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ckpts/llama-3b/global_step100/bf16_zero_pp_rank_13_mp_rank_01_optim_states.pt

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ckpts/llama-3b/global_step100/bf16_zero_pp_rank_146_mp_rank_01_optim_states.pt

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ckpts/llama-3b/global_step100/bf16_zero_pp_rank_231_mp_rank_00_optim_states.pt

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ckpts/llama-3b/global_step100/bf16_zero_pp_rank_244_mp_rank_03_optim_states.pt

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ckpts/llama-3b/global_step100/bf16_zero_pp_rank_80_mp_rank_03_optim_states.pt

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venv/lib/python3.10/site-packages/examples/basic_example.py

@@ -0,0 +1,60 @@
+from time import sleep
+import multiprocessing
+
+import logging
+from tqdm_multiprocess.logger import setup_logger_tqdm
+logger = logging.getLogger(__name__)
+
+from tqdm_multiprocess import TqdmMultiProcessPool
+
+def some_other_function(tqdm_func):
+    iterations1 = 100
+    iterations2 = 5
+    iterations3 = 2
+    
+    total_iterations = iterations1 * iterations2 * iterations3
+    with tqdm_func(total=total_iterations, dynamic_ncols=True) as progress3:
+        progress3.set_description("outer")
+        for i in range(iterations3):
+            logger.info("outer")
+            total_iterations = iterations1 * iterations2
+            with tqdm_func(total=total_iterations, dynamic_ncols=True) as progress2:
+                progress2.set_description("middle")
+                for j in range(iterations2):
+                    logger.info("middle")
+                    #for k in tqdm_func(range(iterations1), dynamic_ncols=True, desc="inner"):
+                    with tqdm_func(total=iterations1, dynamic_ncols=True) as progress1:
+                        for j in range(iterations1):
+                            # logger.info("inner") # Spam slows down tqdm too much
+                            progress1.set_description("innert")
+                            sleep(0.01)
+                            progress1.update()
+                            progress2.update()
+                            progress3.update()
+
+    logger.warning(f"Warning test message. {multiprocessing.current_process().name}")
+    logger.error(f"Error test message. {multiprocessing.current_process().name}")
+
+        
+# Multiprocessed
+def example_multiprocessing_function(some_input, tqdm_func):  
+    logger.debug(f"Debug test message - I won't show up in console. {multiprocessing.current_process().name}")
+    logger.info(f"Info test message. {multiprocessing.current_process().name}")
+    some_other_function(tqdm_func)
+    return True
+
+def error_callback():
+    print("Error!")
+
+def example():
+    pool = TqdmMultiProcessPool()
+    process_count = 4
+    task_count = 10
+    initial_tasks = [(example_multiprocessing_function, (i,)) for i in range(task_count)]    
+    results = pool.map(process_count, initial_tasks, error_callback)
+    print(results)
+
+if __name__ == '__main__':
+    logfile_path = "tqdm_multiprocessing_example.log"
+    setup_logger_tqdm(logfile_path) # Logger will write messages using tqdm.write
+    example() 

