possibility is to use an async context (e.g. `async with AsyncInferenceClient(): ...`). + """ + await asyncio.gather(*[session.close() for session in self._sessions.keys()]) + + async def audio_classification( + self, + audio: ContentT, + *, + model: Optional[str] = None, + top_k: Optional[int] = None, + function_to_apply: Optional["AudioClassificationOutputTransform"] = None, + ) -> List[AudioClassificationOutputElement]: + """ + Perform audio classification on the provided audio content. + + Args: + audio (Union[str, Path, bytes, BinaryIO]): + The audio content to classify. It can be raw audio bytes, a local audio file, or a URL pointing to an + audio file. + model (`str`, *optional*): + The model to use for audio classification. Can be a model ID hosted on the Hugging Face Hub + or a URL to a deployed Inference Endpoint. If not provided, the default recommended model for + audio classification will be used. + top_k (`int`, *optional*): + When specified, limits the output to the top K most probable classes. + function_to_apply (`"AudioClassificationOutputTransform"`, *optional*): + The function to apply to the model outputs in order to retrieve the scores. + + Returns: + `List[AudioClassificationOutputElement]`: List of [`AudioClassificationOutputElement`] items containing the predicted labels and their confidence. + + Raises: + [`InferenceTimeoutError`]: + If the model is unavailable or the request times out. + `aiohttp.ClientResponseError`: + If the request fails with an HTTP error status code other than HTTP 503. + + Example: + ```py + # Must be run in an async context + >>> from huggingface_hub import AsyncInferenceClient + >>> client = AsyncInferenceClient() + >>> await client.audio_classification("audio.flac") + [ + AudioClassificationOutputElement(score=0.4976358711719513, label='hap'), + AudioClassificationOutputElement(score=0.3677836060523987, label='neu'), + ... + ] + ``` + """ + provider_helper = get_provider_helper(self.provider, task="audio-classification") + request_parameters = provider_helper.prepare_request( + inputs=audio, + parameters={"function_to_apply": function_to_apply, "top_k": top_k}, + headers=self.headers, + model=model or self.model, + api_key=self.token, + ) + response = await self._inner_post(request_parameters) + return AudioClassificationOutputElement.parse_obj_as_list(response) + + async def audio_to_audio( + self, + audio: ContentT, + *, + model: Optional[str] = None, + ) -> List[AudioToAudioOutputElement]: + """ + Performs multiple tasks related to audio-to-audio depending on the model (eg: speech enhancement, source separation). + + Args: + audio (Union[str, Path, bytes, BinaryIO]): + The audio content for the model. It can be raw audio bytes, a local audio file, or a URL pointing to an + audio file. + model (`str`, *optional*): + The model can be any model which takes an audio file and returns another audio file. Can be a model ID hosted on the Hugging Face Hub + or a URL to a deployed Inference Endpoint. If not provided, the default recommended model for + audio_to_audio will be used. + + Returns: + `List[AudioToAudioOutputElement]`: A list of [`AudioToAudioOutputElement`] items containing audios label, content-type, and audio content in blob. + + Raises: + `InferenceTimeoutError`: + If the model is unavailable or the request times out. + `aiohttp.ClientResponseError`: + If the request fails with an HTTP error status code other than HTTP 503. + + Example: + ```py + # Must be run in an async context + >>> from huggingface_hub import AsyncInferenceClient + >>> client = AsyncInferenceClient() + >>> audio_output = await client.audio_to_audio("audio.flac") + >>> async for i, item in enumerate(audio_output): + >>> with open(f"output_{i}.flac", "wb") as f: + f.write(item.blob) + ``` + """ + provider_helper = get_provider_helper(self.provider, task="audio-to-audio") + request_parameters = provider_helper.prepare_request( + inputs=audio, + parameters={}, + headers=self.headers, + model=model or self.model, + api_key=self.token, + ) + response = await self._inner_post(request_parameters) + audio_output = AudioToAudioOutputElement.parse_obj_as_list(response) + for item in audio_output: + item.blob = base64.b64decode(item.blob) + return audio_output + + async def automatic_speech_recognition( + self, + audio: ContentT, + *, + model: Optional[str] = None, + extra_body: Optional[Dict] = None, + ) -> AutomaticSpeechRecognitionOutput: + """ + Perform automatic speech recognition (ASR or audio-to-text) on the given audio content. + + Args: + audio (Union[str, Path, bytes, BinaryIO]): + The content to transcribe. It can be raw audio bytes, local audio file, or a URL to an audio file. + model (`str`, *optional*): + The model to use for ASR. Can be a model ID hosted on the Hugging Face Hub or a URL to a deployed + Inference Endpoint. If not provided, the default recommended model for ASR will be used. + extra_body (`Dict`, *optional*): + Additional provider-specific parameters to pass to the model. Refer to the provider's documentation + for supported parameters. + Returns: + [`AutomaticSpeechRecognitionOutput`]: An item containing the transcribed text and optionally the timestamp chunks. + + Raises: + [`InferenceTimeoutError`]: + If the model is unavailable or the request times out. + `aiohttp.ClientResponseError`: + If the request fails with an HTTP error status code other than HTTP 503. + + Example: + ```py + # Must be run in an async context + >>> from huggingface_hub import AsyncInferenceClient + >>> client = AsyncInferenceClient() + >>> await client.automatic_speech_recognition("hello_world.flac").text + "hello world" + ``` + """ + provider_helper = get_provider_helper(self.provider, task="automatic-speech-recognition") + request_parameters = provider_helper.prepare_request( + inputs=audio, + parameters={**(extra_body or {})}, + headers=self.headers, + model=model or self.model, + api_key=self.token, + ) + response = await self._inner_post(request_parameters) + return AutomaticSpeechRecognitionOutput.parse_obj_as_instance(response) + + @overload + async def chat_completion( # type: ignore + self, + messages: List[Dict], + *, + model: Optional[str] = None, + stream: Literal[False] = False, + frequency_penalty: Optional[float] = None, + logit_bias: Optional[List[float]] = None, + logprobs: Optional[bool] = None, + max_tokens: Optional[int] = None, + n: Optional[int] = None, + presence_penalty: Optional[float] = None, + response_format: Optional[ChatCompletionInputGrammarType] = None, + seed: Optional[int] = None, + stop: Optional[List[str]] = None, + stream_options: Optional[ChatCompletionInputStreamOptions] = None, + temperature: Optional[float] = None, + tool_choice: Optional[Union[ChatCompletionInputToolChoiceClass, "ChatCompletionInputToolChoiceEnum"]] = None, + tool_prompt: Optional[str] = None, + tools: Optional[List[ChatCompletionInputTool]] = None, + top_logprobs: Optional[int] = None, + top_p: Optional[float] = None, + extra_body: Optional[Dict] = None, + ) -> ChatCompletionOutput: ... + + @overload + async def chat_completion( # type: ignore + self, + messages: List[Dict], + *, + model: Optional[str] = None, + stream: Literal[True] = True, + frequency_penalty: Optional[float] = None, + logit_bias: Optional[List[float]] = None, + logprobs: Optional[bool] = None, + max_tokens: Optional[int] = None, + n: Optional[int] = None, + presence_penalty: Optional[float] = None, + response_format: Optional[ChatCompletionInputGrammarType] = None, + seed: Optional[int] = None, + stop: Optional[List[str]] = None, + stream_options: Optional[ChatCompletionInputStreamOptions] = None, + temperature: Optional[float] = None, + tool_choice: Optional[Union[ChatCompletionInputToolChoiceClass, "ChatCompletionInputToolChoiceEnum"]] = None, + tool_prompt: Optional[str] = None, + tools: Optional[List[ChatCompletionInputTool]] = None, + top_logprobs: Optional[int] = None, + top_p: Optional[float] = None, + extra_body: Optional[Dict] = None, + ) -> AsyncIterable[ChatCompletionStreamOutput]: ... + + @overload + async def chat_completion( + self, + messages: List[Dict], + *, + model: Optional[str] = None, + stream: bool = False, + frequency_penalty: Optional[float] = None, + logit_bias: Optional[List[float]] = None, + logprobs: Optional[bool] = None, + max_tokens: Optional[int] = None, + n: Optional[int] = None, + presence_penalty: Optional[float] = None, + response_format: Optional[ChatCompletionInputGrammarType] = None, + seed: Optional[int] = None, + stop: Optional[List[str]] = None, + stream_options: Optional[ChatCompletionInputStreamOptions] = None, + temperature: Optional[float] = None, + tool_choice: Optional[Union[ChatCompletionInputToolChoiceClass, "ChatCompletionInputToolChoiceEnum"]] = None, + tool_prompt: Optional[str] = None, + tools: Optional[List[ChatCompletionInputTool]] = None, + top_logprobs: Optional[int] = None, + top_p: Optional[float] = None, + extra_body: Optional[Dict] = None, + ) -> Union[ChatCompletionOutput, AsyncIterable[ChatCompletionStreamOutput]]: ... + + async def chat_completion( + self, + messages: List[Dict], + *, + model: Optional[str] = None, + stream: bool = False, + # Parameters from ChatCompletionInput (handled manually) + frequency_penalty: Optional[float] = None, + logit_bias: Optional[List[float]] = None, + logprobs: Optional[bool] = None, + max_tokens: Optional[int] = None, + n: Optional[int] = None, + presence_penalty: Optional[float] = None, + response_format: Optional[ChatCompletionInputGrammarType] = None, + seed: Optional[int] = None, + stop: Optional[List[str]] = None, + stream_options: Optional[ChatCompletionInputStreamOptions] = None, + temperature: Optional[float] = None, + tool_choice: Optional[Union[ChatCompletionInputToolChoiceClass, "ChatCompletionInputToolChoiceEnum"]] = None, + tool_prompt: Optional[str] = None, + tools: Optional[List[ChatCompletionInputTool]] = None, + top_logprobs: Optional[int] = None, + top_p: Optional[float] = None, + extra_body: Optional[Dict] = None, + ) -> Union[ChatCompletionOutput, AsyncIterable[ChatCompletionStreamOutput]]: + """ + A method for completing conversations using a specified language model. + + + + The `client.chat_completion` method is aliased as `client.chat.completions.create` for compatibility with OpenAI's client. + Inputs and outputs are strictly the same and using either syntax will yield the same results. + Check out the [Inference guide](https://huggingface.co/docs/huggingface_hub/guides/inference#openai-compatibility) + for more details about OpenAI's compatibility. + + + + + You can pass provider-specific parameters to the model by using the `extra_body` argument. + + + Args: + messages (List of [`ChatCompletionInputMessage`]): + Conversation history consisting of roles and content pairs. + model (`str`, *optional*): + The model to use for chat-completion. Can be a model ID hosted on the Hugging Face Hub or a URL to a deployed + Inference Endpoint. If not provided, the default recommended model for chat-based text-generation will be used. + See https://huggingface.co/tasks/text-generation for more details. + If `model` is a model ID, it is passed to the server as the `model` parameter. If you want to define a + custom URL while setting `model` in the request payload, you must set `base_url` when initializing [`InferenceClient`]. + frequency_penalty (`float`, *optional*): + Penalizes new tokens based on their existing frequency + in the text so far. Range: [-2.0, 2.0]. Defaults to 0.0. + logit_bias (`List[float]`, *optional*): + Adjusts the likelihood of specific tokens appearing in the generated output. + logprobs (`bool`, *optional*): + Whether to return log probabilities of the output tokens or not. If true, returns the log + probabilities of each output token returned in the content of message. + max_tokens (`int`, *optional*): + Maximum number of tokens allowed in the response. Defaults to 100. + n (`int`, *optional*): + The number of completions to generate for each prompt. + presence_penalty (`float`, *optional*): + Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the + text so far, increasing the model's likelihood to talk about new topics. + response_format ([`ChatCompletionInputGrammarType`], *optional*): + Grammar constraints. Can be either a JSONSchema or a regex. + seed (Optional[`int`], *optional*): + Seed for reproducible control flow. Defaults to None. + stop (`List[str]`, *optional*): + Up to four strings which trigger the end of the response. + Defaults to None. + stream (`bool`, *optional*): + Enable realtime streaming of responses. Defaults to False. + stream_options ([`ChatCompletionInputStreamOptions`], *optional*): + Options for streaming completions. + temperature (`float`, *optional*): + Controls randomness of the generations. Lower values ensure + less random completions. Range: [0, 2]. Defaults to 1.0. + top_logprobs (`int`, *optional*): + An integer between 0 and 5 specifying the number of most likely tokens to return at each token + position, each with an associated log probability. logprobs must be set to true if this parameter is + used. + top_p (`float`, *optional*): + Fraction of the most likely next words to sample from. + Must be between 0 and 1. Defaults to 1.0. + tool_choice ([`ChatCompletionInputToolChoiceClass`] or [`ChatCompletionInputToolChoiceEnum`], *optional*): + The tool to use for the completion. Defaults to "auto". + tool_prompt (`str`, *optional*): + A prompt to be appended before the tools. + tools (List of [`ChatCompletionInputTool`], *optional*): + A list of tools the model may call. Currently, only functions are supported as a tool. Use this to + provide a list of functions the model may generate JSON inputs for. + extra_body (`Dict`, *optional*): + Additional provider-specific parameters to pass to the model. Refer to the provider's documentation + for supported parameters. + Returns: + [`ChatCompletionOutput`] or Iterable of [`ChatCompletionStreamOutput`]: + Generated text returned from the server: + - if `stream=False`, the generated text is returned as a [`ChatCompletionOutput`] (default). + - if `stream=True`, the generated text is returned token by token as a sequence of [`ChatCompletionStreamOutput`]. + + Raises: + [`InferenceTimeoutError`]: + If the model is unavailable or the request times out. + `aiohttp.ClientResponseError`: + If the request fails with an HTTP error status code other than HTTP 503. + + Example: + + ```py + # Must be run in an async context + >>> from huggingface_hub import AsyncInferenceClient + >>> messages = [{"role": "user", "content": "What is the capital of France?"}] + >>> client = AsyncInferenceClient("meta-llama/Meta-Llama-3-8B-Instruct") + >>> await client.chat_completion(messages, max_tokens=100) + ChatCompletionOutput( + choices=[ + ChatCompletionOutputComplete( + finish_reason='eos_token', + index=0, + message=ChatCompletionOutputMessage( + role='assistant', + content='The capital of France is Paris.', + name=None, + tool_calls=None + ), + logprobs=None + ) + ], + created=1719907176, + id='', + model='meta-llama/Meta-Llama-3-8B-Instruct', + object='text_completion', + system_fingerprint='2.0.4-sha-f426a33', + usage=ChatCompletionOutputUsage( + completion_tokens=8, + prompt_tokens=17, + total_tokens=25 + ) + ) + ``` + + Example using streaming: + ```py + # Must be run in an async context + >>> from huggingface_hub import AsyncInferenceClient + >>> messages = [{"role": "user", "content": "What is the capital of France?"}] + >>> client = AsyncInferenceClient("meta-llama/Meta-Llama-3-8B-Instruct") + >>> async for token in await client.chat_completion(messages, max_tokens=10, stream=True): + ... print(token) + ChatCompletionStreamOutput(choices=[ChatCompletionStreamOutputChoice(delta=ChatCompletionStreamOutputDelta(content='The', role='assistant'), index=0, finish_reason=None)], created=1710498504) + ChatCompletionStreamOutput(choices=[ChatCompletionStreamOutputChoice(delta=ChatCompletionStreamOutputDelta(content=' capital', role='assistant'), index=0, finish_reason=None)], created=1710498504) + (...) + ChatCompletionStreamOutput(choices=[ChatCompletionStreamOutputChoice(delta=ChatCompletionStreamOutputDelta(content=' may', role='assistant'), index=0, finish_reason=None)], created=1710498504) + ``` + + Example using OpenAI's syntax: + ```py + # Must be run in an async context + # instead of `from openai import OpenAI` + from huggingface_hub import AsyncInferenceClient + + # instead of `client = OpenAI(...)` + client = AsyncInferenceClient( + base_url=..., + api_key=..., + ) + + output = await client.chat.completions.create( + model="meta-llama/Meta-Llama-3-8B-Instruct", + messages=[ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": "Count to 10"}, + ], + stream=True, + max_tokens=1024, + ) + + for chunk in output: + print(chunk.choices[0].delta.content) + ``` + + Example using a third-party provider directly with extra (provider-specific) parameters. Usage will be billed on your Together AI account. + ```py + >>> from huggingface_hub import InferenceClient + >>> client = InferenceClient( + ... provider="together", # Use Together AI provider + ... api_key="", # Pass your Together API key directly + ... ) + >>> client.chat_completion( + ... model="meta-llama/Meta-Llama-3-8B-Instruct", + ... messages=[{"role": "user", "content": "What is the capital of France?"}], + ... extra_body={"safety_model": "Meta-Llama/Llama-Guard-7b"}, + ... ) + ``` + + Example using a third-party provider through Hugging Face Routing. Usage will be billed on your Hugging Face account. + ```py + >>> from huggingface_hub import InferenceClient + >>> client = InferenceClient( + ... provider="sambanova", # Use Sambanova provider + ... api_key="hf_...", # Pass your HF token + ... ) + >>> client.chat_completion( + ... model="meta-llama/Meta-Llama-3-8B-Instruct", + ... messages=[{"role": "user", "content": "What is the capital of France?"}], + ... ) + ``` + + Example using Image + Text as input: + ```py + # Must be run in an async context + >>> from huggingface_hub import AsyncInferenceClient + + # provide a remote URL + >>> image_url ="https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" + # or a base64-encoded image + >>> image_path = "/path/to/image.jpeg" + >>> with open(image_path, "rb") as f: + ... base64_image = base64.b64encode(f.read()).decode("utf-8") + >>> image_url = f"data:image/jpeg;base64,{base64_image}" + + >>> client = AsyncInferenceClient("meta-llama/Llama-3.2-11B-Vision-Instruct") + >>> output = await client.chat.completions.create( + ... messages=[ + ... { + ... "role": "user", + ... "content": [ + ... { + ... "type": "image_url", + ... "image_url": {"url": image_url}, + ... }, + ... { + ... "type": "text", + ... "text": "Describe this image in one sentence.", + ... }, + ... ], + ... }, + ... ], + ... ) + >>> output + The image depicts the iconic Statue of Liberty situated in New York Harbor, New York, on a clear day. + ``` + + Example using tools: + ```py + # Must be run in an async context + >>> client = AsyncInferenceClient("meta-llama/Meta-Llama-3-70B-Instruct") + >>> messages = [ + ... { + ... "role": "system", + ... "content": "Don't make assumptions about what values to plug into functions. Ask for clarification if a user request is ambiguous.", + ... }, + ... { + ... "role": "user", + ... "content": "What's the weather like the next 3 days in San Francisco, CA?", + ... }, + ... ] + >>> tools = [ + ... { + ... "type": "function", + ... "function": { + ... "name": "get_current_weather", + ... "description": "Get the current weather", + ... "parameters": { + ... "type": "object", + ... "properties": { + ... "location": { + ... "type": "string", + ... "description": "The city and state, e.g. San Francisco, CA", + ... }, + ... "format": { + ... "type": "string", + ... "enum": ["celsius", "fahrenheit"], + ... "description": "The temperature unit to use. Infer this from the users location.", + ... }, + ... }, + ... "required": ["location", "format"], + ... }, + ... }, + ... }, + ... { + ... "type": "function", + ... "function": { + ... "name": "get_n_day_weather_forecast", + ... "description": "Get an N-day weather forecast", + ... "parameters": { + ... "type": "object", + ... "properties": { + ... "location": { + ... "type": "string", + ... "description": "The city and state, e.g. San Francisco, CA", + ... }, + ... "format": { + ... "type": "string", + ... "enum": ["celsius", "fahrenheit"], + ... "description": "The temperature unit to use. Infer this from the users location.", + ... }, + ... "num_days": { + ... "type": "integer", + ... "description": "The number of days to forecast", + ... }, + ... }, + ... "required": ["location", "format", "num_days"], + ... }, + ... }, + ... }, + ... ] + + >>> response = await client.chat_completion( + ... model="meta-llama/Meta-Llama-3-70B-Instruct", + ... messages=messages, + ... tools=tools, + ... tool_choice="auto", + ... max_tokens=500, + ... ) + >>> response.choices[0].message.tool_calls[0].function + ChatCompletionOutputFunctionDefinition( + arguments={ + 'location': 'San Francisco, CA', + 'format': 'fahrenheit', + 'num_days': 3 + }, + name='get_n_day_weather_forecast', + description=None + ) + ``` + + Example using response_format: + ```py + # Must be run in an async context + >>> from huggingface_hub import AsyncInferenceClient + >>> client = AsyncInferenceClient("meta-llama/Meta-Llama-3-70B-Instruct") + >>> messages = [ + ... { + ... "role": "user", + ... "content": "I saw a puppy a cat and a raccoon during my bike ride in the park. What did I saw and when?", + ... }, + ... ] + >>> response_format = { + ... "type": "json", + ... "value": { + ... "properties": { + ... "location": {"type": "string"}, + ... "activity": {"type": "string"}, + ... "animals_seen": {"type": "integer", "minimum": 1, "maximum": 5}, + ... "animals": {"type": "array", "items": {"type": "string"}}, + ... }, + ... "required": ["location", "activity", "animals_seen", "animals"], + ... }, + ... } + >>> response = await client.chat_completion( + ... messages=messages, + ... response_format=response_format, + ... max_tokens=500, + ) + >>> response.choices[0].message.content + '{\n\n"activity": "bike ride",\n"animals": ["puppy", "cat", "raccoon"],\n"animals_seen": 3,\n"location": "park"}' + ``` + """ + # Get the provider helper + provider_helper = get_provider_helper(self.provider, task="conversational") + + # Since `chat_completion(..., model=xxx)` is also a payload parameter for the server, we need to handle 'model' differently. + # `self.model` takes precedence over 'model' argument for building URL. + # `model` takes precedence for payload value. + model_id_or_url = self.model or model + payload_model = model or self.model + + # Prepare the payload + parameters = { + "model": payload_model, + "frequency_penalty": frequency_penalty, + "logit_bias": logit_bias, + "logprobs": logprobs, + "max_tokens": max_tokens, + "n": n, + "presence_penalty": presence_penalty, + "response_format": response_format, + "seed": seed, + "stop": stop, + "temperature": temperature, + "tool_choice": tool_choice, + "tool_prompt": tool_prompt, + "tools": tools, + "top_logprobs": top_logprobs, + "top_p": top_p, + "stream": stream, + "stream_options": stream_options, + **(extra_body or {}), + } + request_parameters = provider_helper.prepare_request( + inputs=messages, + parameters=parameters, + headers=self.headers, + model=model_id_or_url, + api_key=self.token, + ) + data = await self._inner_post(request_parameters, stream=stream) + + if stream: + return _async_stream_chat_completion_response(data) # type: ignore[arg-type] + + return ChatCompletionOutput.parse_obj_as_instance(data) # type: ignore[arg-type] + + async def document_question_answering( + self, + image: ContentT, + question: str, + *, + model: Optional[str] = None, + doc_stride: Optional[int] = None, + handle_impossible_answer: Optional[bool] = None, + lang: Optional[str] = None, + max_answer_len: Optional[int] = None, + max_question_len: Optional[int] = None, + max_seq_len: Optional[int] = None, + top_k: Optional[int] = None, + word_boxes: Optional[List[Union[List[float], str]]] = None, + ) -> List[DocumentQuestionAnsweringOutputElement]: + """ + Answer questions on document images. + + Args: + image (`Union[str, Path, bytes, BinaryIO]`): + The input image for the context. It can be raw bytes, an image file, or a URL to an online image. + question (`str`): + Question to be answered. + model (`str`, *optional*): + The model to use for the document question answering task. Can be a model ID hosted on the Hugging Face Hub or a URL to + a deployed Inference Endpoint. If not provided, the default recommended document question answering model will be used. + Defaults to None. + doc_stride (`int`, *optional*): + If the words in the document are too long to fit with the question for the model, it will be split in + several chunks with some overlap. This argument controls the size of that overlap. + handle_impossible_answer (`bool`, *optional*): + Whether to accept impossible as an answer + lang (`str`, *optional*): + Language to use while running OCR. Defaults to english. + max_answer_len (`int`, *optional*): + The maximum length of predicted answers (e.g., only answers with a shorter length are considered). + max_question_len (`int`, *optional*): + The maximum length of the question after tokenization. It will be truncated if needed. + max_seq_len (`int`, *optional*): + The maximum length of the total sentence (context + question) in tokens of each chunk passed to the + model. The context will be split in several chunks (using doc_stride as overlap) if needed. + top_k (`int`, *optional*): + The number of answers to return (will be chosen by order of likelihood). Can return less than top_k + answers if there are not enough options available within the context. + word_boxes (`List[Union[List[float], str`, *optional*): + A list of words and bounding boxes (normalized 0->1000). If provided, the inference will skip the OCR + step and use the provided bounding boxes instead. + Returns: + `List[DocumentQuestionAnsweringOutputElement]`: a list of [`DocumentQuestionAnsweringOutputElement`] items containing the predicted label, associated probability, word ids, and page number. + + Raises: + [`InferenceTimeoutError`]: + If the model is unavailable or the request times out. + `aiohttp.ClientResponseError`: + If the request fails with an HTTP error status code other than HTTP 503. + + + Example: + ```py + # Must be run in an async context + >>> from huggingface_hub import AsyncInferenceClient + >>> client = AsyncInferenceClient() + >>> await client.document_question_answering(image="https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png", question="What is the invoice number?") + [DocumentQuestionAnsweringOutputElement(answer='us-001', end=16, score=0.9999666213989258, start=16)] + ``` + """ + inputs: Dict[str, Any] = {"question": question, "image": _b64_encode(image)} + provider_helper = get_provider_helper(self.provider, task="document-question-answering") + request_parameters = provider_helper.prepare_request( + inputs=inputs, + parameters={ + "doc_stride": doc_stride, + "handle_impossible_answer": handle_impossible_answer, + "lang": lang, + "max_answer_len": max_answer_len, + "max_question_len": max_question_len, + "max_seq_len": max_seq_len, + "top_k": top_k, + "word_boxes": word_boxes, + }, + headers=self.headers, + model=model or self.model, + api_key=self.token, + ) + response = await self._inner_post(request_parameters) + return DocumentQuestionAnsweringOutputElement.parse_obj_as_list(response) + + async def feature_extraction( + self, + text: str, + *, + normalize: Optional[bool] = None, + prompt_name: Optional[str] = None, + truncate: Optional[bool] = None, + truncation_direction: Optional[Literal["Left", "Right"]] = None, + model: Optional[str] = None, + ) -> "np.ndarray": + """ + Generate embeddings for a given text. + + Args: + text (`str`): + The text to embed. + model (`str`, *optional*): + The model to use for the conversational task. Can be a model ID hosted on the Hugging Face Hub or a URL to + a deployed Inference Endpoint. If not provided, the default recommended conversational model will be used. + Defaults to None. + normalize (`bool`, *optional*): + Whether to normalize the embeddings or not. + Only available on server powered by Text-Embedding-Inference. + prompt_name (`str`, *optional*): + The name of the prompt that should be used by for encoding. If not set, no prompt will be applied. + Must be a key in the `Sentence Transformers` configuration `prompts` dictionary. + For example if ``prompt_name`` is "query" and the ``prompts`` is {"query": "query: ",...}, + then the sentence "What is the capital of France?" will be encoded as "query: What is the capital of France?" + because the prompt text will be prepended before any text to encode. + truncate (`bool`, *optional*): + Whether to truncate the embeddings or not. + Only available on server powered by Text-Embedding-Inference. + truncation_direction (`Literal["Left", "Right"]`, *optional*): + Which side of the input should be truncated when `truncate=True` is passed. + + Returns: + `np.ndarray`: The embedding representing the input text as a float32 numpy array. + + Raises: + [`InferenceTimeoutError`]: + If the model is unavailable or the request times out. + `aiohttp.ClientResponseError`: + If the request fails with an HTTP error status code other than HTTP 503. + + Example: + ```py + # Must be run in an async context + >>> from huggingface_hub import AsyncInferenceClient + >>> client = AsyncInferenceClient() + >>> await client.feature_extraction("Hi, who are you?") + array([[ 2.424802 , 2.93384 , 1.1750331 , ..., 1.240499, -0.13776633, -0.7889173 ], + [-0.42943227, -0.6364878 , -1.693462 , ..., 0.41978157, -2.4336355 , 0.6162071 ], + ..., + [ 0.28552425, -0.928395 , -1.2077185 , ..., 0.76810825, -2.1069427 , 0.6236161 ]], dtype=float32) + ``` + """ + provider_helper = get_provider_helper(self.provider, task="feature-extraction") + request_parameters = provider_helper.prepare_request( + inputs=text, + parameters={ + "normalize": normalize, + "prompt_name": prompt_name, + "truncate": truncate, + "truncation_direction": truncation_direction, + }, + headers=self.headers, + model=model or self.model, + api_key=self.token, + ) + response = await self._inner_post(request_parameters) + np = _import_numpy() + return np.array(_bytes_to_dict(response), dtype="float32") + + async def fill_mask( + self, + text: str, + *, + model: Optional[str] = None, + targets: Optional[List[str]] = None, + top_k: Optional[int] = None, + ) -> List[FillMaskOutputElement]: + """ + Fill in a hole with a missing word (token to be precise). + + Args: + text (`str`): + a string to be filled from, must contain the [MASK] token (check model card for exact name of the mask). + model (`str`, *optional*): + The model to use for the fill mask task. Can be a model ID hosted on the Hugging Face Hub or a URL to + a deployed Inference Endpoint. If not provided, the default recommended fill mask model will be used. + targets (`List[str`, *optional*): + When passed, the model will limit the scores to the passed targets instead of looking up in the whole + vocabulary. If the provided targets are not in the model vocab, they will be tokenized and the first + resulting token will be used (with a warning, and that might be slower). + top_k (`int`, *optional*): + When passed, overrides the number of predictions to return. + Returns: + `List[FillMaskOutputElement]`: a list of [`FillMaskOutputElement`] items containing the predicted label, associated + probability, token reference, and completed text. + + Raises: + [`InferenceTimeoutError`]: + If the model is unavailable or the request times out. + `aiohttp.ClientResponseError`: + If the request fails with an HTTP error status code other than HTTP 503. + + Example: + ```py + # Must be run in an async context + >>> from huggingface_hub import AsyncInferenceClient + >>> client = AsyncInferenceClient() + >>> await client.fill_mask("The goal of life is .") + [ + FillMaskOutputElement(score=0.06897063553333282, token=11098, token_str=' happiness', sequence='The goal of life is happiness.'), + FillMaskOutputElement(score=0.06554922461509705, token=45075, token_str=' immortality', sequence='The goal of life is immortality.') + ] + ``` + """ + provider_helper = get_provider_helper(self.provider, task="fill-mask") + request_parameters = provider_helper.prepare_request( + inputs=text, + parameters={"targets": targets, "top_k": top_k}, + headers=self.headers, + model=model or self.model, + api_key=self.token, + ) + response = await self._inner_post(request_parameters) + return FillMaskOutputElement.parse_obj_as_list(response) + + async def image_classification( + self, + image: ContentT, + *, + model: Optional[str] = None, + function_to_apply: Optional["ImageClassificationOutputTransform"] = None, + top_k: Optional[int] = None, + ) -> List[ImageClassificationOutputElement]: + """ + Perform image classification on the given image using the specified model. + + Args: + image (`Union[str, Path, bytes, BinaryIO]`): + The image to classify. It can be raw bytes, an image file, or a URL to an online image. + model (`str`, *optional*): + The model to use for image classification. Can be a model ID hosted on the Hugging Face Hub or a URL to a + deployed Inference Endpoint. If not provided, the default recommended model for image classification will be used. + function_to_apply (`"ImageClassificationOutputTransform"`, *optional*): + The function to apply to the model outputs in order to retrieve the scores. + top_k (`int`, *optional*): + When specified, limits the output to the top K most probable classes. + Returns: + `List[ImageClassificationOutputElement]`: a list of [`ImageClassificationOutputElement`] items containing the predicted label and associated probability. + + Raises: + [`InferenceTimeoutError`]: + If the model is unavailable or the request times out. + `aiohttp.ClientResponseError`: + If the request fails with an HTTP error status code other than HTTP 503. + + Example: + ```py + # Must be run in an async context + >>> from huggingface_hub import AsyncInferenceClient + >>> client = AsyncInferenceClient() + >>> await client.image_classification("https://upload.wikimedia.org/wikipedia/commons/thumb/4/43/Cute_dog.jpg/320px-Cute_dog.jpg") + [ImageClassificationOutputElement(label='Blenheim spaniel', score=0.9779096841812134), ...] + ``` + """ + provider_helper = get_provider_helper(self.provider, task="image-classification") + request_parameters = provider_helper.prepare_request( + inputs=image, + parameters={"function_to_apply": function_to_apply, "top_k": top_k}, + headers=self.headers, + model=model or self.model, + api_key=self.token, + ) + response = await self._inner_post(request_parameters) + return ImageClassificationOutputElement.parse_obj_as_list(response) + + async def image_segmentation( + self, + image: ContentT, + *, + model: Optional[str] = None, + mask_threshold: Optional[float] = None, + overlap_mask_area_threshold: Optional[float] = None, + subtask: Optional["ImageSegmentationSubtask"] = None, + threshold: Optional[float] = None, + ) -> List[ImageSegmentationOutputElement]: + """ + Perform image segmentation on the given image using the specified model. + + + + You must have `PIL` installed if you want to work with images (`pip install Pillow`). + + + + Args: + image (`Union[str, Path, bytes, BinaryIO]`): + The image to segment. It can be raw bytes, an image file, or a URL to an online image. + model (`str`, *optional*): + The model to use for image segmentation. Can be a model ID hosted on the Hugging Face Hub or a URL to a + deployed Inference Endpoint. If not provided, the default recommended model for image segmentation will be used. + mask_threshold (`float`, *optional*): + Threshold to use when turning the predicted masks into binary values. + overlap_mask_area_threshold (`float`, *optional*): + Mask overlap threshold to eliminate small, disconnected segments. + subtask (`"ImageSegmentationSubtask"`, *optional*): + Segmentation task to be performed, depending on model capabilities. + threshold (`float`, *optional*): + Probability threshold to filter out predicted masks. + Returns: + `List[ImageSegmentationOutputElement]`: A list of [`ImageSegmentationOutputElement`] items containing the segmented masks and associated attributes. + + Raises: + [`InferenceTimeoutError`]: + If the model is unavailable or the request times out. + `aiohttp.ClientResponseError`: + If the request fails with an HTTP error status code other than HTTP 503. + + Example: + ```py + # Must be run in an async context + >>> from huggingface_hub import AsyncInferenceClient + >>> client = AsyncInferenceClient() + >>> await client.image_segmentation("cat.jpg") + [ImageSegmentationOutputElement(score=0.989008, label='LABEL_184', mask=), ...] + ``` + """ + provider_helper = get_provider_helper(self.provider, task="audio-classification") + request_parameters = provider_helper.prepare_request( + inputs=image, + parameters={ + "mask_threshold": mask_threshold, + "overlap_mask_area_threshold": overlap_mask_area_threshold, + "subtask": subtask, + "threshold": threshold, + }, + headers=self.headers, + model=model or self.model, + api_key=self.token, + ) + response = await self._inner_post(request_parameters) + output = ImageSegmentationOutputElement.parse_obj_as_list(response) + for item in output: + item.mask = _b64_to_image(item.mask) # type: ignore [assignment] + return output + + async def image_to_image( + self, + image: ContentT, + prompt: Optional[str] = None, + *, + negative_prompt: Optional[str] = None, + num_inference_steps: Optional[int] = None, + guidance_scale: Optional[float] = None, + model: Optional[str] = None, + target_size: Optional[ImageToImageTargetSize] = None, + **kwargs, + ) -> "Image": + """ + Perform image-to-image translation using a specified model. + + + + You must have `PIL` installed if you want to work with images (`pip install Pillow`). + + + + Args: + image (`Union[str, Path, bytes, BinaryIO]`): + The input image for translation. It can be raw bytes, an image file, or a URL to an online image. + prompt (`str`, *optional*): + The text prompt to guide the image generation. + negative_prompt (`str`, *optional*): + One prompt to guide what NOT to include in image generation. + num_inference_steps (`int`, *optional*): + For diffusion models. The number of denoising steps. More denoising steps usually lead to a higher + quality image at the expense of slower inference. + guidance_scale (`float`, *optional*): + For diffusion models. A higher guidance scale value encourages the model to generate images closely + linked to the text prompt at the expense of lower image quality. + model (`str`, *optional*): + The model to use for inference. Can be a model ID hosted on the Hugging Face Hub or a URL to a deployed + Inference Endpoint. This parameter overrides the model defined at the instance level. Defaults to None. + target_size (`ImageToImageTargetSize`, *optional*): + The size in pixel of the output image. + + Returns: + `Image`: The translated image. + + Raises: + [`InferenceTimeoutError`]: + If the model is unavailable or the request times out. + `aiohttp.ClientResponseError`: + If the request fails with an HTTP error status code other than HTTP 503. + + Example: + ```py + # Must be run in an async context + >>> from huggingface_hub import AsyncInferenceClient + >>> client = AsyncInferenceClient() + >>> image = await client.image_to_image("cat.jpg", prompt="turn the cat into a tiger") + >>> image.save("tiger.jpg") + ``` + """ + provider_helper = get_provider_helper(self.provider, task="image-to-image") + request_parameters = provider_helper.prepare_request( + inputs=image, + parameters={ + "prompt": prompt, + "negative_prompt": negative_prompt, + "target_size": target_size, + "num_inference_steps": num_inference_steps, + "guidance_scale": guidance_scale, + **kwargs, + }, + headers=self.headers, + model=model or self.model, + api_key=self.token, + ) + response = await self._inner_post(request_parameters) + return _bytes_to_image(response) + + async def image_to_text(self, image: ContentT, *, model: Optional[str] = None) -> ImageToTextOutput: + """ + Takes an input image and return text. + + Models can have very different outputs depending on your use case (image captioning, optical character recognition + (OCR), Pix2Struct, etc). Please have a look to the model card to learn more about a model's specificities. + + Args: + image (`Union[str, Path, bytes, BinaryIO]`): + The input