structure saas with tools

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

@@ -0,0 +1,422 @@
+# 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
+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,
+    Union,
+    overload,
+)
+
+from requests import HTTPError
+
+from huggingface_hub.errors import (
+    GenerationError,
+    IncompleteGenerationError,
+    OverloadedError,
+    TextGenerationError,
+    UnknownError,
+    ValidationError,
+)
+
+from ..utils import get_session, is_aiohttp_available, is_numpy_available, is_pillow_available
+from ._generated.types import ChatCompletionStreamOutput, TextGenerationStreamOutput
+
+
+if TYPE_CHECKING:
+    from aiohttp import ClientResponse, ClientSession
+    from PIL.Image 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__)
+
+
+@dataclass
+class RequestParameters:
+    url: str
+    task: str
+    model: Optional[str]
+    json: Optional[Union[str, Dict, List]]
+    data: Optional[ContentT]
+    headers: Dict[str, Any]
+
+
+# Add dataclass for ModelStatus. We use this dataclass in get_model_status function.
+@dataclass
+class ModelStatus:
+    """
+    This Dataclass represents the model status in the HF Inference API.
+
+    Args:
+        loaded (`bool`):
+            If the model is currently loaded into HF's Inference API. 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
+
+
+## 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 bytes, 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))
+
+
+def _as_dict(response: Union[bytes, Dict]) -> Dict:
+    return json.loads(response) if isinstance(response, bytes) else response
+
+
+## PAYLOAD UTILS
+
+
+## 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:
+        try:
+            output = _format_text_generation_stream_output(byte_payload, details)
+        except StopIteration:
+            break
+        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:
+        try:
+            output = _format_text_generation_stream_output(byte_payload, details)
+        except StopIteration:
+            break
+        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
+
+    if byte_payload.strip() == b"data: [DONE]":
+        raise StopIteration("[DONE] signal received.")
+
+    # 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(
+    bytes_lines: Iterable[bytes],
+) -> Iterable[ChatCompletionStreamOutput]:
+    """Used in `InferenceClient.chat_completion` if model is served with TGI."""
+    for item in bytes_lines:
+        try:
+            output = _format_chat_completion_stream_output(item)
+        except StopIteration:
+            break
+        if output is not None:
+            yield output
+
+
+async def _async_stream_chat_completion_response(
+    bytes_lines: AsyncIterable[bytes],
+) -> AsyncIterable[ChatCompletionStreamOutput]:
+    """Used in `AsyncInferenceClient.chat_completion`."""
+    async for item in bytes_lines:
+        try:
+            output = _format_chat_completion_stream_output(item)
+        except StopIteration:
+            break
+        if output is not None:
+            yield output
+
+
+def _format_chat_completion_stream_output(
+    byte_payload: bytes,
+) -> Optional[ChatCompletionStreamOutput]:
+    if not byte_payload.startswith(b"data:"):
+        return None  # empty line
+
+    if byte_payload.strip() == b"data: [DONE]":
+        raise StopIteration("[DONE] signal received.")
+
+    # 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
+    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.strip()
+    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. When that's the case, We remember the unsupported
+# attributes for this model in the `_UNSUPPORTED_TEXT_GENERATION_KWARGS` 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.
+
+_UNSUPPORTED_TEXT_GENERATION_KWARGS: Dict[Optional[str], List[str]] = {}
+
+
+def _set_unsupported_text_generation_kwargs(model: Optional[str], unsupported_kwargs: List[str]) -> None:
+    _UNSUPPORTED_TEXT_GENERATION_KWARGS.setdefault(model, []).extend(unsupported_kwargs)
+
+
+def _get_unsupported_text_generation_kwargs(model: Optional[str]) -> List[str]:
+    return _UNSUPPORTED_TEXT_GENERATION_KWARGS.get(model, [])
+
+
+# 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/__init__.py