venv/lib/python3.10/site-packages/huggingface_hub/inference/_common.py

@@ -0,0 +1,482 @@
+# 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 used by both the sync and async inference clients."""
+
+import base64
+import io
+import json
+import logging
+import time
+from contextlib import contextmanager
+from dataclasses import dataclass
+from pathlib import Path
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    AsyncIterable,
+    BinaryIO,
+    ContextManager,
+    Dict,
+    Generator,
+    Iterable,
+    List,
+    Literal,
+    NoReturn,
+    Optional,
+    Set,
+    Union,
+    overload,
+)
+
+from requests import HTTPError
+
+from huggingface_hub.errors import (
+    GenerationError,
+    IncompleteGenerationError,
+    OverloadedError,
+    TextGenerationError,
+    UnknownError,
+    ValidationError,
+)
+
+from ..constants import ENDPOINT
+from ..utils import (
+    build_hf_headers,
+    get_session,
+    hf_raise_for_status,
+    is_aiohttp_available,
+    is_numpy_available,
+    is_pillow_available,
+)
+from ._generated.types import (
+    ChatCompletionStreamOutput,
+    ChatCompletionStreamOutputChoice,
+    ChatCompletionStreamOutputDelta,
+    TextGenerationStreamOutput,
+)
+
+
+if TYPE_CHECKING:
+    from aiohttp import ClientResponse, ClientSession
+    from PIL import Image
+
+# TYPES
+UrlT = str
+PathT = Union[str, Path]
+BinaryT = Union[bytes, BinaryIO]
+ContentT = Union[BinaryT, PathT, UrlT]
+
+# Use to set a Accept: image/png header
+TASKS_EXPECTING_IMAGES = {"text-to-image", "image-to-image"}
+
+logger = logging.getLogger(__name__)
+
+
+# Add dataclass for ModelStatus. We use this dataclass in get_model_status function.
+@dataclass
+class ModelStatus:
+    """
+    This Dataclass represents the the model status in the Hugging Face Inference API.
+
+    Args:
+        loaded (`bool`):
+            If the model is currently loaded into Hugging Face's InferenceAPI. Models
+            are loaded on-demand, leading to the user's first request taking longer.
+            If a model is loaded, you can be assured that it is in a healthy state.
+        state (`str`):
+            The current state of the model. This can be 'Loaded', 'Loadable', 'TooBig'.
+            If a model's state is 'Loadable', it's not too big and has a supported
+            backend. Loadable models are automatically loaded when the user first
+            requests inference on the endpoint. This means it is transparent for the
+            user to load a model, except that the first call takes longer to complete.
+        compute_type (`Dict`):
+            Information about the compute resource the model is using or will use, such as 'gpu' type and number of
+            replicas.
+        framework (`str`):
+            The name of the framework that the model was built with, such as 'transformers'
+            or 'text-generation-inference'.
+    """
+
+    loaded: bool
+    state: str
+    compute_type: Dict
+    framework: str
+
+
+## IMPORT UTILS
+
+
+def _import_aiohttp():
+    # Make sure `aiohttp` is installed on the machine.
+    if not is_aiohttp_available():
+        raise ImportError("Please install aiohttp to use `AsyncInferenceClient` (`pip install aiohttp`).")
+    import aiohttp
+
+    return aiohttp
+
+
+def _import_numpy():
+    """Make sure `numpy` is installed on the machine."""
+    if not is_numpy_available():
+        raise ImportError("Please install numpy to use deal with embeddings (`pip install numpy`).")
+    import numpy
+
+    return numpy
+
+
+def _import_pil_image():
+    """Make sure `PIL` is installed on the machine."""
+    if not is_pillow_available():
+        raise ImportError(
+            "Please install Pillow to use deal with images (`pip install Pillow`). If you don't want the image to be"
+            " post-processed, use `client.post(...)` and get the raw response from the server."
+        )
+    from PIL import Image
+
+    return Image
+
+
+## RECOMMENDED MODELS
+
+# Will be globally fetched only once (see '_fetch_recommended_models')
+_RECOMMENDED_MODELS: Optional[Dict[str, Optional[str]]] = None
+
+
+def _fetch_recommended_models() -> Dict[str, Optional[str]]:
+    global _RECOMMENDED_MODELS
+    if _RECOMMENDED_MODELS is None:
+        response = get_session().get(f"{ENDPOINT}/api/tasks", headers=build_hf_headers())
+        hf_raise_for_status(response)
+        _RECOMMENDED_MODELS = {
+            task: _first_or_none(details["widgetModels"]) for task, details in response.json().items()
+        }
+    return _RECOMMENDED_MODELS
+
+
+def _first_or_none(items: List[Any]) -> Optional[Any]:
+    try:
+        return items[0] or None
+    except IndexError:
+        return None
+
+
+## ENCODING / DECODING UTILS
+
+
+@overload
+def _open_as_binary(
+    content: ContentT,
+) -> ContextManager[BinaryT]: ...  # means "if input is not None, output is not None"
+
+
+@overload
+def _open_as_binary(
+    content: Literal[None],
+) -> ContextManager[Literal[None]]: ...  # means "if input is None, output is None"
+
+
+@contextmanager  # type: ignore
+def _open_as_binary(content: Optional[ContentT]) -> Generator[Optional[BinaryT], None, None]:
+    """Open `content` as a binary file, either from a URL, a local path, or raw bytes.
+
+    Do nothing if `content` is None,
+
+    TODO: handle a PIL.Image as input
+    TODO: handle base64 as input
+    """
+    # If content is a string => must be either a URL or a path
+    if isinstance(content, str):
+        if content.startswith("https://") or content.startswith("http://"):
+            logger.debug(f"Downloading content from {content}")
+            yield get_session().get(content).content  # TODO: retrieve as stream and pipe to post request ?
+            return
+        content = Path(content)
+        if not content.exists():
+            raise FileNotFoundError(
+                f"File not found at {content}. If `data` is a string, it must either be a URL or a path to a local"
+                " file. To pass raw content, please encode it as bytes first."
+            )
+
+    # If content is a Path => open it
+    if isinstance(content, Path):
+        logger.debug(f"Opening content from {content}")
+        with content.open("rb") as f:
+            yield f
+    else:
+        # Otherwise: already a file-like object or None
+        yield content
+
+
+def _b64_encode(content: ContentT) -> str:
+    """Encode a raw file (image, audio) into base64. Can be byes, an opened file, a path or a URL."""
+    with _open_as_binary(content) as data:
+        data_as_bytes = data if isinstance(data, bytes) else data.read()
+        return base64.b64encode(data_as_bytes).decode()
+
+
+def _b64_to_image(encoded_image: str) -> "Image":
+    """Parse a base64-encoded string into a PIL Image."""
+    Image = _import_pil_image()
+    return Image.open(io.BytesIO(base64.b64decode(encoded_image)))
+
+
+def _bytes_to_list(content: bytes) -> List:
+    """Parse bytes from a Response object into a Python list.
+
+    Expects the response body to be JSON-encoded data.
+
+    NOTE: This is exactly the same implementation as `_bytes_to_dict` and will not complain if the returned data is a
+    dictionary. The only advantage of having both is to help the user (and mypy) understand what kind of data to expect.
+    """
+    return json.loads(content.decode())
+
+
+def _bytes_to_dict(content: bytes) -> Dict:
+    """Parse bytes from a Response object into a Python dictionary.
+
+    Expects the response body to be JSON-encoded data.
+
+    NOTE: This is exactly the same implementation as `_bytes_to_list` and will not complain if the returned data is a
+    list. The only advantage of having both is to help the user (and mypy) understand what kind of data to expect.
+    """
+    return json.loads(content.decode())
+
+
+def _bytes_to_image(content: bytes) -> "Image":
+    """Parse bytes from a Response object into a PIL Image.
+
+    Expects the response body to be raw bytes. To deal with b64 encoded images, use `_b64_to_image` instead.
+    """
+    Image = _import_pil_image()
+    return Image.open(io.BytesIO(content))
+
+
+## STREAMING UTILS
+
+
+def _stream_text_generation_response(
+    bytes_output_as_lines: Iterable[bytes], details: bool
+) -> Union[Iterable[str], Iterable[TextGenerationStreamOutput]]:
+    """Used in `InferenceClient.text_generation`."""
+    # Parse ServerSentEvents
+    for byte_payload in bytes_output_as_lines:
+        output = _format_text_generation_stream_output(byte_payload, details)
+        if output is not None:
+            yield output
+
+
+async def _async_stream_text_generation_response(
+    bytes_output_as_lines: AsyncIterable[bytes], details: bool
+) -> Union[AsyncIterable[str], AsyncIterable[TextGenerationStreamOutput]]:
+    """Used in `AsyncInferenceClient.text_generation`."""
+    # Parse ServerSentEvents
+    async for byte_payload in bytes_output_as_lines:
+        output = _format_text_generation_stream_output(byte_payload, details)
+        if output is not None:
+            yield output
+
+
+def _format_text_generation_stream_output(
+    byte_payload: bytes, details: bool
+) -> Optional[Union[str, TextGenerationStreamOutput]]:
+    if not byte_payload.startswith(b"data:"):
+        return None  # empty line
+
+    # Decode payload
+    payload = byte_payload.decode("utf-8")
+    json_payload = json.loads(payload.lstrip("data:").rstrip("/n"))
+
+    # Either an error as being returned
+    if json_payload.get("error") is not None:
+        raise _parse_text_generation_error(json_payload["error"], json_payload.get("error_type"))
+
+    # Or parse token payload
+    output = TextGenerationStreamOutput.parse_obj_as_instance(json_payload)
+    return output.token.text if not details else output
+
+
+def _stream_chat_completion_response_from_text_generation(
+    text_generation_output: Iterable[TextGenerationStreamOutput],
+) -> Iterable[ChatCompletionStreamOutput]:
+    """Used in `InferenceClient.chat_completion`."""
+    created = int(time.time())
+    for item in text_generation_output:
+        yield _format_chat_completion_stream_output_from_text_generation(item, created)
+
+
+async def _async_stream_chat_completion_response_from_text_generation(
+    text_generation_output: AsyncIterable[TextGenerationStreamOutput],
+) -> AsyncIterable[ChatCompletionStreamOutput]:
+    """Used in `AsyncInferenceClient.chat_completion`."""
+    created = int(time.time())
+    async for item in text_generation_output:
+        yield _format_chat_completion_stream_output_from_text_generation(item, created)
+
+
+def _format_chat_completion_stream_output_from_text_generation(
+    item: TextGenerationStreamOutput, created: int
+) -> ChatCompletionStreamOutput:
+    if item.details is None:
+        # new token generated => return delta
+        return ChatCompletionStreamOutput(
+            choices=[
+                ChatCompletionStreamOutputChoice(
+                    delta=ChatCompletionStreamOutputDelta(
+                        role="assistant",
+                        content=item.token.text,
+                    ),
+                    finish_reason=None,
+                    index=0,
+                )
+            ],
+            created=created,
+        )
+    else:
+        # generation is completed => return finish reason
+        return ChatCompletionStreamOutput(
+            choices=[
+                ChatCompletionStreamOutputChoice(
+                    delta=ChatCompletionStreamOutputDelta(),
+                    finish_reason=item.details.finish_reason,
+                    index=0,
+                )
+            ],
+            created=created,
+        )
+
+
+def _stream_chat_completion_response_from_bytes(
+    bytes_lines: Iterable[bytes],
+) -> Iterable[ChatCompletionStreamOutput]:
+    """Used in `InferenceClient.chat_completion` if model is served with TGI."""
+    for item in bytes_lines:
+        output = _format_chat_completion_stream_output_from_text_generation_from_bytes(item)
+        if output is not None:
+            yield output
+
+
+async def _async_stream_chat_completion_response_from_bytes(
+    bytes_lines: AsyncIterable[bytes],
+) -> AsyncIterable[ChatCompletionStreamOutput]:
+    """Used in `AsyncInferenceClient.chat_completion`."""
+    async for item in bytes_lines:
+        output = _format_chat_completion_stream_output_from_text_generation_from_bytes(item)
+        if output is not None:
+            yield output
+
+
+def _format_chat_completion_stream_output_from_text_generation_from_bytes(
+    byte_payload: bytes,
+) -> Optional[ChatCompletionStreamOutput]:
+    if not byte_payload.startswith(b"data:"):
+        return None  # empty line
+
+    # Decode payload
+    payload = byte_payload.decode("utf-8")
+    json_payload = json.loads(payload.lstrip("data:").rstrip("/n"))
+    return ChatCompletionStreamOutput.parse_obj_as_instance(json_payload)
+
+
+async def _async_yield_from(client: "ClientSession", response: "ClientResponse") -> AsyncIterable[bytes]:
+    async for byte_payload in response.content:
+        yield byte_payload
+    await client.close()
+
+
+# "TGI servers" are servers running with the `text-generation-inference` backend.
+# This backend is the go-to solution to run large language models at scale. However,
+# for some smaller models (e.g. "gpt2") the default `transformers` + `api-inference`
+# solution is still in use.
+#
+# Both approaches have very similar APIs, but not exactly the same. What we do first in
+# the `text_generation` method is to assume the model is served via TGI. If we realize
+# it's not the case (i.e. we receive an HTTP 400 Bad Request), we fallback to the
+# default API with a warning message. We remember for each model if it's a TGI server
+# or not using `_NON_TGI_SERVERS` global variable.
+#
+# In addition, TGI servers have a built-in API route for chat-completion, which is not
+# available on the default API. We use this route to provide a more consistent behavior
+# when available.
+#
+# For more details, see https://github.com/huggingface/text-generation-inference and
+# https://huggingface.co/docs/api-inference/detailed_parameters#text-generation-task.
+
+_NON_TGI_SERVERS: Set[Optional[str]] = set()
+
+
+def _set_as_non_tgi(model: Optional[str]) -> None:
+    _NON_TGI_SERVERS.add(model)
+
+
+def _is_tgi_server(model: Optional[str]) -> bool:
+    return model not in _NON_TGI_SERVERS
+
+
+_NON_CHAT_COMPLETION_SERVER: Set[str] = set()
+
+
+def _set_as_non_chat_completion_server(model: str) -> None:
+    print("Set as non chat completion", model)
+    _NON_CHAT_COMPLETION_SERVER.add(model)
+
+
+def _is_chat_completion_server(model: str) -> bool:
+    return model not in _NON_CHAT_COMPLETION_SERVER
+
+
+# TEXT GENERATION ERRORS
+# ----------------------
+# Text-generation errors are parsed separately to handle as much as possible the errors returned by the text generation
+# inference project (https://github.com/huggingface/text-generation-inference).
+# ----------------------
+
+
+def raise_text_generation_error(http_error: HTTPError) -> NoReturn:
+    """
+    Try to parse text-generation-inference error message and raise HTTPError in any case.
+
+    Args:
+        error (`HTTPError`):
+            The HTTPError that have been raised.
+    """
+    # Try to parse a Text Generation Inference error
+
+    try:
+        # Hacky way to retrieve payload in case of aiohttp error
+        payload = getattr(http_error, "response_error_payload", None) or http_error.response.json()
+        error = payload.get("error")
+        error_type = payload.get("error_type")
+    except Exception:  # no payload
+        raise http_error
+
+    # If error_type => more information than `hf_raise_for_status`
+    if error_type is not None:
+        exception = _parse_text_generation_error(error, error_type)
+        raise exception from http_error
+
+    # Otherwise, fallback to default error
+    raise http_error
+
+
+def _parse_text_generation_error(error: Optional[str], error_type: Optional[str]) -> TextGenerationError:
+    if error_type == "generation":
+        return GenerationError(error)  # type: ignore
+    if error_type == "incomplete_generation":
+        return IncompleteGenerationError(error)  # type: ignore
+    if error_type == "overloaded":
+        return OverloadedError(error)  # type: ignore
+    if error_type == "validation":
+        return ValidationError(error)  # type: ignore
+    return UnknownError(error)  # type: ignore

venv/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/audio_to_audio.py

@@ -0,0 +1,31 @@
+# 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 dataclasses import dataclass
+from typing import Any
+
+from .base import BaseInferenceType
+
+
+@dataclass
+class AudioToAudioInput(BaseInferenceType):
+    """Inputs for Audio to Audio inference"""
+
+    inputs: Any
+    """The input audio data"""
+
+
+@dataclass
+class AudioToAudioOutputElement(BaseInferenceType):
+    """Outputs of inference for the Audio To Audio task
+    A generated audio file with its label.
+    """
+
+    blob: Any
+    """The generated audio file."""
+    content_type: str
+    """The content type of audio file."""
+    label: str
+    """The label of the audio file."""

venv/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/document_question_answering.py