image to caption. It can be raw bytes, an image file, or a URL to an online image.. + model (`str`, *optional*): + The model to use for inference. Can be a model ID hosted on the Hugging Face Hub or a URL to a deployed + Inference Endpoint. This parameter overrides the model defined at the instance level. Defaults to None. + + Returns: + [`ImageToTextOutput`]: The generated text. + + Raises: + [`InferenceTimeoutError`]: + If the model is unavailable or the request times out. + `aiohttp.ClientResponseError`: + If the request fails with an HTTP error status code other than HTTP 503. + + Example: + ```py + # Must be run in an async context + >>> from huggingface_hub import AsyncInferenceClient + >>> client = AsyncInferenceClient() + >>> await client.image_to_text("cat.jpg") + 'a cat standing in a grassy field ' + >>> await client.image_to_text("https://upload.wikimedia.org/wikipedia/commons/thumb/4/43/Cute_dog.jpg/320px-Cute_dog.jpg") + 'a dog laying on the grass next to a flower pot ' + ``` + """ + provider_helper = get_provider_helper(self.provider, task="image-to-text") + request_parameters = provider_helper.prepare_request( + inputs=image, + parameters={}, + headers=self.headers, + model=model or self.model, + api_key=self.token, + ) + response = await self._inner_post(request_parameters) + output = ImageToTextOutput.parse_obj(response) + return output[0] if isinstance(output, list) else output + + async def object_detection( + self, image: ContentT, *, model: Optional[str] = None, threshold: Optional[float] = None + ) -> List[ObjectDetectionOutputElement]: + """ + Perform object detection on the given image using the specified model. + + + + You must have `PIL` installed if you want to work with images (`pip install Pillow`). + + + + Args: + image (`Union[str, Path, bytes, BinaryIO]`): + The image to detect objects on. It can be raw bytes, an image file, or a URL to an online image. + model (`str`, *optional*): + The model to use for object detection. Can be a model ID hosted on the Hugging Face Hub or a URL to a + deployed Inference Endpoint. If not provided, the default recommended model for object detection (DETR) will be used. + threshold (`float`, *optional*): + The probability necessary to make a prediction. + Returns: + `List[ObjectDetectionOutputElement]`: A list of [`ObjectDetectionOutputElement`] items containing the bounding boxes and associated attributes. + + Raises: + [`InferenceTimeoutError`]: + If the model is unavailable or the request times out. + `aiohttp.ClientResponseError`: + If the request fails with an HTTP error status code other than HTTP 503. + `ValueError`: + If the request output is not a List. + + Example: + ```py + # Must be run in an async context + >>> from huggingface_hub import AsyncInferenceClient + >>> client = AsyncInferenceClient() + >>> await client.object_detection("people.jpg") + [ObjectDetectionOutputElement(score=0.9486683011054993, label='person', box=ObjectDetectionBoundingBox(xmin=59, ymin=39, xmax=420, ymax=510)), ...] + ``` + """ + provider_helper = get_provider_helper(self.provider, task="object-detection") + request_parameters = provider_helper.prepare_request( + inputs=image, + parameters={"threshold": threshold}, + headers=self.headers, + model=model or self.model, + api_key=self.token, + ) + response = await self._inner_post(request_parameters) + return ObjectDetectionOutputElement.parse_obj_as_list(response) + + async def question_answering( + self, + question: str, + context: str, + *, + model: Optional[str] = None, + align_to_words: Optional[bool] = None, + doc_stride: Optional[int] = None, + handle_impossible_answer: Optional[bool] = None, + max_answer_len: Optional[int] = None, + max_question_len: Optional[int] = None, + max_seq_len: Optional[int] = None, + top_k: Optional[int] = None, + ) -> Union[QuestionAnsweringOutputElement, List[QuestionAnsweringOutputElement]]: + """ + Retrieve the answer to a question from a given text. + + Args: + question (`str`): + Question to be answered. + context (`str`): + The context of the question. + model (`str`): + The model to use for the question answering task. Can be a model ID hosted on the Hugging Face Hub or a URL to + a deployed Inference Endpoint. + align_to_words (`bool`, *optional*): + Attempts to align the answer to real words. Improves quality on space separated languages. Might hurt + on non-space-separated languages (like Japanese or Chinese) + doc_stride (`int`, *optional*): + If the context is too long to fit with the question for the model, it will be split in several chunks + with some overlap. This argument controls the size of that overlap. + handle_impossible_answer (`bool`, *optional*): + Whether to accept impossible as an answer. + max_answer_len (`int`, *optional*): + The maximum length of predicted answers (e.g., only answers with a shorter length are considered). + max_question_len (`int`, *optional*): + The maximum length of the question after tokenization. It will be truncated if needed. + max_seq_len (`int`, *optional*): + The maximum length of the total sentence (context + question) in tokens of each chunk passed to the + model. The context will be split in several chunks (using docStride as overlap) if needed. + top_k (`int`, *optional*): + The number of answers to return (will be chosen by order of likelihood). Note that we return less than + topk answers if there are not enough options available within the context. + + Returns: + Union[`QuestionAnsweringOutputElement`, List[`QuestionAnsweringOutputElement`]]: + When top_k is 1 or not provided, it returns a single `QuestionAnsweringOutputElement`. + When top_k is greater than 1, it returns a list of `QuestionAnsweringOutputElement`. + Raises: + [`InferenceTimeoutError`]: + If the model is unavailable or the request times out. + `aiohttp.ClientResponseError`: + If the request fails with an HTTP error status code other than HTTP 503. + + Example: + ```py + # Must be run in an async context + >>> from huggingface_hub import AsyncInferenceClient + >>> client = AsyncInferenceClient() + >>> await client.question_answering(question="What's my name?", context="My name is Clara and I live in Berkeley.") + QuestionAnsweringOutputElement(answer='Clara', end=16, score=0.9326565265655518, start=11) + ``` + """ + provider_helper = get_provider_helper(self.provider, task="question-answering") + request_parameters = provider_helper.prepare_request( + inputs=None, + parameters={ + "align_to_words": align_to_words, + "doc_stride": doc_stride, + "handle_impossible_answer": handle_impossible_answer, + "max_answer_len": max_answer_len, + "max_question_len": max_question_len, + "max_seq_len": max_seq_len, + "top_k": top_k, + }, + extra_payload={"question": question, "context": context}, + headers=self.headers, + model=model or self.model, + api_key=self.token, + ) + response = await self._inner_post(request_parameters) + # Parse the response as a single `QuestionAnsweringOutputElement` when top_k is 1 or not provided, or a list of `QuestionAnsweringOutputElement` to ensure backward compatibility. + output = QuestionAnsweringOutputElement.parse_obj(response) + return output + + async def sentence_similarity( + self, sentence: str, other_sentences: List[str], *, model: Optional[str] = None + ) -> List[float]: + """ + Compute the semantic similarity between a sentence and a list of other sentences by comparing their embeddings. + + Args: + sentence (`str`): + The main sentence to compare to others. + other_sentences (`List[str]`): + The list of sentences to compare to. + model (`str`, *optional*): + The model to use for the conversational task. Can be a model ID hosted on the Hugging Face Hub or a URL to + a deployed Inference Endpoint. If not provided, the default recommended conversational model will be used. + Defaults to None. + + Returns: + `List[float]`: The embedding representing the input text. + + Raises: + [`InferenceTimeoutError`]: + If the model is unavailable or the request times out. + `aiohttp.ClientResponseError`: + If the request fails with an HTTP error status code other than HTTP 503. + + Example: + ```py + # Must be run in an async context + >>> from huggingface_hub import AsyncInferenceClient + >>> client = AsyncInferenceClient() + >>> await client.sentence_similarity( + ... "Machine learning is so easy.", + ... other_sentences=[ + ... "Deep learning is so straightforward.", + ... "This is so difficult, like rocket science.", + ... "I can't believe how much I struggled with this.", + ... ], + ... ) + [0.7785726189613342, 0.45876261591911316, 0.2906220555305481] + ``` + """ + provider_helper = get_provider_helper(self.provider, task="sentence-similarity") + request_parameters = provider_helper.prepare_request( + inputs=None, + parameters={}, + extra_payload={"source_sentence": sentence, "sentences": other_sentences}, + headers=self.headers, + model=model or self.model, + api_key=self.token, + ) + response = await self._inner_post(request_parameters) + return _bytes_to_list(response) + + async def summarization( + self, + text: str, + *, + model: Optional[str] = None, + clean_up_tokenization_spaces: Optional[bool] = None, + generate_parameters: Optional[Dict[str, Any]] = None, + truncation: Optional["SummarizationTruncationStrategy"] = None, + ) -> SummarizationOutput: + """ + Generate a summary of a given text using a specified model. + + Args: + text (`str`): + The input text to summarize. + model (`str`, *optional*): + The model to use for inference. Can be a model ID hosted on the Hugging Face Hub or a URL to a deployed + Inference Endpoint. If not provided, the default recommended model for summarization will be used. + clean_up_tokenization_spaces (`bool`, *optional*): + Whether to clean up the potential extra spaces in the text output. + generate_parameters (`Dict[str, Any]`, *optional*): + Additional parametrization of the text generation algorithm. + truncation (`"SummarizationTruncationStrategy"`, *optional*): + The truncation strategy to use. + Returns: + [`SummarizationOutput`]: The generated summary text. + + Raises: + [`InferenceTimeoutError`]: + If the model is unavailable or the request times out. + `aiohttp.ClientResponseError`: + If the request fails with an HTTP error status code other than HTTP 503. + + Example: + ```py + # Must be run in an async context + >>> from huggingface_hub import AsyncInferenceClient + >>> client = AsyncInferenceClient() + >>> await client.summarization("The Eiffel tower...") + SummarizationOutput(generated_text="The Eiffel tower is one of the most famous landmarks in the world....") + ``` + """ + parameters = { + "clean_up_tokenization_spaces": clean_up_tokenization_spaces, + "generate_parameters": generate_parameters, + "truncation": truncation, + } + provider_helper = get_provider_helper(self.provider, task="summarization") + request_parameters = provider_helper.prepare_request( + inputs=text, + parameters=parameters, + headers=self.headers, + model=model or self.model, + api_key=self.token, + ) + response = await self._inner_post(request_parameters) + return SummarizationOutput.parse_obj_as_list(response)[0] + + async def table_question_answering( + self, + table: Dict[str, Any], + query: str, + *, + model: Optional[str] = None, + padding: Optional["Padding"] = None, + sequential: Optional[bool] = None, + truncation: Optional[bool] = None, + ) -> TableQuestionAnsweringOutputElement: + """ + Retrieve the answer to a question from information given in a table. + + Args: + table (`str`): + A table of data represented as a dict of lists where entries are headers and the lists are all the + values, all lists must have the same size. + query (`str`): + The query in plain text that you want to ask the table. + model (`str`): + The model to use for the table-question-answering task. Can be a model ID hosted on the Hugging Face + Hub or a URL to a deployed Inference Endpoint. + padding (`"Padding"`, *optional*): + Activates and controls padding. + sequential (`bool`, *optional*): + Whether to do inference sequentially or as a batch. Batching is faster, but models like SQA require the + inference to be done sequentially to extract relations within sequences, given their conversational + nature. + truncation (`bool`, *optional*): + Activates and controls truncation. + + Returns: + [`TableQuestionAnsweringOutputElement`]: a table question answering output containing the answer, coordinates, cells and the aggregator used. + + Raises: + [`InferenceTimeoutError`]: + If the model is unavailable or the request times out. + `aiohttp.ClientResponseError`: + If the request fails with an HTTP error status code other than HTTP 503. + + Example: + ```py + # Must be run in an async context + >>> from huggingface_hub import AsyncInferenceClient + >>> client = AsyncInferenceClient() + >>> query = "How many stars does the transformers repository have?" + >>> table = {"Repository": ["Transformers", "Datasets", "Tokenizers"], "Stars": ["36542", "4512", "3934"]} + >>> await client.table_question_answering(table, query, model="google/tapas-base-finetuned-wtq") + TableQuestionAnsweringOutputElement(answer='36542', coordinates=[[0, 1]], cells=['36542'], aggregator='AVERAGE') + ``` + """ + provider_helper = get_provider_helper(self.provider, task="table-question-answering") + request_parameters = provider_helper.prepare_request( + inputs=None, + parameters={"model": model, "padding": padding, "sequential": sequential, "truncation": truncation}, + extra_payload={"query": query, "table": table}, + headers=self.headers, + model=model or self.model, + api_key=self.token, + ) + response = await self._inner_post(request_parameters) + return TableQuestionAnsweringOutputElement.parse_obj_as_instance(response) + + async def tabular_classification(self, table: Dict[str, Any], *, model: Optional[str] = None) -> List[str]: + """ + Classifying a target category (a group) based on a set of attributes. + + Args: + table (`Dict[str, Any]`): + Set of attributes to classify. + model (`str`, *optional*): + The model to use for the tabular classification task. Can be a model ID hosted on the Hugging Face Hub or a URL to + a deployed Inference Endpoint. If not provided, the default recommended tabular classification model will be used. + Defaults to None. + + Returns: + `List`: a list of labels, one per row in the initial table. + + Raises: + [`InferenceTimeoutError`]: + If the model is unavailable or the request times out. + `aiohttp.ClientResponseError`: + If the request fails with an HTTP error status code other than HTTP 503. + + Example: + ```py + # Must be run in an async context + >>> from huggingface_hub import AsyncInferenceClient + >>> client = AsyncInferenceClient() + >>> table = { + ... "fixed_acidity": ["7.4", "7.8", "10.3"], + ... "volatile_acidity": ["0.7", "0.88", "0.32"], + ... "citric_acid": ["0", "0", "0.45"], + ... "residual_sugar": ["1.9", "2.6", "6.4"], + ... "chlorides": ["0.076", "0.098", "0.073"], + ... "free_sulfur_dioxide": ["11", "25", "5"], + ... "total_sulfur_dioxide": ["34", "67", "13"], + ... "density": ["0.9978", "0.9968", "0.9976"], + ... "pH": ["3.51", "3.2", "3.23"], + ... "sulphates": ["0.56", "0.68", "0.82"], + ... "alcohol": ["9.4", "9.8", "12.6"], + ... } + >>> await client.tabular_classification(table=table, model="julien-c/wine-quality") + ["5", "5", "5"] + ``` + """ + provider_helper = get_provider_helper(self.provider, task="tabular-classification") + request_parameters = provider_helper.prepare_request( + inputs=None, + extra_payload={"table": table}, + parameters={}, + headers=self.headers, + model=model or self.model, + api_key=self.token, + ) + response = await self._inner_post(request_parameters) + return _bytes_to_list(response) + + async def tabular_regression(self, table: Dict[str, Any], *, model: Optional[str] = None) -> List[float]: + """ + Predicting a numerical target value given a set of attributes/features in a table. + + Args: + table (`Dict[str, Any]`): + Set of attributes stored in a table. The attributes used to predict the target can be both numerical and categorical. + model (`str`, *optional*): + The model to use for the tabular regression task. Can be a model ID hosted on the Hugging Face Hub or a URL to + a deployed Inference Endpoint. If not provided, the default recommended tabular regression model will be used. + Defaults to None. + + Returns: + `List`: a list of predicted numerical target values. + + Raises: + [`InferenceTimeoutError`]: + If the model is unavailable or the request times out. + `aiohttp.ClientResponseError`: + If the request fails with an HTTP error status code other than HTTP 503. + + Example: + ```py + # Must be run in an async context + >>> from huggingface_hub import AsyncInferenceClient + >>> client = AsyncInferenceClient() + >>> table = { + ... "Height": ["11.52", "12.48", "12.3778"], + ... "Length1": ["23.2", "24", "23.9"], + ... "Length2": ["25.4", "26.3", "26.5"], + ... "Length3": ["30", "31.2", "31.1"], + ... "Species": ["Bream", "Bream", "Bream"], + ... "Width": ["4.02", "4.3056", "4.6961"], + ... } + >>> await client.tabular_regression(table, model="scikit-learn/Fish-Weight") + [110, 120, 130] + ``` + """ + provider_helper = get_provider_helper(self.provider, task="tabular-regression") + request_parameters = provider_helper.prepare_request( + inputs=None, + parameters={}, + extra_payload={"table": table}, + headers=self.headers, + model=model or self.model, + api_key=self.token, + ) + response = await self._inner_post(request_parameters) + return _bytes_to_list(response) + + async def text_classification( + self, + text: str, + *, + model: Optional[str] = None, + top_k: Optional[int] = None, + function_to_apply: Optional["TextClassificationOutputTransform"] = None, + ) -> List[TextClassificationOutputElement]: + """ + Perform text classification (e.g. sentiment-analysis) on the given text. + + Args: + text (`str`): + A string to be classified. + model (`str`, *optional*): + The model to use for the text classification task. Can be a model ID hosted on the Hugging Face Hub or a URL to + a deployed Inference Endpoint. If not provided, the default recommended text classification model will be used. + Defaults to None. + top_k (`int`, *optional*): + When specified, limits the output to the top K most probable classes. + function_to_apply (`"TextClassificationOutputTransform"`, *optional*): + The function to apply to the model outputs in order to retrieve the scores. + + Returns: + `List[TextClassificationOutputElement]`: a list of [`TextClassificationOutputElement`] items containing the predicted label and associated probability. + + Raises: + [`InferenceTimeoutError`]: + If the model is unavailable or the request times out. + `aiohttp.ClientResponseError`: + If the request fails with an HTTP error status code other than HTTP 503. + + Example: + ```py + # Must be run in an async context + >>> from huggingface_hub import AsyncInferenceClient + >>> client = AsyncInferenceClient() + >>> await client.text_classification("I like you") + [ + TextClassificationOutputElement(label='POSITIVE', score=0.9998695850372314), + TextClassificationOutputElement(label='NEGATIVE', score=0.0001304351753788069), + ] + ``` + """ + provider_helper = get_provider_helper(self.provider, task="text-classification") + request_parameters = provider_helper.prepare_request( + inputs=text, + parameters={ + "function_to_apply": function_to_apply, + "top_k": top_k, + }, + headers=self.headers, + model=model or self.model, + api_key=self.token, + ) + response = await self._inner_post(request_parameters) + return TextClassificationOutputElement.parse_obj_as_list(response)[0] # type: ignore [return-value] + + @overload + async def text_generation( # type: ignore + self, + prompt: str, + *, + details: Literal[False] = ..., + stream: Literal[False] = ..., + model: Optional[str] = None, + # Parameters from `TextGenerationInputGenerateParameters` (maintained manually) + adapter_id: Optional[str] = None, + best_of: Optional[int] = None, + decoder_input_details: Optional[bool] = None, + do_sample: Optional[bool] = False, # Manual default value + frequency_penalty: Optional[float] = None, + grammar: Optional[TextGenerationInputGrammarType] = None, + max_new_tokens: Optional[int] = None, + repetition_penalty: Optional[float] = None, + return_full_text: Optional[bool] = False, # Manual default value + seed: Optional[int] = None, + stop: Optional[List[str]] = None, + stop_sequences: Optional[List[str]] = None, # Deprecated, use `stop` instead + temperature: Optional[float] = None, + top_k: Optional[int] = None, + top_n_tokens: Optional[int] = None, + top_p: Optional[float] = None, + truncate: Optional[int] = None, + typical_p: Optional[float] = None, + watermark: Optional[bool] = None, + ) -> str: ... + + @overload + async def text_generation( # type: ignore + self, + prompt: str, + *, + details: Literal[True] = ..., + stream: Literal[False] = ..., + model: Optional[str] = None, + # Parameters from `TextGenerationInputGenerateParameters` (maintained manually) + adapter_id: Optional[str] = None, + best_of: Optional[int] = None, + decoder_input_details: Optional[bool] = None, + do_sample: Optional[bool] = False, # Manual default value + frequency_penalty: Optional[float] = None, + grammar: Optional[TextGenerationInputGrammarType] = None, + max_new_tokens: Optional[int] = None, + repetition_penalty: Optional[float] = None, + return_full_text: Optional[bool] = False, # Manual default value + seed: Optional[int] = None, + stop: Optional[List[str]] = None, + stop_sequences: Optional[List[str]] = None, # Deprecated, use `stop` instead + temperature: Optional[float] = None, + top_k: Optional[int] = None, + top_n_tokens: Optional[int] = None, + top_p: Optional[float] = None, + truncate: Optional[int] = None, + typical_p: Optional[float] = None, + watermark: Optional[bool] = None, + ) -> TextGenerationOutput: ... + + @overload + async def text_generation( # type: ignore + self, + prompt: str, + *, + details: Literal[False] = ..., + stream: Literal[True] = ..., + model: Optional[str] = None, + # Parameters from `TextGenerationInputGenerateParameters` (maintained manually) + adapter_id: Optional[str] = None, + best_of: Optional[int] = None, + decoder_input_details: Optional[bool] = None, + do_sample: Optional[bool] = False, # Manual default value + frequency_penalty: Optional[float] = None, + grammar: Optional[TextGenerationInputGrammarType] = None, + max_new_tokens: Optional[int] = None, + repetition_penalty: Optional[float] = None, + return_full_text: Optional[bool] = False, # Manual default value + seed: Optional[int] = None, + stop: Optional[List[str]] = None, + stop_sequences: Optional[List[str]] = None, # Deprecated, use `stop` instead + temperature: Optional[float] = None, + top_k: Optional[int] = None, + top_n_tokens: Optional[int] = None, + top_p: Optional[float] = None, + truncate: Optional[int] = None, + typical_p: Optional[float] = None, + watermark: Optional[bool] = None, + ) -> AsyncIterable[str]: ... + + @overload + async def text_generation( # type: ignore + self, + prompt: str, + *, + details: Literal[True] = ..., + stream: Literal[True] = ..., + model: Optional[str] = None, + # Parameters from `TextGenerationInputGenerateParameters` (maintained manually) + adapter_id: Optional[str] = None, + best_of: Optional[int] = None, + decoder_input_details: Optional[bool] = None, + do_sample: Optional[bool] = False, # Manual default value + frequency_penalty: Optional[float] = None, + grammar: Optional[TextGenerationInputGrammarType] = None, + max_new_tokens: Optional[int] = None, + repetition_penalty: Optional[float] = None, + return_full_text: Optional[bool] = False, # Manual default value + seed: Optional[int] = None, + stop: Optional[List[str]] = None, + stop_sequences: Optional[List[str]] = None, # Deprecated, use `stop` instead + temperature: Optional[float] = None, + top_k: Optional[int] = None, + top_n_tokens: Optional[int] = None, + top_p: Optional[float] = None, + truncate: Optional[int] = None, + typical_p: Optional[float] = None, + watermark: Optional[bool] = None, + ) -> AsyncIterable[TextGenerationStreamOutput]: ... + + @overload + async def text_generation( + self, + prompt: str, + *, + details: Literal[True] = ..., + stream: bool = ..., + model: Optional[str] = None, + # Parameters from `TextGenerationInputGenerateParameters` (maintained manually) + adapter_id: Optional[str] = None, + best_of: Optional[int] = None, + decoder_input_details: Optional[bool] = None, + do_sample: Optional[bool] = False, # Manual default value + frequency_penalty: Optional[float] = None, + grammar: Optional[TextGenerationInputGrammarType] = None, + max_new_tokens: Optional[int] = None, + repetition_penalty: Optional[float] = None, + return_full_text: Optional[bool] = False, # Manual default value + seed: Optional[int] = None, + stop: Optional[List[str]] = None, + stop_sequences: Optional[List[str]] = None, # Deprecated, use `stop` instead + temperature: Optional[float] = None, + top_k: Optional[int] = None, + top_n_tokens: Optional[int] = None, + top_p: Optional[float] = None, + truncate: Optional[int] = None, + typical_p: Optional[float] = None, + watermark: Optional[bool] = None, + ) -> Union[TextGenerationOutput, AsyncIterable[TextGenerationStreamOutput]]: ... + + async def text_generation( + self, + prompt: str, + *, + details: bool = False, + stream: bool = False, + model: Optional[str] = None, + # Parameters from `TextGenerationInputGenerateParameters` (maintained manually) + adapter_id: Optional[str] = None, + best_of: Optional[int] = None, + decoder_input_details: Optional[bool] = None, + do_sample: Optional[bool] = False, # Manual default value + frequency_penalty: Optional[float] = None, + grammar: Optional[TextGenerationInputGrammarType] = None, + max_new_tokens: Optional[int] = None, + repetition_penalty: Optional[float] = None, + return_full_text: Optional[bool] = False, # Manual default value + seed: Optional[int] = None, + stop: Optional[List[str]] = None, + stop_sequences: Optional[List[str]] = None, # Deprecated, use `stop` instead + temperature: Optional[float] = None, + top_k: Optional[int] = None, + top_n_tokens: Optional[int] = None, + top_p: Optional[float] = None, + truncate: Optional[int] = None, + typical_p: Optional[float] = None, + watermark: Optional[bool] = None, + ) -> Union[str, TextGenerationOutput, AsyncIterable[str], AsyncIterable[TextGenerationStreamOutput]]: + """ + Given a prompt, generate the following text. + + + + If you want to generate a response from chat messages, you should use the [`InferenceClient.chat_completion`] method. + It accepts a list of messages instead of a single text prompt and handles the chat templating for you. + + + + Args: + prompt (`str`): + Input text. + details (`bool`, *optional*): + By default, text_generation returns a string. Pass `details=True` if you want a detailed output (tokens, + probabilities, seed, finish reason, etc.). Only available for models running on with the + `text-generation-inference` backend. + stream (`bool`, *optional*): + By default, text_generation returns the full generated text. Pass `stream=True` if you want a stream of + tokens to be returned. Only available for models running on with the `text-generation-inference` + backend. + model (`str`, *optional*): + The model to use for inference. Can be a model ID hosted on the Hugging Face Hub or a URL to a deployed + Inference Endpoint. This parameter overrides the model defined at the instance level. Defaults to None. + adapter_id (`str`, *optional*): + Lora adapter id. + best_of (`int`, *optional*): + Generate best_of sequences and return the one if the highest token logprobs. + decoder_input_details (`bool`, *optional*): + Return the decoder input token logprobs and ids. You must set `details=True` as well for it to be taken + into account. Defaults to `False`. + do_sample (`bool`, *optional*): + Activate logits sampling + frequency_penalty (`float`, *optional*): + Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in + the text so far, decreasing the model's likelihood to repeat the same line verbatim. + grammar ([`TextGenerationInputGrammarType`], *optional*): + Grammar constraints. Can be either a JSONSchema or a regex. + max_new_tokens (`int`, *optional*): + Maximum number of generated tokens. Defaults to 100. + repetition_penalty (`float`, *optional*): + The parameter for repetition penalty. 1.0 means no penalty. See [this + paper](https://arxiv.org/pdf/1909.05858.pdf) for more details. + return_full_text (`bool`, *optional*): + Whether to prepend the prompt to the generated text + seed (`int`, *optional*): + Random sampling seed + stop (`List[str]`, *optional*): + Stop generating tokens if a member of `stop` is generated. + stop_sequences (`List[str]`, *optional*): + Deprecated argument. Use `stop` instead. + temperature (`float`, *optional*): + The value used to module the logits distribution. + top_n_tokens (`int`, *optional*): + Return information about the `top_n_tokens` most likely tokens at each generation step, instead of + just the sampled token. + top_k (`int`, *optional`): + The number of highest probability vocabulary tokens to keep for top-k-filtering. + top_p (`float`, *optional`): + If set to < 1, only the smallest set of most probable tokens with probabilities that add up to `top_p` or + higher are kept for generation. + truncate (`int`, *optional`): + Truncate inputs tokens to the given size. + typical_p (`float`, *optional`): + Typical Decoding mass + See [Typical Decoding for Natural Language Generation](https://arxiv.org/abs/2202.00666) for more information + watermark (`bool`, *optional`): + Watermarking with [A Watermark for Large Language Models](https://arxiv.org/abs/2301.10226) + + Returns: + `Union[str, TextGenerationOutput, Iterable[str], Iterable[TextGenerationStreamOutput]]`: + Generated text returned from the server: + - if `stream=False` and `details=False`, the generated text is returned as a `str` (default) + - if `stream=True` and `details=False`, the generated text is returned token by token as a `Iterable[str]` + - if `stream=False` and `details=True`, the generated text is returned with more details as a [`~huggingface_hub.TextGenerationOutput`] + - if `details=True` and `stream=True`, the generated text is returned token by token as a iterable of [`~huggingface_hub.TextGenerationStreamOutput`] + + Raises: + `ValidationError`: + If input values are not valid. No HTTP call is made to the server. + [`InferenceTimeoutError`]: + If the model is unavailable or the request times out. + `aiohttp.ClientResponseError`: + If the request fails with an HTTP error status code other than HTTP 503. + + Example: + ```py + # Must be run in an async context + >>> from huggingface_hub import AsyncInferenceClient + >>> client = AsyncInferenceClient() + + # Case 1: generate text + >>> await client.text_generation("The huggingface_hub library is ", max_new_tokens=12) + '100% open source and built to be easy to use.' + + # Case 2: iterate over the generated tokens. Useful for large generation. + >>> async for token in await client.text_generation("The huggingface_hub library is ", max_new_tokens=12, stream=True): + ... print(token) + 100 + % + open + source + and + built + to + be + easy + to + use + . + + # Case 3: get more details about the generation process. + >>> await client.text_generation("The huggingface_hub library is ", max_new_tokens=12, details=True) + TextGenerationOutput( + generated_text='100% open source and built to be easy to use.', + details=TextGenerationDetails( + finish_reason='length', + generated_tokens=12, + seed=None, + prefill=[ + TextGenerationPrefillOutputToken(id=487, text='The', logprob=None), + TextGenerationPrefillOutputToken(id=53789, text=' hugging', logprob=-13.171875), + (...) + TextGenerationPrefillOutputToken(id=204, text=' ', logprob=-7.0390625) + ], + tokens=[ + TokenElement(id=1425, text='100', logprob=-1.0175781, special=False), + TokenElement(id=16, text='%', logprob=-0.0463562, special=False), + (...) + TokenElement(id=25, text='.', logprob=-0.5703125, special=False) + ], + best_of_sequences=None + ) + ) + + # Case 4: iterate over the generated tokens with more details. + # Last object is more complete, containing the full generated text and the finish reason. + >>> async for details in await client.text_generation("The huggingface_hub library is ", max_new_tokens=12, details=True, stream=True): + ... print(details) + ... + TextGenerationStreamOutput(token=TokenElement(id=1425, text='100', logprob=-1.0175781, special=False), generated_text=None, details=None) + TextGenerationStreamOutput(token=TokenElement(id=16, text='%', logprob=-0.0463562, special=False), generated_text=None, details=None) + TextGenerationStreamOutput(token=TokenElement(id=1314, text=' open', logprob=-1.3359375, special=False), generated_text=None, details=None) + TextGenerationStreamOutput(token=TokenElement(id=3178, text=' source', logprob=-0.28100586, special=False), generated_text=None, details=None) + TextGenerationStreamOutput(token=TokenElement(id=273, text=' and', logprob=-0.5961914, special=False), generated_text=None, details=None) + TextGenerationStreamOutput(token=TokenElement(id=3426, text=' built', logprob=-1.9423828, special=False), generated_text=None, details=None) + TextGenerationStreamOutput(token=TokenElement(id=271, text=' to', logprob=-1.4121094, special=False), generated_text=None, details=None) + TextGenerationStreamOutput(token=TokenElement(id=314, text=' be', logprob=-1.5224609, special=False), generated_text=None, details=None) + TextGenerationStreamOutput(token=TokenElement(id=1833, text=' easy', logprob=-2.1132812, special=False), generated_text=None, details=None) + TextGenerationStreamOutput(token=TokenElement(id=271, text=' to', logprob=-0.08520508, special=False), generated_text=None, details=None) + TextGenerationStreamOutput(token=TokenElement(id=745, text=' use', logprob=-0.39453125, special=False), generated_text=None, details=None) + TextGenerationStreamOutput(token=TokenElement( + id=25, + text='.', + logprob=-0.5703125, + special=False), + generated_text='100% open source and built to be easy to use.', + details=TextGenerationStreamOutputStreamDetails(finish_reason='length', generated_tokens=12, seed=None) + ) + + # Case 5: generate constrained output using grammar + >>> response = await client.text_generation( + ... prompt="I saw a puppy a cat and a raccoon during my bike ride in the park", + ... model="HuggingFaceH4/zephyr-orpo-141b-A35b-v0.1", + ... max_new_tokens=100, + ... repetition_penalty=1.3, + ... grammar={ + ... "type": "json", + ... "value": { + ... "properties": { + ... "location": {"type": "string"}, + ... "activity": {"type": "string"}, + ... "animals_seen": {"type": "integer", "minimum": 1, "maximum": 5}, + ... "animals": {"type": "array", "items": {"type": "string"}}, + ... }, + ... "required": ["location", "activity", "animals_seen", "animals"], + ... }, + ... }, + ... ) + >>> json.loads(response) + { + "activity": "bike riding", + "animals": ["puppy", "cat", "raccoon"], + "animals_seen": 3, + "location": "park" + } + ``` + """ + if decoder_input_details and not details: + warnings.warn( + "`decoder_input_details=True` has been passed to the server but `details=False` is set meaning that" + " the output from the server will be truncated." + ) + decoder_input_details = False + + if stop_sequences is not None: + warnings.warn( + "`stop_sequences` is a deprecated argument for `text_generation` task" + " and will be removed in version '0.28.0'. Use `stop` instead.", + FutureWarning, + ) + if stop is None: + stop = stop_sequences # use deprecated arg if provided + + # Build payload + parameters = { + "adapter_id": adapter_id, + "best_of": best_of, + "decoder_input_details": decoder_input_details, + "details": details, + "do_sample": do_sample, + "frequency_penalty": frequency_penalty, + "grammar": grammar, + "max_new_tokens": max_new_tokens, + "repetition_penalty": repetition_penalty, + "return_full_text": return_full_text, + "seed": seed, + "stop": stop if stop is not None else [], + "temperature": temperature, + "top_k": top_k, + "top_n_tokens": top_n_tokens, + "top_p": top_p, + "truncate": truncate, + "typical_p": typical_p, + "watermark": watermark, + } + + # Remove some parameters if not a TGI server + unsupported_kwargs = _get_unsupported_text_generation_kwargs(model) + if len(unsupported_kwargs) > 0: + # The server does not support some parameters + # => means it is not a TGI server + # => remove unsupported parameters and warn the user + + ignored_parameters = [] + for key in unsupported_kwargs: + if parameters.get(key): + ignored_parameters.append(key) + parameters.pop(key, None) + if len(ignored_parameters) > 0: + warnings.warn( + "API endpoint/model for text-generation is not served via TGI. Ignoring following parameters:" + f" {', '.join(ignored_parameters)}.", + UserWarning, + ) + if details: + warnings.warn( + "API endpoint/model for text-generation is not served via TGI. Parameter `details=True` will" + " be ignored meaning only the generated text will be returned.", + UserWarning, + ) + details = False + if stream: + raise ValueError( + "API endpoint/model for text-generation is not served via TGI. Cannot return output as a stream." + " Please pass `stream=False` as input." + ) + + provider_helper = get_provider_helper(self.provider, task="text-generation") + request_parameters = provider_helper.prepare_request( + inputs=prompt, + parameters=parameters, + extra_payload={"stream": stream}, + headers=self.headers, + model=model or self.model, + api_key=self.token, + ) + + # Handle errors separately for more precise error messages + try: + bytes_output = await self._inner_post(request_parameters, stream=stream) + except _import_aiohttp().ClientResponseError as e: + match = MODEL_KWARGS_NOT_USED_REGEX.search(e.response_error_payload["error"]) + if e.status == 400 and match: + unused_params = [kwarg.strip("' ") for kwarg in match.group(1).split(",")] + _set_unsupported_text_generation_kwargs(model, unused_params) + return await self.text_generation( # type: ignore + prompt=prompt, + details=details, + stream=stream, + model=model or self.model, + adapter_id=adapter_id, + best_of=best_of, + decoder_input_details=decoder_input_details, + do_sample=do_sample, + frequency_penalty=frequency_penalty, + grammar=grammar, + max_new_tokens=max_new_tokens, + repetition_penalty=repetition_penalty, + return_full_text=return_full_text, + seed=seed, + stop=stop, + temperature=temperature, + top_k=top_k, + top_n_tokens=top_n_tokens, + top_p=top_p, + truncate=truncate, + typical_p=typical_p, + watermark=watermark, + ) + raise_text_generation_error(e) + + # Parse output + if stream: + return _async_stream_text_generation_response(bytes_output, details) # type: ignore + + data = _bytes_to_dict(bytes_output) # type: ignore[arg-type] + + # Data can be a single element (dict) or an iterable of dicts where we select the first element of. + if isinstance(data, list): + data = data[0] + + return TextGenerationOutput.parse_obj_as_instance(data) if details else data["generated_text"] + + async def text_to_image( + self, + prompt: str, + *, + negative_prompt: Optional[str] = None, + height: Optional[int] = None, + width: Optional[int] = None, + num_inference_steps: Optional[int] = None, + guidance_scale: Optional[float] = None, + model: Optional[str] = None, + scheduler: Optional[str] = None, + seed: Optional[int] = None, + extra_body: Optional[Dict[str, Any]] = None, + ) -> "Image": + """ + Generate an image based on a given text using a specified model. + + + + You must have `PIL` installed if you want to work with images (`pip install Pillow`). + + + + + You can pass provider-specific parameters to the model by using the `extra_body` argument. + + + Args: + prompt (`str`): + The prompt to generate an image from. + negative_prompt (`str`, *optional*): + One prompt to guide what NOT to include in image generation. + height (`int`, *optional*): + The height in pixels of the output image + width (`int`, *optional*): + The width in pixels of the output image + num_inference_steps (`int`, *optional*): + The number of denoising steps. More denoising steps usually lead to a higher quality image at the + expense of slower inference. + guidance_scale (`float`, *optional*): + A higher guidance scale value encourages the model to generate images closely linked to the text + prompt, but values too high may cause saturation and other artifacts. + model (`str`, *optional*): + The model to use for inference. Can be a model ID hosted on the Hugging Face Hub or a URL to a deployed + Inference Endpoint. If not provided, the default recommended text-to-image model will be used. + Defaults to None. + scheduler (`str`, *optional*): + Override the scheduler with a compatible one. + seed (`int`, *optional*): + Seed for the random number generator. + extra_body (`Dict[str, Any]`, *optional*): + Additional provider-specific parameters to pass to the model. Refer to the provider's documentation + for supported parameters. + + Returns: + `Image`: The generated image. + + Raises: + [`InferenceTimeoutError`]: + If the model is unavailable or the request times out. + `aiohttp.ClientResponseError`: + If the request fails with an HTTP error status code other than HTTP 503. + + Example: + ```py + # Must be run in an async context + >>> from huggingface_hub import AsyncInferenceClient + >>> client = AsyncInferenceClient() + + >>> image = await client.text_to_image("An astronaut riding a horse on the moon.") + >>> image.save("astronaut.png") + + >>> image = await client.text_to_image( + ... "An astronaut riding a horse on the moon.", + ... negative_prompt="low resolution, blurry", + ... model="stabilityai/stable-diffusion-2-1", + ... ) + >>> image.save("better_astronaut.png") + ``` + Example using a third-party provider directly. Usage will be billed on your fal.ai account. + ```py + >>> from huggingface_hub import InferenceClient + >>> client = InferenceClient( + ... provider="fal-ai", # Use fal.ai provider + ... api_key="fal-ai-api-key", # Pass your fal.ai API key + ... ) + >>> image = client.text_to_image( + ... "A majestic lion in a fantasy forest", + ... model="black-forest-labs/FLUX.1-schnell", + ... ) + >>> image.save("lion.png") + ``` + + Example using a third-party provider through Hugging Face Routing. Usage will be billed on your Hugging Face account. + ```py + >>> from huggingface_hub import InferenceClient + >>> client = InferenceClient( + ... provider="replicate", # Use replicate provider + ... api_key="hf_...", # Pass your HF token + ... ) + >>> image = client.text_to_image( + ... "An astronaut riding a horse on the moon.", + ... model="black-forest-labs/FLUX.1-dev", + ... ) + >>> image.save("astronaut.png") + ``` + + Example using Replicate provider with extra parameters + ```py + >>> from huggingface_hub import InferenceClient + >>> client = InferenceClient( + ... provider="replicate", # Use replicate provider + ... api_key="hf_...", # Pass your HF token + ... ) + >>> image = client.text_to_image( + ... "An astronaut riding a horse on the moon.", + ... model="black-forest-labs/FLUX.1-schnell", + ... extra_body={"output_quality": 100}, + ... ) + >>> image.save("astronaut.png") + ``` + """ + provider_helper = get_provider_helper(self.provider, task="text-to-image") + request_parameters = provider_helper.prepare_request( + inputs=prompt, + parameters={ + "negative_prompt": negative_prompt, + "height": height, + "width": width, + "num_inference_steps": num_inference_steps, + "guidance_scale": guidance_scale, + "scheduler": scheduler, + "seed": seed, + **(extra_body or {}), + }, + headers=self.headers, + model=model or self.model, + api_key=self.token, + ) + response = await self._inner_post(request_parameters) + response = provider_helper.get_response(response) + return _bytes_to_image(response) + + async def text_to_video( + self, + prompt: str, + *, + model: Optional[str] = None, + guidance_scale: Optional[float] = None, + negative_prompt: Optional[List[str]] = None, + num_frames: Optional[float] = None, + num_inference_steps: Optional[int] = None, + seed: Optional[int] = None, + extra_body: Optional[Dict[str, Any]] = None, + ) -> bytes: + """ + Generate a video based on a given text. + + + You can pass provider-specific parameters to the model by using the `extra_body` argument. + + + Args: + prompt (`str`): + The prompt to generate a video from. + model (`str`, *optional*): + The model to use for inference. Can be a model ID hosted on the Hugging Face Hub or a URL to a deployed + Inference Endpoint. If not provided, the default recommended text-to-video model will be used. + Defaults to None. + guidance_scale (`float`, *optional*): + A higher guidance scale value encourages the model to generate videos closely linked to the text + prompt, but values too high may cause saturation and other artifacts. + negative_prompt (`List[str]`, *optional*): + One or several prompt to guide what NOT to include in video generation. + num_frames (`float`, *optional*): + The num_frames parameter determines how many video frames are generated. + num_inference_steps (`int`, *optional*): + The number of denoising steps. More denoising steps usually lead to a higher quality video at the + expense of slower inference. + seed (`int`, *optional*): + Seed for the random number generator. + extra_body (`Dict[str, Any]`, *optional*): + Additional provider-specific parameters to pass to the model. Refer to the provider's documentation + for supported parameters. + + Returns: + `bytes`: The generated video. + + Example: + + Example using a third-party provider directly. Usage will be billed on your fal.ai account. + ```py + >>> from huggingface_hub import InferenceClient + >>> client = InferenceClient( + ... provider="fal-ai", # Using fal.ai provider + ... api_key="fal-ai-api-key", # Pass your fal.ai API key + ... ) + >>> video = client.text_to_video( + ... "A majestic lion running in a fantasy forest", + ... model="tencent/HunyuanVideo", + ... ) + >>> with open("lion.mp4", "wb") as file: + ... file.write(video) + ``` + + Example using a third-party provider through Hugging Face Routing. Usage will be billed on your Hugging Face account. + ```py + >>> from huggingface_hub import InferenceClient + >>> client = InferenceClient( + ... provider="replicate", # Using replicate provider + ... api_key="hf_...", # Pass your HF token + ... ) + >>> video = client.text_to_video( + ... "A cat running in a park", + ... model="genmo/mochi-1-preview", + ... ) + >>> with open("cat.mp4", "wb") as file: + ... file.write(video) + ``` + """ + provider_helper = get_provider_helper(self.provider, task="text-to-video") + request_parameters = provider_helper.prepare_request( + inputs=prompt, + parameters={ + "guidance_scale": guidance_scale, + "negative_prompt": negative_prompt, + "num_frames": num_frames, + "num_inference_steps": num_inference_steps, + "seed": seed, + **(extra_body or {}), + }, + headers=self.headers, + model=model or self.model, + api_key=self.token, + ) + response = await self._inner_post(request_parameters) + response = provider_helper.get_response(response) + return response + + async def text_to_speech( + self, + text: str, + *, + model: Optional[str] = None, + do_sample: Optional[bool] = None, + early_stopping: Optional[Union[bool, "TextToSpeechEarlyStoppingEnum"]] = None, + epsilon_cutoff: Optional[float] = None, + eta_cutoff: Optional[float] = None, + max_length: Optional[int] = None, + max_new_tokens: Optional[int] = None, + min_length: Optional[int] = None, + min_new_tokens: Optional[int] = None, + num_beam_groups: Optional[int] = None, + num_beams: Optional[int] = None, + penalty_alpha: Optional[float] = None, + temperature: Optional[float] = None, + top_k: Optional[int] = None, + top_p: Optional[float] = None, + typical_p: Optional[float] = None, + use_cache: Optional[bool] = None, + extra_body: Optional[Dict[str, Any]] = None, + ) -> bytes: + """ + Synthesize an audio of a voice pronouncing a given text. + + + You can pass provider-specific parameters to the model by using the `extra_body` argument. + + + Args: + text (`str`): + The text to synthesize. + model (`str`, *optional*): + The model to use for inference. Can be a model ID hosted on the Hugging Face Hub or a URL to a deployed + Inference Endpoint. If not provided, the default recommended text-to-speech model will be used. + Defaults to None. + do_sample (`bool`, *optional*): + Whether to use sampling instead of greedy decoding when generating new tokens. + early_stopping (`Union[bool, "TextToSpeechEarlyStoppingEnum"]`, *optional*): + Controls the stopping condition for beam-based methods. + epsilon_cutoff (`float`, *optional*): + If set to float strictly between 0 and 1, only tokens with a conditional probability greater than + epsilon_cutoff will be sampled. In the paper, suggested values range from 3e-4 to 9e-4, depending on + the size of the model. See [Truncation Sampling as Language Model + Desmoothing](https://hf.co/papers/2210.15191) for more details. + eta_cutoff (`float`, *optional*): + Eta sampling is a hybrid of locally typical sampling and epsilon sampling. If set to float strictly + between 0 and 1, a token is only considered if it is greater than either eta_cutoff or sqrt(eta_cutoff) + * exp(-entropy(softmax(next_token_logits))). The latter term is intuitively the expected next token + probability, scaled by sqrt(eta_cutoff). In the paper, suggested values range from 3e-4 to 2e-3, + depending on the size of the model. See [Truncation Sampling as Language Model + Desmoothing](https://hf.co/papers/2210.15191) for more details. + max_length (`int`, *optional*): + The maximum length (in tokens) of the generated text, including the input. + max_new_tokens (`int`, *optional*): + The maximum number of tokens to generate. Takes precedence over max_length. + min_length (`int`, *optional*): + The minimum length (in tokens) of the generated text, including the input. + min_new_tokens (`int`, *optional*): + The minimum number of tokens to generate. Takes precedence over min_length. + num_beam_groups (`int`, *optional*): + Number of groups to divide num_beams into in order to ensure diversity among different groups of beams. + See [this paper](https://hf.co/papers/1610.02424) for more details. + num_beams (`int`, *optional*): + Number of beams to use for beam search. + penalty_alpha (`float`, *optional*): + The value balances the model confidence and the degeneration penalty in contrastive search decoding. + temperature (`float`, *optional*): + The value used to modulate the next token probabilities. + top_k (`int`, *optional*): + The number of highest probability vocabulary tokens to keep for top-k-filtering. + top_p (`float`, *optional*): + If set to float < 1, only the smallest set of most probable tokens with probabilities that add up to + top_p or higher are kept for generation. + typical_p (`float`, *optional*): + Local typicality measures how similar the conditional probability of predicting a target token next is + to the expected conditional probability of predicting a random token next, given the partial text + already generated. If set to float < 1, the smallest set of the most locally typical tokens with + probabilities that add up to typical_p or higher are kept for generation. See [this + paper](https://hf.co/papers/2202.00666) for more details. + use_cache (`bool`, *optional*): + Whether the model should use the past last key/values attentions to speed up decoding + extra_body (`Dict[str, Any]`, *optional*): + Additional provider-specific parameters to pass to the model. Refer to the provider's documentation + for supported parameters. + Returns: + `bytes`: The generated audio. + + Raises: + [`InferenceTimeoutError`]: + If the model is unavailable or the request times out. + `aiohttp.ClientResponseError`: + If the request fails with an HTTP error status code other than HTTP 503. + + Example: + ```py + # Must be run in an async context + >>> from pathlib import Path + >>> from huggingface_hub import AsyncInferenceClient + >>> client = AsyncInferenceClient() + + >>> audio = await client.text_to_speech("Hello world") + >>> Path("hello_world.flac").write_bytes(audio) + ``` + + Example using a third-party provider directly. Usage will be billed on your Replicate account. + ```py + >>> from huggingface_hub import InferenceClient + >>> client = InferenceClient( + ... provider="replicate", + ... api_key="your-replicate-api-key", # Pass your Replicate API key directly + ... ) + >>> audio = client.text_to_speech( + ... text="Hello world", + ... model="OuteAI/OuteTTS-0.3-500M", + ... ) + >>> Path("hello_world.flac").write_bytes(audio) + ``` + + Example using a third-party provider through Hugging Face Routing. Usage will be billed on your Hugging Face account. + ```py + >>> from huggingface_hub import InferenceClient + >>> client = InferenceClient( + ... provider="replicate", + ... api_key="hf_...", # Pass your HF token + ... ) + >>> audio =client.text_to_speech( + ... text="Hello world", + ... model="OuteAI/OuteTTS-0.3-500M", + ... ) + >>> Path("hello_world.flac").write_bytes(audio) + ``` + Example using Replicate provider with extra parameters + ```py + >>> from huggingface_hub import InferenceClient + >>> client = InferenceClient( + ... provider="replicate", # Use replicate provider + ... api_key="hf_...", # Pass your HF token + ... ) + >>> audio = client.text_to_speech( + ... "Hello, my name is Kororo, an awesome text-to-speech model.", + ... model="hexgrad/Kokoro-82M", + ... extra_body={"voice": "af_nicole"}, + ... ) + >>> Path("hello.flac").write_bytes(audio) + ``` + + Example music-gen using "YuE-s1-7B-anneal-en-cot" on fal.ai + ```py + >>> from huggingface_hub import InferenceClient + >>> lyrics = ''' + ... [verse] + ... In the town where I was born + ... Lived a man who sailed to sea + ... And he told us of his life + ... In the land of submarines + ... So we sailed on to the sun + ... 'Til we found a sea of green + ... And we lived beneath the waves + ... In our yellow submarine + + ... [chorus] + ... We all live in a yellow submarine + ... Yellow submarine, yellow submarine + ... We all live in a yellow submarine + ... Yellow submarine, yellow submarine + ... ''' + >>> genres = "pavarotti-style tenor voice" + >>> client = InferenceClient( + ... provider="fal-ai", + ... model="m-a-p/YuE-s1-7B-anneal-en-cot", + ... api_key=..., + ... ) + >>> audio = client.text_to_speech(lyrics, extra_body={"genres": genres}) + >>> with open("output.mp3", "wb") as f: + ... f.write(audio) + ``` + """ + provider_helper = get_provider_helper(self.provider, task="text-to-speech") + request_parameters = provider_helper.prepare_request( + inputs=text, + parameters={ + "do_sample": do_sample, + "early_stopping": early_stopping, + "epsilon_cutoff": epsilon_cutoff, + "eta_cutoff": eta_cutoff, + "max_length": max_length, + "max_new_tokens": max_new_tokens, + "min_length": min_length, + "min_new_tokens": min_new_tokens, + "num_beam_groups": num_beam_groups, + "num_beams": num_beams, + "penalty_alpha": penalty_alpha, + "temperature": temperature, + "top_k": top_k, + "top_p": top_p, + "typical_p": typical_p, + "use_cache": use_cache, + **(extra_body or {}), + }, + headers=self.headers, + model=model or self.model, + api_key=self.token, + ) + response = await self._inner_post(request_parameters) + response = provider_helper.get_response(response) + return response + + async def token_classification( + self, + text: str, + *, + model: Optional[str] = None, + aggregation_strategy: Optional["TokenClassificationAggregationStrategy"] = None, + ignore_labels: Optional[List[str]] = None, + stride: Optional[int] = None, + ) -> List[TokenClassificationOutputElement]: + """ + Perform token classification on the given text. + Usually used for sentence parsing, either grammatical, or Named Entity Recognition (NER) to understand keywords contained within text. + + Args: + text (`str`): + A string to be classified. + model (`str`, *optional*): + The model to use for the token classification task. Can be a model ID hosted on the Hugging Face Hub or a URL to + a deployed Inference Endpoint. If not provided, the default recommended token classification model will be used. + Defaults to None. + aggregation_strategy (`"TokenClassificationAggregationStrategy"`, *optional*): + The strategy used to fuse tokens based on model predictions + ignore_labels (`List[str`, *optional*): + A list of labels to ignore + stride (`int`, *optional*): + The number of overlapping tokens between chunks when splitting the input text. + + Returns: + `List[TokenClassificationOutputElement]`: List of [`TokenClassificationOutputElement`] items containing the entity group, confidence score, word, start and end index. + + Raises: + [`InferenceTimeoutError`]: + If the model is unavailable or the request times out. + `aiohttp.ClientResponseError`: + If the request fails with an HTTP error status code other than HTTP 503. + + Example: + ```py + # Must be run in an async context + >>> from huggingface_hub import AsyncInferenceClient + >>> client = AsyncInferenceClient() + >>> await client.token_classification("My name is Sarah Jessica Parker but you can call me Jessica") + [ + TokenClassificationOutputElement( + entity_group='PER', + score=0.9971321225166321, + word='Sarah Jessica Parker', + start=11, + end=31, + ), + TokenClassificationOutputElement( + entity_group='PER', + score=0.9773476123809814, + word='Jessica', + start=52, + end=59, + ) + ] + ``` + """ + provider_helper = get_provider_helper(self.provider, task="token-classification") + request_parameters = provider_helper.prepare_request( + inputs=text, + parameters={ + "aggregation_strategy": aggregation_strategy, + "ignore_labels": ignore_labels, + "stride": stride, + }, + headers=self.headers, + model=model or self.model, + api_key=self.token, + ) + response = await self._inner_post(request_parameters) + return TokenClassificationOutputElement.parse_obj_as_list(response) + + async def translation( + self, + text: str, + *, + model: Optional[str] = None, + src_lang: Optional[str] = None, + tgt_lang: Optional[str] = None, + clean_up_tokenization_spaces: Optional[bool] = None, + truncation: Optional["TranslationTruncationStrategy"] = None, + generate_parameters: Optional[Dict[str, Any]] = None, + ) -> TranslationOutput: + """ + Convert text from one language to another. + + Check out https://huggingface.co/tasks/translation for more information on how to choose the best model for + your specific use case. Source and target languages usually depend on the model. + However, it is possible to specify source and target languages for certain models. If you are working with one of these models, + you can use `src_lang` and `tgt_lang` arguments to pass the relevant information. + + Args: + text (`str`): + A string to be translated. + model (`str`, *optional*): + The model to use for the translation task. Can be a model ID hosted on the Hugging Face Hub or a URL to + a deployed Inference Endpoint. If not provided, the default recommended translation model will be used. + Defaults to None. + src_lang (`str`, *optional*): + The source language of the text. Required for models that can translate from multiple languages. + tgt_lang (`str`, *optional*): + Target language to translate to. Required for models that can translate to multiple languages. + clean_up_tokenization_spaces (`bool`, *optional*): + Whether to clean up the potential extra spaces in the text output. + truncation (`"TranslationTruncationStrategy"`, *optional*): + The truncation strategy to use. + generate_parameters (`Dict[str, Any]`, *optional*): + Additional parametrization of the text generation algorithm. + + Returns: + [`TranslationOutput`]: The generated translated text. + + Raises: + [`InferenceTimeoutError`]: + If the model is unavailable or the request times out. + `aiohttp.ClientResponseError`: + If the request fails with an HTTP error status code other than HTTP 503. + `ValueError`: + If only one of the `src_lang` and `tgt_lang` arguments are provided. + + Example: + ```py + # Must be run in an async context + >>> from huggingface_hub import AsyncInferenceClient + >>> client = AsyncInferenceClient() + >>> await client.translation("My name is Wolfgang and I live in Berlin") + 'Mein Name ist Wolfgang und ich lebe in Berlin.' + >>> await client.translation("My name is Wolfgang and I live in Berlin", model="Helsinki-NLP/opus-mt-en-fr") + TranslationOutput(translation_text='Je m'appelle Wolfgang et je vis à Berlin.') + ``` + + Specifying languages: + ```py + >>> client.translation("My name is Sarah Jessica Parker but you can call me Jessica", model="facebook/mbart-large-50-many-to-many-mmt", src_lang="en_XX", tgt_lang="fr_XX") + "Mon nom est Sarah Jessica Parker mais vous pouvez m'appeler Jessica" + ``` + """ + # Throw error if only one of `src_lang` and `tgt_lang` was given + if src_lang is not None and tgt_lang is None: + raise ValueError("You cannot specify `src_lang` without specifying `tgt_lang`.") + + if src_lang is None and tgt_lang is not None: + raise ValueError("You cannot specify `tgt_lang` without specifying `src_lang`.") + + provider_helper = get_provider_helper(self.provider, task="translation") + request_parameters = provider_helper.prepare_request( + inputs=text, + parameters={ + "src_lang": src_lang, + "tgt_lang": tgt_lang, + "clean_up_tokenization_spaces": clean_up_tokenization_spaces, + "truncation": truncation, + "generate_parameters": generate_parameters, + }, + headers=self.headers, + model=model or self.model, + api_key=self.token, + ) + response = await self._inner_post(request_parameters) + return TranslationOutput.parse_obj_as_list(response)[0] + + async def visual_question_answering( + self, + image: ContentT, + question: str, + *, + model: Optional[str] = None, + top_k: Optional[int] = None, + ) -> List[VisualQuestionAnsweringOutputElement]: + """ + Answering open-ended questions based on an image. + + Args: + image (`Union[str, Path, bytes, BinaryIO]`): + The input image for the context. It can be raw bytes, an image file, or a URL to an online image. + question (`str`): + Question to be answered. + model (`str`, *optional*): + The model to use for the visual question answering task. Can be a model ID hosted on the Hugging Face Hub or a URL to + a deployed Inference Endpoint. If not provided, the default recommended visual question answering model will be used. + Defaults to None. + top_k (`int`, *optional*): + The number of answers to return (will be chosen by order of likelihood). Note that we return less than + topk answers if there are not enough options available within the context. + Returns: + `List[VisualQuestionAnsweringOutputElement]`: a list of [`VisualQuestionAnsweringOutputElement`] items containing the predicted label and associated probability. + + Raises: + `InferenceTimeoutError`: + If the model is unavailable or the request times out. + `aiohttp.ClientResponseError`: + If the request fails with an HTTP error status code other than HTTP 503. + + Example: + ```py + # Must be run in an async context + >>> from huggingface_hub import AsyncInferenceClient + >>> client = AsyncInferenceClient() + >>> await client.visual_question_answering( + ... image="https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", + ... question="What is the animal doing?" + ... ) + [ + VisualQuestionAnsweringOutputElement(score=0.778609573841095, answer='laying down'), + VisualQuestionAnsweringOutputElement(score=0.6957435607910156, answer='sitting'), + ] + ``` + """ + provider_helper = get_provider_helper(self.provider, task="visual-question-answering") + request_parameters = provider_helper.prepare_request( + inputs=image, + parameters={"top_k": top_k}, + headers=self.headers, + model=model or self.model, + api_key=self.token, + extra_payload={"question": question, "image": _b64_encode(image)}, + ) + response = await self._inner_post(request_parameters) + return VisualQuestionAnsweringOutputElement.parse_obj_as_list(response) + + @_deprecate_arguments( + version="0.30.0", + deprecated_args=["labels"], + custom_message="`labels`has been renamed to `candidate_labels` and will be removed in huggingface_hub>=0.30.0.", + ) + async def zero_shot_classification( + self, + text: str, + # temporarily keeping it optional for backward compatibility. + candidate_labels: List[str] = None, # type: ignore + *, + multi_label: Optional[bool] = False, + hypothesis_template: Optional[str] = None, + model: Optional[str] = None, + # deprecated argument + labels: List[str] = None, # type: ignore + ) -> List[ZeroShotClassificationOutputElement]: + """ + Provide as input a text and a set of candidate labels to classify the input text. + + Args: + text (`str`): + The input text to classify. + candidate_labels (`List[str]`): + The set of possible class labels to classify the text into. + labels (`List[str]`, *optional*): + (deprecated) List of strings. Each string is the verbalization of a possible label for the input text. + multi_label (`bool`, *optional*): + Whether multiple candidate labels can be true. If false, the scores are normalized such that the sum of + the label likelihoods for each sequence is 1. If true, the labels are considered independent and + probabilities are normalized for each candidate. + hypothesis_template (`str`, *optional*): + The sentence used in conjunction with `candidate_labels` to attempt the text classification by + replacing the placeholder with the candidate labels. + model (`str`, *optional*): + The model to use for inference. Can be a model ID hosted on the Hugging Face Hub or a URL to a deployed + Inference Endpoint. This parameter overrides the model defined at the instance level. If not provided, the default recommended zero-shot classification model will be used. + + + Returns: + `List[ZeroShotClassificationOutputElement]`: List of [`ZeroShotClassificationOutputElement`] items containing the predicted labels and their confidence. + + Raises: + [`InferenceTimeoutError`]: + If the model is unavailable or the request times out. + `aiohttp.ClientResponseError`: + If the request fails with an HTTP error status code other than HTTP 503. + + Example with `multi_label=False`: + ```py + # Must be run in an async context + >>> from huggingface_hub import AsyncInferenceClient + >>> client = AsyncInferenceClient() + >>> text = ( + ... "A new model offers an explanation for how the Galilean satellites formed around the solar system's" + ... "largest world. Konstantin Batygin did not set out to solve one of the solar system's most puzzling" + ... " mysteries when he went for a run up a hill in Nice, France." + ... ) + >>> labels = ["space & cosmos", "scientific discovery", "microbiology", "robots", "archeology"] + >>> await client.zero_shot_classification(text, labels) + [ + ZeroShotClassificationOutputElement(label='scientific discovery', score=0.7961668968200684), + ZeroShotClassificationOutputElement(label='space & cosmos', score=0.18570658564567566), + ZeroShotClassificationOutputElement(label='microbiology', score=0.00730885099619627), + ZeroShotClassificationOutputElement(label='archeology', score=0.006258360575884581), + ZeroShotClassificationOutputElement(label='robots', score=0.004559356719255447), + ] + >>> await client.zero_shot_classification(text, labels, multi_label=True) + [ + ZeroShotClassificationOutputElement(label='scientific discovery', score=0.9829297661781311), + ZeroShotClassificationOutputElement(label='space & cosmos', score=0.755190908908844), + ZeroShotClassificationOutputElement(label='microbiology', score=0.0005462635890580714), + ZeroShotClassificationOutputElement(label='archeology', score=0.00047131875180639327), + ZeroShotClassificationOutputElement(label='robots', score=0.00030448526376858354), + ] + ``` + + Example with `multi_label=True` and a custom `hypothesis_template`: + ```py + # Must be run in an async context + >>> from huggingface_hub import AsyncInferenceClient + >>> client = AsyncInferenceClient() + >>> await client.zero_shot_classification( + ... text="I really like our dinner and I'm very happy. I don't like the weather though.", + ... labels=["positive", "negative", "pessimistic", "optimistic"], + ... multi_label=True, + ... hypothesis_template="This text is {} towards the weather" + ... ) + [ + ZeroShotClassificationOutputElement(label='negative', score=0.9231801629066467), + ZeroShotClassificationOutputElement(label='pessimistic', score=0.8760990500450134), + ZeroShotClassificationOutputElement(label='optimistic', score=0.0008674879791215062), + ZeroShotClassificationOutputElement(label='positive', score=0.0005250611575320363) + ] + ``` + """ + # handle deprecation + if labels is not None: + if candidate_labels is not None: + raise ValueError( + "Cannot specify both `labels` and `candidate_labels`. Use `candidate_labels` instead." + ) + candidate_labels = labels + elif candidate_labels is None: + raise ValueError("Must specify `candidate_labels`") + + provider_helper = get_provider_helper(self.provider, task="zero-shot-classification") + request_parameters = provider_helper.prepare_request( + inputs=text, + parameters={ + "candidate_labels": candidate_labels, + "multi_label": multi_label, + "hypothesis_template": hypothesis_template, + }, + headers=self.headers, + model=model or self.model, + api_key=self.token, + ) + response = await self._inner_post(request_parameters) + output = _bytes_to_dict(response) + return [ + ZeroShotClassificationOutputElement.parse_obj_as_instance({"label": label, "score": score}) + for label, score in zip(output["labels"], output["scores"]) + ] + + @_deprecate_arguments( + version="0.30.0", + deprecated_args=["labels"], + custom_message="`labels`has been renamed to `candidate_labels` and will be removed in huggingface_hub>=0.30.0.", + ) + async def zero_shot_image_classification( + self, + image: ContentT, + # temporarily keeping it optional for backward compatibility. + candidate_labels: List[str] = None, # type: ignore + *, + model: Optional[str] = None, + hypothesis_template: Optional[str] = None, + # deprecated argument + labels: List[str] = None, # type: ignore + ) -> List[ZeroShotImageClassificationOutputElement]: + """ + Provide input image and text labels to predict text labels for the image. + + Args: + image (`Union[str, Path, bytes, BinaryIO]`): + The input image to caption. It can be raw bytes, an image file, or a URL to an online image. + candidate_labels (`List[str]`): + The candidate labels for this image + labels (`List[str]`, *optional*): + (deprecated) List of string possible labels. There must be at least 2 labels. + model (`str`, *optional*): + The model to use for inference. Can be a model ID hosted on the Hugging Face Hub or a URL to a deployed + Inference Endpoint. This parameter overrides the model defined at the instance level. If not provided, the default recommended zero-shot image classification model will be used. + hypothesis_template (`str`, *optional*): + The sentence used in conjunction with `candidate_labels` to attempt the image classification by + replacing the placeholder with the candidate labels. + + Returns: + `List[ZeroShotImageClassificationOutputElement]`: List of [`ZeroShotImageClassificationOutputElement`] items containing the predicted labels and their confidence. + + Raises: + [`InferenceTimeoutError`]: + If the model is unavailable or the request times out. + `aiohttp.ClientResponseError`: + If the request fails with an HTTP error status code other than HTTP 503. + + Example: + ```py + # Must be run in an async context + >>> from huggingface_hub import AsyncInferenceClient + >>> client = AsyncInferenceClient() + + >>> await client.zero_shot_image_classification( + ... "https://upload.wikimedia.org/wikipedia/commons/thumb/4/43/Cute_dog.jpg/320px-Cute_dog.jpg", + ... labels=["dog", "cat", "horse"], + ... ) + [ZeroShotImageClassificationOutputElement(label='dog', score=0.956),...] + ``` + """ + # handle deprecation + if labels is not None: + if candidate_labels is not None: + raise ValueError( + "Cannot specify both `labels` and `candidate_labels`. Use `candidate_labels` instead." + ) + candidate_labels = labels + elif candidate_labels is None: + raise ValueError("Must specify `candidate_labels`") + # Raise ValueError if input is less than 2 labels + if len(candidate_labels) < 2: + raise ValueError("You must specify at least 2 classes to compare.") + + provider_helper = get_provider_helper(self.provider, task="zero-shot-image-classification") + request_parameters = provider_helper.prepare_request( + inputs=image, + parameters={ + "candidate_labels": candidate_labels, + "hypothesis_template": hypothesis_template, + }, + headers=self.headers, + model=model or self.model, + api_key=self.token, + ) + response = await self._inner_post(request_parameters) + return ZeroShotImageClassificationOutputElement.parse_obj_as_list(response) + + @_deprecate_method( + version="0.33.0", + message=( + "HF Inference API is getting revamped and will only support warm models in the future (no cold start allowed)." + " Use `HfApi.list_models(..., inference_provider='...')` to list warm models per provider." + ), + ) + async def list_deployed_models( + self, frameworks: Union[None, str, Literal["all"], List[str]] = None + ) -> Dict[str, List[str]]: + """ + List models deployed on the HF Serverless Inference API service. + + This helper checks deployed models framework by framework. By default, it will check the 4 main frameworks that + are supported and account for 95% of the hosted models. However, if you want a complete list of models you can + specify `frameworks="all"` as input. Alternatively, if you know before-hand which framework you are interested + in, you can also restrict to search to this one (e.g. `frameworks="text-generation-inference"`). The more + frameworks are checked, the more time it will take. + + + + This endpoint method does not return a live list of all models available for the HF Inference API service. + It searches over a cached list of models that were recently available and the list may not be up to date. + If you want to know the live status of a specific model, use [`~InferenceClient.get_model_status`]. + + + + + + This endpoint method is mostly useful for discoverability. If you already know which model you want to use and want to + check its availability, you can directly use [`~InferenceClient.get_model_status`]. + + + + Args: + frameworks (`Literal["all"]` or `List[str]` or `str`, *optional*): + The frameworks to filter on. By default only a subset of the available frameworks are tested. If set to + "all", all available frameworks will be tested. It is also possible to provide a single framework or a + custom set of frameworks to check. + + Returns: + `Dict[str, List[str]]`: A dictionary mapping task names to a sorted list of model IDs. + + Example: + ```py + # Must be run in an async contextthon + >>> from huggingface_hub import AsyncInferenceClient + >>> client = AsyncInferenceClient() + + # Discover zero-shot-classification models currently deployed + >>> models = await client.list_deployed_models() + >>> models["zero-shot-classification"] + ['Narsil/deberta-large-mnli-zero-cls', 'facebook/bart-large-mnli', ...] + + # List from only 1 framework + >>> await client.list_deployed_models("text-generation-inference") + {'text-generation': ['bigcode/starcoder', 'meta-llama/Llama-2-70b-chat-hf', ...], ...} + ``` + """ + if self.provider != "hf-inference": + raise ValueError(f"Listing deployed models is not supported on '{self.provider}'.") + + # Resolve which frameworks to check + if frameworks is None: + frameworks = constants.MAIN_INFERENCE_API_FRAMEWORKS + elif frameworks == "all": + frameworks = constants.ALL_INFERENCE_API_FRAMEWORKS + elif isinstance(frameworks, str): + frameworks = [frameworks] + frameworks = list(set(frameworks)) + + # Fetch them iteratively + models_by_task: Dict[str, List[str]] = {} + + def _unpack_response(framework: str, items: List[Dict]) -> None: + for model in items: + if framework == "sentence-transformers": + # Model running with the `sentence-transformers` framework can work with both tasks even if not + # branded as such in the API response + models_by_task.setdefault("feature-extraction", []).append(model["model_id"]) + models_by_task.setdefault("sentence-similarity", []).append(model["model_id"]) + else: + models_by_task.setdefault(model["task"], []).append(model["model_id"]) + + for framework in frameworks: + response = get_session().get( + f"{constants.INFERENCE_ENDPOINT}/framework/{framework}", headers=build_hf_headers(token=self.token) + ) + hf_raise_for_status(response) + _unpack_response(framework, response.json()) + + # Sort alphabetically for discoverability and return + for task, models in models_by_task.items(): + models_by_task[task] = sorted(set(models), key=lambda x: x.lower()) + return models_by_task + + def _get_client_session(self, headers: Optional[Dict] = None) -> "ClientSession": + aiohttp = _import_aiohttp() + client_headers = self.headers.copy() + if headers is not None: + client_headers.update(headers) + + # Return a new aiohttp ClientSession with correct settings. + session = aiohttp.ClientSession( + headers=client_headers, + cookies=self.cookies, + timeout=aiohttp.ClientTimeout(self.timeout), + trust_env=self.trust_env, + ) + + # Keep track of sessions to close them later + self._sessions[session] = set() + + # Override the `._request` method to register responses to be closed + session._wrapped_request = session._request + + async def _request(method, url, **kwargs): + response = await session._wrapped_request(method, url, **kwargs) + self._sessions[session].add(response) + return response + + session._request = _request + + # Override the 'close' method to + # 1. close ongoing responses + # 2. deregister the session when closed + session._close = session.close + + async def close_session(): + for response in self._sessions[session]: + response.close() + await session._close() + self._sessions.pop(session, None) + + session.close = close_session + return session + + async def get_endpoint_info(self, *, model: Optional[str] = None) -> Dict[str, Any]: + """ + Get information about the deployed endpoint. + + This endpoint is only available on endpoints powered by Text-Generation-Inference (TGI) or Text-Embedding-Inference (TEI). + Endpoints powered by `transformers` return an empty payload. + + Args: + model (`str`, *optional*): + The model to use for inference. Can be a model ID hosted on the Hugging Face Hub or a URL to a deployed + Inference Endpoint. This parameter overrides the model defined at the instance level. Defaults to None. + + Returns: + `Dict[str, Any]`: Information about the endpoint. + + Example: + ```py + # Must be run in an async context + >>> from huggingface_hub import AsyncInferenceClient + >>> client = AsyncInferenceClient("meta-llama/Meta-Llama-3-70B-Instruct") + >>> await client.get_endpoint_info() + { + 'model_id': 'meta-llama/Meta-Llama-3-70B-Instruct', + 'model_sha': None, + 'model_dtype': 'torch.float16', + 'model_device_type': 'cuda', + 'model_pipeline_tag': None, + 'max_concurrent_requests': 128, + 'max_best_of': 2, + 'max_stop_sequences': 4, + 'max_input_length': 8191, + 'max_total_tokens': 8192, + 'waiting_served_ratio': 0.3, + 'max_batch_total_tokens': 1259392, + 'max_waiting_tokens': 20, + 'max_batch_size': None, + 'validation_workers': 32, + 'max_client_batch_size': 4, + 'version': '2.0.2', + 'sha': 'dccab72549635c7eb5ddb17f43f0b7cdff07c214', + 'docker_label': 'sha-dccab72' + } + ``` + """ + if self.provider != "hf-inference": + raise ValueError(f"Getting endpoint info is not supported on '{self.provider}'.") + + model = model or self.model + if model is None: + raise ValueError("Model id not provided.") + if model.startswith(("http://", "https://")): + url = model.rstrip("/") + "/info" + else: + url = f"{constants.INFERENCE_ENDPOINT}/models/{model}/info" + + async with self._get_client_session(headers=build_hf_headers(token=self.token)) as client: + response = await client.get(url, proxy=self.proxies) + response.raise_for_status() + return await response.json() + + async def health_check(self, model: Optional[str] = None) -> bool: + """ + Check the health of the deployed endpoint. + + Health check is only available with Inference Endpoints powered by Text-Generation-Inference (TGI) or Text-Embedding-Inference (TEI). + For Inference API, please use [`InferenceClient.get_model_status`] instead. + + Args: + model (`str`, *optional*): + URL of the Inference Endpoint. This parameter overrides the model defined at the instance level. Defaults to None. + + Returns: + `bool`: True if everything is working fine. + + Example: + ```py + # Must be run in an async context + >>> from huggingface_hub import AsyncInferenceClient + >>> client = AsyncInferenceClient("https://jzgu0buei5.us-east-1.aws.endpoints.huggingface.cloud") + >>> await client.health_check() + True + ``` + """ + if self.provider != "hf-inference": + raise ValueError(f"Health check is not supported on '{self.provider}'.") + + model = model or self.model + if model is None: + raise ValueError("Model id not provided.") + if not model.startswith(("http://", "https://")): + raise ValueError( + "Model must be an Inference Endpoint URL. For serverless Inference API, please use `InferenceClient.get_model_status`." + ) + url = model.rstrip("/") + "/health" + + async with self._get_client_session(headers=build_hf_headers(token=self.token)) as client: + response = await client.get(url, proxy=self.proxies) + return response.status == 200 + + @_deprecate_method( + version="0.33.0", + message=( + "HF Inference API is getting revamped and will only support warm models in the future (no cold start allowed)." + " Use `HfApi.model_info` to get the model status both with HF Inference API and external providers." + ), + ) + async def get_model_status(self, model: Optional[str] = None) -> ModelStatus: + """ + Get the status of a model hosted on the HF Inference API. + + + + This endpoint is mostly useful when you already know which model you want to use and want to check its + availability. If you want to discover already deployed models, you should rather use [`~InferenceClient.list_deployed_models`]. + + + + Args: + model (`str`, *optional*): + Identifier of the model for witch the status gonna be checked. If model is not provided, + the model associated with this instance of [`InferenceClient`] will be used. Only HF Inference API service can be checked so the + identifier cannot be a URL. + + + Returns: + [`ModelStatus`]: An instance of ModelStatus dataclass, containing information, + about the state of the model: load, state, compute type and framework. + + Example: + ```py + # Must be run in an async context + >>> from huggingface_hub import AsyncInferenceClient + >>> client = AsyncInferenceClient() + >>> await client.get_model_status("meta-llama/Meta-Llama-3-8B-Instruct") + ModelStatus(loaded=True, state='Loaded', compute_type='gpu', framework='text-generation-inference') + ``` + """ + if self.provider != "hf-inference": + raise ValueError(f"Getting model status is not supported on '{self.provider}'.") + + model = model or self.model + if model is None: + raise ValueError("Model id not provided.") + if model.startswith("https://"): + raise NotImplementedError("Model status is only available for Inference API endpoints.") + url = f"{constants.INFERENCE_ENDPOINT}/status/{model}" + + async with self._get_client_session(headers=build_hf_headers(token=self.token)) as client: + response = await client.get(url, proxy=self.proxies) + response.raise_for_status() + response_data = await response.json() + + if "error" in response_data: + raise ValueError(response_data["error"]) + + return ModelStatus( + loaded=response_data["loaded"], + state=response_data["state"], + compute_type=response_data["compute_type"], + framework=response_data["framework"], + ) + + @property + def chat(self) -> "ProxyClientChat": + return ProxyClientChat(self) + + +class _ProxyClient: + """Proxy class to be able to call `client.chat.completion.create(...)