@@ -0,0 +1,85 @@
+# 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 dataclasses import dataclass
+from typing import Any, List, Optional, Union
+
+from .base import BaseInferenceType
+
+
+@dataclass
+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
+class DocumentQuestionAnsweringParameters(BaseInferenceType):
+    """Additional inference parameters
+    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
+class DocumentQuestionAnsweringInput(BaseInferenceType):
+    """Inputs for Document Question Answering inference"""
+
+    inputs: DocumentQuestionAnsweringInputData
+    """One (document, question) pair to answer"""
+    parameters: Optional[DocumentQuestionAnsweringParameters] = None
+    """Additional inference parameters"""
+
+
+@dataclass
+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).
+    """
+    words: List[int]
+    """The index of each word/box pair that is in the answer"""

venv/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/fill_mask.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 dataclasses import dataclass
+from typing import Any, List, Optional
+
+from .base import BaseInferenceType
+
+
+@dataclass
+class FillMaskParameters(BaseInferenceType):
+    """Additional inference parameters
+    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
+class FillMaskInput(BaseInferenceType):
+    """Inputs for Fill Mask inference"""
+
+    inputs: str
+    """The text with masked tokens"""
+    parameters: Optional[FillMaskParameters] = None
+    """Additional inference parameters"""
+
+
+@dataclass
+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)."""

venv/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/image_segmentation.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 dataclasses import dataclass
+from typing import Any, Literal, Optional
+
+from .base import BaseInferenceType
+
+
+ImageSegmentationSubtask = Literal["instance", "panoptic", "semantic"]
+
+
+@dataclass
+class ImageSegmentationParameters(BaseInferenceType):
+    """Additional inference parameters
+    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
+class ImageSegmentationInput(BaseInferenceType):
+    """Inputs for Image Segmentation inference"""
+
+    inputs: Any
+    """The input image data"""
+    parameters: Optional[ImageSegmentationParameters] = None
+    """Additional inference parameters"""
+
+
+@dataclass
+class ImageSegmentationOutputElement(BaseInferenceType):
+    """Outputs of inference for the Image Segmentation task
+    A predicted mask / segment
+    """
+
+    label: str
+    """The label of the predicted segment"""
+    mask: Any
+    """The corresponding mask as a black-and-white image"""
+    score: Optional[float] = None
+    """The score or confidence degreee the model has"""

venv/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/image_to_image.py

@@ -0,0 +1,55 @@
+# 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 dataclasses import dataclass
+from typing import Any, List, Optional
+
+from .base import BaseInferenceType
+
+
+@dataclass
+class ImageToImageTargetSize(BaseInferenceType):
+    """The size in pixel of the output image"""
+
+    height: int
+    width: int
+
+
+@dataclass
+class ImageToImageParameters(BaseInferenceType):
+    """Additional inference parameters
+    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[List[str]] = None
+    """One or several 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
+class ImageToImageInput(BaseInferenceType):
+    """Inputs for Image To Image inference"""
+
+    inputs: Any
+    """The input image data"""
+    parameters: Optional[ImageToImageParameters] = None
+    """Additional inference parameters"""
+
+
+@dataclass
+class ImageToImageOutput(BaseInferenceType):
+    """Outputs of inference for the Image To Image task"""
+
+    image: Any
+    """The output image"""

venv/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/image_to_text.py

@@ -0,0 +1,105 @@
+# 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 dataclasses import dataclass
+from typing import Any, Literal, Optional, Union
+
+from .base import BaseInferenceType
+
+
+EarlyStoppingEnum = Literal["never"]
+
+
+@dataclass
+class ImageToTextGenerationParameters(BaseInferenceType):
+    """Parametrization of the text generation process
+    Ad-hoc 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, "EarlyStoppingEnum"]] = 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 maxLength."""
+    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 maxLength."""
+    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
+class ImageToTextParameters(BaseInferenceType):
+    """Additional inference parameters
+    Additional inference parameters for Image To Text
+    """
+
+    generate: Optional[ImageToTextGenerationParameters] = None
+    """Parametrization of the text generation process"""
+    max_new_tokens: Optional[int] = None
+    """The amount of maximum tokens to generate."""
+
+
+@dataclass
+class ImageToTextInput(BaseInferenceType):
+    """Inputs for Image To Text inference"""
+
+    inputs: Any
+    """The input image data"""
+    parameters: Optional[ImageToTextParameters] = None
+    """Additional inference parameters"""
+
+
+@dataclass
+class ImageToTextOutput(BaseInferenceType):
+    """Outputs of inference for the Image To Text task"""
+
+    generated_text: Any
+    image_to_text_output_generated_text: Optional[str] = None
+    """The generated text."""

venv/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/object_detection.py

@@ -0,0 +1,55 @@
+# 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 dataclasses import dataclass
+from typing import Any, Optional
+
+from .base import BaseInferenceType
+
+
+@dataclass
+class ObjectDetectionParameters(BaseInferenceType):
+    """Additional inference parameters
+    Additional inference parameters for Object Detection
+    """
+
+    threshold: Optional[float] = None
+    """The probability necessary to make a prediction."""
+
+
+@dataclass
+class ObjectDetectionInput(BaseInferenceType):
+    """Inputs for Object Detection inference"""
+
+    inputs: Any
+    """The input image data"""
+    parameters: Optional[ObjectDetectionParameters] = None
+    """Additional inference parameters"""
+
+
+@dataclass
+class ObjectDetectionBoundingBox(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
+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"""

venv/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/summarization.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 dataclasses import dataclass
+from typing import Any, Dict, Literal, Optional
+
+from .base import BaseInferenceType
+
+
+SummarizationGenerationTruncationStrategy = Literal["do_not_truncate", "longest_first", "only_first", "only_second"]
+
+
+@dataclass
+class SummarizationGenerationParameters(BaseInferenceType):
+    """Additional inference parameters
+    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["SummarizationGenerationTruncationStrategy"] = None
+    """The truncation strategy to use"""
+
+
+@dataclass
+class SummarizationInput(BaseInferenceType):
+    """Inputs for Summarization inference
+    Inputs for Text2text Generation inference
+    """
+
+    inputs: str
+    """The input text data"""
+    parameters: Optional[SummarizationGenerationParameters] = None
+    """Additional inference parameters"""
+
+
+@dataclass
+class SummarizationOutput(BaseInferenceType):
+    """Outputs of inference for the Summarization task"""
+
+    summary_text: str
+    """The summarized text."""

venv/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/table_question_answering.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 dataclasses import dataclass
+from typing import Any, Dict, List, Optional
+
+from .base import BaseInferenceType
+
+
+@dataclass
+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"""
+
+
+@dataclass
+class TableQuestionAnsweringInput(BaseInferenceType):
+    """Inputs for Table Question Answering inference"""
+
+    inputs: TableQuestionAnsweringInputData
+    """One (table, question) pair to answer"""
+    parameters: Optional[Dict[str, Any]] = None
+    """Additional inference parameters"""
+
+
+@dataclass
+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."""

venv/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/text_generation.py

@@ -0,0 +1,161 @@
+# 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 dataclasses import dataclass
+from typing import List, Literal, Optional
+
+from .base import BaseInferenceType
+
+
+@dataclass
+class TextGenerationParameters(BaseInferenceType):
+    """Additional inference parameters
+    Additional inference parameters for Text Generation
+    """
+
+    best_of: Optional[int] = None
+    """The number of sampling queries to run. Only the best one (in terms of total logprob) will
+    be returned.
+    """
+    decoder_input_details: Optional[bool] = None
+    """Whether or not to output decoder input details"""
+    details: Optional[bool] = None
+    """Whether or not to output details"""
+    do_sample: Optional[bool] = None
+    """Whether to use logits sampling instead of greedy decoding when generating new tokens."""
+    max_new_tokens: Optional[int] = None
+    """The maximum number of tokens to generate."""
+    repetition_penalty: Optional[float] = None
+    """The parameter for repetition penalty. A value of 1.0 means no penalty. See [this
+    paper](https://hf.co/papers/1909.05858) for more details.
+    """
+    return_full_text: Optional[bool] = None
+    """Whether to prepend the prompt to the generated text."""
+    seed: Optional[int] = None
+    """The random sampling seed."""
+    stop_sequences: Optional[List[str]] = None
+    """Stop generating tokens if a member of `stop_sequences` is generated."""
+    temperature: Optional[float] = None
+    """The value used to modulate the logits distribution."""
+    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 < 1, only the smallest set of most probable tokens with probabilities that add
+    up to `top_p` or higher are kept for generation.
+    """
+    truncate: Optional[int] = None
+    """Truncate input tokens to the given size."""
+    typical_p: Optional[float] = None
+    """Typical Decoding mass. See [Typical Decoding for Natural Language
+    Generation](https://hf.co/papers/2202.00666) for more information
+    """
+    watermark: Optional[bool] = None
+    """Watermarking with [A Watermark for Large Language Models](https://hf.co/papers/2301.10226)"""
+
+
+@dataclass
+class TextGenerationInput(BaseInferenceType):
+    """Inputs for Text Generation inference"""
+
+    inputs: str
+    """The text to initialize generation with"""
+    parameters: Optional[TextGenerationParameters] = None
+    """Additional inference parameters"""
+    stream: Optional[bool] = None
+    """Whether to stream output tokens"""
+
+
+TextGenerationFinishReason = Literal["length", "eos_token", "stop_sequence"]
+
+
+@dataclass
+class TextGenerationPrefillToken(BaseInferenceType):
+    id: int
+    logprob: float
+    text: str
+    """The text associated with that token"""
+
+
+@dataclass
+class TextGenerationOutputToken(BaseInferenceType):
+    """Generated token."""
+
+    id: int
+    special: bool
+    """Whether or not that token is a special one"""
+    text: str
+    """The text associated with that token"""
+    logprob: Optional[float] = None
+
+
+@dataclass
+class TextGenerationOutputSequenceDetails(BaseInferenceType):
+    finish_reason: "TextGenerationFinishReason"
+    generated_text: str
+    """The generated text"""
+    generated_tokens: int
+    """The number of generated tokens"""
+    prefill: List[TextGenerationPrefillToken]
+    tokens: List[TextGenerationOutputToken]
+    """The generated tokens and associated details"""
+    seed: Optional[int] = None
+    """The random seed used for generation"""
+    top_tokens: Optional[List[List[TextGenerationOutputToken]]] = None
+    """Most likely tokens"""
+
+
+@dataclass
+class TextGenerationOutputDetails(BaseInferenceType):
+    """When enabled, details about the generation"""
+
+    finish_reason: "TextGenerationFinishReason"
+    """The reason why the generation was stopped."""
+    generated_tokens: int
+    """The number of generated tokens"""
+    prefill: List[TextGenerationPrefillToken]
+    tokens: List[TextGenerationOutputToken]
+    """The generated tokens and associated details"""
+    best_of_sequences: Optional[List[TextGenerationOutputSequenceDetails]] = None
+    """Details about additional sequences when best_of is provided"""
+    seed: Optional[int] = None
+    """The random seed used for generation"""
+    top_tokens: Optional[List[List[TextGenerationOutputToken]]] = None
+    """Most likely tokens"""
+
+
+@dataclass
+class TextGenerationOutput(BaseInferenceType):
+    """Outputs for Text Generation inference"""
+
+    generated_text: str
+    """The generated text"""
+    details: Optional[TextGenerationOutputDetails] = None
+    """When enabled, details about the generation"""
+
+
+@dataclass
+class TextGenerationStreamDetails(BaseInferenceType):
+    """Generation details. Only available when the generation is finished."""
+
+    finish_reason: "TextGenerationFinishReason"
+    """The reason why the generation was stopped."""
+    generated_tokens: int
+    """The number of generated tokens"""
+    seed: int
+    """The random seed used for generation"""
+
+
+@dataclass
+class TextGenerationStreamOutput(BaseInferenceType):
+    """Text Generation Stream Output"""
+
+    token: TextGenerationOutputToken
+    """Generated token."""
+    details: Optional[TextGenerationStreamDetails] = None
+    """Generation details. Only available when the generation is finished."""
+    generated_text: Optional[str] = None
+    """The complete generated text. Only available when the generation is finished."""
+    index: Optional[int] = None
+    """The token index within the stream. Optional to support older clients that omit it."""