` as OpenAI client.""" + + def __init__(self, client: AsyncInferenceClient): + self._client = client + + +class ProxyClientChat(_ProxyClient): + """Proxy class to be able to call `client.chat.completion.create(...)` as OpenAI client.""" + + @property + def completions(self) -> "ProxyClientChatCompletions": + return ProxyClientChatCompletions(self._client) + + +class ProxyClientChatCompletions(_ProxyClient): + """Proxy class to be able to call `client.chat.completion.create(...)` as OpenAI client.""" + + @property + def create(self): + return self._client.chat_completion diff --git a/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/__init__.py b/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..edbc967cb634014ceeb9108be9f86aa7387010e9 --- /dev/null +++ b/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/__init__.py @@ -0,0 +1,187 @@ +# This file is auto-generated by `utils/generate_inference_types.py`. +# Do not modify it manually. +# +# ruff: noqa: F401 + +from .audio_classification import ( + AudioClassificationInput, + AudioClassificationOutputElement, + AudioClassificationOutputTransform, + AudioClassificationParameters, +) +from .audio_to_audio import AudioToAudioInput, AudioToAudioOutputElement +from .automatic_speech_recognition import ( + AutomaticSpeechRecognitionEarlyStoppingEnum, + AutomaticSpeechRecognitionGenerationParameters, + AutomaticSpeechRecognitionInput, + AutomaticSpeechRecognitionOutput, + AutomaticSpeechRecognitionOutputChunk, + AutomaticSpeechRecognitionParameters, +) +from .base import BaseInferenceType +from .chat_completion import ( + ChatCompletionInput, + ChatCompletionInputFunctionDefinition, + ChatCompletionInputFunctionName, + ChatCompletionInputGrammarType, + ChatCompletionInputGrammarTypeType, + ChatCompletionInputMessage, + ChatCompletionInputMessageChunk, + ChatCompletionInputMessageChunkType, + ChatCompletionInputStreamOptions, + ChatCompletionInputTool, + ChatCompletionInputToolChoiceClass, + ChatCompletionInputToolChoiceEnum, + ChatCompletionInputURL, + ChatCompletionOutput, + ChatCompletionOutputComplete, + ChatCompletionOutputFunctionDefinition, + ChatCompletionOutputLogprob, + ChatCompletionOutputLogprobs, + ChatCompletionOutputMessage, + ChatCompletionOutputToolCall, + ChatCompletionOutputTopLogprob, + ChatCompletionOutputUsage, + ChatCompletionStreamOutput, + ChatCompletionStreamOutputChoice, + ChatCompletionStreamOutputDelta, + ChatCompletionStreamOutputDeltaToolCall, + ChatCompletionStreamOutputFunction, + ChatCompletionStreamOutputLogprob, + ChatCompletionStreamOutputLogprobs, + ChatCompletionStreamOutputTopLogprob, + ChatCompletionStreamOutputUsage, +) +from .depth_estimation import DepthEstimationInput, DepthEstimationOutput +from .document_question_answering import ( + DocumentQuestionAnsweringInput, + DocumentQuestionAnsweringInputData, + DocumentQuestionAnsweringOutputElement, + DocumentQuestionAnsweringParameters, +) +from .feature_extraction import FeatureExtractionInput, FeatureExtractionInputTruncationDirection +from .fill_mask import FillMaskInput, FillMaskOutputElement, FillMaskParameters +from .image_classification import ( + ImageClassificationInput, + ImageClassificationOutputElement, + ImageClassificationOutputTransform, + ImageClassificationParameters, +) +from .image_segmentation import ( + ImageSegmentationInput, + ImageSegmentationOutputElement, + ImageSegmentationParameters, + ImageSegmentationSubtask, +) +from .image_to_image import ImageToImageInput, ImageToImageOutput, ImageToImageParameters, ImageToImageTargetSize +from .image_to_text import ( + ImageToTextEarlyStoppingEnum, + ImageToTextGenerationParameters, + ImageToTextInput, + ImageToTextOutput, + ImageToTextParameters, +) +from .object_detection import ( + ObjectDetectionBoundingBox, + ObjectDetectionInput, + ObjectDetectionOutputElement, + ObjectDetectionParameters, +) +from .question_answering import ( + QuestionAnsweringInput, + QuestionAnsweringInputData, + QuestionAnsweringOutputElement, + QuestionAnsweringParameters, +) +from .sentence_similarity import SentenceSimilarityInput, SentenceSimilarityInputData +from .summarization import ( + SummarizationInput, + SummarizationOutput, + SummarizationParameters, + SummarizationTruncationStrategy, +) +from .table_question_answering import ( + Padding, + TableQuestionAnsweringInput, + TableQuestionAnsweringInputData, + TableQuestionAnsweringOutputElement, + TableQuestionAnsweringParameters, +) +from .text2text_generation import ( + Text2TextGenerationInput, + Text2TextGenerationOutput, + Text2TextGenerationParameters, + Text2TextGenerationTruncationStrategy, +) +from .text_classification import ( + TextClassificationInput, + TextClassificationOutputElement, + TextClassificationOutputTransform, + TextClassificationParameters, +) +from .text_generation import ( + TextGenerationInput, + TextGenerationInputGenerateParameters, + TextGenerationInputGrammarType, + TextGenerationOutput, + TextGenerationOutputBestOfSequence, + TextGenerationOutputDetails, + TextGenerationOutputFinishReason, + TextGenerationOutputPrefillToken, + TextGenerationOutputToken, + TextGenerationStreamOutput, + TextGenerationStreamOutputStreamDetails, + TextGenerationStreamOutputToken, + TypeEnum, +) +from .text_to_audio import ( + TextToAudioEarlyStoppingEnum, + TextToAudioGenerationParameters, + TextToAudioInput, + TextToAudioOutput, + TextToAudioParameters, +) +from .text_to_image import TextToImageInput, TextToImageOutput, TextToImageParameters +from .text_to_speech import ( + TextToSpeechEarlyStoppingEnum, + TextToSpeechGenerationParameters, + TextToSpeechInput, + TextToSpeechOutput, + TextToSpeechParameters, +) +from .text_to_video import TextToVideoInput, TextToVideoOutput, TextToVideoParameters +from .token_classification import ( + TokenClassificationAggregationStrategy, + TokenClassificationInput, + TokenClassificationOutputElement, + TokenClassificationParameters, +) +from .translation import TranslationInput, TranslationOutput, TranslationParameters, TranslationTruncationStrategy +from .video_classification import ( + VideoClassificationInput, + VideoClassificationOutputElement, + VideoClassificationOutputTransform, + VideoClassificationParameters, +) +from .visual_question_answering import ( + VisualQuestionAnsweringInput, + VisualQuestionAnsweringInputData, + VisualQuestionAnsweringOutputElement, + VisualQuestionAnsweringParameters, +) +from .zero_shot_classification import ( + ZeroShotClassificationInput, + ZeroShotClassificationOutputElement, + ZeroShotClassificationParameters, +) +from .zero_shot_image_classification import ( + ZeroShotImageClassificationInput, + ZeroShotImageClassificationOutputElement, + ZeroShotImageClassificationParameters, +) +from .zero_shot_object_detection import ( + ZeroShotObjectDetectionBoundingBox, + ZeroShotObjectDetectionInput, + ZeroShotObjectDetectionOutputElement, + ZeroShotObjectDetectionParameters, +) diff --git a/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/__pycache__/audio_classification.cpython-310.pyc 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index 0000000000000000000000000000000000000000..053055787bce933e1fbd393cfbc00d81c43c8c2d --- /dev/null +++ b/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/audio_classification.py @@ -0,0 +1,43 @@ +# Inference code generated from the JSON schema spec in @huggingface/tasks. +# +# See: +# - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts +# - specs: https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks. +from typing import Literal, Optional + +from .base import BaseInferenceType, dataclass_with_extra + + +AudioClassificationOutputTransform = Literal["sigmoid", "softmax", "none"] + + +@dataclass_with_extra +class AudioClassificationParameters(BaseInferenceType): + """Additional inference parameters for Audio Classification""" + + function_to_apply: Optional["AudioClassificationOutputTransform"] = None + """The function to apply to the model outputs in order to retrieve the scores.""" + top_k: Optional[int] = None + """When specified, limits the output to the top K most probable classes.""" + + +@dataclass_with_extra +class AudioClassificationInput(BaseInferenceType): + """Inputs for Audio Classification inference""" + + inputs: str + """The input audio data as a base64-encoded string. If no `parameters` are provided, you can + also provide the audio data as a raw bytes payload. + """ + parameters: Optional[AudioClassificationParameters] = None + """Additional inference parameters for Audio Classification""" + + +@dataclass_with_extra +class AudioClassificationOutputElement(BaseInferenceType): + """Outputs for Audio Classification inference""" + + label: str + """The predicted class label.""" + score: float + """The corresponding probability.""" diff --git a/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/automatic_speech_recognition.py b/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/automatic_speech_recognition.py new file mode 100644 index 0000000000000000000000000000000000000000..1e885b6c6efe389cc5fb93a6ef7c9f956b1c3acc --- /dev/null +++ b/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/automatic_speech_recognition.py @@ -0,0 +1,114 @@ +# Inference code generated from the JSON schema spec in @huggingface/tasks. +# +# See: +# - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts +# - specs: https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks. +from typing import List, Literal, Optional, Union + +from .base import BaseInferenceType, dataclass_with_extra + + +AutomaticSpeechRecognitionEarlyStoppingEnum = Literal["never"] + + +@dataclass_with_extra +class AutomaticSpeechRecognitionGenerationParameters(BaseInferenceType): + """Parametrization of the text generation process""" + + do_sample: Optional[bool] = None + """Whether to use sampling instead of greedy decoding when generating new tokens.""" + early_stopping: Optional[Union[bool, "AutomaticSpeechRecognitionEarlyStoppingEnum"]] = None + """Controls the stopping condition for beam-based methods.""" + epsilon_cutoff: Optional[float] = None + """If set to float strictly between 0 and 1, only tokens with a conditional probability + greater than epsilon_cutoff will be sampled. In the paper, suggested values range from + 3e-4 to 9e-4, depending on the size of the model. See [Truncation Sampling as Language + Model Desmoothing](https://hf.co/papers/2210.15191) for more details. + """ + eta_cutoff: Optional[float] = None + """Eta sampling is a hybrid of locally typical sampling and epsilon sampling. If set to + float strictly between 0 and 1, a token is only considered if it is greater than either + eta_cutoff or sqrt(eta_cutoff) * exp(-entropy(softmax(next_token_logits))). The latter + term is intuitively the expected next token probability, scaled by sqrt(eta_cutoff). In + the paper, suggested values range from 3e-4 to 2e-3, depending on the size of the model. + See [Truncation Sampling as Language Model Desmoothing](https://hf.co/papers/2210.15191) + for more details. + """ + max_length: Optional[int] = None + """The maximum length (in tokens) of the generated text, including the input.""" + max_new_tokens: Optional[int] = None + """The maximum number of tokens to generate. Takes precedence over max_length.""" + min_length: Optional[int] = None + """The minimum length (in tokens) of the generated text, including the input.""" + min_new_tokens: Optional[int] = None + """The minimum number of tokens to generate. Takes precedence over min_length.""" + num_beam_groups: Optional[int] = None + """Number of groups to divide num_beams into in order to ensure diversity among different + groups of beams. See [this paper](https://hf.co/papers/1610.02424) for more details. + """ + num_beams: Optional[int] = None + """Number of beams to use for beam search.""" + penalty_alpha: Optional[float] = None + """The value balances the model confidence and the degeneration penalty in contrastive + search decoding. + """ + temperature: Optional[float] = None + """The value used to modulate the next token probabilities.""" + top_k: Optional[int] = None + """The number of highest probability vocabulary tokens to keep for top-k-filtering.""" + top_p: Optional[float] = None + """If set to float < 1, only the smallest set of most probable tokens with probabilities + that add up to top_p or higher are kept for generation. + """ + typical_p: Optional[float] = None + """Local typicality measures how similar the conditional probability of predicting a target + token next is to the expected conditional probability of predicting a random token next, + given the partial text already generated. If set to float < 1, the smallest set of the + most locally typical tokens with probabilities that add up to typical_p or higher are + kept for generation. See [this paper](https://hf.co/papers/2202.00666) for more details. + """ + use_cache: Optional[bool] = None + """Whether the model should use the past last key/values attentions to speed up decoding""" + + +@dataclass_with_extra +class AutomaticSpeechRecognitionParameters(BaseInferenceType): + """Additional inference parameters for Automatic Speech Recognition""" + + return_timestamps: Optional[bool] = None + """Whether to output corresponding timestamps with the generated text""" + # Will be deprecated in the future when the renaming to `generation_parameters` is implemented in transformers + generate_kwargs: Optional[AutomaticSpeechRecognitionGenerationParameters] = None + """Parametrization of the text generation process""" + + +@dataclass_with_extra +class AutomaticSpeechRecognitionInput(BaseInferenceType): + """Inputs for Automatic Speech Recognition inference""" + + inputs: str + """The input audio data as a base64-encoded string. If no `parameters` are provided, you can + also provide the audio data as a raw bytes payload. + """ + parameters: Optional[AutomaticSpeechRecognitionParameters] = None + """Additional inference parameters for Automatic Speech Recognition""" + + +@dataclass_with_extra +class AutomaticSpeechRecognitionOutputChunk(BaseInferenceType): + text: str + """A chunk of text identified by the model""" + timestamp: List[float] + """The start and end timestamps corresponding with the text""" + + +@dataclass_with_extra +class AutomaticSpeechRecognitionOutput(BaseInferenceType): + """Outputs of inference for the Automatic Speech Recognition task""" + + text: str + """The recognized text.""" + chunks: Optional[List[AutomaticSpeechRecognitionOutputChunk]] = None + """When returnTimestamps is enabled, chunks contains a list of audio chunks identified by + the model. + """ diff --git a/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/base.py b/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/base.py new file mode 100644 index 0000000000000000000000000000000000000000..1f0c4687ceccbfb738da3f38c583c2516d065a01 --- /dev/null +++ b/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/base.py @@ -0,0 +1,161 @@ +# Copyright 2024 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. +"""Contains a base class for all inference types.""" + +import inspect +import json +from dataclasses import asdict, dataclass +from typing import Any, Dict, List, Type, TypeVar, Union, get_args + + +T = TypeVar("T", bound="BaseInferenceType") + + +def _repr_with_extra(self): + fields = list(self.__dataclass_fields__.keys()) + other_fields = list(k for k in self.__dict__ if k not in fields) + return f"{self.__class__.__name__}({', '.join(f'{k}={self.__dict__[k]!r}' for k in fields + other_fields)})" + + +def dataclass_with_extra(cls: Type[T]) -> Type[T]: + """Decorator to add a custom __repr__ method to a dataclass, showing all fields, including extra ones. + + This decorator only works with dataclasses that inherit from `BaseInferenceType`. + """ + cls = dataclass(cls) + cls.__repr__ = _repr_with_extra # type: ignore[method-assign] + return cls + + +@dataclass +class BaseInferenceType(dict): + """Base class for all inference types. + + Object is a dataclass and a dict for backward compatibility but plan is to remove the dict part in the future. + + Handle parsing from dict, list and json strings in a permissive way to ensure future-compatibility (e.g. all fields + are made optional, and non-expected fields are added as dict attributes). + """ + + @classmethod + def parse_obj_as_list(cls: Type[T], data: Union[bytes, str, List, Dict]) -> List[T]: + """Alias to parse server response and return a single instance. + + See `parse_obj` for more details. + """ + output = cls.parse_obj(data) + if not isinstance(output, list): + raise ValueError(f"Invalid input data for {cls}. Expected a list, but got {type(output)}.") + return output + + @classmethod + def parse_obj_as_instance(cls: Type[T], data: Union[bytes, str, List, Dict]) -> T: + """Alias to parse server response and return a single instance. + + See `parse_obj` for more details. + """ + output = cls.parse_obj(data) + if isinstance(output, list): + raise ValueError(f"Invalid input data for {cls}. Expected a single instance, but got a list.") + return output + + @classmethod + def parse_obj(cls: Type[T], data: Union[bytes, str, List, Dict]) -> Union[List[T], T]: + """Parse server response as a dataclass or list of dataclasses. + + To enable future-compatibility, we want to handle cases where the server return more fields than expected. + In such cases, we don't want to raise an error but still create the dataclass object. Remaining fields are + added as dict attributes. + """ + # Parse server response (from bytes) + if isinstance(data, bytes): + data = data.decode() + if isinstance(data, str): + data = json.loads(data) + + # If a list, parse each item individually + if isinstance(data, List): + return [cls.parse_obj(d) for d in data] # type: ignore [misc] + + # At this point, we expect a dict + if not isinstance(data, dict): + raise ValueError(f"Invalid data type: {type(data)}") + + init_values = {} + other_values = {} + for key, value in data.items(): + key = normalize_key(key) + if key in cls.__dataclass_fields__ and cls.__dataclass_fields__[key].init: + if isinstance(value, dict) or isinstance(value, list): + field_type = cls.__dataclass_fields__[key].type + + # if `field_type` is a `BaseInferenceType`, parse it + if inspect.isclass(field_type) and issubclass(field_type, BaseInferenceType): + value = field_type.parse_obj(value) + + # otherwise, recursively parse nested dataclasses (if possible) + # `get_args` returns handle Union and Optional for us + else: + expected_types = get_args(field_type) + for expected_type in expected_types: + if getattr(expected_type, "_name", None) == "List": + expected_type = get_args(expected_type)[ + 0 + ] # assume same type for all items in the list + if inspect.isclass(expected_type) and issubclass(expected_type, BaseInferenceType): + value = expected_type.parse_obj(value) + break + init_values[key] = value + else: + other_values[key] = value + + # Make all missing fields default to None + # => ensure that dataclass initialization will never fail even if the server does not return all fields. + for key in cls.__dataclass_fields__: + if key not in init_values: + init_values[key] = None + + # Initialize dataclass with expected values + item = cls(**init_values) + + # Add remaining fields as dict attributes + item.update(other_values) + + # Add remaining fields as extra dataclass fields. + # They won't be part of the dataclass fields but will be accessible as attributes. + # Use @dataclass_with_extra to show them in __repr__. + item.__dict__.update(other_values) + return item + + def __post_init__(self): + self.update(asdict(self)) + + def __setitem__(self, __key: Any, __value: Any) -> None: + # Hacky way to keep dataclass values in sync when dict is updated + super().__setitem__(__key, __value) + if __key in self.__dataclass_fields__ and getattr(self, __key, None) != __value: + self.__setattr__(__key, __value) + return + + def __setattr__(self, __name: str, __value: Any) -> None: + # Hacky way to keep dict values is sync when dataclass is updated + super().__setattr__(__name, __value) + if self.get(__name) != __value: + self[__name] = __value + return + + +def normalize_key(key: str) -> str: + # e.g "content-type" -> "content_type", "Accept" -> "accept" + return key.replace("-", "_").replace(" ", "_").lower() diff --git a/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/document_question_answering.py b/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/document_question_answering.py new file mode 100644 index 0000000000000000000000000000000000000000..2457d2c8c237f055f660e0e8291d846bb036949d --- /dev/null +++ b/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/document_question_answering.py @@ -0,0 +1,80 @@ +# Inference code generated from the JSON schema spec in @huggingface/tasks. +# +# See: +# - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts +# - specs: https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks. +from typing import Any, List, Optional, Union + +from .base import BaseInferenceType, dataclass_with_extra + + +@dataclass_with_extra +class DocumentQuestionAnsweringInputData(BaseInferenceType): + """One (document, question) pair to answer""" + + image: Any + """The image on which the question is asked""" + question: str + """A question to ask of the document""" + + +@dataclass_with_extra +class DocumentQuestionAnsweringParameters(BaseInferenceType): + """Additional inference parameters for Document Question Answering""" + + doc_stride: Optional[int] = None + """If the words in the document are too long to fit with the question for the model, it will + be split in several chunks with some overlap. This argument controls the size of that + overlap. + """ + handle_impossible_answer: Optional[bool] = None + """Whether to accept impossible as an answer""" + lang: Optional[str] = None + """Language to use while running OCR. Defaults to english.""" + max_answer_len: Optional[int] = None + """The maximum length of predicted answers (e.g., only answers with a shorter length are + considered). + """ + max_question_len: Optional[int] = None + """The maximum length of the question after tokenization. It will be truncated if needed.""" + max_seq_len: Optional[int] = None + """The maximum length of the total sentence (context + question) in tokens of each chunk + passed to the model. The context will be split in several chunks (using doc_stride as + overlap) if needed. + """ + top_k: Optional[int] = None + """The number of answers to return (will be chosen by order of likelihood). Can return less + than top_k answers if there are not enough options available within the context. + """ + word_boxes: Optional[List[Union[List[float], str]]] = None + """A list of words and bounding boxes (normalized 0->1000). If provided, the inference will + skip the OCR step and use the provided bounding boxes instead. + """ + + +@dataclass_with_extra +class DocumentQuestionAnsweringInput(BaseInferenceType): + """Inputs for Document Question Answering inference""" + + inputs: DocumentQuestionAnsweringInputData + """One (document, question) pair to answer""" + parameters: Optional[DocumentQuestionAnsweringParameters] = None + """Additional inference parameters for Document Question Answering""" + + +@dataclass_with_extra +class DocumentQuestionAnsweringOutputElement(BaseInferenceType): + """Outputs of inference for the Document Question Answering task""" + + answer: str + """The answer to the question.""" + end: int + """The end word index of the answer (in the OCR’d version of the input or provided word + boxes). + """ + score: float + """The probability associated to the answer.""" + start: int + """The start word index of the answer (in the OCR’d version of the input or provided word + boxes). + """ diff --git a/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/feature_extraction.py b/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/feature_extraction.py new file mode 100644 index 0000000000000000000000000000000000000000..e965ddbac2af0a5bf73e662a7c18c847611d18a1 --- /dev/null +++ b/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/feature_extraction.py @@ -0,0 +1,36 @@ +# Inference code generated from the JSON schema spec in @huggingface/tasks. +# +# See: +# - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts +# - specs: https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks. +from typing import List, Literal, Optional, Union + +from .base import BaseInferenceType, dataclass_with_extra + + +FeatureExtractionInputTruncationDirection = Literal["Left", "Right"] + + +@dataclass_with_extra +class FeatureExtractionInput(BaseInferenceType): + """Feature Extraction Input. + Auto-generated from TEI specs. + For more details, check out + https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-tei-import.ts. + """ + + inputs: Union[List[str], str] + """The text or list of texts to embed.""" + normalize: Optional[bool] = None + prompt_name: Optional[str] = None + """The name of the prompt that should be used by for encoding. If not set, no prompt + will be applied. + Must be a key in the `sentence-transformers` configuration `prompts` dictionary. + For example if ``prompt_name`` is "query" and the ``prompts`` is {"query": "query: ", + ...}, + then the sentence "What is the capital of France?" will be encoded as + "query: What is the capital of France?" because the prompt text will be prepended before + any text to encode. + """ + truncate: Optional[bool] = None + truncation_direction: Optional["FeatureExtractionInputTruncationDirection"] = None diff --git a/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/fill_mask.py b/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/fill_mask.py new file mode 100644 index 0000000000000000000000000000000000000000..dfcdc56bc507e50280d38e0f63b024ada6a7ea94 --- /dev/null +++ b/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/fill_mask.py @@ -0,0 +1,47 @@ +# Inference code generated from the JSON schema spec in @huggingface/tasks. +# +# See: +# - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts +# - specs: https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks. +from typing import Any, List, Optional + +from .base import BaseInferenceType, dataclass_with_extra + + +@dataclass_with_extra +class FillMaskParameters(BaseInferenceType): + """Additional inference parameters for Fill Mask""" + + targets: Optional[List[str]] = None + """When passed, the model will limit the scores to the passed targets instead of looking up + in the whole vocabulary. If the provided targets are not in the model vocab, they will be + tokenized and the first resulting token will be used (with a warning, and that might be + slower). + """ + top_k: Optional[int] = None + """When passed, overrides the number of predictions to return.""" + + +@dataclass_with_extra +class FillMaskInput(BaseInferenceType): + """Inputs for Fill Mask inference""" + + inputs: str + """The text with masked tokens""" + parameters: Optional[FillMaskParameters] = None + """Additional inference parameters for Fill Mask""" + + +@dataclass_with_extra +class FillMaskOutputElement(BaseInferenceType): + """Outputs of inference for the Fill Mask task""" + + score: float + """The corresponding probability""" + sequence: str + """The corresponding input with the mask token prediction.""" + token: int + """The predicted token id (to replace the masked one).""" + token_str: Any + fill_mask_output_token_str: Optional[str] = None + """The predicted token (to replace the masked one).""" diff --git a/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/image_classification.py b/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/image_classification.py new file mode 100644 index 0000000000000000000000000000000000000000..0fdda6c83ff4c7aee5dc7794f0530e89d6b43047 --- /dev/null +++ b/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/image_classification.py @@ -0,0 +1,43 @@ +# Inference code generated from the JSON schema spec in @huggingface/tasks. +# +# See: +# - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts +# - specs: https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks. +from typing import Literal, Optional + +from .base import BaseInferenceType, dataclass_with_extra + + +ImageClassificationOutputTransform = Literal["sigmoid", "softmax", "none"] + + +@dataclass_with_extra +class ImageClassificationParameters(BaseInferenceType): + """Additional inference parameters for Image Classification""" + + function_to_apply: Optional["ImageClassificationOutputTransform"] = None + """The function to apply to the model outputs in order to retrieve the scores.""" + top_k: Optional[int] = None + """When specified, limits the output to the top K most probable classes.""" + + +@dataclass_with_extra +class ImageClassificationInput(BaseInferenceType): + """Inputs for Image Classification inference""" + + inputs: str + """The input image data as a base64-encoded string. If no `parameters` are provided, you can + also provide the image data as a raw bytes payload. + """ + parameters: Optional[ImageClassificationParameters] = None + """Additional inference parameters for Image Classification""" + + +@dataclass_with_extra +class ImageClassificationOutputElement(BaseInferenceType): + """Outputs of inference for the Image Classification task""" + + label: str + """The predicted class label.""" + score: float + """The corresponding probability.""" diff --git a/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/image_segmentation.py b/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/image_segmentation.py new file mode 100644 index 0000000000000000000000000000000000000000..3dbf61db83ec2ae6ceafd901c4425567cd2e5b03 --- /dev/null +++ b/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/image_segmentation.py @@ -0,0 +1,51 @@ +# Inference code generated from the JSON schema spec in @huggingface/tasks. +# +# See: +# - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts +# - specs: https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks. +from typing import Literal, Optional + +from .base import BaseInferenceType, dataclass_with_extra + + +ImageSegmentationSubtask = Literal["instance", "panoptic", "semantic"] + + +@dataclass_with_extra +class ImageSegmentationParameters(BaseInferenceType): + """Additional inference parameters for Image Segmentation""" + + mask_threshold: Optional[float] = None + """Threshold to use when turning the predicted masks into binary values.""" + overlap_mask_area_threshold: Optional[float] = None + """Mask overlap threshold to eliminate small, disconnected segments.""" + subtask: Optional["ImageSegmentationSubtask"] = None + """Segmentation task to be performed, depending on model capabilities.""" + threshold: Optional[float] = None + """Probability threshold to filter out predicted masks.""" + + +@dataclass_with_extra +class ImageSegmentationInput(BaseInferenceType): + """Inputs for Image Segmentation inference""" + + inputs: str + """The input image data as a base64-encoded string. If no `parameters` are provided, you can + also provide the image data as a raw bytes payload. + """ + parameters: Optional[ImageSegmentationParameters] = None + """Additional inference parameters for Image Segmentation""" + + +@dataclass_with_extra +class ImageSegmentationOutputElement(BaseInferenceType): + """Outputs of inference for the Image Segmentation task + A predicted mask / segment + """ + + label: str + """The label of the predicted segment.""" + mask: str + """The corresponding mask as a black-and-white image (base64-encoded).""" + score: Optional[float] = None + """The score or confidence degree the model has.""" diff --git a/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/image_to_image.py b/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/image_to_image.py new file mode 100644 index 0000000000000000000000000000000000000000..0deb0374dcab5a9a13616d2860a313fc17b35f7b --- /dev/null +++ b/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/image_to_image.py @@ -0,0 +1,54 @@ +# Inference code generated from the JSON schema spec in @huggingface/tasks. +# +# See: +# - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts +# - specs: https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks. +from typing import Any, Optional + +from .base import BaseInferenceType, dataclass_with_extra + + +@dataclass_with_extra +class ImageToImageTargetSize(BaseInferenceType): + """The size in pixel of the output image.""" + + height: int + width: int + + +@dataclass_with_extra +class ImageToImageParameters(BaseInferenceType): + """Additional inference parameters for Image To Image""" + + guidance_scale: Optional[float] = None + """For diffusion models. A higher guidance scale value encourages the model to generate + images closely linked to the text prompt at the expense of lower image quality. + """ + negative_prompt: Optional[str] = None + """One prompt to guide what NOT to include in image generation.""" + num_inference_steps: Optional[int] = None + """For diffusion models. The number of denoising steps. More denoising steps usually lead to + a higher quality image at the expense of slower inference. + """ + target_size: Optional[ImageToImageTargetSize] = None + """The size in pixel of the output image.""" + + +@dataclass_with_extra +class ImageToImageInput(BaseInferenceType): + """Inputs for Image To Image inference""" + + inputs: str + """The input image data as a base64-encoded string. If no `parameters` are provided, you can + also provide the image data as a raw bytes payload. + """ + parameters: Optional[ImageToImageParameters] = None + """Additional inference parameters for Image To Image""" + + +@dataclass_with_extra +class ImageToImageOutput(BaseInferenceType): + """Outputs of inference for the Image To Image task""" + + image: Any + """The output image returned as raw bytes in the payload.""" diff --git a/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/object_detection.py b/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/object_detection.py new file mode 100644 index 0000000000000000000000000000000000000000..75f3ebcfe1199462d0df60879b5ba6e517f7001e --- /dev/null +++ b/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/object_detection.py @@ -0,0 +1,58 @@ +# Inference code generated from the JSON schema spec in @huggingface/tasks. +# +# See: +# - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts +# - specs: https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks. +from typing import Optional + +from .base import BaseInferenceType, dataclass_with_extra + + +@dataclass_with_extra +class ObjectDetectionParameters(BaseInferenceType): + """Additional inference parameters for Object Detection""" + + threshold: Optional[float] = None + """The probability necessary to make a prediction.""" + + +@dataclass_with_extra +class ObjectDetectionInput(BaseInferenceType): + """Inputs for Object Detection inference""" + + inputs: str + """The input image data as a base64-encoded string. If no `parameters` are provided, you can + also provide the image data as a raw bytes payload. + """ + parameters: Optional[ObjectDetectionParameters] = None + """Additional inference parameters for Object Detection""" + + +@dataclass_with_extra +class ObjectDetectionBoundingBox(BaseInferenceType): + """The predicted bounding box. Coordinates are relative to the top left corner of the input + image. + """ + + xmax: int + """The x-coordinate of the bottom-right corner of the bounding box.""" + xmin: int + """The x-coordinate of the top-left corner of the bounding box.""" + ymax: int + """The y-coordinate of the bottom-right corner of the bounding box.""" + ymin: int + """The y-coordinate of the top-left corner of the bounding box.""" + + +@dataclass_with_extra +class ObjectDetectionOutputElement(BaseInferenceType): + """Outputs of inference for the Object Detection task""" + + box: ObjectDetectionBoundingBox + """The predicted bounding box. Coordinates are relative to the top left corner of the input + image. + """ + label: str + """The predicted label for the bounding box.""" + score: float + """The associated score / probability.""" diff --git a/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/question_answering.py b/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/question_answering.py new file mode 100644 index 0000000000000000000000000000000000000000..014ab41893c560a2c266bc04a1d60bc933be31c7 --- /dev/null +++ b/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/question_answering.py @@ -0,0 +1,74 @@ +# Inference code generated from the JSON schema spec in @huggingface/tasks. +# +# See: +# - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts +# - specs: https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks. +from typing import Optional + +from .base import BaseInferenceType, dataclass_with_extra + + +@dataclass_with_extra +class QuestionAnsweringInputData(BaseInferenceType): + """One (context, question) pair to answer""" + + context: str + """The context to be used for answering the question""" + question: str + """The question to be answered""" + + +@dataclass_with_extra +class QuestionAnsweringParameters(BaseInferenceType): + """Additional inference parameters for Question Answering""" + + align_to_words: Optional[bool] = None + """Attempts to align the answer to real words. Improves quality on space separated + languages. Might hurt on non-space-separated languages (like Japanese or Chinese) + """ + doc_stride: Optional[int] = None + """If the context is too long to fit with the question for the model, it will be split in + several chunks with some overlap. This argument controls the size of that overlap. + """ + handle_impossible_answer: Optional[bool] = None + """Whether to accept impossible as an answer.""" + max_answer_len: Optional[int] = None + """The