venv/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/text_to_image.py

@@ -0,0 +1,57 @@
+# 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 dataclasses import dataclass
+from typing import Any, List, Optional
+
+from .base import BaseInferenceType
+
+
+@dataclass
+class TextToImageTargetSize(BaseInferenceType):
+    """The size in pixel of the output image"""
+
+    height: int
+    width: int
+
+
+@dataclass
+class TextToImageParameters(BaseInferenceType):
+    """Additional inference parameters
+    Additional inference parameters for Text 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[List[str]] = None
+    """One or several 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.
+    """
+    scheduler: Optional[str] = None
+    """For diffusion models. Override the scheduler with a compatible one"""
+    target_size: Optional[TextToImageTargetSize] = None
+    """The size in pixel of the output image"""
+
+
+@dataclass
+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"""
+
+
+@dataclass
+class TextToImageOutput(BaseInferenceType):
+    """Outputs of inference for the Text To Image task"""
+
+    image: Any
+    """The generated image"""

venv/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/token_classification.py

@@ -0,0 +1,53 @@
+# 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 dataclasses import dataclass
+from typing import Any, List, Literal, Optional
+
+from .base import BaseInferenceType
+
+
+TokenClassificationAggregationStrategy = Literal["none", "simple", "first", "average", "max"]
+
+
+@dataclass
+class TokenClassificationParameters(BaseInferenceType):
+    """Additional inference parameters
+    Additional inference parameters for Token Classification
+    """
+
+    aggregation_strategy: Optional["TokenClassificationAggregationStrategy"] = None
+    """The strategy used to fuse tokens based on model predictions"""
+    ignore_labels: Optional[List[str]] = None
+    """A list of labels to ignore"""
+    stride: Optional[int] = None
+    """The number of overlapping tokens between chunks when splitting the input text."""
+
+
+@dataclass
+class TokenClassificationInput(BaseInferenceType):
+    """Inputs for Token Classification inference"""
+
+    inputs: str
+    """The input text data"""
+    parameters: Optional[TokenClassificationParameters] = None
+    """Additional inference parameters"""
+
+
+@dataclass
+class TokenClassificationOutputElement(BaseInferenceType):
+    """Outputs of inference for the Token Classification task"""
+
+    label: Any
+    score: float
+    """The associated score / probability"""
+    end: Optional[int] = None
+    """The character position in the input where this group ends."""
+    entity_group: Optional[str] = None
+    """The predicted label for that group of tokens"""
+    start: Optional[int] = None
+    """The character position in the input where this group begins."""
+    word: Optional[str] = None
+    """The corresponding text"""

venv/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/visual_question_answering.py

@@ -0,0 +1,53 @@
+# 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 dataclasses import dataclass
+from typing import Any, Optional
+
+from .base import BaseInferenceType
+
+
+@dataclass
+class VisualQuestionAnsweringInputData(BaseInferenceType):
+    """One (image, question) pair to answer"""
+
+    image: Any
+    """The image."""
+    question: Any
+    """The question to answer based on the image."""
+
+
+@dataclass
+class VisualQuestionAnsweringParameters(BaseInferenceType):
+    """Additional inference parameters
+    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
+class VisualQuestionAnsweringInput(BaseInferenceType):
+    """Inputs for Visual Question Answering inference"""
+
+    inputs: VisualQuestionAnsweringInputData
+    """One (image, question) pair to answer"""
+    parameters: Optional[VisualQuestionAnsweringParameters] = None
+    """Additional inference parameters"""
+
+
+@dataclass
+class VisualQuestionAnsweringOutputElement(BaseInferenceType):
+    """Outputs of inference for the Visual Question Answering task"""
+
+    label: Any
+    score: float
+    """The associated score / probability"""
+    answer: Optional[str] = None
+    """The answer to the question"""

venv/lib/python3.10/site-packages/huggingface_hub/inference/_templating.py

@@ -0,0 +1,105 @@
+from functools import lru_cache
+from typing import Callable, Dict, List, Optional, Union
+
+from ..utils import HfHubHTTPError, RepositoryNotFoundError, is_minijinja_available
+
+
+class TemplateError(Exception):
+    """Any error raised while trying to fetch or render a chat template."""
+
+
+def _import_minijinja():
+    if not is_minijinja_available():
+        raise ImportError("Cannot render template. Please install minijinja using `pip install minijinja`.")
+    import minijinja  # noqa: F401
+
+    return minijinja
+
+
+def render_chat_prompt(
+    *,
+    model_id: str,
+    messages: List[Dict[str, str]],
+    token: Union[str, bool, None] = None,
+    add_generation_prompt: bool = True,
+    **kwargs,
+) -> str:
+    """Render a chat prompt using a model's chat template.
+
+    Args:
+        model_id (`str`):
+            The model id.
+        messages (`List[Dict[str, str]]`):
+            The list of messages to render.
+        token (`str` or `bool`, *optional*):
+            Hugging Face token. Will default to the locally saved token if not provided.
+
+    Returns:
+        `str`: The rendered chat prompt.
+
+    Raises:
+        `TemplateError`: If there's any issue while fetching, compiling or rendering the chat template.
+    """
+    minijinja = _import_minijinja()
+    template = _fetch_and_compile_template(model_id=model_id, token=token)
+
+    try:
+        return template(messages=messages, add_generation_prompt=add_generation_prompt, **kwargs)
+    except minijinja.TemplateError as e:
+        raise TemplateError(f"Error while trying to render chat prompt for model '{model_id}': {e}") from e
+
+
+@lru_cache  # TODO: lru_cache for raised exceptions
+def _fetch_and_compile_template(*, model_id: str, token: Union[str, None]) -> Callable:
+    """Fetch and compile a model's chat template.
+
+    Method is cached to avoid fetching the same model's config multiple times.
+
+    Args:
+        model_id (`str`):
+            The model id.
+        token (`str` or `bool`, *optional*):
+            Hugging Face token. Will default to the locally saved token if not provided.
+
+    Returns:
+        `Callable`: A callable that takes a list of messages and returns the rendered chat prompt.
+    """
+    from huggingface_hub.hf_api import HfApi
+
+    minijinja = _import_minijinja()
+
+    # 1. fetch config from API
+    try:
+        config = HfApi(token=token).model_info(model_id).config
+    except RepositoryNotFoundError as e:
+        raise TemplateError(f"Cannot render chat template: model '{model_id}' not found.") from e
+    except HfHubHTTPError as e:
+        raise TemplateError(f"Error while trying to fetch chat template for model '{model_id}': {e}") from e
+
+    # 2. check config validity
+    if config is None:
+        raise TemplateError(f"Config not found for model '{model_id}'.")
+    tokenizer_config = config.get("tokenizer_config")
+    if tokenizer_config is None:
+        raise TemplateError(f"Tokenizer config not found for model '{model_id}'.")
+    if tokenizer_config.get("chat_template") is None:
+        raise TemplateError(f"Chat template not found in tokenizer_config for model '{model_id}'.")
+    chat_template = tokenizer_config["chat_template"]
+    if not isinstance(chat_template, str):
+        raise TemplateError(f"Chat template must be a string, not '{type(chat_template)}' (model: {model_id}).")
+
+    special_tokens: Dict[str, Optional[str]] = {}
+    for key, value in tokenizer_config.items():
+        if "token" in key:
+            if isinstance(value, str):
+                special_tokens[key] = value
+            elif isinstance(value, dict) and value.get("__type") == "AddedToken":
+                special_tokens[key] = value.get("content")
+
+    # 3. compile template and return
+    env = minijinja.Environment()
+    try:
+        env.add_template("chat_template", chat_template)
+    except minijinja.TemplateError as e:
+        raise TemplateError(f"Error while trying to compile chat template for model '{model_id}': {e}") from e
+    return lambda **kwargs: env.render_template("chat_template", **kwargs, **special_tokens)