maximum length of predicted answers (e.g., only answers with a shorter length are + considered). + """ + max_question_len: Optional[int] = None + """The maximum length of the question after tokenization. It will be truncated if needed.""" + max_seq_len: Optional[int] = None + """The maximum length of the total sentence (context + question) in tokens of each chunk + passed to the model. The context will be split in several chunks (using docStride as + overlap) if needed. + """ + top_k: Optional[int] = None + """The number of answers to return (will be chosen by order of likelihood). Note that we + return less than topk answers if there are not enough options available within the + context. + """ + + +@dataclass_with_extra +class QuestionAnsweringInput(BaseInferenceType): + """Inputs for Question Answering inference""" + + inputs: QuestionAnsweringInputData + """One (context, question) pair to answer""" + parameters: Optional[QuestionAnsweringParameters] = None + """Additional inference parameters for Question Answering""" + + +@dataclass_with_extra +class QuestionAnsweringOutputElement(BaseInferenceType): + """Outputs of inference for the Question Answering task""" + + answer: str + """The answer to the question.""" + end: int + """The character position in the input where the answer ends.""" + score: float + """The probability associated to the answer.""" + start: int + """The character position in the input where the answer begins.""" diff --git a/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/summarization.py b/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/summarization.py new file mode 100644 index 0000000000000000000000000000000000000000..33eae6fcba0e8724babf145f93be005868429c33 --- /dev/null +++ b/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/summarization.py @@ -0,0 +1,41 @@ +# Inference code generated from the JSON schema spec in @huggingface/tasks. +# +# See: +# - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts +# - specs: https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks. +from typing import Any, Dict, Literal, Optional + +from .base import BaseInferenceType, dataclass_with_extra + + +SummarizationTruncationStrategy = Literal["do_not_truncate", "longest_first", "only_first", "only_second"] + + +@dataclass_with_extra +class SummarizationParameters(BaseInferenceType): + """Additional inference parameters for summarization.""" + + clean_up_tokenization_spaces: Optional[bool] = None + """Whether to clean up the potential extra spaces in the text output.""" + generate_parameters: Optional[Dict[str, Any]] = None + """Additional parametrization of the text generation algorithm.""" + truncation: Optional["SummarizationTruncationStrategy"] = None + """The truncation strategy to use.""" + + +@dataclass_with_extra +class SummarizationInput(BaseInferenceType): + """Inputs for Summarization inference""" + + inputs: str + """The input text to summarize.""" + parameters: Optional[SummarizationParameters] = None + """Additional inference parameters for summarization.""" + + +@dataclass_with_extra +class SummarizationOutput(BaseInferenceType): + """Outputs of inference for the Summarization task""" + + summary_text: str + """The summarized text.""" diff --git a/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/table_question_answering.py b/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/table_question_answering.py new file mode 100644 index 0000000000000000000000000000000000000000..10e208eeeb50a689d2826a160432a2b005ec006c --- /dev/null +++ b/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/table_question_answering.py @@ -0,0 +1,62 @@ +# Inference code generated from the JSON schema spec in @huggingface/tasks. +# +# See: +# - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts +# - specs: https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks. +from typing import Dict, List, Literal, Optional + +from .base import BaseInferenceType, dataclass_with_extra + + +@dataclass_with_extra +class TableQuestionAnsweringInputData(BaseInferenceType): + """One (table, question) pair to answer""" + + question: str + """The question to be answered about the table""" + table: Dict[str, List[str]] + """The table to serve as context for the questions""" + + +Padding = Literal["do_not_pad", "longest", "max_length"] + + +@dataclass_with_extra +class TableQuestionAnsweringParameters(BaseInferenceType): + """Additional inference parameters for Table Question Answering""" + + padding: Optional["Padding"] = None + """Activates and controls padding.""" + sequential: Optional[bool] = None + """Whether to do inference sequentially or as a batch. Batching is faster, but models like + SQA require the inference to be done sequentially to extract relations within sequences, + given their conversational nature. + """ + truncation: Optional[bool] = None + """Activates and controls truncation.""" + + +@dataclass_with_extra +class TableQuestionAnsweringInput(BaseInferenceType): + """Inputs for Table Question Answering inference""" + + inputs: TableQuestionAnsweringInputData + """One (table, question) pair to answer""" + parameters: Optional[TableQuestionAnsweringParameters] = None + """Additional inference parameters for Table Question Answering""" + + +@dataclass_with_extra +class TableQuestionAnsweringOutputElement(BaseInferenceType): + """Outputs of inference for the Table Question Answering task""" + + answer: str + """The answer of the question given the table. If there is an aggregator, the answer will be + preceded by `AGGREGATOR >`. + """ + cells: List[str] + """List of strings made up of the answer cell values.""" + coordinates: List[List[int]] + """Coordinates of the cells of the answers.""" + aggregator: Optional[str] = None + """If the model has an aggregator, this returns the aggregator.""" diff --git a/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/text2text_generation.py b/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/text2text_generation.py new file mode 100644 index 0000000000000000000000000000000000000000..34ac74e21e8a30d889f1a251f648d4c365325be6 --- /dev/null +++ b/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/text2text_generation.py @@ -0,0 +1,42 @@ +# Inference code generated from the JSON schema spec in @huggingface/tasks. +# +# See: +# - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts +# - specs: https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks. +from typing import Any, Dict, Literal, Optional + +from .base import BaseInferenceType, dataclass_with_extra + + +Text2TextGenerationTruncationStrategy = Literal["do_not_truncate", "longest_first", "only_first", "only_second"] + + +@dataclass_with_extra +class Text2TextGenerationParameters(BaseInferenceType): + """Additional inference parameters for Text2text Generation""" + + clean_up_tokenization_spaces: Optional[bool] = None + """Whether to clean up the potential extra spaces in the text output.""" + generate_parameters: Optional[Dict[str, Any]] = None + """Additional parametrization of the text generation algorithm""" + truncation: Optional["Text2TextGenerationTruncationStrategy"] = None + """The truncation strategy to use""" + + +@dataclass_with_extra +class Text2TextGenerationInput(BaseInferenceType): + """Inputs for Text2text Generation inference""" + + inputs: str + """The input text data""" + parameters: Optional[Text2TextGenerationParameters] = None + """Additional inference parameters for Text2text Generation""" + + +@dataclass_with_extra +class Text2TextGenerationOutput(BaseInferenceType): + """Outputs of inference for the Text2text Generation task""" + + generated_text: Any + text2_text_generation_output_generated_text: Optional[str] = None + """The generated text.""" diff --git a/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/text_classification.py b/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/text_classification.py new file mode 100644 index 0000000000000000000000000000000000000000..9a172b23f844fa58f757a644d52138a18e7b6ddb --- /dev/null +++ b/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/text_classification.py @@ -0,0 +1,41 @@ +# Inference code generated from the JSON schema spec in @huggingface/tasks. +# +# See: +# - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts +# - specs: https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks. +from typing import Literal, Optional + +from .base import BaseInferenceType, dataclass_with_extra + + +TextClassificationOutputTransform = Literal["sigmoid", "softmax", "none"] + + +@dataclass_with_extra +class TextClassificationParameters(BaseInferenceType): + """Additional inference parameters for Text Classification""" + + function_to_apply: Optional["TextClassificationOutputTransform"] = None + """The function to apply to the model outputs in order to retrieve the scores.""" + top_k: Optional[int] = None + """When specified, limits the output to the top K most probable classes.""" + + +@dataclass_with_extra +class TextClassificationInput(BaseInferenceType): + """Inputs for Text Classification inference""" + + inputs: str + """The text to classify""" + parameters: Optional[TextClassificationParameters] = None + """Additional inference parameters for Text Classification""" + + +@dataclass_with_extra +class TextClassificationOutputElement(BaseInferenceType): + """Outputs of inference for the Text Classification task""" + + label: str + """The predicted class label.""" + score: float + """The corresponding probability.""" diff --git a/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/text_to_audio.py b/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/text_to_audio.py new file mode 100644 index 0000000000000000000000000000000000000000..963b4406e0c5a40e654ddb8a059731c051b6257a --- /dev/null +++ b/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/text_to_audio.py @@ -0,0 +1,100 @@ +# Inference code generated from the JSON schema spec in @huggingface/tasks. +# +# See: +# - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts +# - specs: https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks. +from typing import Any, Literal, Optional, Union + +from .base import BaseInferenceType, dataclass_with_extra + + +TextToAudioEarlyStoppingEnum = Literal["never"] + + +@dataclass_with_extra +class TextToAudioGenerationParameters(BaseInferenceType): + """Parametrization of the text generation process""" + + do_sample: Optional[bool] = None + """Whether to use sampling instead of greedy decoding when generating new tokens.""" + early_stopping: Optional[Union[bool, "TextToAudioEarlyStoppingEnum"]] = None + """Controls the stopping condition for beam-based methods.""" + epsilon_cutoff: Optional[float] = None + """If set to float strictly between 0 and 1, only tokens with a conditional probability + greater than epsilon_cutoff will be sampled. In the paper, suggested values range from + 3e-4 to 9e-4, depending on the size of the model. See [Truncation Sampling as Language + Model Desmoothing](https://hf.co/papers/2210.15191) for more details. + """ + eta_cutoff: Optional[float] = None + """Eta sampling is a hybrid of locally typical sampling and epsilon sampling. If set to + float strictly between 0 and 1, a token is only considered if it is greater than either + eta_cutoff or sqrt(eta_cutoff) * exp(-entropy(softmax(next_token_logits))). The latter + term is intuitively the expected next token probability, scaled by sqrt(eta_cutoff). In + the paper, suggested values range from 3e-4 to 2e-3, depending on the size of the model. + See [Truncation Sampling as Language Model Desmoothing](https://hf.co/papers/2210.15191) + for more details. + """ + max_length: Optional[int] = None + """The maximum length (in tokens) of the generated text, including the input.""" + max_new_tokens: Optional[int] = None + """The maximum number of tokens to generate. Takes precedence over max_length.""" + min_length: Optional[int] = None + """The minimum length (in tokens) of the generated text, including the input.""" + min_new_tokens: Optional[int] = None + """The minimum number of tokens to generate. Takes precedence over min_length.""" + num_beam_groups: Optional[int] = None + """Number of groups to divide num_beams into in order to ensure diversity among different + groups of beams. See [this paper](https://hf.co/papers/1610.02424) for more details. + """ + num_beams: Optional[int] = None + """Number of beams to use for beam search.""" + penalty_alpha: Optional[float] = None + """The value balances the model confidence and the degeneration penalty in contrastive + search decoding. + """ + temperature: Optional[float] = None + """The value used to modulate the next token probabilities.""" + top_k: Optional[int] = None + """The number of highest probability vocabulary tokens to keep for top-k-filtering.""" + top_p: Optional[float] = None + """If set to float < 1, only the smallest set of most probable tokens with probabilities + that add up to top_p or higher are kept for generation. + """ + typical_p: Optional[float] = None + """Local typicality measures how similar the conditional probability of predicting a target + token next is to the expected conditional probability of predicting a random token next, + given the partial text already generated. If set to float < 1, the smallest set of the + most locally typical tokens with probabilities that add up to typical_p or higher are + kept for generation. See [this paper](https://hf.co/papers/2202.00666) for more details. + """ + use_cache: Optional[bool] = None + """Whether the model should use the past last key/values attentions to speed up decoding""" + + +@dataclass_with_extra +class TextToAudioParameters(BaseInferenceType): + """Additional inference parameters for Text To Audio""" + + # Will be deprecated in the future when the renaming to `generation_parameters` is implemented in transformers + generate_kwargs: Optional[TextToAudioGenerationParameters] = None + """Parametrization of the text generation process""" + + +@dataclass_with_extra +class TextToAudioInput(BaseInferenceType): + """Inputs for Text To Audio inference""" + + inputs: str + """The input text data""" + parameters: Optional[TextToAudioParameters] = None + """Additional inference parameters for Text To Audio""" + + +@dataclass_with_extra +class TextToAudioOutput(BaseInferenceType): + """Outputs of inference for the Text To Audio task""" + + audio: Any + """The generated audio waveform.""" + sampling_rate: float + """The sampling rate of the generated audio waveform.""" diff --git a/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/text_to_image.py b/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/text_to_image.py new file mode 100644 index 0000000000000000000000000000000000000000..20c963731371339975019ca5d40c95303d79209b --- /dev/null +++ b/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/text_to_image.py @@ -0,0 +1,50 @@ +# Inference code generated from the JSON schema spec in @huggingface/tasks. +# +# See: +# - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts +# - specs: https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks. +from typing import Any, Optional + +from .base import BaseInferenceType, dataclass_with_extra + + +@dataclass_with_extra +class TextToImageParameters(BaseInferenceType): + """Additional inference parameters for Text To Image""" + + guidance_scale: Optional[float] = None + """A higher guidance scale value encourages the model to generate images closely linked to + the text prompt, but values too high may cause saturation and other artifacts. + """ + height: Optional[int] = None + """The height in pixels of the output image""" + negative_prompt: Optional[str] = None + """One prompt to guide what NOT to include in image generation.""" + num_inference_steps: Optional[int] = None + """The number of denoising steps. More denoising steps usually lead to a higher quality + image at the expense of slower inference. + """ + scheduler: Optional[str] = None + """Override the scheduler with a compatible one.""" + seed: Optional[int] = None + """Seed for the random number generator.""" + width: Optional[int] = None + """The width in pixels of the output image""" + + +@dataclass_with_extra +class TextToImageInput(BaseInferenceType): + """Inputs for Text To Image inference""" + + inputs: str + """The input text data (sometimes called "prompt")""" + parameters: Optional[TextToImageParameters] = None + """Additional inference parameters for Text To Image""" + + +@dataclass_with_extra +class TextToImageOutput(BaseInferenceType): + """Outputs of inference for the Text To Image task""" + + image: Any + """The generated image returned as raw bytes in the payload.""" diff --git a/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/text_to_speech.py b/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/text_to_speech.py new file mode 100644 index 0000000000000000000000000000000000000000..4399e2aea0fc86127cf9696c29f5ed8f5360d0d5 --- /dev/null +++ b/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/text_to_speech.py @@ -0,0 +1,100 @@ +# Inference code generated from the JSON schema spec in @huggingface/tasks. +# +# See: +# - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts +# - specs: https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks. +from typing import Any, Literal, Optional, Union + +from .base import BaseInferenceType, dataclass_with_extra + + +TextToSpeechEarlyStoppingEnum = Literal["never"] + + +@dataclass_with_extra +class TextToSpeechGenerationParameters(BaseInferenceType): + """Parametrization of the text generation process""" + + do_sample: Optional[bool] = None + """Whether to use sampling instead of greedy decoding when generating new tokens.""" + early_stopping: Optional[Union[bool, "TextToSpeechEarlyStoppingEnum"]] = None + """Controls the stopping condition for beam-based methods.""" + epsilon_cutoff: Optional[float] = None + """If set to float strictly between 0 and 1, only tokens with a conditional probability + greater than epsilon_cutoff will be sampled. In the paper, suggested values range from + 3e-4 to 9e-4, depending on the size of the model. See [Truncation Sampling as Language + Model Desmoothing](https://hf.co/papers/2210.15191) for more details. + """ + eta_cutoff: Optional[float] = None + """Eta sampling is a hybrid of locally typical sampling and epsilon sampling. If set to + float strictly between 0 and 1, a token is only considered if it is greater than either + eta_cutoff or sqrt(eta_cutoff) * exp(-entropy(softmax(next_token_logits))). The latter + term is intuitively the expected next token probability, scaled by sqrt(eta_cutoff). In + the paper, suggested values range from 3e-4 to 2e-3, depending on the size of the model. + See [Truncation Sampling as Language Model Desmoothing](https://hf.co/papers/2210.15191) + for more details. + """ + max_length: Optional[int] = None + """The maximum length (in tokens) of the generated text, including the input.""" + max_new_tokens: Optional[int] = None + """The maximum number of tokens to generate. Takes precedence over max_length.""" + min_length: Optional[int] = None + """The minimum length (in tokens) of the generated text, including the input.""" + min_new_tokens: Optional[int] = None + """The minimum number of tokens to generate. Takes precedence over min_length.""" + num_beam_groups: Optional[int] = None + """Number of groups to divide num_beams into in order to ensure diversity among different + groups of beams. See [this paper](https://hf.co/papers/1610.02424) for more details. + """ + num_beams: Optional[int] = None + """Number of beams to use for beam search.""" + penalty_alpha: Optional[float] = None + """The value balances the model confidence and the degeneration penalty in contrastive + search decoding. + """ + temperature: Optional[float] = None + """The value used to modulate the next token probabilities.""" + top_k: Optional[int] = None + """The number of highest probability vocabulary tokens to keep for top-k-filtering.""" + top_p: Optional[float] = None + """If set to float < 1, only the smallest set of most probable tokens with probabilities + that add up to top_p or higher are kept for generation. + """ + typical_p: Optional[float] = None + """Local typicality measures how similar the conditional probability of predicting a target + token next is to the expected conditional probability of predicting a random token next, + given the partial text already generated. If set to float < 1, the smallest set of the + most locally typical tokens with probabilities that add up to typical_p or higher are + kept for generation. See [this paper](https://hf.co/papers/2202.00666) for more details. + """ + use_cache: Optional[bool] = None + """Whether the model should use the past last key/values attentions to speed up decoding""" + + +@dataclass_with_extra +class TextToSpeechParameters(BaseInferenceType): + """Additional inference parameters for Text To Speech""" + + # Will be deprecated in the future when the renaming to `generation_parameters` is implemented in transformers + generate_kwargs: Optional[TextToSpeechGenerationParameters] = None + """Parametrization of the text generation process""" + + +@dataclass_with_extra +class TextToSpeechInput(BaseInferenceType): + """Inputs for Text To Speech inference""" + + inputs: str + """The input text data""" + parameters: Optional[TextToSpeechParameters] = None + """Additional inference parameters for Text To Speech""" + + +@dataclass_with_extra +class TextToSpeechOutput(BaseInferenceType): + """Outputs of inference for the Text To Speech task""" + + audio: Any + """The generated audio""" + sampling_rate: Optional[float] = None + """The sampling rate of the generated audio waveform.""" diff --git a/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/text_to_video.py b/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/text_to_video.py new file mode 100644 index 0000000000000000000000000000000000000000..e54a1bc094e4aaf7132e502aa268bc052ab34f0a --- /dev/null +++ b/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/text_to_video.py @@ -0,0 +1,46 @@ +# Inference code generated from the JSON schema spec in @huggingface/tasks. +# +# See: +# - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts +# - specs: https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks. +from typing import Any, List, Optional + +from .base import BaseInferenceType, dataclass_with_extra + + +@dataclass_with_extra +class TextToVideoParameters(BaseInferenceType): + """Additional inference parameters for Text To Video""" + + guidance_scale: Optional[float] = None + """A higher guidance scale value encourages the model to generate videos closely linked to + the text prompt, but values too high may cause saturation and other artifacts. + """ + negative_prompt: Optional[List[str]] = None + """One or several prompt to guide what NOT to include in video generation.""" + num_frames: Optional[float] = None + """The num_frames parameter determines how many video frames are generated.""" + num_inference_steps: Optional[int] = None + """The number of denoising steps. More denoising steps usually lead to a higher quality + video at the expense of slower inference. + """ + seed: Optional[int] = None + """Seed for the random number generator.""" + + +@dataclass_with_extra +class TextToVideoInput(BaseInferenceType): + """Inputs for Text To Video inference""" + + inputs: str + """The input text data (sometimes called "prompt")""" + parameters: Optional[TextToVideoParameters] = None + """Additional inference parameters for Text To Video""" + + +@dataclass_with_extra +class TextToVideoOutput(BaseInferenceType): + """Outputs of inference for the Text To Video task""" + + video: Any + """The generated video returned as raw bytes in the payload.""" diff --git a/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/translation.py b/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/translation.py new file mode 100644 index 0000000000000000000000000000000000000000..df95b7dbb1f4ce5b80cec034e004bb6e71387be8 --- /dev/null +++ b/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/translation.py @@ -0,0 +1,49 @@ +# Inference code generated from the JSON schema spec in @huggingface/tasks. +# +# See: +# - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts +# - specs: https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks. +from typing import Any, Dict, Literal, Optional + +from .base import BaseInferenceType, dataclass_with_extra + + +TranslationTruncationStrategy = Literal["do_not_truncate", "longest_first", "only_first", "only_second"] + + +@dataclass_with_extra +class TranslationParameters(BaseInferenceType): + """Additional inference parameters for Translation""" + + clean_up_tokenization_spaces: Optional[bool] = None + """Whether to clean up the potential extra spaces in the text output.""" + generate_parameters: Optional[Dict[str, Any]] = None + """Additional parametrization of the text generation algorithm.""" + src_lang: Optional[str] = None + """The source language of the text. Required for models that can translate from multiple + languages. + """ + tgt_lang: Optional[str] = None + """Target language to translate to. Required for models that can translate to multiple + languages. + """ + truncation: Optional["TranslationTruncationStrategy"] = None + """The truncation strategy to use.""" + + +@dataclass_with_extra +class TranslationInput(BaseInferenceType): + """Inputs for Translation inference""" + + inputs: str + """The text to translate.""" + parameters: Optional[TranslationParameters] = None + """Additional inference parameters for Translation""" + + +@dataclass_with_extra +class TranslationOutput(BaseInferenceType): + """Outputs of inference for the Translation task""" + + translation_text: str + """The translated text.""" diff --git a/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/video_classification.py b/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/video_classification.py new file mode 100644 index 0000000000000000000000000000000000000000..e1d7a15bb4ee5fa63aa6ebc3750191bd38549212 --- /dev/null +++ b/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/video_classification.py @@ -0,0 +1,45 @@ +# Inference code generated from the JSON schema spec in @huggingface/tasks. +# +# See: +# - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts +# - specs: https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks. +from typing import Any, Literal, Optional + +from .base import BaseInferenceType, dataclass_with_extra + + +VideoClassificationOutputTransform = Literal["sigmoid", "softmax", "none"] + + +@dataclass_with_extra +class VideoClassificationParameters(BaseInferenceType): + """Additional inference parameters for Video Classification""" + + frame_sampling_rate: Optional[int] = None + """The sampling rate used to select frames from the video.""" + function_to_apply: Optional["VideoClassificationOutputTransform"] = None + """The function to apply to the model outputs in order to retrieve the scores.""" + num_frames: Optional[int] = None + """The number of sampled frames to consider for classification.""" + top_k: Optional[int] = None + """When specified, limits the output to the top K most probable classes.""" + + +@dataclass_with_extra +class VideoClassificationInput(BaseInferenceType): + """Inputs for Video Classification inference""" + + inputs: Any + """The input video data""" + parameters: Optional[VideoClassificationParameters] = None + """Additional inference parameters for Video Classification""" + + +@dataclass_with_extra +class VideoClassificationOutputElement(BaseInferenceType): + """Outputs of inference for the Video Classification task""" + + label: str + """The predicted class label.""" + score: float + """The corresponding probability.""" diff --git a/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/visual_question_answering.py b/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/visual_question_answering.py new file mode 100644 index 0000000000000000000000000000000000000000..d368f1621289bc11a17be3e590cf8a040019d455 --- /dev/null +++ b/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/visual_question_answering.py @@ -0,0 +1,49 @@ +# Inference code generated from the JSON schema spec in @huggingface/tasks. +# +# See: +# - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts +# - specs: https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks. +from typing import Any, Optional + +from .base import BaseInferenceType, dataclass_with_extra + + +@dataclass_with_extra +class VisualQuestionAnsweringInputData(BaseInferenceType): + """One (image, question) pair to answer""" + + image: Any + """The image.""" + question: str + """The question to answer based on the image.""" + + +@dataclass_with_extra +class VisualQuestionAnsweringParameters(BaseInferenceType): + """Additional inference parameters for Visual Question Answering""" + + top_k: Optional[int] = None + """The number of answers to return (will be chosen by order of likelihood). Note that we + return less than topk answers if there are not enough options available within the + context. + """ + + +@dataclass_with_extra +class VisualQuestionAnsweringInput(BaseInferenceType): + """Inputs for Visual Question Answering inference""" + + inputs: VisualQuestionAnsweringInputData + """One (image, question) pair to answer""" + parameters: Optional[VisualQuestionAnsweringParameters] = None + """Additional inference parameters for Visual Question Answering""" + + +@dataclass_with_extra +class VisualQuestionAnsweringOutputElement(BaseInferenceType): + """Outputs of inference for the Visual Question Answering task""" + + score: float + """The associated score / probability""" + answer: Optional[str] = None + """The answer to the question""" diff --git a/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/zero_shot_classification.py b/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/zero_shot_classification.py new file mode 100644 index 0000000000000000000000000000000000000000..47b32492e358edcc0de6aa09d53635b0a8156b25 --- /dev/null +++ b/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/zero_shot_classification.py @@ -0,0 +1,45 @@ +# Inference code generated from the JSON schema spec in @huggingface/tasks. +# +# See: +# - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts +# - specs: https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks. +from typing import List, Optional + +from .base import BaseInferenceType, dataclass_with_extra + + +@dataclass_with_extra +class ZeroShotClassificationParameters(BaseInferenceType): + """Additional inference parameters for Zero Shot Classification""" + + candidate_labels: List[str] + """The set of possible class labels to classify the text into.""" + hypothesis_template: Optional[str] = None + """The sentence used in conjunction with `candidate_labels` to attempt the text + classification by replacing the placeholder with the candidate labels. + """ + multi_label: Optional[bool] = None + """Whether multiple candidate labels can be true. If false, the scores are normalized such + that the sum of the label likelihoods for each sequence is 1. If true, the labels are + considered independent and probabilities are normalized for each candidate. + """ + + +@dataclass_with_extra +class ZeroShotClassificationInput(BaseInferenceType): + """Inputs for Zero Shot Classification inference""" + + inputs: str + """The text to classify""" + parameters: ZeroShotClassificationParameters + """Additional inference parameters for Zero Shot Classification""" + + +@dataclass_with_extra +class ZeroShotClassificationOutputElement(BaseInferenceType): + """Outputs of inference for the Zero Shot Classification task""" + + label: str + """The predicted class label.""" + score: float + """The corresponding probability.""" diff --git a/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/zero_shot_image_classification.py b/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/zero_shot_image_classification.py new file mode 100644 index 0000000000000000000000000000000000000000..998d66b6b4e3356f0f09a0ad25ebdaf2e76cd03f --- /dev/null +++ b/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/zero_shot_image_classification.py @@ -0,0 +1,40 @@ +# Inference code generated from the JSON schema spec in @huggingface/tasks. +# +# See: +# - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts +# - specs: https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks. +from typing import List, Optional + +from .base import BaseInferenceType, dataclass_with_extra + + +@dataclass_with_extra +class ZeroShotImageClassificationParameters(BaseInferenceType): + """Additional inference parameters for Zero Shot Image Classification""" + + candidate_labels: List[str] + """The candidate labels for this image""" + hypothesis_template: Optional[str] = None + """The sentence used in conjunction with `candidate_labels` to attempt the image + classification by replacing the placeholder with the candidate labels. + """ + + +@dataclass_with_extra +class ZeroShotImageClassificationInput(BaseInferenceType): + """Inputs for Zero Shot Image Classification inference""" + + inputs: str + """The input image data to classify as a base64-encoded string.""" + parameters: ZeroShotImageClassificationParameters + """Additional inference parameters for Zero Shot Image Classification""" + + +@dataclass_with_extra +class ZeroShotImageClassificationOutputElement(BaseInferenceType): + """Outputs of inference for the Zero Shot Image Classification task""" + + label: str + """The predicted class label.""" + score: float + """The corresponding probability.""" diff --git a/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/zero_shot_object_detection.py b/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/zero_shot_object_detection.py new file mode 100644 index 0000000000000000000000000000000000000000..8ef76b5fcb93e8126266e4b1464934d01024b1b7 --- /dev/null +++ b/parrot/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/zero_shot_object_detection.py @@ -0,0 +1,52 @@ +# Inference code generated from the JSON schema spec in @huggingface/tasks. +# +# See: +# - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts +# - specs: https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks. +from typing import List + +from .base import BaseInferenceType, dataclass_with_extra + + +@dataclass_with_extra +class ZeroShotObjectDetectionParameters(BaseInferenceType): + """Additional inference parameters for Zero Shot Object Detection""" + + candidate_labels: List[str] + """The candidate labels for this image""" + + +@dataclass_with_extra +class ZeroShotObjectDetectionInput(BaseInferenceType): + """Inputs for Zero Shot Object Detection inference""" + + inputs: str + """The input image data as a base64-encoded string.""" + parameters: ZeroShotObjectDetectionParameters + """Additional inference parameters for Zero Shot Object Detection""" + + +@dataclass_with_extra +class ZeroShotObjectDetectionBoundingBox(BaseInferenceType): + """The predicted bounding box. Coordinates are relative to the top left corner of the input + image. + """ + + xmax: int + xmin: int + ymax: int + ymin: int + + +@dataclass_with_extra +class ZeroShotObjectDetectionOutputElement(BaseInferenceType): + """Outputs of inference for the Zero Shot Object Detection task""" + + box: ZeroShotObjectDetectionBoundingBox + """The predicted bounding box. Coordinates are relative to the top left corner of the input + image. + """ + label: str + """A candidate label""" + score: float + """The associated score / probability""" diff --git a/parrot/lib/python3.10/site-packages/huggingface_hub/utils/__init__.py b/parrot/lib/python3.10/site-packages/huggingface_hub/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..b9715dc0ad0b278fb91ba14e92a4c68905979762 --- /dev/null +++ b/parrot/lib/python3.10/site-packages/huggingface_hub/utils/__init__.py @@ -0,0 +1,110 @@ +# coding=utf-8 +# Copyright 2021 The HuggingFace Inc. 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 + +# ruff: noqa: F401 + +from huggingface_hub.errors import ( + BadRequestError, + CacheNotFound, + CorruptedCacheException, + DisabledRepoError, + EntryNotFoundError, + FileMetadataError, + GatedRepoError, + HfHubHTTPError, + HFValidationError, + LocalEntryNotFoundError, + LocalTokenNotFoundError, + NotASafetensorsRepoError, + OfflineModeIsEnabled, + RepositoryNotFoundError, + RevisionNotFoundError, + SafetensorsParsingError, +) + +from . import tqdm as _tqdm # _tqdm is the module +from ._auth import get_stored_tokens, get_token +from ._cache_assets import cached_assets_path +from ._cache_manager import ( + CachedFileInfo, + CachedRepoInfo, + CachedRevisionInfo, + DeleteCacheStrategy, + HFCacheInfo, + scan_cache_dir, +) +from ._chunk_utils import chunk_iterable +from ._datetime import parse_datetime +from ._experimental import experimental +from ._fixes import SoftTemporaryDirectory, WeakFileLock, yaml_dump +from ._git_credential import list_credential_helpers, set_git_credential, unset_git_credential +from ._headers import build_hf_headers, get_token_to_send +from ._hf_folder import HfFolder +from ._http import ( + configure_http_backend, + fix_hf_endpoint_in_url, + get_session, + hf_raise_for_status, + http_backoff, + reset_sessions, +) +from ._pagination import paginate +from ._paths import DEFAULT_IGNORE_PATTERNS, FORBIDDEN_FOLDERS, filter_repo_objects +from ._runtime import ( + dump_environment_info, + get_aiohttp_version, + get_fastai_version, + get_fastapi_version, + get_fastcore_version, + get_gradio_version, + get_graphviz_version, + get_hf_hub_version, + get_hf_transfer_version, + get_jinja_version, + get_numpy_version, + get_pillow_version, + get_pydantic_version, + get_pydot_version, + get_python_version, + get_tensorboard_version, + get_tf_version, + get_torch_version, + is_aiohttp_available, + is_colab_enterprise, + is_fastai_available, + is_fastapi_available, + is_fastcore_available, + is_google_colab, + is_gradio_available, + is_graphviz_available, + is_hf_transfer_available, + is_jinja_available, + is_notebook, + is_numpy_available, + is_package_available, + is_pillow_available, + is_pydantic_available, + is_pydot_available, + is_safetensors_available, + is_tensorboard_available, + is_tf_available, + is_torch_available, +) +from ._safetensors import SafetensorsFileMetadata, SafetensorsRepoMetadata, TensorInfo +from ._subprocess import capture_output, run_interactive_subprocess, run_subprocess +from ._telemetry import send_telemetry +from ._typing import is_jsonable, is_simple_optional_type, unwrap_simple_optional_type +from ._validators import smoothly_deprecate_use_auth_token, validate_hf_hub_args, validate_repo_id +from .tqdm import are_progress_bars_disabled, disable_progress_bars, enable_progress_bars, tqdm, tqdm_stream_file diff --git a/parrot/lib/python3.10/site-packages/huggingface_hub/utils/__pycache__/_cache_assets.cpython-310.pyc b/parrot/lib/python3.10/site-packages/huggingface_hub/utils/__pycache__/_cache_assets.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..6096775834d3ee9b00bea6f362c4aad75670608d Binary files /dev/null and b/parrot/lib/python3.10/site-packages/huggingface_hub/utils/__pycache__/_cache_assets.cpython-310.pyc differ diff --git a/parrot/lib/python3.10/site-packages/huggingface_hub/utils/__pycache__/_git_credential.cpython-310.pyc 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-0,0 +1,214 @@ +# Copyright 2023 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. +"""Contains an helper to get the token from machine (env variable, secret or config file).""" + +import configparser +import logging +import os +import warnings +from pathlib import Path +from threading import Lock +from typing import Dict, Optional + +from .. import constants +from ._runtime import is_colab_enterprise, is_google_colab + + +_IS_GOOGLE_COLAB_CHECKED = False +_GOOGLE_COLAB_SECRET_LOCK = Lock() +_GOOGLE_COLAB_SECRET: Optional[str] = None + +logger = logging.getLogger(__name__) + + +def get_token() -> Optional[str]: + """ + Get token if user is logged in. + + Note: in most cases, you should use [`huggingface_hub.utils.build_hf_headers`] instead. This method is only useful + if you want to retrieve the token for other purposes than sending an HTTP request. + + Token is retrieved in priority from the `HF_TOKEN` environment variable. Otherwise, we read the token file located + in the Hugging Face home folder. Returns None if user is not logged in. To log in, use [`login`] or + `huggingface-cli login`. + + Returns: + `str` or `None`: The token, `None` if it doesn't exist. + """ + return _get_token_from_google_colab() or _get_token_from_environment() or _get_token_from_file() + + +def _get_token_from_google_colab() -> Optional[str]: + """Get token from Google Colab secrets vault using `google.colab.userdata.get(...)