venv/lib/python3.10/site-packages/huggingface_hub/inference/_types.py

@@ -0,0 +1,52 @@
+# 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.
+
+from typing import List, TypedDict
+
+
+# Legacy types
+# Types are now generated from the JSON schema spec in @huggingface/tasks.
+# See ./src/huggingface_hub/inference/_generated/types
+
+
+class ConversationalOutputConversation(TypedDict):
+    """Dictionary containing the "conversation" part of a [`~InferenceClient.conversational`] task.
+
+    Args:
+        generated_responses (`List[str]`):
+            A list of the responses from the model.
+        past_user_inputs (`List[str]`):
+            A list of the inputs from the user. Must be the same length as `generated_responses`.
+    """
+
+    generated_responses: List[str]
+    past_user_inputs: List[str]
+
+
+class ConversationalOutput(TypedDict):
+    """Dictionary containing the output of a  [`~InferenceClient.conversational`] task.
+
+    Args:
+        generated_text (`str`):
+            The last response from the model.
+        conversation (`ConversationalOutputConversation`):
+            The past conversation.
+        warnings (`List[str]`):
+            A list of warnings associated with the process.
+    """
+
+    conversation: ConversationalOutputConversation
+    generated_text: str
+    warnings: List[str]

venv/lib/python3.10/site-packages/huggingface_hub/utils/__init__.py

@@ -0,0 +1,122 @@
+#!/usr/bin/env python
+# 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 . import tqdm as _tqdm  # _tqdm is the module
+from ._cache_assets import cached_assets_path
+from ._cache_manager import (
+    CachedFileInfo,
+    CachedRepoInfo,
+    CachedRevisionInfo,
+    CacheNotFound,
+    CorruptedCacheException,
+    DeleteCacheStrategy,
+    HFCacheInfo,
+    scan_cache_dir,
+)
+from ._chunk_utils import chunk_iterable
+from ._datetime import parse_datetime
+from ._errors import (
+    BadRequestError,
+    DisabledRepoError,
+    EntryNotFoundError,
+    FileMetadataError,
+    GatedRepoError,
+    HfHubHTTPError,
+    LocalEntryNotFoundError,
+    RepositoryNotFoundError,
+    RevisionNotFoundError,
+    hf_raise_for_status,
+)
+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 LocalTokenNotFoundError, build_hf_headers, get_token_to_send
+from ._hf_folder import HfFolder
+from ._http import (
+    OfflineModeIsEnabled,
+    configure_http_backend,
+    fix_hf_endpoint_in_url,
+    get_session,
+    http_backoff,
+    reset_sessions,
+)
+from ._pagination import paginate
+from ._paths import IGNORE_GIT_FOLDER_PATTERNS, filter_repo_objects
+from ._runtime import (
+    dump_environment_info,
+    get_aiohttp_version,
+    get_fastai_version,
+    get_fastcore_version,
+    get_gradio_version,
+    get_graphviz_version,
+    get_hf_hub_version,
+    get_hf_transfer_version,
+    get_jinja_version,
+    get_minijinja_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_fastai_available,
+    is_fastcore_available,
+    is_google_colab,
+    is_gradio_available,
+    is_graphviz_available,
+    is_hf_transfer_available,
+    is_jinja_available,
+    is_minijinja_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 (
+    NotASafetensorsRepoError,
+    SafetensorsFileMetadata,
+    SafetensorsParsingError,
+    SafetensorsRepoMetadata,
+    TensorInfo,
+)
+from ._subprocess import capture_output, run_interactive_subprocess, run_subprocess
+from ._telemetry import send_telemetry
+from ._token import get_token
+from ._typing import is_jsonable
+from ._validators import (
+    HFValidationError,
+    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,
+)

venv/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

venv/lib/python3.10/site-packages/huggingface_hub/utils/_cache_manager.py

@@ -0,0 +1,813 @@
+# 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 ..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"]
+
+
+class CacheNotFound(Exception):
+    """Exception thrown when the Huggingface cache is not found."""
+
+    cache_dir: Union[str, Path]
+
+    def __init__(self, msg: str, cache_dir: Union[str, Path], *args, **kwargs):
+        super().__init__(msg, *args, **kwargs)
+        self.cache_dir = cache_dir
+
+
+class CorruptedCacheException(Exception):
+    """Exception for any unexpected structure in the Huggingface cache-system."""
+
+
+@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.
+
+    <Tip warning={true}>
+
+    `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.
+
+    </Tip>
+    """
+
+    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.
+
+    <Tip warning={true}>
+
+    `last_accessed` cannot be determined correctly on a single revision as blob files
+    are shared across revisions.
+
+    </Tip>
+
+    <Tip warning={true}>
+
+    `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.
+
+    </Tip>
+    """
+
+    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.
+
+    <Tip warning={true}>
+
+    `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.
+
+    </Tip>
+
+    <Tip warning={true}>
+
+    `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.
+
+    </Tip>
+    """
+
+    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.
+
+        <Tip warning={true}>
+
+        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.
+
+        </Tip>
+
+        <Tip warning={true}>
+
+        This method is irreversible. If executed, cached files are erased and must be
+        downloaded again.
+
+        </Tip>
+        """
+        # 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.
+
+    <Tip warning={true}>
+
+    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.
+
+    </Tip>
+    """
+
+    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.
+        ```
+
+        <Tip warning={true}>
+
+        `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.
+
+        </Tip>
+        """
+        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 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.
+
+    <Tip warning={true}>
+
+    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.
+
+    </Tip>
+
+    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():
+                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)

venv/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

venv/lib/python3.10/site-packages/huggingface_hub/utils/_errors.py

@@ -0,0 +1,397 @@
+import re
+from typing import Optional
+
+from requests import HTTPError, Response
+
+from ._fixes import JSONDecodeError
+
+
+REPO_API_REGEX = re.compile(
+    r"""
+        # staging or production endpoint
+        ^https://[^/]+
+        (
+            # on /api/repo_type/repo_id
+            /api/(models|datasets|spaces)/(.+)
+            |
+            # or /repo_id/resolve/revision/...
+            /(.+)/resolve/(.+)
+        )
+    """,
+    flags=re.VERBOSE,
+)
+
+
+class FileMetadataError(OSError):
+    """Error triggered when the metadata of a file on the Hub cannot be retrieved (missing ETag or commit_hash).
+
+    Inherits from `OSError` for backward compatibility.
+    """
+
+
+class HfHubHTTPError(HTTPError):
+    """
+    HTTPError to inherit from for any custom HTTP Error raised in HF Hub.
+
+    Any HTTPError is converted at least into a `HfHubHTTPError`. If some information is
+    sent back by the server, it will be added to the error message.
+
+    Added details:
+    - Request id from "X-Request-Id" header if exists.
+    - Server error message from the header "X-Error-Message".
+    - Server error message if we can found one in the response body.
+
+    Example:
+    ```py
+        import requests
+        from huggingface_hub.utils import get_session, hf_raise_for_status, HfHubHTTPError
+
+        response = get_session().post(...)
+        try:
+            hf_raise_for_status(response)
+        except HfHubHTTPError as e:
+            print(str(e)) # formatted message
+            e.request_id, e.server_message # details returned by server
+
+            # Complete the error message with additional information once it's raised
+            e.append_to_message("\n`create_commit` expects the repository to exist.")
+            raise
+    ```
+    """
+
+    request_id: Optional[str] = None
+    server_message: Optional[str] = None
+
+    def __init__(self, message: str, response: Optional[Response] = None):
+        # Parse server information if any.
+        if response is not None:
+            self.request_id = response.headers.get("X-Request-Id")
+            try:
+                server_data = response.json()
+            except JSONDecodeError:
+                server_data = {}
+
+            # Retrieve server error message from multiple sources
+            server_message_from_headers = response.headers.get("X-Error-Message")
+            server_message_from_body = server_data.get("error")
+            server_multiple_messages_from_body = "\n".join(
+                error["message"] for error in server_data.get("errors", []) if "message" in error
+            )
+
+            # Concatenate error messages
+            _server_message = ""
+            if server_message_from_headers is not None:  # from headers
+                _server_message += server_message_from_headers + "\n"
+            if server_message_from_body is not None:  # from body "error"
+                if isinstance(server_message_from_body, list):
+                    server_message_from_body = "\n".join(server_message_from_body)
+                if server_message_from_body not in _server_message:
+                    _server_message += server_message_from_body + "\n"
+            if server_multiple_messages_from_body is not None:  # from body "errors"
+                if server_multiple_messages_from_body not in _server_message:
+                    _server_message += server_multiple_messages_from_body + "\n"
+            _server_message = _server_message.strip()
+
+            # Set message to `HfHubHTTPError` (if any)
+            if _server_message != "":
+                self.server_message = _server_message
+
+        super().__init__(
+            _format_error_message(
+                message,
+                request_id=self.request_id,
+                server_message=self.server_message,
+            ),
+            response=response,  # type: ignore
+            request=response.request if response is not None else None,  # type: ignore
+        )
+
+    def append_to_message(self, additional_message: str) -> None:
+        """Append additional information to the `HfHubHTTPError` initial message."""
+        self.args = (self.args[0] + additional_message,) + self.args[1:]
+
+
+class RepositoryNotFoundError(HfHubHTTPError):
+    """
+    Raised when trying to access a hf.co URL with an invalid repository name, or
+    with a private repo name the user does not have access to.
+
+    Example:
+
+    ```py
+    >>> from huggingface_hub import model_info
+    >>> model_info("<non_existent_repository>")
+    (...)
+    huggingface_hub.utils._errors.RepositoryNotFoundError: 401 Client Error. (Request ID: PvMw_VjBMjVdMz53WKIzP)
+
+    Repository Not Found for url: https://huggingface.co/api/models/%3Cnon_existent_repository%3E.
+    Please make sure you specified the correct `repo_id` and `repo_type`.
+    If the repo is private, make sure you are authenticated.
+    Invalid username or password.
+    ```
+    """
+
+
+class GatedRepoError(RepositoryNotFoundError):
+    """
+    Raised when trying to access a gated repository for which the user is not on the
+    authorized list.
+
+    Note: derives from `RepositoryNotFoundError` to ensure backward compatibility.
+
+    Example:
+
+    ```py
+    >>> from huggingface_hub import model_info
+    >>> model_info("<gated_repository>")
+    (...)
+    huggingface_hub.utils._errors.GatedRepoError: 403 Client Error. (Request ID: ViT1Bf7O_026LGSQuVqfa)
+
+    Cannot access gated repo for url https://huggingface.co/api/models/ardent-figment/gated-model.
+    Access to model ardent-figment/gated-model is restricted and you are not in the authorized list.
+    Visit https://huggingface.co/ardent-figment/gated-model to ask for access.
+    ```
+    """
+
+
+class DisabledRepoError(HfHubHTTPError):
+    """
+    Raised when trying to access a repository that has been disabled by its author.
+
+    Example:
+
+    ```py
+    >>> from huggingface_hub import dataset_info
+    >>> dataset_info("laion/laion-art")
+    (...)
+    huggingface_hub.utils._errors.DisabledRepoError: 403 Client Error. (Request ID: Root=1-659fc3fa-3031673e0f92c71a2260dbe2;bc6f4dfb-b30a-4862-af0a-5cfe827610d8)
+
+    Cannot access repository for url https://huggingface.co/api/datasets/laion/laion-art.
+    Access to this resource is disabled.
+    ```
+    """
+
+
+class RevisionNotFoundError(HfHubHTTPError):
+    """
+    Raised when trying to access a hf.co URL with a valid repository but an invalid
+    revision.
+
+    Example:
+
+    ```py
+    >>> from huggingface_hub import hf_hub_download
+    >>> hf_hub_download('bert-base-cased', 'config.json', revision='<non-existent-revision>')
+    (...)
+    huggingface_hub.utils._errors.RevisionNotFoundError: 404 Client Error. (Request ID: Mwhe_c3Kt650GcdKEFomX)
+
+    Revision Not Found for url: https://huggingface.co/bert-base-cased/resolve/%3Cnon-existent-revision%3E/config.json.
+    ```
+    """
+
+
+class EntryNotFoundError(HfHubHTTPError):
+    """
+    Raised when trying to access a hf.co URL with a valid repository and revision
+    but an invalid filename.
+
+    Example:
+
+    ```py
+    >>> from huggingface_hub import hf_hub_download
+    >>> hf_hub_download('bert-base-cased', '<non-existent-file>')
+    (...)
+    huggingface_hub.utils._errors.EntryNotFoundError: 404 Client Error. (Request ID: 53pNl6M0MxsnG5Sw8JA6x)
+
+    Entry Not Found for url: https://huggingface.co/bert-base-cased/resolve/main/%3Cnon-existent-file%3E.
+    ```
+    """
+
+
+class LocalEntryNotFoundError(EntryNotFoundError, FileNotFoundError, ValueError):
+    """
+    Raised when trying to access a file or snapshot that is not on the disk when network is
+    disabled or unavailable (connection issue). The entry may exist on the Hub.
+
+    Note: `ValueError` type is to ensure backward compatibility.
+    Note: `LocalEntryNotFoundError` derives from `HTTPError` because of `EntryNotFoundError`
+          even when it is not a network issue.
+
+    Example:
+
+    ```py
+    >>> from huggingface_hub import hf_hub_download
+    >>> hf_hub_download('bert-base-cased', '<non-cached-file>',  local_files_only=True)
+    (...)
+    huggingface_hub.utils._errors.LocalEntryNotFoundError: Cannot find the requested files in the disk cache and outgoing traffic has been disabled. To enable hf.co look-ups and downloads online, set 'local_files_only' to False.
+    ```
+    """
+
+    def __init__(self, message: str):
+        super().__init__(message, response=None)
+
+
+class BadRequestError(HfHubHTTPError, ValueError):
+    """
+    Raised by `hf_raise_for_status` when the server returns a HTTP 400 error.
+
+    Example:
+
+    ```py
+    >>> resp = requests.post("hf.co/api/check", ...)
+    >>> hf_raise_for_status(resp, endpoint_name="check")
+    huggingface_hub.utils._errors.BadRequestError: Bad request for check endpoint: {details} (Request ID: XXX)
+    ```
+    """
+
+
+def hf_raise_for_status(response: Response, endpoint_name: Optional[str] = None) -> None:
+    """
+    Internal version of `response.raise_for_status()` that will refine a
+    potential HTTPError. Raised exception will be an instance of `HfHubHTTPError`.
+
+    This helper is meant to be the unique method to raise_for_status when making a call
+    to the Hugging Face Hub.
+
+    Example:
+    ```py
+        import requests
+        from huggingface_hub.utils import get_session, hf_raise_for_status, HfHubHTTPError
+
+        response = get_session().post(...)
+        try:
+            hf_raise_for_status(response)
+        except HfHubHTTPError as e:
+            print(str(e)) # formatted message
+            e.request_id, e.server_message # details returned by server
+
+            # Complete the error message with additional information once it's raised
+            e.append_to_message("\n`create_commit` expects the repository to exist.")
+            raise
+    ```
+
+    Args:
+        response (`Response`):
+            Response from the server.
+        endpoint_name (`str`, *optional*):
+            Name of the endpoint that has been called. If provided, the error message
+            will be more complete.