`. + + Token is read from the vault only once per session and then stored in a global variable to avoid re-requesting + access to the vault. + """ + # If it's not a Google Colab or it's Colab Enterprise, fallback to environment variable or token file authentication + if not is_google_colab() or is_colab_enterprise(): + return None + + # `google.colab.userdata` is not thread-safe + # This can lead to a deadlock if multiple threads try to access it at the same time + # (typically when using `snapshot_download`) + # => use a lock + # See https://github.com/huggingface/huggingface_hub/issues/1952 for more details. + with _GOOGLE_COLAB_SECRET_LOCK: + global _GOOGLE_COLAB_SECRET + global _IS_GOOGLE_COLAB_CHECKED + + if _IS_GOOGLE_COLAB_CHECKED: # request access only once + return _GOOGLE_COLAB_SECRET + + try: + from google.colab import userdata # type: ignore + from google.colab.errors import Error as ColabError # type: ignore + except ImportError: + return None + + try: + token = userdata.get("HF_TOKEN") + _GOOGLE_COLAB_SECRET = _clean_token(token) + except userdata.NotebookAccessError: + # Means the user has a secret call `HF_TOKEN` and got a popup "please grand access to HF_TOKEN" and refused it + # => warn user but ignore error => do not re-request access to user + warnings.warn( + "\nAccess to the secret `HF_TOKEN` has not been granted on this notebook." + "\nYou will not be requested again." + "\nPlease restart the session if you want to be prompted again." + ) + _GOOGLE_COLAB_SECRET = None + except userdata.SecretNotFoundError: + # Means the user did not define a `HF_TOKEN` secret => warn + warnings.warn( + "\nThe secret `HF_TOKEN` does not exist in your Colab secrets." + "\nTo authenticate with the Hugging Face Hub, create a token in your settings tab " + "(https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session." + "\nYou will be able to reuse this secret in all of your notebooks." + "\nPlease note that authentication is recommended but still optional to access public models or datasets." + ) + _GOOGLE_COLAB_SECRET = None + except ColabError as e: + # Something happen but we don't know what => recommend to open a GitHub issue + warnings.warn( + f"\nError while fetching `HF_TOKEN` secret value from your vault: '{str(e)}'." + "\nYou are not authenticated with the Hugging Face Hub in this notebook." + "\nIf the error persists, please let us know by opening an issue on GitHub " + "(https://github.com/huggingface/huggingface_hub/issues/new)." + ) + _GOOGLE_COLAB_SECRET = None + + _IS_GOOGLE_COLAB_CHECKED = True + return _GOOGLE_COLAB_SECRET + + +def _get_token_from_environment() -> Optional[str]: + # `HF_TOKEN` has priority (keep `HUGGING_FACE_HUB_TOKEN` for backward compatibility) + return _clean_token(os.environ.get("HF_TOKEN") or os.environ.get("HUGGING_FACE_HUB_TOKEN")) + + +def _get_token_from_file() -> Optional[str]: + try: + return _clean_token(Path(constants.HF_TOKEN_PATH).read_text()) + except FileNotFoundError: + return None + + +def get_stored_tokens() -> Dict[str, str]: + """ + Returns the parsed INI file containing the access tokens. + The file is located at `HF_STORED_TOKENS_PATH`, defaulting to `~/.cache/huggingface/stored_tokens`. + If the file does not exist, an empty dictionary is returned. + + Returns: `Dict[str, str]` + Key is the token name and value is the token. + """ + tokens_path = Path(constants.HF_STORED_TOKENS_PATH) + if not tokens_path.exists(): + stored_tokens = {} + config = configparser.ConfigParser() + try: + config.read(tokens_path) + stored_tokens = {token_name: config.get(token_name, "hf_token") for token_name in config.sections()} + except configparser.Error as e: + logger.error(f"Error parsing stored tokens file: {e}") + stored_tokens = {} + return stored_tokens + + +def _save_stored_tokens(stored_tokens: Dict[str, str]) -> None: + """ + Saves the given configuration to the stored tokens file. + + Args: + stored_tokens (`Dict[str, str]`): + The stored tokens to save. Key is the token name and value is the token. + """ + stored_tokens_path = Path(constants.HF_STORED_TOKENS_PATH) + + # Write the stored tokens into an INI file + config = configparser.ConfigParser() + for token_name in sorted(stored_tokens.keys()): + config.add_section(token_name) + config.set(token_name, "hf_token", stored_tokens[token_name]) + + stored_tokens_path.parent.mkdir(parents=True, exist_ok=True) + with stored_tokens_path.open("w") as config_file: + config.write(config_file) + + +def _get_token_by_name(token_name: str) -> Optional[str]: + """ + Get the token by name. + + Args: + token_name (`str`): + The name of the token to get. + + Returns: + `str` or `None`: The token, `None` if it doesn't exist. + + """ + stored_tokens = get_stored_tokens() + if token_name not in stored_tokens: + return None + return _clean_token(stored_tokens[token_name]) + + +def _save_token(token: str, token_name: str) -> None: + """ + Save the given token. + + If the stored tokens file does not exist, it will be created. + Args: + token (`str`): + The token to save. + token_name (`str`): + The name of the token. + """ + tokens_path = Path(constants.HF_STORED_TOKENS_PATH) + stored_tokens = get_stored_tokens() + stored_tokens[token_name] = token + _save_stored_tokens(stored_tokens) + logger.info(f"The token `{token_name}` has been saved to {tokens_path}") + + +def _clean_token(token: Optional[str]) -> Optional[str]: + """Clean token by removing trailing and leading spaces and newlines. + + If token is an empty string, return None. + """ + if token is None: + return None + return token.replace("\r", "").replace("\n", "").strip() or None diff --git a/parrot/lib/python3.10/site-packages/huggingface_hub/utils/_cache_assets.py b/parrot/lib/python3.10/site-packages/huggingface_hub/utils/_cache_assets.py new file mode 100644 index 0000000000000000000000000000000000000000..e5d435df9b0bb0c67c0bcb5ef65711e9aef367f6 --- /dev/null +++ b/parrot/lib/python3.10/site-packages/huggingface_hub/utils/_cache_assets.py @@ -0,0 +1,135 @@ +# coding=utf-8 +# Copyright 2019-present, the HuggingFace Inc. team. +# +# 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 pathlib import Path +from typing import Union + +from ..constants import HF_ASSETS_CACHE + + +def cached_assets_path( + library_name: str, + namespace: str = "default", + subfolder: str = "default", + *, + assets_dir: Union[str, Path, None] = None, +): + """Return a folder path to cache arbitrary files. + + `huggingface_hub` provides a canonical folder path to store assets. This is the + recommended way to integrate cache in a downstream library as it will benefit from + the builtins tools to scan and delete the cache properly. + + The distinction is made between files cached from the Hub and assets. Files from the + Hub are cached in a git-aware manner and entirely managed by `huggingface_hub`. See + [related documentation](https://huggingface.co/docs/huggingface_hub/how-to-cache). + All other files that a downstream library caches are considered to be "assets" + (files downloaded from external sources, extracted from a .tar archive, preprocessed + for training,...). + + Once the folder path is generated, it is guaranteed to exist and to be a directory. + The path is based on 3 levels of depth: the library name, a namespace and a + subfolder. Those 3 levels grants flexibility while allowing `huggingface_hub` to + expect folders when scanning/deleting parts of the assets cache. Within a library, + it is expected that all namespaces share the same subset of subfolder names but this + is not a mandatory rule. The downstream library has then full control on which file + structure to adopt within its cache. Namespace and subfolder are optional (would + default to a `"default/"` subfolder) but library name is mandatory as we want every + downstream library to manage its own cache. + + Expected tree: + ```text + assets/ + └── datasets/ + │ ├── SQuAD/ + │ │ ├── downloaded/ + │ │ ├── extracted/ + │ │ └── processed/ + │ ├── Helsinki-NLP--tatoeba_mt/ + │ ├── downloaded/ + │ ├── extracted/ + │ └── processed/ + └── transformers/ + ├── default/ + │ ├── something/ + ├── bert-base-cased/ + │ ├── default/ + │ └── training/ + hub/ + └── models--julien-c--EsperBERTo-small/ + ├── blobs/ + │ ├── (...) + │ ├── (...) + ├── refs/ + │ └── (...) + └── [ 128] snapshots/ + ├── 2439f60ef33a0d46d85da5001d52aeda5b00ce9f/ + │ ├── (...) + └── bbc77c8132af1cc5cf678da3f1ddf2de43606d48/ + └── (...) + ``` + + + Args: + library_name (`str`): + Name of the library that will manage the cache folder. Example: `"dataset"`. + namespace (`str`, *optional*, defaults to "default"): + Namespace to which the data belongs. Example: `"SQuAD"`. + subfolder (`str`, *optional*, defaults to "default"): + Subfolder in which the data will be stored. Example: `extracted`. + assets_dir (`str`, `Path`, *optional*): + Path to the folder where assets are cached. This must not be the same folder + where Hub files are cached. Defaults to `HF_HOME / "assets"` if not provided. + Can also be set with `HF_ASSETS_CACHE` environment variable. + + Returns: + Path to the cache folder (`Path`). + + Example: + ```py + >>> from huggingface_hub import cached_assets_path + + >>> cached_assets_path(library_name="datasets", namespace="SQuAD", subfolder="download") + PosixPath('/home/wauplin/.cache/huggingface/extra/datasets/SQuAD/download') + + >>> cached_assets_path(library_name="datasets", namespace="SQuAD", subfolder="extracted") + PosixPath('/home/wauplin/.cache/huggingface/extra/datasets/SQuAD/extracted') + + >>> cached_assets_path(library_name="datasets", namespace="Helsinki-NLP/tatoeba_mt") + PosixPath('/home/wauplin/.cache/huggingface/extra/datasets/Helsinki-NLP--tatoeba_mt/default') + + >>> cached_assets_path(library_name="datasets", assets_dir="/tmp/tmp123456") + PosixPath('/tmp/tmp123456/datasets/default/default') + ``` + """ + # Resolve assets_dir + if assets_dir is None: + assets_dir = HF_ASSETS_CACHE + assets_dir = Path(assets_dir).expanduser().resolve() + + # Avoid names that could create path issues + for part in (" ", "/", "\\"): + library_name = library_name.replace(part, "--") + namespace = namespace.replace(part, "--") + subfolder = subfolder.replace(part, "--") + + # Path to subfolder is created + path = assets_dir / library_name / namespace / subfolder + try: + path.mkdir(exist_ok=True, parents=True) + except (FileExistsError, NotADirectoryError): + raise ValueError(f"Corrupted assets folder: cannot create directory because of an existing file ({path}).") + + # Return + return path diff --git a/parrot/lib/python3.10/site-packages/huggingface_hub/utils/_cache_manager.py b/parrot/lib/python3.10/site-packages/huggingface_hub/utils/_cache_manager.py new file mode 100644 index 0000000000000000000000000000000000000000..21469c97aff138a4bd015dc537d5809ef97cf88e --- /dev/null +++ b/parrot/lib/python3.10/site-packages/huggingface_hub/utils/_cache_manager.py @@ -0,0 +1,896 @@ +# coding=utf-8 +# Copyright 2022-present, the HuggingFace Inc. team. +# +# 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. +"""Contains utilities to manage the HF cache directory.""" + +import os +import shutil +import time +from collections import defaultdict +from dataclasses import dataclass +from pathlib import Path +from typing import Dict, FrozenSet, List, Literal, Optional, Set, Union + +from huggingface_hub.errors import CacheNotFound, CorruptedCacheException + +from ..commands._cli_utils import tabulate +from ..constants import HF_HUB_CACHE +from . import logging + + +logger = logging.get_logger(__name__) + +REPO_TYPE_T = Literal["model", "dataset", "space"] + +# List of OS-created helper files that need to be ignored +FILES_TO_IGNORE = [".DS_Store"] + + +@dataclass(frozen=True) +class CachedFileInfo: + """Frozen data structure holding information about a single cached file. + + Args: + file_name (`str`): + Name of the file. Example: `config.json`. + file_path (`Path`): + Path of the file in the `snapshots` directory. The file path is a symlink + referring to a blob in the `blobs` folder. + blob_path (`Path`): + Path of the blob file. This is equivalent to `file_path.resolve()`. + size_on_disk (`int`): + Size of the blob file in bytes. + blob_last_accessed (`float`): + Timestamp of the last time the blob file has been accessed (from any + revision). + blob_last_modified (`float`): + Timestamp of the last time the blob file has been modified/created. + + + + `blob_last_accessed` and `blob_last_modified` reliability can depend on the OS you + are using. See [python documentation](https://docs.python.org/3/library/os.html#os.stat_result) + for more details. + + + """ + + file_name: str + file_path: Path + blob_path: Path + size_on_disk: int + + blob_last_accessed: float + blob_last_modified: float + + @property + def blob_last_accessed_str(self) -> str: + """ + (property) Timestamp of the last time the blob file has been accessed (from any + revision), returned as a human-readable string. + + Example: "2 weeks ago". + """ + return _format_timesince(self.blob_last_accessed) + + @property + def blob_last_modified_str(self) -> str: + """ + (property) Timestamp of the last time the blob file has been modified, returned + as a human-readable string. + + Example: "2 weeks ago". + """ + return _format_timesince(self.blob_last_modified) + + @property + def size_on_disk_str(self) -> str: + """ + (property) Size of the blob file as a human-readable string. + + Example: "42.2K". + """ + return _format_size(self.size_on_disk) + + +@dataclass(frozen=True) +class CachedRevisionInfo: + """Frozen data structure holding information about a revision. + + A revision correspond to a folder in the `snapshots` folder and is populated with + the exact tree structure as the repo on the Hub but contains only symlinks. A + revision can be either referenced by 1 or more `refs` or be "detached" (no refs). + + Args: + commit_hash (`str`): + Hash of the revision (unique). + Example: `"9338f7b671827df886678df2bdd7cc7b4f36dffd"`. + snapshot_path (`Path`): + Path to the revision directory in the `snapshots` folder. It contains the + exact tree structure as the repo on the Hub. + files: (`FrozenSet[CachedFileInfo]`): + Set of [`~CachedFileInfo`] describing all files contained in the snapshot. + refs (`FrozenSet[str]`): + Set of `refs` pointing to this revision. If the revision has no `refs`, it + is considered detached. + Example: `{"main", "2.4.0"}` or `{"refs/pr/1"}`. + size_on_disk (`int`): + Sum of the blob file sizes that are symlink-ed by the revision. + last_modified (`float`): + Timestamp of the last time the revision has been created/modified. + + + + `last_accessed` cannot be determined correctly on a single revision as blob files + are shared across revisions. + + + + + + `size_on_disk` is not necessarily the sum of all file sizes because of possible + duplicated files. Besides, only blobs are taken into account, not the (negligible) + size of folders and symlinks. + + + """ + + commit_hash: str + snapshot_path: Path + size_on_disk: int + files: FrozenSet[CachedFileInfo] + refs: FrozenSet[str] + + last_modified: float + + @property + def last_modified_str(self) -> str: + """ + (property) Timestamp of the last time the revision has been modified, returned + as a human-readable string. + + Example: "2 weeks ago". + """ + return _format_timesince(self.last_modified) + + @property + def size_on_disk_str(self) -> str: + """ + (property) Sum of the blob file sizes as a human-readable string. + + Example: "42.2K". + """ + return _format_size(self.size_on_disk) + + @property + def nb_files(self) -> int: + """ + (property) Total number of files in the revision. + """ + return len(self.files) + + +@dataclass(frozen=True) +class CachedRepoInfo: + """Frozen data structure holding information about a cached repository. + + Args: + repo_id (`str`): + Repo id of the repo on the Hub. Example: `"google/fleurs"`. + repo_type (`Literal["dataset", "model", "space"]`): + Type of the cached repo. + repo_path (`Path`): + Local path to the cached repo. + size_on_disk (`int`): + Sum of the blob file sizes in the cached repo. + nb_files (`int`): + Total number of blob files in the cached repo. + revisions (`FrozenSet[CachedRevisionInfo]`): + Set of [`~CachedRevisionInfo`] describing all revisions cached in the repo. + last_accessed (`float`): + Timestamp of the last time a blob file of the repo has been accessed. + last_modified (`float`): + Timestamp of the last time a blob file of the repo has been modified/created. + + + + `size_on_disk` is not necessarily the sum of all revisions sizes because of + duplicated files. Besides, only blobs are taken into account, not the (negligible) + size of folders and symlinks. + + + + + + `last_accessed` and `last_modified` reliability can depend on the OS you are using. + See [python documentation](https://docs.python.org/3/library/os.html#os.stat_result) + for more details. + + + """ + + repo_id: str + repo_type: REPO_TYPE_T + repo_path: Path + size_on_disk: int + nb_files: int + revisions: FrozenSet[CachedRevisionInfo] + + last_accessed: float + last_modified: float + + @property + def last_accessed_str(self) -> str: + """ + (property) Last time a blob file of the repo has been accessed, returned as a + human-readable string. + + Example: "2 weeks ago". + """ + return _format_timesince(self.last_accessed) + + @property + def last_modified_str(self) -> str: + """ + (property) Last time a blob file of the repo has been modified, returned as a + human-readable string. + + Example: "2 weeks ago". + """ + return _format_timesince(self.last_modified) + + @property + def size_on_disk_str(self) -> str: + """ + (property) Sum of the blob file sizes as a human-readable string. + + Example: "42.2K". + """ + return _format_size(self.size_on_disk) + + @property + def refs(self) -> Dict[str, CachedRevisionInfo]: + """ + (property) Mapping between `refs` and revision data structures. + """ + return {ref: revision for revision in self.revisions for ref in revision.refs} + + +@dataclass(frozen=True) +class DeleteCacheStrategy: + """Frozen data structure holding the strategy to delete cached revisions. + + This object is not meant to be instantiated programmatically but to be returned by + [`~utils.HFCacheInfo.delete_revisions`]. See documentation for usage example. + + Args: + expected_freed_size (`float`): + Expected freed size once strategy is executed. + blobs (`FrozenSet[Path]`): + Set of blob file paths to be deleted. + refs (`FrozenSet[Path]`): + Set of reference file paths to be deleted. + repos (`FrozenSet[Path]`): + Set of entire repo paths to be deleted. + snapshots (`FrozenSet[Path]`): + Set of snapshots to be deleted (directory of symlinks). + """ + + expected_freed_size: int + blobs: FrozenSet[Path] + refs: FrozenSet[Path] + repos: FrozenSet[Path] + snapshots: FrozenSet[Path] + + @property + def expected_freed_size_str(self) -> str: + """ + (property) Expected size that will be freed as a human-readable string. + + Example: "42.2K". + """ + return _format_size(self.expected_freed_size) + + def execute(self) -> None: + """Execute the defined strategy. + + + + If this method is interrupted, the cache might get corrupted. Deletion order is + implemented so that references and symlinks are deleted before the actual blob + files. + + + + + + This method is irreversible. If executed, cached files are erased and must be + downloaded again. + + + """ + # Deletion order matters. Blobs are deleted in last so that the user can't end + # up in a state where a `ref`` refers to a missing snapshot or a snapshot + # symlink refers to a deleted blob. + + # Delete entire repos + for path in self.repos: + _try_delete_path(path, path_type="repo") + + # Delete snapshot directories + for path in self.snapshots: + _try_delete_path(path, path_type="snapshot") + + # Delete refs files + for path in self.refs: + _try_delete_path(path, path_type="ref") + + # Delete blob files + for path in self.blobs: + _try_delete_path(path, path_type="blob") + + logger.info(f"Cache deletion done. Saved {self.expected_freed_size_str}.") + + +@dataclass(frozen=True) +class HFCacheInfo: + """Frozen data structure holding information about the entire cache-system. + + This data structure is returned by [`scan_cache_dir`] and is immutable. + + Args: + size_on_disk (`int`): + Sum of all valid repo sizes in the cache-system. + repos (`FrozenSet[CachedRepoInfo]`): + Set of [`~CachedRepoInfo`] describing all valid cached repos found on the + cache-system while scanning. + warnings (`List[CorruptedCacheException]`): + List of [`~CorruptedCacheException`] that occurred while scanning the cache. + Those exceptions are captured so that the scan can continue. Corrupted repos + are skipped from the scan. + + + + Here `size_on_disk` is equal to the sum of all repo sizes (only blobs). However if + some cached repos are corrupted, their sizes are not taken into account. + + + """ + + size_on_disk: int + repos: FrozenSet[CachedRepoInfo] + warnings: List[CorruptedCacheException] + + @property + def size_on_disk_str(self) -> str: + """ + (property) Sum of all valid repo sizes in the cache-system as a human-readable + string. + + Example: "42.2K". + """ + return _format_size(self.size_on_disk) + + def delete_revisions(self, *revisions: str) -> DeleteCacheStrategy: + """Prepare the strategy to delete one or more revisions cached locally. + + Input revisions can be any revision hash. If a revision hash is not found in the + local cache, a warning is thrown but no error is raised. Revisions can be from + different cached repos since hashes are unique across repos, + + Examples: + ```py + >>> from huggingface_hub import scan_cache_dir + >>> cache_info = scan_cache_dir() + >>> delete_strategy = cache_info.delete_revisions( + ... "81fd1d6e7847c99f5862c9fb81387956d99ec7aa" + ... ) + >>> print(f"Will free {delete_strategy.expected_freed_size_str}.") + Will free 7.9K. + >>> delete_strategy.execute() + Cache deletion done. Saved 7.9K. + ``` + + ```py + >>> from huggingface_hub import scan_cache_dir + >>> scan_cache_dir().delete_revisions( + ... "81fd1d6e7847c99f5862c9fb81387956d99ec7aa", + ... "e2983b237dccf3ab4937c97fa717319a9ca1a96d", + ... "6c0e6080953db56375760c0471a8c5f2929baf11", + ... ).execute() + Cache deletion done. Saved 8.6G. + ``` + + + + `delete_revisions` returns a [`~utils.DeleteCacheStrategy`] object that needs to + be executed. The [`~utils.DeleteCacheStrategy`] is not meant to be modified but + allows having a dry run before actually executing the deletion. + + + """ + hashes_to_delete: Set[str] = set(revisions) + + repos_with_revisions: Dict[CachedRepoInfo, Set[CachedRevisionInfo]] = defaultdict(set) + + for repo in self.repos: + for revision in repo.revisions: + if revision.commit_hash in hashes_to_delete: + repos_with_revisions[repo].add(revision) + hashes_to_delete.remove(revision.commit_hash) + + if len(hashes_to_delete) > 0: + logger.warning(f"Revision(s) not found - cannot delete them: {', '.join(hashes_to_delete)}") + + delete_strategy_blobs: Set[Path] = set() + delete_strategy_refs: Set[Path] = set() + delete_strategy_repos: Set[Path] = set() + delete_strategy_snapshots: Set[Path] = set() + delete_strategy_expected_freed_size = 0 + + for affected_repo, revisions_to_delete in repos_with_revisions.items(): + other_revisions = affected_repo.revisions - revisions_to_delete + + # If no other revisions, it means all revisions are deleted + # -> delete the entire cached repo + if len(other_revisions) == 0: + delete_strategy_repos.add(affected_repo.repo_path) + delete_strategy_expected_freed_size += affected_repo.size_on_disk + continue + + # Some revisions of the repo will be deleted but not all. We need to filter + # which blob files will not be linked anymore. + for revision_to_delete in revisions_to_delete: + # Snapshot dir + delete_strategy_snapshots.add(revision_to_delete.snapshot_path) + + # Refs dir + for ref in revision_to_delete.refs: + delete_strategy_refs.add(affected_repo.repo_path / "refs" / ref) + + # Blobs dir + for file in revision_to_delete.files: + if file.blob_path not in delete_strategy_blobs: + is_file_alone = True + for revision in other_revisions: + for rev_file in revision.files: + if file.blob_path == rev_file.blob_path: + is_file_alone = False + break + if not is_file_alone: + break + + # Blob file not referenced by remaining revisions -> delete + if is_file_alone: + delete_strategy_blobs.add(file.blob_path) + delete_strategy_expected_freed_size += file.size_on_disk + + # Return the strategy instead of executing it. + return DeleteCacheStrategy( + blobs=frozenset(delete_strategy_blobs), + refs=frozenset(delete_strategy_refs), + repos=frozenset(delete_strategy_repos), + snapshots=frozenset(delete_strategy_snapshots), + expected_freed_size=delete_strategy_expected_freed_size, + ) + + def export_as_table(self, *, verbosity: int = 0) -> str: + """Generate a table from the [`HFCacheInfo`] object. + + Pass `verbosity=0` to get a table with a single row per repo, with columns + "repo_id", "repo_type", "size_on_disk", "nb_files", "last_accessed", "last_modified", "refs", "local_path". + + Pass `verbosity=1` to get a table with a row per repo and revision (thus multiple rows can appear for a single repo), with columns + "repo_id", "repo_type", "revision", "size_on_disk", "nb_files", "last_modified", "refs", "local_path". + + Example: + ```py + >>> from huggingface_hub.utils import scan_cache_dir + + >>> hf_cache_info = scan_cache_dir() + HFCacheInfo(...) + + >>> print(hf_cache_info.export_as_table()) + REPO ID REPO TYPE SIZE ON DISK NB FILES LAST_ACCESSED LAST_MODIFIED REFS LOCAL PATH + --------------------------------------------------- --------- ------------ -------- ------------- ------------- ---- -------------------------------------------------------------------------------------------------- + roberta-base model 2.7M 5 1 day ago 1 week ago main ~/.cache/huggingface/hub/models--roberta-base + suno/bark model 8.8K 1 1 week ago 1 week ago main ~/.cache/huggingface/hub/models--suno--bark + t5-base model 893.8M 4 4 days ago 7 months ago main ~/.cache/huggingface/hub/models--t5-base + t5-large model 3.0G 4 5 weeks ago 5 months ago main ~/.cache/huggingface/hub/models--t5-large + + >>> print(hf_cache_info.export_as_table(verbosity=1)) + REPO ID REPO TYPE REVISION SIZE ON DISK NB FILES LAST_MODIFIED REFS LOCAL PATH + --------------------------------------------------- --------- ---------------------------------------- ------------ -------- ------------- ---- ----------------------------------------------------------------------------------------------------------------------------------------------------- + roberta-base model e2da8e2f811d1448a5b465c236feacd80ffbac7b 2.7M 5 1 week ago main ~/.cache/huggingface/hub/models--roberta-base/snapshots/e2da8e2f811d1448a5b465c236feacd80ffbac7b + suno/bark model 70a8a7d34168586dc5d028fa9666aceade177992 8.8K 1 1 week ago main ~/.cache/huggingface/hub/models--suno--bark/snapshots/70a8a7d34168586dc5d028fa9666aceade177992 + t5-base model a9723ea7f1b39c1eae772870f3b547bf6ef7e6c1 893.8M 4 7 months ago main ~/.cache/huggingface/hub/models--t5-base/snapshots/a9723ea7f1b39c1eae772870f3b547bf6ef7e6c1 + t5-large model 150ebc2c4b72291e770f58e6057481c8d2ed331a 3.0G 4 5 months ago main ~/.cache/huggingface/hub/models--t5-large/snapshots/150ebc2c4b72291e770f58e6057481c8d2ed331a + ``` + + Args: + verbosity (`int`, *optional*): + The verbosity level. Defaults to 0. + + Returns: + `str`: The table as a string. + """ + if verbosity == 0: + return tabulate( + rows=[ + [ + repo.repo_id, + repo.repo_type, + "{:>12}".format(repo.size_on_disk_str), + repo.nb_files, + repo.last_accessed_str, + repo.last_modified_str, + ", ".join(sorted(repo.refs)), + str(repo.repo_path), + ] + for repo in sorted(self.repos, key=lambda repo: repo.repo_path) + ], + headers=[ + "REPO ID", + "REPO TYPE", + "SIZE ON DISK", + "NB FILES", + "LAST_ACCESSED", + "LAST_MODIFIED", + "REFS", + "LOCAL PATH", + ], + ) + else: + return tabulate( + rows=[ + [ + repo.repo_id, + repo.repo_type, + revision.commit_hash, + "{:>12}".format(revision.size_on_disk_str), + revision.nb_files, + revision.last_modified_str, + ", ".join(sorted(revision.refs)), + str(revision.snapshot_path), + ] + for repo in sorted(self.repos, key=lambda repo: repo.repo_path) + for revision in sorted(repo.revisions, key=lambda revision: revision.commit_hash) + ], + headers=[ + "REPO ID", + "REPO TYPE", + "REVISION", + "SIZE ON DISK", + "NB FILES", + "LAST_MODIFIED", + "REFS", + "LOCAL PATH", + ], + ) + + +def scan_cache_dir(cache_dir: Optional[Union[str, Path]] = None) -> HFCacheInfo: + """Scan the entire HF cache-system and return a [`~HFCacheInfo`] structure. + + Use `scan_cache_dir` in order to programmatically scan your cache-system. The cache + will be scanned repo by repo. If a repo is corrupted, a [`~CorruptedCacheException`] + will be thrown internally but captured and returned in the [`~HFCacheInfo`] + structure. Only valid repos get a proper report. + + ```py + >>> from huggingface_hub import scan_cache_dir + + >>> hf_cache_info = scan_cache_dir() + HFCacheInfo( + size_on_disk=3398085269, + repos=frozenset({ + CachedRepoInfo( + repo_id='t5-small', + repo_type='model', + repo_path=PosixPath(...), + size_on_disk=970726914, + nb_files=11, + revisions=frozenset({ + CachedRevisionInfo( + commit_hash='d78aea13fa7ecd06c29e3e46195d6341255065d5', + size_on_disk=970726339, + snapshot_path=PosixPath(...), + files=frozenset({ + CachedFileInfo( + file_name='config.json', + size_on_disk=1197 + file_path=PosixPath(...), + blob_path=PosixPath(...), + ), + CachedFileInfo(...), + ... + }), + ), + CachedRevisionInfo(...), + ... + }), + ), + CachedRepoInfo(...), + ... + }), + warnings=[ + CorruptedCacheException("Snapshots dir doesn't exist in cached repo: ..."), + CorruptedCacheException(...), + ... + ], + ) + ``` + + You can also print a detailed report directly from the `huggingface-cli` using: + ```text + > huggingface-cli scan-cache + REPO ID REPO TYPE SIZE ON DISK NB FILES REFS LOCAL PATH + --------------------------- --------- ------------ -------- ------------------- ------------------------------------------------------------------------- + glue dataset 116.3K 15 1.17.0, main, 2.4.0 /Users/lucain/.cache/huggingface/hub/datasets--glue + google/fleurs dataset 64.9M 6 main, refs/pr/1 /Users/lucain/.cache/huggingface/hub/datasets--google--fleurs + Jean-Baptiste/camembert-ner model 441.0M 7 main /Users/lucain/.cache/huggingface/hub/models--Jean-Baptiste--camembert-ner + bert-base-cased model 1.9G 13 main /Users/lucain/.cache/huggingface/hub/models--bert-base-cased + t5-base model 10.1K 3 main /Users/lucain/.cache/huggingface/hub/models--t5-base + t5-small model 970.7M 11 refs/pr/1, main /Users/lucain/.cache/huggingface/hub/models--t5-small + + Done in 0.0s. Scanned 6 repo(s) for a total of 3.4G. + Got 1 warning(s) while scanning. Use -vvv to print details. + ``` + + Args: + cache_dir (`str` or `Path`, `optional`): + Cache directory to cache. Defaults to the default HF cache directory. + + + + Raises: + + `CacheNotFound` + If the cache directory does not exist. + + [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError) + If the cache directory is a file, instead of a directory. + + + + Returns: a [`~HFCacheInfo`] object. + """ + if cache_dir is None: + cache_dir = HF_HUB_CACHE + + cache_dir = Path(cache_dir).expanduser().resolve() + if not cache_dir.exists(): + raise CacheNotFound( + f"Cache directory not found: {cache_dir}. Please use `cache_dir` argument or set `HF_HUB_CACHE` environment variable.", + cache_dir=cache_dir, + ) + + if cache_dir.is_file(): + raise ValueError( + f"Scan cache expects a directory but found a file: {cache_dir}. Please use `cache_dir` argument or set `HF_HUB_CACHE` environment variable." + ) + + repos: Set[CachedRepoInfo] = set() + warnings: List[CorruptedCacheException] = [] + for repo_path in cache_dir.iterdir(): + if repo_path.name == ".locks": # skip './.locks/' folder + continue + try: + repos.add(_scan_cached_repo(repo_path)) + except CorruptedCacheException as e: + warnings.append(e) + + return HFCacheInfo( + repos=frozenset(repos), + size_on_disk=sum(repo.size_on_disk for repo in repos), + warnings=warnings, + ) + + +def _scan_cached_repo(repo_path: Path) -> CachedRepoInfo: + """Scan a single cache repo and return information about it. + + Any unexpected behavior will raise a [`~CorruptedCacheException`]. + """ + if not repo_path.is_dir(): + raise CorruptedCacheException(f"Repo path is not a directory: {repo_path}") + + if "--" not in repo_path.name: + raise CorruptedCacheException(f"Repo path is not a valid HuggingFace cache directory: {repo_path}") + + repo_type, repo_id = repo_path.name.split("--", maxsplit=1) + repo_type = repo_type[:-1] # "models" -> "model" + repo_id = repo_id.replace("--", "/") # google/fleurs -> "google/fleurs" + + if repo_type not in {"dataset", "model", "space"}: + raise CorruptedCacheException( + f"Repo type must be `dataset`, `model` or `space`, found `{repo_type}` ({repo_path})." + ) + + blob_stats: Dict[Path, os.stat_result] = {} # Key is blob_path, value is blob stats + + snapshots_path = repo_path / "snapshots" + refs_path = repo_path / "refs" + + if not snapshots_path.exists() or not snapshots_path.is_dir(): + raise CorruptedCacheException(f"Snapshots dir doesn't exist in cached repo: {snapshots_path}") + + # Scan over `refs` directory + + # key is revision hash, value is set of refs + refs_by_hash: Dict[str, Set[str]] = defaultdict(set) + if refs_path.exists(): + # Example of `refs` directory + # ── refs + # ├── main + # └── refs + # └── pr + # └── 1 + if refs_path.is_file(): + raise CorruptedCacheException(f"Refs directory cannot be a file: {refs_path}") + + for ref_path in refs_path.glob("**/*"): + # glob("**/*") iterates over all files and directories -> skip directories + if ref_path.is_dir() or ref_path.name in FILES_TO_IGNORE: + continue + + ref_name = str(ref_path.relative_to(refs_path)) + with ref_path.open() as f: + commit_hash = f.read() + + refs_by_hash[commit_hash].add(ref_name) + + # Scan snapshots directory + cached_revisions: Set[CachedRevisionInfo] = set() + for revision_path in snapshots_path.iterdir(): + # Ignore OS-created helper files + if revision_path.name in FILES_TO_IGNORE: + continue + if revision_path.is_file(): + raise CorruptedCacheException(f"Snapshots folder corrupted. Found a file: {revision_path}") + + cached_files = set() + for file_path in revision_path.glob("**/*"): + # glob("**/*") iterates over all files and directories -> skip directories + if file_path.is_dir(): + continue + + blob_path = Path(file_path).resolve() + if not blob_path.exists(): + raise CorruptedCacheException(f"Blob missing (broken symlink): {blob_path}") + + if blob_path not in blob_stats: + blob_stats[blob_path] = blob_path.stat() + + cached_files.add( + CachedFileInfo( + file_name=file_path.name, + file_path=file_path, + size_on_disk=blob_stats[blob_path].st_size, + blob_path=blob_path, + blob_last_accessed=blob_stats[blob_path].st_atime, + blob_last_modified=blob_stats[blob_path].st_mtime, + ) + ) + + # Last modified is either the last modified blob file or the revision folder + # itself if it is empty + if len(cached_files) > 0: + revision_last_modified = max(blob_stats[file.blob_path].st_mtime for file in cached_files) + else: + revision_last_modified = revision_path.stat().st_mtime + + cached_revisions.add( + CachedRevisionInfo( + commit_hash=revision_path.name, + files=frozenset(cached_files), + refs=frozenset(refs_by_hash.pop(revision_path.name, set())), + size_on_disk=sum( + blob_stats[blob_path].st_size for blob_path in set(file.blob_path for file in cached_files) + ), + snapshot_path=revision_path, + last_modified=revision_last_modified, + ) + ) + + # Check that all refs referred to an existing revision + if len(refs_by_hash) > 0: + raise CorruptedCacheException( + f"Reference(s) refer to missing commit hashes: {dict(refs_by_hash)} ({repo_path})." + ) + + # Last modified is either the last modified blob file or the repo folder itself if + # no blob files has been found. Same for last accessed. + if len(blob_stats) > 0: + repo_last_accessed = max(stat.st_atime for stat in blob_stats.values()) + repo_last_modified = max(stat.st_mtime for stat in blob_stats.values()) + else: + repo_stats = repo_path.stat() + repo_last_accessed = repo_stats.st_atime + repo_last_modified = repo_stats.st_mtime + + # Build and return frozen structure + return CachedRepoInfo( + nb_files=len(blob_stats), + repo_id=repo_id, + repo_path=repo_path, + repo_type=repo_type, # type: ignore + revisions=frozenset(cached_revisions), + size_on_disk=sum(stat.st_size for stat in blob_stats.values()), + last_accessed=repo_last_accessed, + last_modified=repo_last_modified, + ) + + +def _format_size(num: int) -> str: + """Format size in bytes into a human-readable string. + + Taken from https://stackoverflow.com/a/1094933 + """ + num_f = float(num) + for unit in ["", "K", "M", "G", "T", "P", "E", "Z"]: + if abs(num_f) < 1000.0: + return f"{num_f:3.1f}{unit}" + num_f /= 1000.0 + return f"{num_f:.1f}Y" + + +_TIMESINCE_CHUNKS = ( + # Label, divider, max value + ("second", 1, 60), + ("minute", 60, 60), + ("hour", 60 * 60, 24), + ("day", 60 * 60 * 24, 6), + ("week", 60 * 60 * 24 * 7, 6), + ("month", 60 * 60 * 24 * 30, 11), + ("year", 60 * 60 * 24 * 365, None), +) + + +def _format_timesince(ts: float) -> str: + """Format timestamp in seconds into a human-readable string, relative to now. + + Vaguely inspired by Django's `timesince` formatter. + """ + delta = time.time() - ts + if delta < 20: + return "a few seconds ago" + for label, divider, max_value in _TIMESINCE_CHUNKS: # noqa: B007 + value = round(delta / divider) + if max_value is not None and value <= max_value: + break + return f"{value} {label}{'s' if value > 1 else ''} ago" + + +def _try_delete_path(path: Path, path_type: str) -> None: + """Try to delete a local file or folder. + + If the path does not exists, error is logged as a warning and then ignored. + + Args: + path (`Path`) + Path to delete. Can be a file or a folder. + path_type (`str`) + What path are we deleting ? Only for logging purposes. Example: "snapshot". + """ + logger.info(f"Delete {path_type}: {path}") + try: + if path.is_file(): + os.remove(path) + else: + shutil.rmtree(path) + except FileNotFoundError: + logger.warning(f"Couldn't delete {path_type}: file not found ({path})", exc_info=True) + except PermissionError: + logger.warning(f"Couldn't delete {path_type}: permission denied ({path})", exc_info=True) diff --git a/parrot/lib/python3.10/site-packages/huggingface_hub/utils/_chunk_utils.py b/parrot/lib/python3.10/site-packages/huggingface_hub/utils/_chunk_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..b0af032ae6a68f03676ad7fdb8e483248d9853f8 --- /dev/null +++ b/parrot/lib/python3.10/site-packages/huggingface_hub/utils/_chunk_utils.py @@ -0,0 +1,65 @@ +# coding=utf-8 +# Copyright 2022-present, the HuggingFace Inc. team. +# +# 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. +"""Contains a utility to iterate by chunks over an iterator.""" + +import itertools +from typing import Iterable, TypeVar + + +T = TypeVar("T") + + +def chunk_iterable(iterable: Iterable[T], chunk_size: int) -> Iterable[Iterable[T]]: + """Iterates over an iterator chunk by chunk. + + Taken from https://stackoverflow.com/a/8998040. + See also https://github.com/huggingface/huggingface_hub/pull/920#discussion_r938793088. + + Args: + iterable (`Iterable`): + The iterable on which we want to iterate. + chunk_size (`int`): + Size of the chunks. Must be a strictly positive integer (e.g. >0). + + Example: + + ```python + >>> from huggingface_hub.utils import chunk_iterable + + >>> for items in chunk_iterable(range(17), chunk_size=8): + ... print(items) + # [0, 1, 2, 3, 4, 5, 6, 7] + # [8, 9, 10, 11, 12, 13, 14, 15] + # [16] # smaller last chunk + ``` + + Raises: + [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError) + If `chunk_size` <= 0. + + + The last chunk can be smaller than `chunk_size`. + + """ + if not isinstance(chunk_size, int) or chunk_size <= 0: + raise ValueError("`chunk_size` must be a strictly positive integer (>0).") + + iterator = iter(iterable) + while True: + try: + next_item = next(iterator) + except StopIteration: + return + yield itertools.chain((next_item,), itertools.islice(iterator, chunk_size - 1)) diff --git a/parrot/lib/python3.10/site-packages/huggingface_hub/utils/_deprecation.py b/parrot/lib/python3.10/site-packages/huggingface_hub/utils/_deprecation.py new file mode 100644 index 0000000000000000000000000000000000000000..4cb8d6e418c76accd1ecd61158b4bdd265e12f71 --- /dev/null +++ b/parrot/lib/python3.10/site-packages/huggingface_hub/utils/_deprecation.py @@ -0,0 +1,136 @@ +import warnings +from functools import wraps +from inspect import Parameter, signature +from typing import Iterable, Optional + + +def _deprecate_positional_args(*, version: str): + """Decorator for methods that issues warnings for positional arguments. + Using the keyword-only argument syntax in pep 3102, arguments after the + * will issue a warning when passed as a positional argument. + + Args: + version (`str`): + The version when positional arguments will result in error. + """ + + def _inner_deprecate_positional_args(f): + sig = signature(f) + kwonly_args = [] + all_args = [] + for name, param in sig.parameters.items(): + if param.kind == Parameter.POSITIONAL_OR_KEYWORD: + all_args.append(name) + elif param.kind == Parameter.KEYWORD_ONLY: + kwonly_args.append(name) + + @wraps(f) + def inner_f(*args, **kwargs): + extra_args = len(args) - len(all_args) + if extra_args <= 0: + return f(*args, **kwargs) + # extra_args > 0 + args_msg = [ + f"{name}='{arg}'" if isinstance(arg, str) else f"{name}={arg}" + for name, arg in zip(kwonly_args[:extra_args], args[-extra_args:]) + ] + args_msg = ", ".join(args_msg) + warnings.warn( + f"Deprecated positional argument(s) used in '{f.