+
+    <Tip warning={true}>
+
+    Raises when the request has failed:
+
+        - [`~utils.RepositoryNotFoundError`]
+            If the repository to download from cannot be found. This may be because it
+            doesn't exist, because `repo_type` is not set correctly, or because the repo
+            is `private` and you do not have access.
+        - [`~utils.GatedRepoError`]
+            If the repository exists but is gated and the user is not on the authorized
+            list.
+        - [`~utils.RevisionNotFoundError`]
+            If the repository exists but the revision couldn't be find.
+        - [`~utils.EntryNotFoundError`]
+            If the repository exists but the entry (e.g. the requested file) couldn't be
+            find.
+        - [`~utils.BadRequestError`]
+            If request failed with a HTTP 400 BadRequest error.
+        - [`~utils.HfHubHTTPError`]
+            If request failed for a reason not listed above.
+
+    </Tip>
+    """
+    try:
+        response.raise_for_status()
+    except HTTPError as e:
+        error_code = response.headers.get("X-Error-Code")
+        error_message = response.headers.get("X-Error-Message")
+
+        if error_code == "RevisionNotFound":
+            message = f"{response.status_code} Client Error." + "\n\n" + f"Revision Not Found for url: {response.url}."
+            raise RevisionNotFoundError(message, response) from e
+
+        elif error_code == "EntryNotFound":
+            message = f"{response.status_code} Client Error." + "\n\n" + f"Entry Not Found for url: {response.url}."
+            raise EntryNotFoundError(message, response) from e
+
+        elif error_code == "GatedRepo":
+            message = (
+                f"{response.status_code} Client Error." + "\n\n" + f"Cannot access gated repo for url {response.url}."
+            )
+            raise GatedRepoError(message, response) from e
+
+        elif error_message == "Access to this resource is disabled.":
+            message = (
+                f"{response.status_code} Client Error."
+                + "\n\n"
+                + f"Cannot access repository for url {response.url}."
+                + "\n"
+                + "Access to this resource is disabled."
+            )
+            raise DisabledRepoError(message, response) from e
+
+        elif error_code == "RepoNotFound" or (
+            response.status_code == 401
+            and response.request is not None
+            and response.request.url is not None
+            and REPO_API_REGEX.search(response.request.url) is not None
+        ):
+            # 401 is misleading as it is returned for:
+            #    - private and gated repos if user is not authenticated
+            #    - missing repos
+            # => for now, we process them as `RepoNotFound` anyway.
+            # See https://gist.github.com/Wauplin/46c27ad266b15998ce56a6603796f0b9
+            message = (
+                f"{response.status_code} Client Error."
+                + "\n\n"
+                + f"Repository Not Found for url: {response.url}."
+                + "\nPlease make sure you specified the correct `repo_id` and"
+                " `repo_type`.\nIf you are trying to access a private or gated repo,"
+                " make sure you are authenticated."
+            )
+            raise RepositoryNotFoundError(message, response) from e
+
+        elif response.status_code == 400:
+            message = (
+                f"\n\nBad request for {endpoint_name} endpoint:" if endpoint_name is not None else "\n\nBad request:"
+            )
+            raise BadRequestError(message, response=response) from e
+
+        elif response.status_code == 403:
+            message = (
+                f"\n\n{response.status_code} Forbidden: {error_message}."
+                + f"\nCannot access content at: {response.url}."
+                + "\nIf you are trying to create or update content,"
+                + "make sure you have a token with the `write` role."
+            )
+            raise HfHubHTTPError(message, response=response) from e
+
+        # Convert `HTTPError` into a `HfHubHTTPError` to display request information
+        # as well (request id and/or server error message)
+        raise HfHubHTTPError(str(e), response=response) from e
+
+
+def _format_error_message(message: str, request_id: Optional[str], server_message: Optional[str]) -> str:
+    """
+    Format the `HfHubHTTPError` error message based on initial message and information
+    returned by the server.
+
+    Used when initializing `HfHubHTTPError`.
+    """
+    # Add message from response body
+    if server_message is not None and len(server_message) > 0 and server_message.lower() not in message.lower():
+        if "\n\n" in message:
+            message += "\n" + server_message
+        else:
+            message += "\n\n" + server_message
+
+    # Add Request ID
+    if request_id is not None and str(request_id).lower() not in message.lower():
+        request_id_message = f" (Request ID: {request_id})"
+        if "\n" in message:
+            newline_index = message.index("\n")
+            message = message[:newline_index] + request_id_message + message[newline_index:]
+        else:
+            message += request_id_message
+
+    return message

venv/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

venv/lib/python3.10/site-packages/huggingface_hub/utils/_fixes.py

@@ -0,0 +1,93 @@
+# JSONDecodeError was introduced in requests=2.27 released in 2022.
+# This allows us to support older requests for users
+# More information: https://github.com/psf/requests/pull/5856
+try:
+    from requests import JSONDecodeError  # type: ignore  # noqa: F401
+except ImportError:
+    try:
+        from simplejson import JSONDecodeError  # type: ignore # noqa: F401
+    except ImportError:
+        from json import JSONDecodeError  # type: ignore  # noqa: F401
+import contextlib
+import os
+import shutil
+import stat
+import tempfile
+from functools import partial
+from pathlib import Path
+from typing import Callable, Generator, Optional, Union
+
+import yaml
+from filelock import BaseFileLock, FileLock
+
+
+# Wrap `yaml.dump` to set `allow_unicode=True` by default.
+#
+# Example:
+# ```py
+# >>> yaml.dump({"emoji": "👀", "some unicode": "日本か"})
+# 'emoji: "\\U0001F440"\nsome unicode: "\\u65E5\\u672C\\u304B"\n'
+#
+# >>> yaml_dump({"emoji": "👀", "some unicode": "日本か"})
+# 'emoji: "👀"\nsome unicode: "日本か"\n'
+# ```
+yaml_dump: Callable[..., str] = partial(yaml.dump, stream=None, allow_unicode=True)  # type: ignore
+
+
+@contextlib.contextmanager
+def SoftTemporaryDirectory(
+    suffix: Optional[str] = None,
+    prefix: Optional[str] = None,
+    dir: Optional[Union[Path, str]] = None,
+    **kwargs,
+) -> Generator[Path, None, None]:
+    """
+    Context manager to create a temporary directory and safely delete it.
+
+    If tmp directory cannot be deleted normally, we set the WRITE permission and retry.
+    If cleanup still fails, we give up but don't raise an exception. This is equivalent
+    to  `tempfile.TemporaryDirectory(..., ignore_cleanup_errors=True)` introduced in
+    Python 3.10.
+
+    See https://www.scivision.dev/python-tempfile-permission-error-windows/.
+    """
+    tmpdir = tempfile.TemporaryDirectory(prefix=prefix, suffix=suffix, dir=dir, **kwargs)
+    yield Path(tmpdir.name).resolve()
+
+    try:
+        # First once with normal cleanup
+        shutil.rmtree(tmpdir.name)
+    except Exception:
+        # If failed, try to set write permission and retry
+        try:
+            shutil.rmtree(tmpdir.name, onerror=_set_write_permission_and_retry)
+        except Exception:
+            pass
+
+    # And finally, cleanup the tmpdir.
+    # If it fails again, give up but do not throw error
+    try:
+        tmpdir.cleanup()
+    except Exception:
+        pass
+
+
+def _set_write_permission_and_retry(func, path, excinfo):
+    os.chmod(path, stat.S_IWRITE)
+    func(path)
+
+
+@contextlib.contextmanager
+def WeakFileLock(lock_file: Union[str, Path]) -> Generator[BaseFileLock, None, None]:
+    lock = FileLock(lock_file)
+    lock.acquire()
+
+    yield lock
+
+    try:
+        return lock.release()
+    except OSError:
+        try:
+            Path(lock_file).unlink()
+        except OSError:
+            pass

venv/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)
+        )
+    )

venv/lib/python3.10/site-packages/huggingface_hub/utils/_headers.py

@@ -0,0 +1,241 @@
+# 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 .. import constants
+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 ._token import get_token
+from ._validators import validate_hf_hub_args
+
+
+class LocalTokenNotFoundError(EnvironmentError):
+    """Raised if local token is required but not found."""
+
+
+@validate_hf_hub_args
+def build_hf_headers(
+    *,
+    token: Optional[Union[bool, str]] = None,
+    is_write_action: bool = False,
+    library_name: Optional[str] = None,
+    library_version: Optional[str] = None,
+    user_agent: Union[Dict, str, None] = None,
+    headers: Optional[Dict[str, str]] = None,
+) -> 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.
+        is_write_action (`bool`, default to `False`):
+            Set to True if the API call requires a write access. If `True`, the token
+            will be validated (cannot be `None`, cannot start by `"api_org***"`).
+        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.
+
+    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(token="api_org_***", is_write_action=True)
+        ValueError: You must use your personal account token for write-access methods.
+
+        >>> 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)
+    _validate_token_to_send(token_to_send, is_write_action=is_write_action)
+
+    # 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 _validate_token_to_send(token: Optional[str], is_write_action: bool) -> None:
+    if is_write_action:
+        if token is None:
+            raise ValueError(
+                "Token is required (write-access action) 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."
+            )
+        if token.startswith("api_org"):
+            raise ValueError(
+                "You must use your personal account token for write-access methods. To"
+                " generate a write-access token, go to"
+                " https://huggingface.co/settings/tokens"
+            )
+
+
+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
+
+    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())

venv/lib/python3.10/site-packages/huggingface_hub/utils/_hf_folder.py

@@ -0,0 +1,96 @@
+# 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."""
+
+import warnings
+from pathlib import Path
+from typing import Optional
+
+from .. import constants
+from ._token import get_token
+
+
+class HfFolder:
+    path_token = Path(constants.HF_TOKEN_PATH)
+    # Private attribute. Will be removed in v0.15
+    _old_path_token = Path(constants._OLD_HF_TOKEN_PATH)
+
+    # 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`]
+        """
+        cls.path_token.parent.mkdir(parents=True, exist_ok=True)
+        cls.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.
+        """
+        # 0. Check if token exist in old path but not new location
+        try:
+            cls._copy_to_new_path_and_warn()
+        except Exception:  # if not possible (e.g. PermissionError), do not raise
+            pass
+
+        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:
+            cls.path_token.unlink()
+        except FileNotFoundError:
+            pass
+
+        try:
+            cls._old_path_token.unlink()
+        except FileNotFoundError:
+            pass
+
+    @classmethod
+    def _copy_to_new_path_and_warn(cls):
+        if cls._old_path_token.exists() and not cls.path_token.exists():
+            cls.save_token(cls._old_path_token.read_text())
+            warnings.warn(
+                f"A token has been found in `{cls._old_path_token}`. This is the old"
+                " path where tokens were stored. The new location is"
+                f" `{cls.path_token}` which is configurable using `HF_HOME` environment"
+                " variable. Your token has been copied to this new location. You can"
+                " now safely delete the old token file manually or use"
+                " `huggingface-cli logout`."
+            )

venv/lib/python3.10/site-packages/huggingface_hub/utils/_http.py

@@ -0,0 +1,321 @@
+# 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 HTTP requests in Huggingface Hub."""
+
+import io
+import os
+import threading
+import time
+import uuid
+from functools import lru_cache
+from http import HTTPStatus
+from typing import Callable, Optional, Tuple, Type, Union
+
+import requests
+from requests import Response
+from requests.adapters import HTTPAdapter
+from requests.models import PreparedRequest
+
+from .. import constants
+from . import logging
+from ._typing import HTTP_METHOD_T
+
+
+logger = logging.get_logger(__name__)
+
+# Both headers are used by the Hub to debug failed requests.
+# `X_AMZN_TRACE_ID` is better as it also works to debug on Cloudfront and ALB.
+# If `X_AMZN_TRACE_ID` is set, the Hub will use it as well.
+X_AMZN_TRACE_ID = "X-Amzn-Trace-Id"
+X_REQUEST_ID = "x-request-id"
+
+
+class OfflineModeIsEnabled(ConnectionError):
+    """Raised when a request is made but `HF_HUB_OFFLINE=1` is set as environment variable."""
+
+
+class UniqueRequestIdAdapter(HTTPAdapter):
+    X_AMZN_TRACE_ID = "X-Amzn-Trace-Id"
+
+    def add_headers(self, request, **kwargs):
+        super().add_headers(request, **kwargs)
+
+        # Add random request ID => easier for server-side debug
+        if X_AMZN_TRACE_ID not in request.headers:
+            request.headers[X_AMZN_TRACE_ID] = request.headers.get(X_REQUEST_ID) or str(uuid.uuid4())
+
+        # Add debug log
+        has_token = str(request.headers.get("authorization", "")).startswith("Bearer hf_")
+        logger.debug(
+            f"Request {request.headers[X_AMZN_TRACE_ID]}: {request.method} {request.url} (authenticated: {has_token})"
+        )
+
+    def send(self, request: PreparedRequest, *args, **kwargs) -> Response:
+        """Catch any RequestException to append request id to the error message for debugging."""
+        try:
+            return super().send(request, *args, **kwargs)
+        except requests.RequestException as e:
+            request_id = request.headers.get(X_AMZN_TRACE_ID)
+            if request_id is not None:
+                # Taken from https://stackoverflow.com/a/58270258
+                e.args = (*e.args, f"(Request ID: {request_id})")
+            raise
+
+
+class OfflineAdapter(HTTPAdapter):
+    def send(self, request: PreparedRequest, *args, **kwargs) -> Response:
+        raise OfflineModeIsEnabled(
+            f"Cannot reach {request.url}: offline mode is enabled. To disable it, please unset the `HF_HUB_OFFLINE` environment variable."
+        )
+
+
+def _default_backend_factory() -> requests.Session:
+    session = requests.Session()
+    if constants.HF_HUB_OFFLINE:
+        session.mount("http://", OfflineAdapter())
+        session.mount("https://", OfflineAdapter())
+    else:
+        session.mount("http://", UniqueRequestIdAdapter())
+        session.mount("https://", UniqueRequestIdAdapter())
+    return session
+
+
+BACKEND_FACTORY_T = Callable[[], requests.Session]
+_GLOBAL_BACKEND_FACTORY: BACKEND_FACTORY_T = _default_backend_factory
+
+
+def configure_http_backend(backend_factory: BACKEND_FACTORY_T = _default_backend_factory) -> None:
+    """
+    Configure the HTTP backend by providing a `backend_factory`. Any HTTP calls made by `huggingface_hub` will use a
+    Session object instantiated by this factory. This can be useful if you are running your scripts in a specific
+    environment requiring custom configuration (e.g. custom proxy or certifications).
+
+    Use [`get_session`] to get a configured Session. Since `requests.Session` is not guaranteed to be thread-safe,
+    `huggingface_hub` creates 1 Session instance per thread. They are all instantiated using the same `backend_factory`
+    set in [`configure_http_backend`]. A LRU cache is used to cache the created sessions (and connections) between
+    calls. Max size is 128 to avoid memory leaks if thousands of threads are spawned.
+
+    See [this issue](https://github.com/psf/requests/issues/2766) to know more about thread-safety in `requests`.
+
+    Example:
+    ```py
+    import requests
+    from huggingface_hub import configure_http_backend, get_session
+
+    # Create a factory function that returns a Session with configured proxies
+    def backend_factory() -> requests.Session:
+        session = requests.Session()
+        session.proxies = {"http": "http://10.10.1.10:3128", "https": "https://10.10.1.11:1080"}
+        return session
+
+    # Set it as the default session factory
+    configure_http_backend(backend_factory=backend_factory)
+
+    # In practice, this is mostly done internally in `huggingface_hub`
+    session = get_session()
+    ```
+    """
+    global _GLOBAL_BACKEND_FACTORY
+    _GLOBAL_BACKEND_FACTORY = backend_factory
+    reset_sessions()
+
+
+def get_session() -> requests.Session:
+    """
+    Get a `requests.Session` object, using the session factory from the user.
+
+    Use [`get_session`] to get a configured Session. Since `requests.Session` is not guaranteed to be thread-safe,
+    `huggingface_hub` creates 1 Session instance per thread. They are all instantiated using the same `backend_factory`
+    set in [`configure_http_backend`]. A LRU cache is used to cache the created sessions (and connections) between
+    calls. Max size is 128 to avoid memory leaks if thousands of threads are spawned.
+
+    See [this issue](https://github.com/psf/requests/issues/2766) to know more about thread-safety in `requests`.
+
+    Example:
+    ```py
+    import requests
+    from huggingface_hub import configure_http_backend, get_session
+
+    # Create a factory function that returns a Session with configured proxies
+    def backend_factory() -> requests.Session:
+        session = requests.Session()
+        session.proxies = {"http": "http://10.10.1.10:3128", "https": "https://10.10.1.11:1080"}
+        return session
+
+    # Set it as the default session factory
+    configure_http_backend(backend_factory=backend_factory)
+
+    # In practice, this is mostly done internally in `huggingface_hub`
+    session = get_session()
+    ```
+    """
+    return _get_session_from_cache(process_id=os.getpid(), thread_id=threading.get_ident())
+
+
+def reset_sessions() -> None:
+    """Reset the cache of sessions.
+
+    Mostly used internally when sessions are reconfigured or an SSLError is raised.
+    See [`configure_http_backend`] for more details.
+    """
+    _get_session_from_cache.cache_clear()
+
+
+@lru_cache
+def _get_session_from_cache(process_id: int, thread_id: int) -> requests.Session:
+    """
+    Create a new session per thread using global factory. Using LRU cache (maxsize 128) to avoid memory leaks when
+    using thousands of threads. Cache is cleared when `configure_http_backend` is called.
+    """
+    return _GLOBAL_BACKEND_FACTORY()
+
+
+def http_backoff(
+    method: HTTP_METHOD_T,
+    url: str,
+    *,
+    max_retries: int = 5,
+    base_wait_time: float = 1,
+    max_wait_time: float = 8,
+    retry_on_exceptions: Union[Type[Exception], Tuple[Type[Exception], ...]] = (
+        requests.Timeout,
+        requests.ConnectionError,
+    ),
+    retry_on_status_codes: Union[int, Tuple[int, ...]] = HTTPStatus.SERVICE_UNAVAILABLE,
+    **kwargs,
+) -> Response:
+    """Wrapper around requests to retry calls on an endpoint, with exponential backoff.
+
+    Endpoint call is retried on exceptions (ex: connection timeout, proxy error,...)
+    and/or on specific status codes (ex: service unavailable). If the call failed more
+    than `max_retries`, the exception is thrown or `raise_for_status` is called on the
+    response object.
+
+    Re-implement mechanisms from the `backoff` library to avoid adding an external
+    dependencies to `hugging_face_hub`. See https://github.com/litl/backoff.
+
+    Args:
+        method (`Literal["GET", "OPTIONS", "HEAD", "POST", "PUT", "PATCH", "DELETE"]`):
+            HTTP method to perform.
+        url (`str`):
+            The URL of the resource to fetch.
+        max_retries (`int`, *optional*, defaults to `5`):
+            Maximum number of retries, defaults to 5 (no retries).
+        base_wait_time (`float`, *optional*, defaults to `1`):
+            Duration (in seconds) to wait before retrying the first time.
+            Wait time between retries then grows exponentially, capped by
+            `max_wait_time`.
+        max_wait_time (`float`, *optional*, defaults to `8`):
+            Maximum duration (in seconds) to wait before retrying.
+        retry_on_exceptions (`Type[Exception]` or `Tuple[Type[Exception]]`, *optional*):
+            Define which exceptions must be caught to retry the request. Can be a single type or a tuple of types.
+            By default, retry on `requests.Timeout` and `requests.ConnectionError`.
+        retry_on_status_codes (`int` or `Tuple[int]`, *optional*, defaults to `503`):
+            Define on which status codes the request must be retried. By default, only
+            HTTP 503 Service Unavailable is retried.
+        **kwargs (`dict`, *optional*):
+            kwargs to pass to `requests.request`.
+
+    Example:
+    ```
+    >>> from huggingface_hub.utils import http_backoff
+
+    # Same usage as "requests.request".
+    >>> response = http_backoff("GET", "https://www.google.com")
+    >>> response.raise_for_status()
+
+    # If you expect a Gateway Timeout from time to time
+    >>> http_backoff("PUT", upload_url, data=data, retry_on_status_codes=504)
+    >>> response.raise_for_status()
+    ```
+
+    <Tip warning={true}>
+
+    When using `requests` it is possible to stream data by passing an iterator to the
+    `data` argument. On http backoff this is a problem as the iterator is not reset
+    after a failed call. This issue is mitigated for file objects or any IO streams
+    by saving the initial position of the cursor (with `data.tell()`) and resetting the
+    cursor between each call (with `data.seek()`). For arbitrary iterators, http backoff
+    will fail. If this is a hard constraint for you, please let us know by opening an
+    issue on [Github](https://github.com/huggingface/huggingface_hub).
+
+    </Tip>
+    """
+    if isinstance(retry_on_exceptions, type):  # Tuple from single exception type
+        retry_on_exceptions = (retry_on_exceptions,)
+
+    if isinstance(retry_on_status_codes, int):  # Tuple from single status code
+        retry_on_status_codes = (retry_on_status_codes,)
+
+    nb_tries = 0
+    sleep_time = base_wait_time
+
+    # If `data` is used and is a file object (or any IO), it will be consumed on the
+    # first HTTP request. We need to save the initial position so that the full content
+    # of the file is re-sent on http backoff. See warning tip in docstring.
+    io_obj_initial_pos = None
+    if "data" in kwargs and isinstance(kwargs["data"], io.IOBase):
+        io_obj_initial_pos = kwargs["data"].tell()
+
+    session = get_session()
+    while True:
+        nb_tries += 1
+        try:
+            # If `data` is used and is a file object (or any IO), set back cursor to
+            # initial position.
+            if io_obj_initial_pos is not None:
+                kwargs["data"].seek(io_obj_initial_pos)
+
+            # Perform request and return if status_code is not in the retry list.
+            response = session.request(method=method, url=url, **kwargs)
+            if response.status_code not in retry_on_status_codes:
+                return response
+
+            # Wrong status code returned (HTTP 503 for instance)
+            logger.warning(f"HTTP Error {response.status_code} thrown while requesting {method} {url}")
+            if nb_tries > max_retries:
+                response.raise_for_status()  # Will raise uncaught exception
+                # We return response to avoid infinite loop in the corner case where the
+                # user ask for retry on a status code that doesn't raise_for_status.
+                return response
+
+        except retry_on_exceptions as err:
+            logger.warning(f"'{err}' thrown while requesting {method} {url}")
+
+            if isinstance(err, requests.ConnectionError):
+                reset_sessions()  # In case of SSLError it's best to reset the shared requests.Session objects
+
+            if nb_tries > max_retries:
+                raise err
+
+        # Sleep for X seconds
+        logger.warning(f"Retrying in {sleep_time}s [Retry {nb_tries}/{max_retries}].")
+        time.sleep(sleep_time)
+
+        # Update sleep time for next retry
+        sleep_time = min(max_wait_time, sleep_time * 2)  # Exponential backoff
+
+
+def fix_hf_endpoint_in_url(url: str, endpoint: Optional[str]) -> str:
+    """Replace the default endpoint in a URL by a custom one.
+
+    This is useful when using a proxy and the Hugging Face Hub returns a URL with the default endpoint.
+    """
+    endpoint = endpoint or constants.ENDPOINT
+    # check if a proxy has been set => if yes, update the returned URL to use the proxy
+    if endpoint not in (None, constants._HF_DEFAULT_ENDPOINT, constants._HF_DEFAULT_STAGING_ENDPOINT):
+        url = url.replace(constants._HF_DEFAULT_ENDPOINT, endpoint)
+        url = url.replace(constants._HF_DEFAULT_STAGING_ENDPOINT, endpoint)
+    return url

venv/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")