__name__}': pass" + f" {args_msg} as keyword args. From version {version} passing these" + " as positional arguments will result in an error,", + FutureWarning, + ) + kwargs.update(zip(sig.parameters, args)) + return f(**kwargs) + + return inner_f + + return _inner_deprecate_positional_args + + +def _deprecate_arguments( + *, + version: str, + deprecated_args: Iterable[str], + custom_message: Optional[str] = None, +): + """Decorator to issue warnings when using deprecated arguments. + + TODO: could be useful to be able to set a custom error message. + + Args: + version (`str`): + The version when deprecated arguments will result in error. + deprecated_args (`List[str]`): + List of the arguments to be deprecated. + custom_message (`str`, *optional*): + Warning message that is raised. If not passed, a default warning message + will be created. + """ + + def _inner_deprecate_positional_args(f): + sig = signature(f) + + @wraps(f) + def inner_f(*args, **kwargs): + # Check for used deprecated arguments + used_deprecated_args = [] + for _, parameter in zip(args, sig.parameters.values()): + if parameter.name in deprecated_args: + used_deprecated_args.append(parameter.name) + for kwarg_name, kwarg_value in kwargs.items(): + if ( + # If argument is deprecated but still used + kwarg_name in deprecated_args + # And then the value is not the default value + and kwarg_value != sig.parameters[kwarg_name].default + ): + used_deprecated_args.append(kwarg_name) + + # Warn and proceed + if len(used_deprecated_args) > 0: + message = ( + f"Deprecated argument(s) used in '{f.__name__}':" + f" {', '.join(used_deprecated_args)}. Will not be supported from" + f" version '{version}'." + ) + if custom_message is not None: + message += "\n\n" + custom_message + warnings.warn(message, FutureWarning) + return f(*args, **kwargs) + + return inner_f + + return _inner_deprecate_positional_args + + +def _deprecate_method(*, version: str, message: Optional[str] = None): + """Decorator to issue warnings when using a deprecated method. + + Args: + version (`str`): + The version when deprecated arguments will result in error. + message (`str`, *optional*): + Warning message that is raised. If not passed, a default warning message + will be created. + """ + + def _inner_deprecate_method(f): + name = f.__name__ + if name == "__init__": + name = f.__qualname__.split(".")[0] # class name instead of method name + + @wraps(f) + def inner_f(*args, **kwargs): + warning_message = ( + f"'{name}' (from '{f.__module__}') is deprecated and will be removed from version '{version}'." + ) + if message is not None: + warning_message += " " + message + warnings.warn(warning_message, FutureWarning) + return f(*args, **kwargs) + + return inner_f + + return _inner_deprecate_method diff --git a/parrot/lib/python3.10/site-packages/huggingface_hub/utils/_experimental.py b/parrot/lib/python3.10/site-packages/huggingface_hub/utils/_experimental.py new file mode 100644 index 0000000000000000000000000000000000000000..34141eba09123c06fbca55c929a19a0264e5788e --- /dev/null +++ b/parrot/lib/python3.10/site-packages/huggingface_hub/utils/_experimental.py @@ -0,0 +1,66 @@ +# coding=utf-8 +# Copyright 2023-present, the HuggingFace Inc. team. +# +# 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. +"""Contains utilities to flag a feature as "experimental" in Huggingface Hub.""" + +import warnings +from functools import wraps +from typing import Callable + +from .. import constants + + +def experimental(fn: Callable) -> Callable: + """Decorator to flag a feature as experimental. + + An experimental feature trigger a warning when used as it might be subject to breaking changes in the future. + Warnings can be disabled by setting the environment variable `HF_EXPERIMENTAL_WARNING` to `0`. + + Args: + fn (`Callable`): + The function to flag as experimental. + + Returns: + `Callable`: The decorated function. + + Example: + + ```python + >>> from huggingface_hub.utils import experimental + + >>> @experimental + ... def my_function(): + ... print("Hello world!") + + >>> my_function() + UserWarning: 'my_function' is experimental and might be subject to breaking changes in the future. You can disable + this warning by setting `HF_HUB_DISABLE_EXPERIMENTAL_WARNING=1` as environment variable. + Hello world! + ``` + """ + # For classes, put the "experimental" around the "__new__" method => __new__ will be removed in warning message + name = fn.__qualname__[: -len(".__new__")] if fn.__qualname__.endswith(".__new__") else fn.__qualname__ + + @wraps(fn) + def _inner_fn(*args, **kwargs): + if not constants.HF_HUB_DISABLE_EXPERIMENTAL_WARNING: + warnings.warn( + f"'{name}' is experimental and might be subject to breaking changes in the future." + " You can disable this warning by setting `HF_HUB_DISABLE_EXPERIMENTAL_WARNING=1` as environment" + " variable.", + UserWarning, + ) + return fn(*args, **kwargs) + + return _inner_fn diff --git a/parrot/lib/python3.10/site-packages/huggingface_hub/utils/_git_credential.py b/parrot/lib/python3.10/site-packages/huggingface_hub/utils/_git_credential.py new file mode 100644 index 0000000000000000000000000000000000000000..a8ed77f4e49ca88ff4fa9aba48cbf00195036013 --- /dev/null +++ b/parrot/lib/python3.10/site-packages/huggingface_hub/utils/_git_credential.py @@ -0,0 +1,121 @@ +# coding=utf-8 +# Copyright 2022-present, the HuggingFace Inc. team. +# +# 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. +"""Contains utilities to manage Git credentials.""" + +import re +import subprocess +from typing import List, Optional + +from ..constants import ENDPOINT +from ._subprocess import run_interactive_subprocess, run_subprocess + + +GIT_CREDENTIAL_REGEX = re.compile( + r""" + ^\s* # start of line + credential\.helper # credential.helper value + \s*=\s* # separator + (\w+) # the helper name (group 1) + (\s|$) # whitespace or end of line + """, + flags=re.MULTILINE | re.IGNORECASE | re.VERBOSE, +) + + +def list_credential_helpers(folder: Optional[str] = None) -> List[str]: + """Return the list of git credential helpers configured. + + See https://git-scm.com/docs/gitcredentials. + + Credentials are saved in all configured helpers (store, cache, macOS keychain,...). + Calls "`git credential approve`" internally. See https://git-scm.com/docs/git-credential. + + Args: + folder (`str`, *optional*): + The folder in which to check the configured helpers. + """ + try: + output = run_subprocess("git config --list", folder=folder).stdout + parsed = _parse_credential_output(output) + return parsed + except subprocess.CalledProcessError as exc: + raise EnvironmentError(exc.stderr) + + +def set_git_credential(token: str, username: str = "hf_user", folder: Optional[str] = None) -> None: + """Save a username/token pair in git credential for HF Hub registry. + + Credentials are saved in all configured helpers (store, cache, macOS keychain,...). + Calls "`git credential approve`" internally. See https://git-scm.com/docs/git-credential. + + Args: + username (`str`, defaults to `"hf_user"`): + A git username. Defaults to `"hf_user"`, the default user used in the Hub. + token (`str`, defaults to `"hf_user"`): + A git password. In practice, the User Access Token for the Hub. + See https://huggingface.co/settings/tokens. + folder (`str`, *optional*): + The folder in which to check the configured helpers. + """ + with run_interactive_subprocess("git credential approve", folder=folder) as ( + stdin, + _, + ): + stdin.write(f"url={ENDPOINT}\nusername={username.lower()}\npassword={token}\n\n") + stdin.flush() + + +def unset_git_credential(username: str = "hf_user", folder: Optional[str] = None) -> None: + """Erase credentials from git credential for HF Hub registry. + + Credentials are erased from the configured helpers (store, cache, macOS + keychain,...), if any. If `username` is not provided, any credential configured for + HF Hub endpoint is erased. + Calls "`git credential erase`" internally. See https://git-scm.com/docs/git-credential. + + Args: + username (`str`, defaults to `"hf_user"`): + A git username. Defaults to `"hf_user"`, the default user used in the Hub. + folder (`str`, *optional*): + The folder in which to check the configured helpers. + """ + with run_interactive_subprocess("git credential reject", folder=folder) as ( + stdin, + _, + ): + standard_input = f"url={ENDPOINT}\n" + if username is not None: + standard_input += f"username={username.lower()}\n" + standard_input += "\n" + + stdin.write(standard_input) + stdin.flush() + + +def _parse_credential_output(output: str) -> List[str]: + """Parse the output of `git credential fill` to extract the password. + + Args: + output (`str`): + The output of `git credential fill`. + """ + # NOTE: If user has set an helper for a custom URL, it will not we caught here. + # Example: `credential.https://huggingface.co.helper=store` + # See: https://github.com/huggingface/huggingface_hub/pull/1138#discussion_r1013324508 + return sorted( # Sort for nice printing + set( # Might have some duplicates + match[0] for match in GIT_CREDENTIAL_REGEX.findall(output) + ) + ) diff --git a/parrot/lib/python3.10/site-packages/huggingface_hub/utils/_headers.py b/parrot/lib/python3.10/site-packages/huggingface_hub/utils/_headers.py new file mode 100644 index 0000000000000000000000000000000000000000..f0e1ddd87a51e170e7d0518c55cfaa3ab4f0ebb7 --- /dev/null +++ b/parrot/lib/python3.10/site-packages/huggingface_hub/utils/_headers.py @@ -0,0 +1,228 @@ +# coding=utf-8 +# Copyright 2022-present, the HuggingFace Inc. team. +# +# 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. +"""Contains utilities to handle headers to send in calls to Huggingface Hub.""" + +from typing import Dict, Optional, Union + +from huggingface_hub.errors import LocalTokenNotFoundError + +from .. import constants +from ._auth import get_token +from ._deprecation import _deprecate_arguments +from ._runtime import ( + get_fastai_version, + get_fastcore_version, + get_hf_hub_version, + get_python_version, + get_tf_version, + get_torch_version, + is_fastai_available, + is_fastcore_available, + is_tf_available, + is_torch_available, +) +from ._validators import validate_hf_hub_args + + +@_deprecate_arguments( + version="1.0", + deprecated_args="is_write_action", + custom_message="This argument is ignored and we let the server handle the permission error instead (if any).", +) +@validate_hf_hub_args +def build_hf_headers( + *, + token: Optional[Union[bool, str]] = None, + library_name: Optional[str] = None, + library_version: Optional[str] = None, + user_agent: Union[Dict, str, None] = None, + headers: Optional[Dict[str, str]] = None, + is_write_action: bool = False, +) -> Dict[str, str]: + """ + Build headers dictionary to send in a HF Hub call. + + By default, authorization token is always provided either from argument (explicit + use) or retrieved from the cache (implicit use). To explicitly avoid sending the + token to the Hub, set `token=False` or set the `HF_HUB_DISABLE_IMPLICIT_TOKEN` + environment variable. + + In case of an API call that requires write access, an error is thrown if token is + `None` or token is an organization token (starting with `"api_org***"`). + + In addition to the auth header, a user-agent is added to provide information about + the installed packages (versions of python, huggingface_hub, torch, tensorflow, + fastai and fastcore). + + Args: + token (`str`, `bool`, *optional*): + The token to be sent in authorization header for the Hub call: + - if a string, it is used as the Hugging Face token + - if `True`, the token is read from the machine (cache or env variable) + - if `False`, authorization header is not set + - if `None`, the token is read from the machine only except if + `HF_HUB_DISABLE_IMPLICIT_TOKEN` env variable is set. + library_name (`str`, *optional*): + The name of the library that is making the HTTP request. Will be added to + the user-agent header. + library_version (`str`, *optional*): + The version of the library that is making the HTTP request. Will be added + to the user-agent header. + user_agent (`str`, `dict`, *optional*): + The user agent info in the form of a dictionary or a single string. It will + be completed with information about the installed packages. + headers (`dict`, *optional*): + Additional headers to include in the request. Those headers take precedence + over the ones generated by this function. + is_write_action (`bool`): + Ignored and deprecated argument. + + Returns: + A `Dict` of headers to pass in your API call. + + Example: + ```py + >>> build_hf_headers(token="hf_***") # explicit token + {"authorization": "Bearer hf_***", "user-agent": ""} + + >>> build_hf_headers(token=True) # explicitly use cached token + {"authorization": "Bearer hf_***",...} + + >>> build_hf_headers(token=False) # explicitly don't use cached token + {"user-agent": ...} + + >>> build_hf_headers() # implicit use of the cached token + {"authorization": "Bearer hf_***",...} + + # HF_HUB_DISABLE_IMPLICIT_TOKEN=True # to set as env variable + >>> build_hf_headers() # token is not sent + {"user-agent": ...} + + >>> build_hf_headers(library_name="transformers", library_version="1.2.3") + {"authorization": ..., "user-agent": "transformers/1.2.3; hf_hub/0.10.2; python/3.10.4; tensorflow/1.55"} + ``` + + Raises: + [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError) + If organization token is passed and "write" access is required. + [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError) + If "write" access is required but token is not passed and not saved locally. + [`EnvironmentError`](https://docs.python.org/3/library/exceptions.html#EnvironmentError) + If `token=True` but token is not saved locally. + """ + # Get auth token to send + token_to_send = get_token_to_send(token) + + # Combine headers + hf_headers = { + "user-agent": _http_user_agent( + library_name=library_name, + library_version=library_version, + user_agent=user_agent, + ) + } + if token_to_send is not None: + hf_headers["authorization"] = f"Bearer {token_to_send}" + if headers is not None: + hf_headers.update(headers) + return hf_headers + + +def get_token_to_send(token: Optional[Union[bool, str]]) -> Optional[str]: + """Select the token to send from either `token` or the cache.""" + # Case token is explicitly provided + if isinstance(token, str): + return token + + # Case token is explicitly forbidden + if token is False: + return None + + # Token is not provided: we get it from local cache + cached_token = get_token() + + # Case token is explicitly required + if token is True: + if cached_token is None: + raise LocalTokenNotFoundError( + "Token is required (`token=True`), but no token found. You" + " need to provide a token or be logged in to Hugging Face with" + " `huggingface-cli login` or `huggingface_hub.login`. See" + " https://huggingface.co/settings/tokens." + ) + return cached_token + + # Case implicit use of the token is forbidden by env variable + if constants.HF_HUB_DISABLE_IMPLICIT_TOKEN: + return None + + # Otherwise: we use the cached token as the user has not explicitly forbidden it + return cached_token + + +def _http_user_agent( + *, + library_name: Optional[str] = None, + library_version: Optional[str] = None, + user_agent: Union[Dict, str, None] = None, +) -> str: + """Format a user-agent string containing information about the installed packages. + + Args: + library_name (`str`, *optional*): + The name of the library that is making the HTTP request. + library_version (`str`, *optional*): + The version of the library that is making the HTTP request. + user_agent (`str`, `dict`, *optional*): + The user agent info in the form of a dictionary or a single string. + + Returns: + The formatted user-agent string. + """ + if library_name is not None: + ua = f"{library_name}/{library_version}" + else: + ua = "unknown/None" + ua += f"; hf_hub/{get_hf_hub_version()}" + ua += f"; python/{get_python_version()}" + + if not constants.HF_HUB_DISABLE_TELEMETRY: + if is_torch_available(): + ua += f"; torch/{get_torch_version()}" + if is_tf_available(): + ua += f"; tensorflow/{get_tf_version()}" + if is_fastai_available(): + ua += f"; fastai/{get_fastai_version()}" + if is_fastcore_available(): + ua += f"; fastcore/{get_fastcore_version()}" + + if isinstance(user_agent, dict): + ua += "; " + "; ".join(f"{k}/{v}" for k, v in user_agent.items()) + elif isinstance(user_agent, str): + ua += "; " + user_agent + + # Retrieve user-agent origin headers from environment variable + origin = constants.HF_HUB_USER_AGENT_ORIGIN + if origin is not None: + ua += "; origin/" + origin + + return _deduplicate_user_agent(ua) + + +def _deduplicate_user_agent(user_agent: str) -> str: + """Deduplicate redundant information in the generated user-agent.""" + # Split around ";" > Strip whitespaces > Store as dict keys (ensure unicity) > format back as string + # Order is implicitly preserved by dictionary structure (see https://stackoverflow.com/a/53657523). + return "; ".join({key.strip(): None for key in user_agent.split(";")}.keys()) diff --git a/parrot/lib/python3.10/site-packages/huggingface_hub/utils/_hf_folder.py b/parrot/lib/python3.10/site-packages/huggingface_hub/utils/_hf_folder.py new file mode 100644 index 0000000000000000000000000000000000000000..6418bf2fd2c59b4bcf301c1dd82bc468f2f42ddf --- /dev/null +++ b/parrot/lib/python3.10/site-packages/huggingface_hub/utils/_hf_folder.py @@ -0,0 +1,68 @@ +# coding=utf-8 +# Copyright 2022-present, the HuggingFace Inc. team. +# +# 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. +"""Contain helper class to retrieve/store token from/to local cache.""" + +from pathlib import Path +from typing import Optional + +from .. import constants +from ._auth import get_token + + +class HfFolder: + # TODO: deprecate when adapted in transformers/datasets/gradio + # @_deprecate_method(version="1.0", message="Use `huggingface_hub.login` instead.") + @classmethod + def save_token(cls, token: str) -> None: + """ + Save token, creating folder as needed. + + Token is saved in the huggingface home folder. You can configure it by setting + the `HF_HOME` environment variable. + + Args: + token (`str`): + The token to save to the [`HfFolder`] + """ + path_token = Path(constants.HF_TOKEN_PATH) + path_token.parent.mkdir(parents=True, exist_ok=True) + path_token.write_text(token) + + # TODO: deprecate when adapted in transformers/datasets/gradio + # @_deprecate_method(version="1.0", message="Use `huggingface_hub.get_token` instead.") + @classmethod + def get_token(cls) -> Optional[str]: + """ + Get token or None if not existent. + + This method is deprecated in favor of [`huggingface_hub.get_token`] but is kept for backward compatibility. + Its behavior is the same as [`huggingface_hub.get_token`]. + + Returns: + `str` or `None`: The token, `None` if it doesn't exist. + """ + return get_token() + + # TODO: deprecate when adapted in transformers/datasets/gradio + # @_deprecate_method(version="1.0", message="Use `huggingface_hub.logout` instead.") + @classmethod + def delete_token(cls) -> None: + """ + Deletes the token from storage. Does not fail if token does not exist. + """ + try: + Path(constants.HF_TOKEN_PATH).unlink() + except FileNotFoundError: + pass diff --git a/parrot/lib/python3.10/site-packages/huggingface_hub/utils/_lfs.py b/parrot/lib/python3.10/site-packages/huggingface_hub/utils/_lfs.py new file mode 100644 index 0000000000000000000000000000000000000000..307f371ffa79a8ae726ee03458c52e230a792898 --- /dev/null +++ b/parrot/lib/python3.10/site-packages/huggingface_hub/utils/_lfs.py @@ -0,0 +1,110 @@ +# coding=utf-8 +# Copyright 2019-present, the HuggingFace Inc. team. +# +# 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. +"""Git LFS related utilities""" + +import io +import os +from contextlib import AbstractContextManager +from typing import BinaryIO + + +class SliceFileObj(AbstractContextManager): + """ + Utility context manager to read a *slice* of a seekable file-like object as a seekable, file-like object. + + This is NOT thread safe + + Inspired by stackoverflow.com/a/29838711/593036 + + Credits to @julien-c + + Args: + fileobj (`BinaryIO`): + A file-like object to slice. MUST implement `tell()` and `seek()` (and `read()` of course). + `fileobj` will be reset to its original position when exiting the context manager. + seek_from (`int`): + The start of the slice (offset from position 0 in bytes). + read_limit (`int`): + The maximum number of bytes to read from the slice. + + Attributes: + previous_position (`int`): + The previous position + + Examples: + + Reading 200 bytes with an offset of 128 bytes from a file (ie bytes 128 to 327): + ```python + >>> with open("path/to/file", "rb") as file: + ... with SliceFileObj(file, seek_from=128, read_limit=200) as fslice: + ... fslice.read(...) + ``` + + Reading a file in chunks of 512 bytes + ```python + >>> import os + >>> chunk_size = 512 + >>> file_size = os.getsize("path/to/file") + >>> with open("path/to/file", "rb") as file: + ... for chunk_idx in range(ceil(file_size / chunk_size)): + ... with SliceFileObj(file, seek_from=chunk_idx * chunk_size, read_limit=chunk_size) as fslice: + ... chunk = fslice.read(...) + + ``` + """ + + def __init__(self, fileobj: BinaryIO, seek_from: int, read_limit: int): + self.fileobj = fileobj + self.seek_from = seek_from + self.read_limit = read_limit + + def __enter__(self): + self._previous_position = self.fileobj.tell() + end_of_stream = self.fileobj.seek(0, os.SEEK_END) + self._len = min(self.read_limit, end_of_stream - self.seek_from) + # ^^ The actual number of bytes that can be read from the slice + self.fileobj.seek(self.seek_from, io.SEEK_SET) + return self + + def __exit__(self, exc_type, exc_value, traceback): + self.fileobj.seek(self._previous_position, io.SEEK_SET) + + def read(self, n: int = -1): + pos = self.tell() + if pos >= self._len: + return b"" + remaining_amount = self._len - pos + data = self.fileobj.read(remaining_amount if n < 0 else min(n, remaining_amount)) + return data + + def tell(self) -> int: + return self.fileobj.tell() - self.seek_from + + def seek(self, offset: int, whence: int = os.SEEK_SET) -> int: + start = self.seek_from + end = start + self._len + if whence in (os.SEEK_SET, os.SEEK_END): + offset = start + offset if whence == os.SEEK_SET else end + offset + offset = max(start, min(offset, end)) + whence = os.SEEK_SET + elif whence == os.SEEK_CUR: + cur_pos = self.fileobj.tell() + offset = max(start - cur_pos, min(offset, end - cur_pos)) + else: + raise ValueError(f"whence value {whence} is not supported") + return self.fileobj.seek(offset, whence) - self.seek_from + + def __iter__(self): + yield self.read(n=4 * 1024 * 1024) diff --git a/parrot/lib/python3.10/site-packages/huggingface_hub/utils/_pagination.py b/parrot/lib/python3.10/site-packages/huggingface_hub/utils/_pagination.py new file mode 100644 index 0000000000000000000000000000000000000000..f7ab4fe7cba9bd13f01d9c81854a00fd30b7f0d9 --- /dev/null +++ b/parrot/lib/python3.10/site-packages/huggingface_hub/utils/_pagination.py @@ -0,0 +1,52 @@ +# coding=utf-8 +# Copyright 2022-present, the HuggingFace Inc. team. +# +# 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. +"""Contains utilities to handle pagination on Huggingface Hub.""" + +from typing import Dict, Iterable, Optional + +import requests + +from . import get_session, hf_raise_for_status, logging + + +logger = logging.get_logger(__name__) + + +def paginate(path: str, params: Dict, headers: Dict) -> Iterable: + """Fetch a list of models/datasets/spaces and paginate through results. + + This is using the same "Link" header format as GitHub. + See: + - https://requests.readthedocs.io/en/latest/api/#requests.Response.links + - https://docs.github.com/en/rest/guides/traversing-with-pagination#link-header + """ + session = get_session() + r = session.get(path, params=params, headers=headers) + hf_raise_for_status(r) + yield from r.json() + + # Follow pages + # Next link already contains query params + next_page = _get_next_page(r) + while next_page is not None: + logger.debug(f"Pagination detected. Requesting next page: {next_page}") + r = session.get(next_page, headers=headers) + hf_raise_for_status(r) + yield from r.json() + next_page = _get_next_page(r) + + +def _get_next_page(response: requests.Response) -> Optional[str]: + return response.links.get("next", {}).get("url") diff --git a/parrot/lib/python3.10/site-packages/huggingface_hub/utils/_paths.py b/parrot/lib/python3.10/site-packages/huggingface_hub/utils/_paths.py new file mode 100644 index 0000000000000000000000000000000000000000..4f2c0ebce070bbde4900e919a3aca7cfc331e747 --- /dev/null +++ b/parrot/lib/python3.10/site-packages/huggingface_hub/utils/_paths.py @@ -0,0 +1,141 @@ +# coding=utf-8 +# Copyright 2022-present, the HuggingFace Inc. team. +# +# 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. +"""Contains utilities to handle paths in Huggingface Hub.""" + +from fnmatch import fnmatch +from pathlib import Path +from typing import Callable, Generator, Iterable, List, Optional, TypeVar, Union + + +T = TypeVar("T") + +# Always ignore `.git` and `.cache/huggingface` folders in commits +DEFAULT_IGNORE_PATTERNS = [ + ".git", + ".git/*", + "*/.git", + "**/.git/**", + ".cache/huggingface", + ".cache/huggingface/*", + "*/.cache/huggingface", + "**/.cache/huggingface/**", +] +# Forbidden to commit these folders +FORBIDDEN_FOLDERS = [".git", ".cache"] + + +def filter_repo_objects( + items: Iterable[T], + *, + allow_patterns: Optional[Union[List[str], str]] = None, + ignore_patterns: Optional[Union[List[str], str]] = None, + key: Optional[Callable[[T], str]] = None, +) -> Generator[T, None, None]: + """Filter repo objects based on an allowlist and a denylist. + + Input must be a list of paths (`str` or `Path`) or a list of arbitrary objects. + In the later case, `key` must be provided and specifies a function of one argument + that is used to extract a path from each element in iterable. + + Patterns are Unix shell-style wildcards which are NOT regular expressions. See + https://docs.python.org/3/library/fnmatch.html for more details. + + Args: + items (`Iterable`): + List of items to filter. + allow_patterns (`str` or `List[str]`, *optional*): + Patterns constituting the allowlist. If provided, item paths must match at + least one pattern from the allowlist. + ignore_patterns (`str` or `List[str]`, *optional*): + Patterns constituting the denylist. If provided, item paths must not match + any patterns from the denylist. + key (`Callable[[T], str]`, *optional*): + Single-argument function to extract a path from each item. If not provided, + the `items` must already be `str` or `Path`. + + Returns: + Filtered list of objects, as a generator. + + Raises: + :class:`ValueError`: + If `key` is not provided and items are not `str` or `Path`. + + Example usage with paths: + ```python + >>> # Filter only PDFs that are not hidden. + >>> list(filter_repo_objects( + ... ["aaa.PDF", "bbb.jpg", ".ccc.pdf", ".ddd.png"], + ... allow_patterns=["*.pdf"], + ... ignore_patterns=[".*"], + ... )) + ["aaa.pdf"] + ``` + + Example usage with objects: + ```python + >>> list(filter_repo_objects( + ... [ + ... CommitOperationAdd(path_or_fileobj="/tmp/aaa.pdf", path_in_repo="aaa.pdf") + ... CommitOperationAdd(path_or_fileobj="/tmp/bbb.jpg", path_in_repo="bbb.jpg") + ... CommitOperationAdd(path_or_fileobj="/tmp/.ccc.pdf", path_in_repo=".ccc.pdf") + ... CommitOperationAdd(path_or_fileobj="/tmp/.ddd.png", path_in_repo=".ddd.png") + ... ], + ... allow_patterns=["*.pdf"], + ... ignore_patterns=[".*"], + ... key=lambda x: x.repo_in_path + ... )) + [CommitOperationAdd(path_or_fileobj="/tmp/aaa.pdf", path_in_repo="aaa.pdf")] + ``` + """ + if isinstance(allow_patterns, str): + allow_patterns = [allow_patterns] + + if isinstance(ignore_patterns, str): + ignore_patterns = [ignore_patterns] + + if allow_patterns is not None: + allow_patterns = [_add_wildcard_to_directories(p) for p in allow_patterns] + if ignore_patterns is not None: + ignore_patterns = [_add_wildcard_to_directories(p) for p in ignore_patterns] + + if key is None: + + def _identity(item: T) -> str: + if isinstance(item, str): + return item + if isinstance(item, Path): + return str(item) + raise ValueError(f"Please provide `key` argument in `filter_repo_objects`: `{item}` is not a string.") + + key = _identity # Items must be `str` or `Path`, otherwise raise ValueError + + for item in items: + path = key(item) + + # Skip if there's an allowlist and path doesn't match any + if allow_patterns is not None and not any(fnmatch(path, r) for r in allow_patterns): + continue + + # Skip if there's a denylist and path matches any + if ignore_patterns is not None and any(fnmatch(path, r) for r in ignore_patterns): + continue + + yield item + + +def _add_wildcard_to_directories(pattern: str) -> str: + if pattern[-1] == "/": + return pattern + "*" + return pattern diff --git a/parrot/lib/python3.10/site-packages/huggingface_hub/utils/_runtime.py b/parrot/lib/python3.10/site-packages/huggingface_hub/utils/_runtime.py new file mode 100644 index 0000000000000000000000000000000000000000..c8d82d4129d4846422ecbf278b6550491c986e50 --- /dev/null +++ b/parrot/lib/python3.10/site-packages/huggingface_hub/utils/_runtime.py @@ -0,0 +1,379 @@ +# coding=utf-8 +# Copyright 2022-present, the HuggingFace Inc. team. +# +# 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. +"""Check presence of installed packages at runtime.""" + +import importlib.metadata +import os +import platform +import sys +import warnings +from typing import Any, Dict + +from .. import __version__, constants + + +_PY_VERSION: str = sys.version.split()[0].rstrip("+") + +_package_versions = {} + +_CANDIDATES = { + "aiohttp": {"aiohttp"}, + "fastai": {"fastai"}, + "fastapi": {"fastapi"}, + "fastcore": {"fastcore"}, + "gradio": {"gradio"}, + "graphviz": {"graphviz"}, + "hf_transfer": {"hf_transfer"}, + "jinja": {"Jinja2"}, + "keras": {"keras"}, + "numpy": {"numpy"}, + "pillow": {"Pillow"}, + "pydantic": {"pydantic"}, + "pydot": {"pydot"}, + "safetensors": {"safetensors"}, + "tensorboard": {"tensorboardX"}, + "tensorflow": ( + "tensorflow", + "tensorflow-cpu", + "tensorflow-gpu", + "tf-nightly", + "tf-nightly-cpu", + "tf-nightly-gpu", + "intel-tensorflow", + "intel-tensorflow-avx512", + "tensorflow-rocm", + "tensorflow-macos", + ), + "torch": {"torch"}, +} + +# Check once at runtime +for candidate_name, package_names in _CANDIDATES.items(): + _package_versions[candidate_name] = "N/A" + for name in package_names: + try: + _package_versions[candidate_name] = importlib.metadata.version(name) + break + except importlib.metadata.PackageNotFoundError: + pass + + +def _get_version(package_name: str) -> str: + return _package_versions.get(package_name, "N/A") + + +def is_package_available(package_name: str) -> bool: + return _get_version(package_name) != "N/A" + + +# Python +def get_python_version() -> str: + return _PY_VERSION + + +# Huggingface Hub +def get_hf_hub_version() -> str: + return __version__ + + +# aiohttp +def is_aiohttp_available() -> bool: + return is_package_available("aiohttp") + + +def get_aiohttp_version() -> str: + return _get_version("aiohttp") + + +# FastAI +def is_fastai_available() -> bool: + return is_package_available("fastai") + + +def get_fastai_version() -> str: + return _get_version("fastai") + + +# FastAPI +def is_fastapi_available() -> bool: + return is_package_available("fastapi") + + +def get_fastapi_version() -> str: + return _get_version("fastapi") + + +# Fastcore +def is_fastcore_available() -> bool: + return is_package_available("fastcore") + + +def get_fastcore_version() -> str: + return _get_version("fastcore") + + +# FastAI +def is_gradio_available() -> bool: + return is_package_available("gradio") + + +def get_gradio_version() -> str: + return _get_version("gradio") + + +# Graphviz +def is_graphviz_available() -> bool: + return is_package_available("graphviz") + + +def get_graphviz_version() -> str: + return _get_version("graphviz") + + +# hf_transfer +def is_hf_transfer_available() -> bool: + return is_package_available("hf_transfer") + + +def get_hf_transfer_version() -> str: + return _get_version("hf_transfer") + + +# keras +def is_keras_available() -> bool: + return is_package_available("keras") + + +def get_keras_version() -> str: + return _get_version("keras") + + +# Numpy +def is_numpy_available() -> bool: + return is_package_available("numpy") + + +def get_numpy_version() -> str: + return _get_version("numpy") + + +# Jinja +def is_jinja_available() -> bool: + return is_package_available("jinja") + + +def get_jinja_version() -> str: + return _get_version("jinja") + + +# Pillow +def is_pillow_available() -> bool: + return is_package_available("pillow") + + +def get_pillow_version() -> str: + return _get_version("pillow") + + +# Pydantic +def is_pydantic_available() -> bool: + if not is_package_available("pydantic"): + return False + # For Pydantic, we add an extra check to test whether it is correctly installed or not. If both pydantic 2.x and + # typing_extensions<=4.5.0 are installed, then pydantic will fail at import time. This should not happen when + # it is installed with `pip install huggingface_hub[inference]` but it can happen when it is installed manually + # by the user in an environment that we don't control. + # + # Usually we won't need to do this kind of check on optional dependencies. However, pydantic is a special case + # as it is automatically imported when doing `from huggingface_hub import ...` even if the user doesn't use it. + # + # See https://github.com/huggingface/huggingface_hub/pull/1829 for more details. + try: + from pydantic import validator # noqa: F401 + except ImportError: + # Example: "ImportError: cannot import name 'TypeAliasType' from 'typing_extensions'" + warnings.warn( + "Pydantic is installed but cannot be imported. Please check your installation. `huggingface_hub` will " + "default to not using Pydantic. Error message: '{e}'" + ) + return False + return True + + +def get_pydantic_version() -> str: + return _get_version("pydantic") + + +# Pydot +def is_pydot_available() -> bool: + return is_package_available("pydot") + + +def get_pydot_version() -> str: + return _get_version("pydot") + + +# Tensorboard +def is_tensorboard_available() -> bool: + return is_package_available("tensorboard") + + +def get_tensorboard_version() -> str: + return _get_version("tensorboard") + + +# Tensorflow +def is_tf_available() -> bool: + return is_package_available("tensorflow") + + +def get_tf_version() -> str: + return _get_version("tensorflow") + + +# Torch +def is_torch_available() -> bool: + return is_package_available("torch") + + +def get_torch_version() -> str: + return _get_version("torch") + + +# Safetensors +def is_safetensors_available() -> bool: + return is_package_available("safetensors") + + +# Shell-related helpers +try: + # Set to `True` if script is running in a Google Colab notebook. + # If running in Google Colab, git credential store is set globally which makes the + # warning disappear. See https://github.com/huggingface/huggingface_hub/issues/1043 + # + # Taken from https://stackoverflow.com/a/63519730. + _is_google_colab = "google.colab" in str(get_ipython()) # type: ignore # noqa: F821 +except NameError: + _is_google_colab = False + + +def is_notebook() -> bool: + """Return `True` if code is executed in a notebook (Jupyter, Colab, QTconsole). + + Taken from https://stackoverflow.com/a/39662359. + Adapted to make it work with Google colab as well. + """ + try: + shell_class = get_ipython().__class__ # type: ignore # noqa: F821 + for parent_class in shell_class.__mro__: # e.g. "is subclass of" + if parent_class.__name__ == "ZMQInteractiveShell": + return True # Jupyter notebook, Google colab or qtconsole + return False + except NameError: + return False # Probably standard Python interpreter + + +def is_google_colab() -> bool: + """Return `True` if code is executed in a Google colab. + + Taken from https://stackoverflow.com/a/63519730. + """ + return _is_google_colab + + +def is_colab_enterprise() -> bool: + """Return `True` if code is executed in a Google Colab Enterprise environment.""" + return os.environ.get("VERTEX_PRODUCT") == "COLAB_ENTERPRISE" + + +def dump_environment_info() -> Dict[str, Any]: + """Dump information about the machine to help debugging issues. + + Similar helper exist in: + - `datasets` (https://github.com/huggingface/datasets/blob/main/src/datasets/commands/env.py) + - `diffusers` (https://github.com/huggingface/diffusers/blob/main/src/diffusers/commands/env.py) + - `transformers` (https://github.com/huggingface/transformers/blob/main/src/transformers/commands/env.py) + """ + from huggingface_hub import get_token, whoami + from huggingface_hub.utils import list_credential_helpers + + token = get_token() + + # Generic machine info + info: Dict[str, Any] = { + "huggingface_hub version": get_hf_hub_version(), + "Platform": platform.platform(), + "Python version": get_python_version(), + } + + # Interpreter info + try: + shell_class = get_ipython().__class__ # type: ignore # noqa: F821 + info["Running in iPython ?"] = "Yes" + info["iPython shell"] = shell_class.__name__ + except NameError: + info["Running in iPython ?"] = "No" + info["Running in notebook ?"] = "Yes" if is_notebook() else "No" + info["Running in Google Colab ?"] = "Yes" if is_google_colab() else "No" + info["Running in Google Colab Enterprise ?"] = "Yes" if is_colab_enterprise() else "No" + # Login info + info["Token path ?"] = constants.HF_TOKEN_PATH + info["Has saved token ?"] = token is not None + if token is not None: + try: + info["Who am I ?"] = whoami()["name"] + except Exception: + pass + + try: + info["Configured git credential helpers"] = ", ".join(list_credential_helpers()) + except Exception: + pass + + # Installed dependencies + info["FastAI"] = get_fastai_version() + info["Tensorflow"] = get_tf_version() + info["Torch"] = get_torch_version() + info["Jinja2"] = get_jinja_version() + info["Graphviz"] = get_graphviz_version() + info["keras"] = get_keras_version() + info["Pydot"] = get_pydot_version() + info["Pillow"] = get_pillow_version() + info["hf_transfer"] = get_hf_transfer_version() + info["gradio"] = get_gradio_version() + info["tensorboard"] = get_tensorboard_version() + info["numpy"] = get_numpy_version() + info["pydantic"] = get_pydantic_version() + info["aiohttp"] = get_aiohttp_version() + + # Environment variables + info["ENDPOINT"] = constants.ENDPOINT + info["HF_HUB_CACHE"] = constants.HF_HUB_CACHE + info["HF_ASSETS_CACHE"] = constants.HF_ASSETS_CACHE + info["HF_TOKEN_PATH"] = constants.HF_TOKEN_PATH + info["HF_STORED_TOKENS_PATH"] = constants.HF_STORED_TOKENS_PATH + info["HF_HUB_OFFLINE"] = constants.HF_HUB_OFFLINE + info["HF_HUB_DISABLE_TELEMETRY"] = constants.HF_HUB_DISABLE_TELEMETRY + info["HF_HUB_DISABLE_PROGRESS_BARS"] = constants.HF_HUB_DISABLE_PROGRESS_BARS + info["HF_HUB_DISABLE_SYMLINKS_WARNING"] = constants.HF_HUB_DISABLE_SYMLINKS_WARNING + info["HF_HUB_DISABLE_EXPERIMENTAL_WARNING"] = constants.HF_HUB_DISABLE_EXPERIMENTAL_WARNING + info["HF_HUB_DISABLE_IMPLICIT_TOKEN"] = constants.HF_HUB_DISABLE_IMPLICIT_TOKEN + info["HF_HUB_ENABLE_HF_TRANSFER"] = constants.HF_HUB_ENABLE_HF_TRANSFER + info["HF_HUB_ETAG_TIMEOUT"] = constants.HF_HUB_ETAG_TIMEOUT + info["HF_HUB_DOWNLOAD_TIMEOUT"] = constants.HF_HUB_DOWNLOAD_TIMEOUT + + print("\nCopy-and-paste the text below in your GitHub issue.\n") + print("\n".join([f"- {prop}: {val}" for prop, val in info.items()]) + "\n") + return info diff --git a/parrot/lib/python3.10/site-packages/huggingface_hub/utils/_safetensors.py b/parrot/lib/python3.10/site-packages/huggingface_hub/utils/_safetensors.py new file mode 100644 index 0000000000000000000000000000000000000000..38546c6d34db786c62861e1706f747a21b7012bf --- /dev/null +++ b/parrot/lib/python3.10/site-packages/huggingface_hub/utils/_safetensors.py @@ -0,0 +1,111 @@ +import functools +import operator +from collections import defaultdict +from dataclasses import dataclass, field +from typing import Dict, List, Literal, Optional, Tuple + + +FILENAME_T = str +TENSOR_NAME_T = str +DTYPE_T = Literal["F64", "F32", "F16", "BF16", "I64", "I32", "I16", "I8", "U8", "BOOL"] + + +@dataclass +class TensorInfo: + """Information about a tensor. + + For more details regarding the safetensors format, check out https://huggingface.co/docs/safetensors/index#format. + + Attributes: + dtype (`str`): + The data type of the tensor ("F64", "F32", "F16", "BF16", "I64", "I32", "I16", "I8", "U8", "BOOL"). + shape (`List[int]`): + The shape of the tensor. + data_offsets (`Tuple[int, int]`): + The offsets of the data in the file as a tuple `[BEGIN, END]`. + parameter_count (`int`): + The number of parameters in the tensor. + """ + + dtype: DTYPE_T + shape: List[int] + data_offsets: Tuple[int, int] + parameter_count: int = field(init=False) + + def __post_init__(self) -> None: + # Taken from https://stackoverflow.com/a/13840436 + try: + self.parameter_count = functools.reduce(operator.mul, self.shape) + except TypeError: + self.parameter_count = 1 # scalar value has no shape + + +@dataclass +class SafetensorsFileMetadata: + """Metadata for a Safetensors file hosted on the Hub. + + This class is returned by [`parse_safetensors_file_metadata`]. + + For more details regarding the safetensors format, check out https://huggingface.co/docs/safetensors/index#format. + + Attributes: + metadata (`Dict`): + The metadata contained in the file. + tensors (`Dict[str, TensorInfo]`): + A map of all tensors. Keys are tensor names and values are information about the corresponding tensor, as a + [`TensorInfo`] object. + parameter_count (`Dict[str, int]`): + A map of the number of parameters per data type. Keys are data types and values are the number of parameters + of that data type. + """ + + metadata: Dict[str, str] + tensors: Dict[TENSOR_NAME_T, TensorInfo] + parameter_count: Dict[DTYPE_T, int] = field(init=False) + + def __post_init__(self) -> None: + parameter_count: Dict[DTYPE_T, int] = defaultdict(int) + for tensor in self.tensors.values(): + parameter_count[tensor.dtype] += tensor.parameter_count + self.parameter_count = dict(parameter_count) + + +@dataclass +class SafetensorsRepoMetadata: + """Metadata for a Safetensors repo. + + A repo is considered to be a Safetensors repo if it contains either a 'model.safetensors' weight file (non-shared + model) or a 'model.safetensors.index.json' index file (sharded model) at its root. + + This class is returned by [`get_safetensors_metadata`]. + + For more details regarding the safetensors format, check out https://huggingface.co/docs/safetensors/index#format. + + Attributes: + metadata (`Dict`, *optional*): + The metadata contained in the 'model.safetensors.index.json' file, if it exists. Only populated for sharded + models. + sharded (`bool`): + Whether the repo contains a sharded model or not. + weight_map (`Dict[str, str]`): + A map of all weights. Keys are tensor names and values are filenames of the files containing the tensors. + files_metadata (`Dict[str, SafetensorsFileMetadata]`): + A map of all files metadata. Keys are filenames and values are the metadata of the corresponding file, as + a [`SafetensorsFileMetadata`] object. + parameter_count (`Dict[str, int]`): + A map of the number of parameters per data type. Keys are data types and values are the number of parameters + of that data type. + """ + + metadata: Optional[Dict] + sharded: bool + weight_map: Dict[TENSOR_NAME_T, FILENAME_T] # tensor name -> filename + files_metadata: Dict[FILENAME_T, SafetensorsFileMetadata] # filename -> metadata + parameter_count: Dict[DTYPE_T, int] = field(init=False) + + def __post_init__(self) -> None: + parameter_count: Dict[DTYPE_T, int] = defaultdict(int) + for file_metadata in self.files_metadata.values(): + for dtype, nb_parameters_ in file_metadata.parameter_count.items(): + parameter_count[dtype] += nb_parameters_ + self.parameter_count = dict(parameter_count) diff --git a/parrot/lib/python3.10/site-packages/huggingface_hub/utils/_telemetry.py b/parrot/lib/python3.10/site-packages/huggingface_hub/utils/_telemetry.py new file mode 100644 index 0000000000000000000000000000000000000000..2ba4a6349a8de1c565263ec73d235d36f88b68cf --- /dev/null +++ b/parrot/lib/python3.10/site-packages/huggingface_hub/utils/_telemetry.py @@ -0,0 +1,126 @@ +from queue import Queue +from threading import Lock, Thread +from typing import Dict, Optional, Union +from urllib.parse import quote + +from .. import constants, logging +from . import build_hf_headers, get_session, hf_raise_for_status + + +logger = logging.get_logger(__name__) + +# Telemetry is sent by a separate thread to avoid blocking the main thread. +# A daemon thread is started once and consume tasks from the _TELEMETRY_QUEUE. +# If the thread stops for some reason -shouldn't happen-, we restart a new one. +_TELEMETRY_THREAD: Optional[Thread] = None +_TELEMETRY_THREAD_LOCK = Lock() # Lock to avoid starting multiple threads in parallel +_TELEMETRY_QUEUE: Queue = Queue() + + +def send_telemetry( + topic: str, + *, + library_name: Optional[str] = None, + library_version: Optional[str] = None, + user_agent: Union[Dict, str, None] = None, +) -> None: + """ + Sends telemetry that helps tracking usage of different HF libraries. + + This usage data helps us debug issues and prioritize new features. However, we understand that not everyone wants + to share additional information, and we respect your privacy. You can disable telemetry collection by setting the + `HF_HUB_DISABLE_TELEMETRY=1` as environment variable. Telemetry is also disabled in offline mode (i.e. when setting + `HF_HUB_OFFLINE=1`). + + Telemetry collection is run in a separate thread to minimize impact for the user. + + Args: + topic (`str`): + Name of the topic that is monitored. The topic is directly used to build the URL. If you want to monitor + subtopics, just use "/" separation. Examples: "gradio", "transformers/examples",... + library_name (`str`, *optional*): + The name of the library that is making the HTTP request. Will be added to the user-agent header. + library_version (`str`, *optional*): + The version of the library that is making the HTTP request. Will be added to the user-agent header. + user_agent (`str`, `dict`, *optional*): + The user agent info in the form of a dictionary or a single string. It will be completed with information about the installed packages. + + Example: + ```py + >>> from huggingface_hub.utils import send_telemetry + + # Send telemetry without library information + >>> send_telemetry("ping") + + # Send telemetry to subtopic with library information + >>> send_telemetry("gradio/local_link", library_name="gradio", library_version="3.22.1") + + # Send telemetry with additional data + >>> send_telemetry( + ... topic="examples", + ... library_name="transformers", + ... library_version="4.26.0", + ... user_agent={"pipeline": "text_classification", "framework": "flax"}, + ... ) + ``` + """ + if constants.HF_HUB_OFFLINE or constants.HF_HUB_DISABLE_TELEMETRY: + return + + _start_telemetry_thread() # starts thread only if doesn't exist yet + _TELEMETRY_QUEUE.put( + {"topic": topic, "library_name": library_name, "library_version": library_version, "user_agent": user_agent} + ) + + +def _start_telemetry_thread(): + """Start a daemon thread to consume tasks from the telemetry queue. + + If the thread is interrupted, start a new one. + """ + with _TELEMETRY_THREAD_LOCK: # avoid to start multiple threads if called concurrently + global _TELEMETRY_THREAD + if _TELEMETRY_THREAD is None or not _TELEMETRY_THREAD.is_alive(): + _TELEMETRY_THREAD = Thread(target=_telemetry_worker, daemon=True) + _TELEMETRY_THREAD.start() + + +def _telemetry_worker(): + """Wait for a task and consume it.""" + while True: + kwargs = _TELEMETRY_QUEUE.get() + _send_telemetry_in_thread(**kwargs) + _TELEMETRY_QUEUE.task_done() + + +def _send_telemetry_in_thread( + topic: str, + *, + library_name: Optional[str] = None, + library_version: Optional[str] = None, + user_agent: Union[Dict, str, None] = None, +) -> None: + """Contains the actual data sending data to the Hub. + + This function is called directly in gradio's analytics because + it is not possible to send telemetry from a daemon thread. + + See here: https://github.com/gradio-app/gradio/pull/8180 + + Please do not rename or remove this function. + """ + path = "/".join(quote(part) for part in topic.split("/") if len(part) > 0) + try: + r = get_session().head( + f"{constants.ENDPOINT}/api/telemetry/{path}", + headers=build_hf_headers( + token=False, # no need to send a token for telemetry + library_name=library_name, + library_version=library_version, + user_agent=user_agent, + ), + ) + hf_raise_for_status(r) + except Exception as e: + # We don't want to error in case of connection errors of any kind. + logger.debug(f"Error while sending telemetry: {e}") diff --git a/parrot/lib/python3.10/site-packages/huggingface_hub/utils/_typing.py b/parrot/lib/python3.10/site-packages/huggingface_hub/utils/_typing.py new file mode 100644 index 0000000000000000000000000000000000000000..b8388ca0c003e4741a44b298f02ef69932737f93 --- /dev/null +++ b/parrot/lib/python3.10/site-packages/huggingface_hub/utils/_typing.py @@ -0,0 +1,75 @@ +# coding=utf-8 +# Copyright 2022-present, the HuggingFace Inc. team. +# +# 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. +"""Handle typing imports based on system compatibility.""" + +import sys +from typing import Any, Callable, List, Literal, Type, TypeVar, Union, get_args, get_origin + + +UNION_TYPES: List[Any] = [Union] +if sys.version_info >= (3, 10): + from types import UnionType + + UNION_TYPES += [UnionType] + + +HTTP_METHOD_T = Literal["GET", "OPTIONS", "HEAD", "POST", "PUT", "PATCH", "DELETE"] + +# type hint meaning "function signature not changed by decorator" +CallableT = TypeVar("CallableT", bound=Callable) + +_JSON_SERIALIZABLE_TYPES = (int, float, str, bool, type(None)) + + +def is_jsonable(obj: Any) -> bool: + """Check if an object is JSON serializable. + + This is a weak check, as it does not check for the actual JSON serialization, but only for the types of the object. + It works correctly for basic use cases but do not guarantee an exhaustive check. + + Object is considered to be recursively json serializable if: + - it is an instance of int, float, str, bool, or NoneType + - it is a list or tuple and all its items are json serializable + - it is a dict and all its keys are strings and all its values are json serializable + """ + try: + if isinstance(obj, _JSON_SERIALIZABLE_TYPES): + return True + if isinstance(obj, (list, tuple)): + return all(is_jsonable(item) for item in obj) + if isinstance(obj, dict): + return all(isinstance(key, _JSON_SERIALIZABLE_TYPES) and is_jsonable(value) for key, value in obj.items()) + if hasattr(obj, "__json__"): + return True + return False + except RecursionError: + return False + + +def is_simple_optional_type(type_: Type) -> bool: + """Check if a type is optional, i.e. Optional[Type] or Union[Type, None] or Type | None, where Type is a non-composite type.""" + if get_origin(type_) in UNION_TYPES: + union_args = get_args(type_) + if len(union_args) == 2 and type(None) in union_args: + return True + return False + + +def unwrap_simple_optional_type(optional_type: Type) -> Type: + """Unwraps a simple optional type, i.e. returns Type from Optional[Type].""" + for arg in get_args(optional_type): + if arg is not type(None): + return arg + raise ValueError(f"'{optional_type}' is not an optional type") diff --git a/parrot/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py b/parrot/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py new file mode 100644 index 0000000000000000000000000000000000000000..27833f28e3e2030680fb72b95a547521bc08831b --- /dev/null +++ b/parrot/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py @@ -0,0 +1,226 @@ +# coding=utf-8 +# Copyright 2022-present, the HuggingFace Inc. team. +# +# 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. +"""Contains utilities to validate argument values in `huggingface_hub`.""" + +import inspect +import re +import warnings +from functools import wraps +from itertools import chain +from typing import Any, Dict + +from huggingface_hub.errors import HFValidationError + +from ._typing import CallableT + + +REPO_ID_REGEX = re.compile( + r""" + ^ + (\b[\w\-.]+\b/)? # optional namespace (username or organization) + \b # starts with a word boundary + [\w\-.]{1,96} # repo_name: alphanumeric + . _ - + \b # ends with a word boundary + $ + """, + flags=re.VERBOSE, +) + + +def validate_hf_hub_args(fn: CallableT) -> CallableT: + """Validate values received as argument for any public method of `huggingface_hub`. + + The goal of this decorator is to harmonize validation of arguments reused + everywhere. By default, all defined validators are tested. + + Validators: + - [`~utils.validate_repo_id`]: `repo_id` must be `"repo_name"` + or `"namespace/repo_name"`. Namespace is a username or an organization. + - [`~utils.smoothly_deprecate_use_auth_token`]: Use `token` instead of + `use_auth_token` (only if `use_auth_token` is not expected by the decorated + function - in practice, always the case in `huggingface_hub`). + + Example: + ```py + >>> from huggingface_hub.utils import validate_hf_hub_args + + >>> @validate_hf_hub_args + ... def my_cool_method(repo_id: str): + ... print(repo_id) + + >>> my_cool_method(repo_id="valid_repo_id") + valid_repo_id + + >>> my_cool_method("other..repo..id") + huggingface_hub.utils._validators.HFValidationError: Cannot have -- or .. in repo_id: 'other..repo..id'. + + >>> my_cool_method(repo_id="other..repo..id") + huggingface_hub.utils._validators.HFValidationError: Cannot have -- or .. in repo_id: 'other..repo..id'. + + >>> @validate_hf_hub_args + ... def my_cool_auth_method(token: str): + ... print(token) + + >>> my_cool_auth_method(token="a token") + "a token" + + >>> my_cool_auth_method(use_auth_token="a use_auth_token") + "a use_auth_token" + + >>> my_cool_auth_method(token="a token", use_auth_token="a use_auth_token") + UserWarning: Both `token` and `use_auth_token` are passed (...) + "a token" + ``` + + Raises: + [`~utils.HFValidationError`]: + If an input is not valid. + """ + # TODO: add an argument to opt-out validation for specific argument? + signature = inspect.signature(fn) + + # Should the validator switch `use_auth_token` values to `token`? In practice, always + # True in `huggingface_hub`. Might not be the case in a downstream library. + check_use_auth_token = "use_auth_token" not in signature.parameters and "token" in signature.parameters + + @wraps(fn) + def _inner_fn(*args, **kwargs): + has_token = False + for arg_name, arg_value in chain( + zip(signature.parameters, args), # Args values + kwargs.items(), # Kwargs values + ): + if arg_name in ["repo_id", "from_id", "to_id"]: + validate_repo_id(arg_value) + + elif arg_name == "token" and arg_value is not None: + has_token = True + + if check_use_auth_token: + kwargs = smoothly_deprecate_use_auth_token(fn_name=fn.__name__, has_token=has_token, kwargs=kwargs) + + return fn(*args, **kwargs) + + return _inner_fn # type: ignore + + +def validate_repo_id(repo_id: str) -> None: + """Validate `repo_id` is valid. + + This is not meant to replace the proper validation made on the Hub but rather to + avoid local inconsistencies whenever possible (example: passing `repo_type` in the + `repo_id` is forbidden). + + Rules: + - Between 1 and 96 characters. + - Either "repo_name" or "namespace/repo_name" + - [a-zA-Z0-9] or "-", "_", "." + - "--" and ".." are forbidden + + Valid: `"foo"`, `"foo/bar"`, `"123"`, `"Foo-BAR_foo.bar123"` + + Not valid: `"datasets/foo/bar"`, `".repo_id"`, `"foo--bar"`, `"foo.git"` + + Example: + ```py + >>> from huggingface_hub.utils import validate_repo_id + >>> validate_repo_id(repo_id="valid_repo_id") + >>> validate_repo_id(repo_id="other..repo..id") + huggingface_hub.utils._validators.HFValidationError: Cannot have -- or .. in repo_id: 'other..repo..id'. + ``` + + Discussed in https://github.com/huggingface/huggingface_hub/issues/1008. + In moon-landing (internal repository): + - https://github.com/huggingface/moon-landing/blob/main/server/lib/Names.ts#L27 + - https://github.com/huggingface/moon-landing/blob/main/server/views/components/NewRepoForm/NewRepoForm.svelte#L138 + """ + if not isinstance(repo_id, str): + # Typically, a Path is not a repo_id + raise HFValidationError(f"Repo id must be a string, not {type(repo_id)}: '{repo_id}'.") + + if repo_id.count("/") > 1: + raise HFValidationError( + "Repo id must be in the form 'repo_name' or 'namespace/repo_name':" + f" '{repo_id}'. Use `repo_type` argument if needed." + ) + + if not REPO_ID_REGEX.match(repo_id): + raise HFValidationError( + "Repo id must use alphanumeric chars or '-', '_', '.', '--' and '..' are" + " forbidden, '-' and '.' cannot start or end the name, max length is 96:" + f" '{repo_id}'." + ) + + if "--" in repo_id or ".." in repo_id: + raise HFValidationError(f"Cannot have -- or .. in repo_id: '{repo_id}'.") + + if repo_id.endswith(".git"): + raise HFValidationError(f"Repo_id cannot end by '.git': '{repo_id}'.") + + +def smoothly_deprecate_use_auth_token(fn_name: str, has_token: bool, kwargs: Dict[str, Any]) -> Dict[str, Any]: + """Smoothly deprecate `use_auth_token` in the `huggingface_hub` codebase. + + The long-term goal is to remove any mention of `use_auth_token` in the codebase in + favor of a unique and less verbose `token` argument. This will be done a few steps: + + 0. Step 0: methods that require a read-access to the Hub use the `use_auth_token` + argument (`str`, `bool` or `None`). Methods requiring write-access have a `token` + argument (`str`, `None`). This implicit rule exists to be able to not send the + token when not necessary (`use_auth_token=False`) even if logged in. + + 1. Step 1: we want to harmonize everything and use `token` everywhere (supporting + `token=False` for read-only methods). In order not to break existing code, if + `use_auth_token` is passed to a function, the `use_auth_token` value is passed + as `token` instead, without any warning. + a. Corner case: if both `use_auth_token` and `token` values are passed, a warning + is thrown and the `use_auth_token` value is ignored. + + 2. Step 2: Once it is release, we should push downstream libraries to switch from + `use_auth_token` to `token` as much as possible, but without throwing a warning + (e.g. manually create issues on the corresponding repos). + + 3. Step 3: After a transitional period (6 months e.g. until April 2023?), we update + `huggingface_hub` to throw a warning on `use_auth_token`. Hopefully, very few + users will be impacted as it would have already been fixed. + In addition, unit tests in `huggingface_hub` must be adapted to expect warnings + to be thrown (but still use `use_auth_token` as before). + + 4. Step 4: After a normal deprecation cycle (3 releases ?), remove this validator. + `use_auth_token` will definitely not be supported. + In addition, we update unit tests in `huggingface_hub` to use `token` everywhere. + + This has been discussed in: + - https://github.com/huggingface/huggingface_hub/issues/1094. + - https://github.com/huggingface/huggingface_hub/pull/928 + - (related) https://github.com/huggingface/huggingface_hub/pull/1064 + """ + new_kwargs = kwargs.copy() # do not mutate input ! + + use_auth_token = new_kwargs.pop("use_auth_token", None) # remove from kwargs + if use_auth_token is not None: + if has_token: + warnings.warn( + "Both `token` and `use_auth_token` are passed to" + f" `{fn_name}` with non-None values. `token` is now the" + " preferred argument to pass a User Access Token." + " `use_auth_token` value will be ignored." + ) + else: + # `token` argument is not passed and a non-None value is passed in + # `use_auth_token` => use `use_auth_token` value as `token` kwarg. + new_kwargs["token"] = use_auth_token + + return new_kwargs diff --git a/parrot/lib/python3.10/site-packages/huggingface_hub/utils/endpoint_helpers.py b/parrot/lib/python3.10/site-packages/huggingface_hub/utils/endpoint_helpers.py new file mode 100644 index 0000000000000000000000000000000000000000..85cd86011b78bcdc57034aeebc3c01e9e721ab50 --- /dev/null +++ b/parrot/lib/python3.10/site-packages/huggingface_hub/utils/endpoint_helpers.py @@ -0,0 +1,66 @@ +# 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. +""" +Helpful utility functions and classes in relation to exploring API endpoints +with the aim for a user-friendly interface. +""" + +import math +import re +from typing import TYPE_CHECKING + +from ..repocard_data import ModelCardData + + +if TYPE_CHECKING: + from ..hf_api import ModelInfo + + +def _is_emission_within_threshold(model_info: "ModelInfo", minimum_threshold: float, maximum_threshold: float) -> bool: + """Checks if a model's emission is within a given threshold. + + Args: + model_info (`ModelInfo`): + A model info object containing the model's emission information. + minimum_threshold (`float`): + A minimum carbon threshold to filter by, such as 1. + maximum_threshold (`float`): + A maximum carbon threshold to filter by, such as 10. + + Returns: + `bool`: Whether the model's emission is within the given threshold. + """ + if minimum_threshold is None and maximum_threshold is None: + raise ValueError("Both `minimum_threshold` and `maximum_threshold` cannot both be `None`") + if minimum_threshold is None: + minimum_threshold = -1 + if maximum_threshold is None: + maximum_threshold = math.inf + + card_data = getattr(model_info, "card_data", None) + if card_data is None or not isinstance(card_data, (dict, ModelCardData)): + return False + + # Get CO2 emission metadata + emission = card_data.get("co2_eq_emissions", None) + if isinstance(emission, dict): + emission = emission["emissions"] + if not emission: + return False + + # Filter out if value is missing or out of range + matched = re.search(r"\d+\.\d+|\d+", str(emission)) + if matched is None: + return False + + emission_value = float(matched.group(0)) + return minimum_threshold <= emission_value <= maximum_threshold diff --git a/parrot/lib/python3.10/site-packages/huggingface_hub/utils/insecure_hashlib.py b/parrot/lib/python3.10/site-packages/huggingface_hub/utils/insecure_hashlib.py new file mode 100644 index 0000000000000000000000000000000000000000..f232ee0adcfc52dcc18b5ea4d9c913b206521f71 --- /dev/null +++ b/parrot/lib/python3.10/site-packages/huggingface_hub/utils/insecure_hashlib.py @@ -0,0 +1,34 @@ +# Taken from https://github.com/mlflow/mlflow/pull/10119 +# +# DO NOT use this function for security purposes (e.g., password hashing). +# +# In Python >= 3.9, insecure hashing algorithms such as MD5 fail in FIPS-compliant +# environments unless `usedforsecurity=False` is explicitly passed. +# +# References: +# - https://github.com/mlflow/mlflow/issues/9905 +# - https://github.com/mlflow/mlflow/pull/10119 +# - https://docs.python.org/3/library/hashlib.html +# - https://github.com/huggingface/transformers/pull/27038 +# +# Usage: +# ```python +# # Use +# from huggingface_hub.utils.insecure_hashlib import sha256 +# # instead of +# from hashlib import sha256 +# +# # Use +# from huggingface_hub.utils import insecure_hashlib +# # instead of +# import hashlib +# ``` +import functools +import hashlib +import sys + + +_kwargs = {"usedforsecurity": False} if sys.version_info >= (3, 9) else {} +md5 = functools.partial(hashlib.md5, **_kwargs) +sha1 = functools.partial(hashlib.sha1, **_kwargs) +sha256 = functools.partial(hashlib.sha256, **_kwargs) diff --git a/parrot/lib/python3.10/site-packages/huggingface_hub/utils/logging.py b/parrot/lib/python3.10/site-packages/huggingface_hub/utils/logging.py new file mode 100644 index 0000000000000000000000000000000000000000..813719683a54cc65768bab5488e7ea153ad08d7e --- /dev/null +++ b/parrot/lib/python3.10/site-packages/huggingface_hub/utils/logging.py @@ -0,0 +1,188 @@ +# coding=utf-8 +# Copyright 2020 Optuna, Hugging Face +# +# 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. +"""Logging utilities.""" + +import logging +import os +from logging import ( + CRITICAL, # NOQA + DEBUG, # NOQA + ERROR, # NOQA + FATAL, # NOQA + INFO, # NOQA + NOTSET, # NOQA + WARN, # NOQA + WARNING, # NOQA +) +from typing import Optional + +from .. import constants + + +log_levels = { + "debug": logging.DEBUG, + "info": logging.INFO, + "warning": logging.WARNING, + "error": logging.ERROR, + "critical": logging.CRITICAL, +} + +_default_log_level = logging.WARNING + + +def _get_library_name() -> str: + return __name__.split(".")[0] + + +def _get_library_root_logger() -> logging.Logger: + return logging.getLogger(_get_library_name()) + + +def _get_default_logging_level(): + """ + If `HF_HUB_VERBOSITY` env var is set to one of the valid choices return that as the new default level. If it is not + - fall back to `_default_log_level` + """ + env_level_str = os.getenv("HF_HUB_VERBOSITY", None) + if env_level_str: + if env_level_str in log_levels: + return log_levels[env_level_str] + else: + logging.getLogger().warning( + f"Unknown option HF_HUB_VERBOSITY={env_level_str}, has to be one of: {', '.join(log_levels.keys())}" + ) + return _default_log_level + + +def _configure_library_root_logger() -> None: + library_root_logger = _get_library_root_logger() + library_root_logger.addHandler(logging.StreamHandler()) + library_root_logger.setLevel(_get_default_logging_level()) + + +def _reset_library_root_logger() -> None: + library_root_logger = _get_library_root_logger() + library_root_logger.setLevel(logging.NOTSET) + + +def get_logger(name: Optional[str] = None) -> logging.Logger: + """ + Returns a logger with the specified name. This function is not supposed + to be directly accessed by library users. + + Args: + name (`str`, *optional*): + The name of the logger to get, usually the filename + + Example: + + ```python + >>> from huggingface_hub import get_logger + + >>> logger = get_logger(__file__) + >>> logger.set_verbosity_info() + ``` + """ + + if name is None: + name = _get_library_name() + + return logging.getLogger(name) + + +def get_verbosity() -> int: + """Return the current level for the HuggingFace Hub's root logger. + + Returns: + Logging level, e.g., `huggingface_hub.logging.DEBUG` and + `huggingface_hub.logging.INFO`. + + + + HuggingFace Hub has following logging levels: + + - `huggingface_hub.logging.CRITICAL`, `huggingface_hub.logging.FATAL` + - `huggingface_hub.logging.ERROR` + - `huggingface_hub.logging.WARNING`, `huggingface_hub.logging.WARN` + - `huggingface_hub.logging.INFO` + - `huggingface_hub.logging.DEBUG` + + + """ + return _get_library_root_logger().getEffectiveLevel() + + +def set_verbosity(verbosity: int) -> None: + """ + Sets the level for the HuggingFace Hub's root logger. + + Args: + verbosity (`int`): + Logging level, e.g., `huggingface_hub.logging.DEBUG` and + `huggingface_hub.logging.INFO`. + """ + _get_library_root_logger().setLevel(verbosity) + + +def set_verbosity_info(): + """ + Sets the verbosity to `logging.INFO`. + """ + return set_verbosity(INFO) + + +def set_verbosity_warning(): + """ + Sets the verbosity to `logging.WARNING`. + """ + return set_verbosity(WARNING) + + +def set_verbosity_debug(): + """ + Sets the verbosity to `logging.DEBUG`. + """ + return set_verbosity(DEBUG) + + +def set_verbosity_error(): + """ + Sets the verbosity to `logging.ERROR`. + """ + return set_verbosity(ERROR) + + +def disable_propagation() -> None: + """ + Disable propagation of the library log outputs. Note that log propagation is + disabled by default. + """ + _get_library_root_logger().propagate = False + + +def enable_propagation() -> None: + """ + Enable propagation of the library log outputs. Please disable the + HuggingFace Hub's default handler to prevent double logging if the root + logger has been configured. + """ + _get_library_root_logger().propagate = True + + +_configure_library_root_logger() + +if constants.HF_DEBUG: + # If `HF_DEBUG` environment variable is set, set the verbosity of `huggingface_hub` logger to `DEBUG`. + set_verbosity_debug() diff --git a/parrot/lib/python3.10/site-packages/huggingface_hub/utils/sha.py b/parrot/lib/python3.10/site-packages/huggingface_hub/utils/sha.py new file mode 100644 index 0000000000000000000000000000000000000000..001c3fe8b2f37a64e890888ca3d521c10ec8f03b --- /dev/null +++ b/parrot/lib/python3.10/site-packages/huggingface_hub/utils/sha.py @@ -0,0 +1,64 @@ +"""Utilities to efficiently compute the SHA 256 hash of a bunch of bytes.""" + +from typing import BinaryIO, Optional + +from .insecure_hashlib import sha1, sha256 + + +def sha_fileobj(fileobj: BinaryIO, chunk_size: Optional[int] = None) -> bytes: + """ + Computes the sha256 hash of the given file object, by chunks of size `chunk_size`. + + Args: + fileobj (file-like object): + The File object to compute sha256 for, typically obtained with `open(path, "rb")` + chunk_size (`int`, *optional*): + The number of bytes to read from `fileobj` at once, defaults to 1MB. + + Returns: + `bytes`: `fileobj`'s sha256 hash as bytes + """ + chunk_size = chunk_size if chunk_size is not None else 1024 * 1024 + + sha = sha256() + while True: + chunk = fileobj.read(chunk_size) + sha.update(chunk) + if not chunk: + break + return sha.digest() + + +def git_hash(data: bytes) -> str: + """ + Computes the git-sha1 hash of the given bytes, using the same algorithm as git. + + This is equivalent to running `git hash-object`. See https://git-scm.com/docs/git-hash-object + for more details. + + Note: this method is valid for regular files. For LFS files, the proper git hash is supposed to be computed on the + pointer file content, not the actual file content. However, for simplicity, we directly compare the sha256 of + the LFS file content when we want to compare LFS files. + + Args: + data (`bytes`): + The data to compute the git-hash for. + + Returns: + `str`: the git-hash of `data` as an hexadecimal string. + + Example: + ```python + >>> from huggingface_hub.utils.sha import git_hash + >>> git_hash(b"Hello, World!") + 'b45ef6fec89518d314f546fd6c3025367b721684' + ``` + """ + # Taken from https://gist.github.com/msabramo/763200 + # Note: no need to optimize by reading the file in chunks as we're not supposed to hash huge files (5MB maximum). + sha = sha1() + sha.update(b"blob ") + sha.update(str(len(data)).encode()) + sha.update(b"\0") + sha.update(data) + return sha.hexdigest() diff --git a/parrot/lib/python3.10/site-packages/huggingface_hub/utils/tqdm.py b/parrot/lib/python3.10/site-packages/huggingface_hub/utils/tqdm.py new file mode 100644 index 0000000000000000000000000000000000000000..b22b797c32b6a0938eb73272b4365a5caa3ba250 --- /dev/null +++ b/parrot/lib/python3.10/site-packages/huggingface_hub/utils/tqdm.py @@ -0,0 +1,279 @@ +# coding=utf-8 +# Copyright 2021 The HuggingFace Inc. 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 +"""Utility helpers to handle progress bars in `huggingface_hub`. + +Example: + 1. Use `huggingface_hub.utils.tqdm` as you would use `tqdm.tqdm` or `tqdm.auto.tqdm`. + 2. To disable progress bars, either use `disable_progress_bars()` helper or set the + environment variable `HF_HUB_DISABLE_PROGRESS_BARS` to 1. + 3. To re-enable progress bars, use `enable_progress_bars()`. + 4. To check whether progress bars are disabled, use `are_progress_bars_disabled()`. + +NOTE: Environment variable `HF_HUB_DISABLE_PROGRESS_BARS` has the priority. + +Example: + ```py + >>> from huggingface_hub.utils import are_progress_bars_disabled, disable_progress_bars, enable_progress_bars, tqdm + + # Disable progress bars globally + >>> disable_progress_bars() + + # Use as normal `tqdm` + >>> for _ in tqdm(range(5)): + ... pass + + # Still not showing progress bars, as `disable=False` is overwritten to `True`. + >>> for _ in tqdm(range(5), disable=False): + ... pass + + >>> are_progress_bars_disabled() + True + + # Re-enable progress bars globally + >>> enable_progress_bars() + + # Progress bar will be shown ! + >>> for _ in tqdm(range(5)): + ... pass + 100%|███████████████████████████████████████| 5/5 [00:00<00:00, 117817.53it/s] + ``` + +Group-based control: + ```python + # Disable progress bars for a specific group + >>> disable_progress_bars("peft.foo") + + # Check state of different groups + >>> assert not are_progress_bars_disabled("peft")) + >>> assert not are_progress_bars_disabled("peft.something") + >>> assert are_progress_bars_disabled("peft.foo")) + >>> assert are_progress_bars_disabled("peft.foo.bar")) + + # Enable progress bars for a subgroup + >>> enable_progress_bars("peft.foo.bar") + + # Check if enabling a subgroup affects the parent group + >>> assert are_progress_bars_disabled("peft.foo")) + >>> assert not are_progress_bars_disabled("peft.foo.bar")) + + # No progress bar for `name="peft.foo"` + >>> for _ in tqdm(range(5), name="peft.foo"): + ... pass + + # Progress bar will be shown for `name="peft.foo.bar"` + >>> for _ in tqdm(range(5), name="peft.foo.bar"): + ... pass + 100%|███████████████████████████████████████| 5/5 [00:00<00:00, 117817.53it/s] + + ``` +""" + +import io +import logging +import os +import warnings +from contextlib import contextmanager +from pathlib import Path +from typing import Dict, Iterator, Optional, Union + +from tqdm.auto import tqdm as old_tqdm + +from ..constants import HF_HUB_DISABLE_PROGRESS_BARS + + +# The `HF_HUB_DISABLE_PROGRESS_BARS` environment variable can be True, False, or not set (None), +# allowing for control over progress bar visibility. When set, this variable takes precedence +# over programmatic settings, dictating whether progress bars should be shown or hidden globally. +# Essentially, the environment variable's setting overrides any code-based configurations. +# +# If `HF_HUB_DISABLE_PROGRESS_BARS` is not defined (None), it implies that users can manage +# progress bar visibility through code. By default, progress bars are turned on. + + +progress_bar_states: Dict[str, bool] = {} + + +def disable_progress_bars(name: Optional[str] = None) -> None: + """ + Disable progress bars either globally or for a specified group. + + This function updates the state of progress bars based on a group name. + If no group name is provided, all progress bars are disabled. The operation + respects the `HF_HUB_DISABLE_PROGRESS_BARS` environment variable's setting. + + Args: + name (`str`, *optional*): + The name of the group for which to disable the progress bars. If None, + progress bars are disabled globally. + + Raises: + Warning: If the environment variable precludes changes. + """ + if HF_HUB_DISABLE_PROGRESS_BARS is False: + warnings.warn( + "Cannot disable progress bars: environment variable `HF_HUB_DISABLE_PROGRESS_BARS=0` is set and has priority." + ) + return + + if name is None: + progress_bar_states.clear() + progress_bar_states["_global"] = False + else: + keys_to_remove = [key for key in progress_bar_states if key.startswith(f"{name}.")] + for key in keys_to_remove: + del progress_bar_states[key] + progress_bar_states[name] = False + + +def enable_progress_bars(name: Optional[str] = None) -> None: + """ + Enable progress bars either globally or for a specified group. + + This function sets the progress bars to enabled for the specified group or globally + if no group is specified. The operation is subject to the `HF_HUB_DISABLE_PROGRESS_BARS` + environment setting. + + Args: + name (`str`, *optional*): + The name of the group for which to enable the progress bars. If None, + progress bars are enabled globally. + + Raises: + Warning: If the environment variable precludes changes. + """ + if HF_HUB_DISABLE_PROGRESS_BARS is True: + warnings.warn( + "Cannot enable progress bars: environment variable `HF_HUB_DISABLE_PROGRESS_BARS=1` is set and has priority." + ) + return + + if name is None: + progress_bar_states.clear() + progress_bar_states["_global"] = True + else: + keys_to_remove = [key for key in progress_bar_states if key.startswith(f"{name}.")] + for key in keys_to_remove: + del progress_bar_states[key] + progress_bar_states[name] = True + + +def are_progress_bars_disabled(name: Optional[str] = None) -> bool: + """ + Check if progress bars are disabled globally or for a specific group. + + This function returns whether progress bars are disabled for a given group or globally. + It checks the `HF_HUB_DISABLE_PROGRESS_BARS` environment variable first, then the programmatic + settings. + + Args: + name (`str`, *optional*): + The group name to check; if None, checks the global setting. + + Returns: + `bool`: True if progress bars are disabled, False otherwise. + """ + if HF_HUB_DISABLE_PROGRESS_BARS is True: + return True + + if name is None: + return not progress_bar_states.get("_global", True) + + while name: + if name in progress_bar_states: + return not progress_bar_states[name] + name = ".".join(name.split(".")[:-1]) + + return not progress_bar_states.get("_global", True) + + +def is_tqdm_disabled(log_level: int) -> Optional[bool]: + """ + Determine if tqdm progress bars should be disabled based on logging level and environment settings. + + see https://github.com/huggingface/huggingface_hub/pull/2000 and https://github.com/huggingface/huggingface_hub/pull/2698. + """ + if log_level == logging.NOTSET: + return True + if os.getenv("TQDM_POSITION") == "-1": + return False + return None + + +class tqdm(old_tqdm): + """ + Class to override `disable` argument in case progress bars are globally disabled. + + Taken from https://github.com/tqdm/tqdm/issues/619#issuecomment-619639324. + """ + + def __init__(self, *args, **kwargs): + name = kwargs.pop("name", None) # do not pass `name` to `tqdm` + if are_progress_bars_disabled(name): + kwargs["disable"] = True + super().__init__(*args, **kwargs) + + def __delattr__(self, attr: str) -> None: + """Fix for https://github.com/huggingface/huggingface_hub/issues/1603""" + try: + super().__delattr__(attr) + except AttributeError: + if attr != "_lock": + raise + + +@contextmanager +def tqdm_stream_file(path: Union[Path, str]) -> Iterator[io.BufferedReader]: + """ + Open a file as binary and wrap the `read` method to display a progress bar when it's streamed. + + First implemented in `transformers` in 2019 but removed when switched to git-lfs. Used in `huggingface_hub` to show + progress bar when uploading an LFS file to the Hub. See github.com/huggingface/transformers/pull/2078#discussion_r354739608 + for implementation details. + + Note: currently implementation handles only files stored on disk as it is the most common use case. Could be + extended to stream any `BinaryIO` object but we might have to debug some corner cases. + + Example: + ```py + >>> with tqdm_stream_file("config.json") as f: + >>> requests.put(url, data=f) + config.json: 100%|█████████████████████████| 8.19k/8.19k [00:02<00:00, 3.72kB/s] + ``` + """ + if isinstance(path, str): + path = Path(path) + + with path.open("rb") as f: + total_size = path.stat().st_size + pbar = tqdm( + unit="B", + unit_scale=True, + total=total_size, + initial=0, + desc=path.name, + ) + + f_read = f.read + + def _inner_read(size: Optional[int] = -1) -> bytes: + data = f_read(size) + pbar.update(len(data)) + return data + + f.read = _inner_read # type: ignore + + yield f + + pbar.close() diff --git a/parrot/lib/python3.10/site-packages/wandb/sdk/internal/__pycache__/internal_api.cpython-310.pyc b/parrot/lib/python3.10/site-packages/wandb/sdk/internal/__pycache__/internal_api.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..66cafaa78054497f75ca642ba5144952380347ed --- /dev/null +++ b/parrot/lib/python3.10/site-packages/wandb/sdk/internal/__pycache__/internal_api.cpython-310.pyc @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:dfe3e89ebad2182ed0181760607b4f938d938dbfce56ad394c9b6289e867dbea +size 110261