@@ -0,0 +1,188 @@
+# This file is auto-generated by `utils/generate_inference_types.py`.
+# Do not modify it manually.
+#
+# ruff: noqa: F401
+
+from .audio_classification import (
+    AudioClassificationInput,
+    AudioClassificationOutputElement,
+    AudioClassificationOutputTransform,
+    AudioClassificationParameters,
+)
+from .audio_to_audio import AudioToAudioInput, AudioToAudioOutputElement
+from .automatic_speech_recognition import (
+    AutomaticSpeechRecognitionEarlyStoppingEnum,
+    AutomaticSpeechRecognitionGenerationParameters,
+    AutomaticSpeechRecognitionInput,
+    AutomaticSpeechRecognitionOutput,
+    AutomaticSpeechRecognitionOutputChunk,
+    AutomaticSpeechRecognitionParameters,
+)
+from .base import BaseInferenceType
+from .chat_completion import (
+    ChatCompletionInput,
+    ChatCompletionInputFunctionDefinition,
+    ChatCompletionInputFunctionName,
+    ChatCompletionInputGrammarType,
+    ChatCompletionInputGrammarTypeType,
+    ChatCompletionInputMessage,
+    ChatCompletionInputMessageChunk,
+    ChatCompletionInputMessageChunkType,
+    ChatCompletionInputStreamOptions,
+    ChatCompletionInputTool,
+    ChatCompletionInputToolCall,
+    ChatCompletionInputToolChoiceClass,
+    ChatCompletionInputToolChoiceEnum,
+    ChatCompletionInputURL,
+    ChatCompletionOutput,
+    ChatCompletionOutputComplete,
+    ChatCompletionOutputFunctionDefinition,
+    ChatCompletionOutputLogprob,
+    ChatCompletionOutputLogprobs,
+    ChatCompletionOutputMessage,
+    ChatCompletionOutputToolCall,
+    ChatCompletionOutputTopLogprob,
+    ChatCompletionOutputUsage,
+    ChatCompletionStreamOutput,
+    ChatCompletionStreamOutputChoice,
+    ChatCompletionStreamOutputDelta,
+    ChatCompletionStreamOutputDeltaToolCall,
+    ChatCompletionStreamOutputFunction,
+    ChatCompletionStreamOutputLogprob,
+    ChatCompletionStreamOutputLogprobs,
+    ChatCompletionStreamOutputTopLogprob,
+    ChatCompletionStreamOutputUsage,
+)
+from .depth_estimation import DepthEstimationInput, DepthEstimationOutput
+from .document_question_answering import (
+    DocumentQuestionAnsweringInput,
+    DocumentQuestionAnsweringInputData,
+    DocumentQuestionAnsweringOutputElement,
+    DocumentQuestionAnsweringParameters,
+)
+from .feature_extraction import FeatureExtractionInput, FeatureExtractionInputTruncationDirection
+from .fill_mask import FillMaskInput, FillMaskOutputElement, FillMaskParameters
+from .image_classification import (
+    ImageClassificationInput,
+    ImageClassificationOutputElement,
+    ImageClassificationOutputTransform,
+    ImageClassificationParameters,
+)
+from .image_segmentation import (
+    ImageSegmentationInput,
+    ImageSegmentationOutputElement,
+    ImageSegmentationParameters,
+    ImageSegmentationSubtask,
+)
+from .image_to_image import ImageToImageInput, ImageToImageOutput, ImageToImageParameters, ImageToImageTargetSize
+from .image_to_text import (
+    ImageToTextEarlyStoppingEnum,
+    ImageToTextGenerationParameters,
+    ImageToTextInput,
+    ImageToTextOutput,
+    ImageToTextParameters,
+)
+from .object_detection import (
+    ObjectDetectionBoundingBox,
+    ObjectDetectionInput,
+    ObjectDetectionOutputElement,
+    ObjectDetectionParameters,
+)
+from .question_answering import (
+    QuestionAnsweringInput,
+    QuestionAnsweringInputData,
+    QuestionAnsweringOutputElement,
+    QuestionAnsweringParameters,
+)
+from .sentence_similarity import SentenceSimilarityInput, SentenceSimilarityInputData
+from .summarization import (
+    SummarizationInput,
+    SummarizationOutput,
+    SummarizationParameters,
+    SummarizationTruncationStrategy,
+)
+from .table_question_answering import (
+    Padding,
+    TableQuestionAnsweringInput,
+    TableQuestionAnsweringInputData,
+    TableQuestionAnsweringOutputElement,
+    TableQuestionAnsweringParameters,
+)
+from .text2text_generation import (
+    Text2TextGenerationInput,
+    Text2TextGenerationOutput,
+    Text2TextGenerationParameters,
+    Text2TextGenerationTruncationStrategy,
+)
+from .text_classification import (
+    TextClassificationInput,
+    TextClassificationOutputElement,
+    TextClassificationOutputTransform,
+    TextClassificationParameters,
+)
+from .text_generation import (
+    TextGenerationInput,
+    TextGenerationInputGenerateParameters,
+    TextGenerationInputGrammarType,
+    TextGenerationOutput,
+    TextGenerationOutputBestOfSequence,
+    TextGenerationOutputDetails,
+    TextGenerationOutputFinishReason,
+    TextGenerationOutputPrefillToken,
+    TextGenerationOutputToken,
+    TextGenerationStreamOutput,
+    TextGenerationStreamOutputStreamDetails,
+    TextGenerationStreamOutputToken,
+    TypeEnum,
+)
+from .text_to_audio import (
+    TextToAudioEarlyStoppingEnum,
+    TextToAudioGenerationParameters,
+    TextToAudioInput,
+    TextToAudioOutput,
+    TextToAudioParameters,
+)
+from .text_to_image import TextToImageInput, TextToImageOutput, TextToImageParameters
+from .text_to_speech import (
+    TextToSpeechEarlyStoppingEnum,
+    TextToSpeechGenerationParameters,
+    TextToSpeechInput,
+    TextToSpeechOutput,
+    TextToSpeechParameters,
+)
+from .text_to_video import TextToVideoInput, TextToVideoOutput, TextToVideoParameters
+from .token_classification import (
+    TokenClassificationAggregationStrategy,
+    TokenClassificationInput,
+    TokenClassificationOutputElement,
+    TokenClassificationParameters,
+)
+from .translation import TranslationInput, TranslationOutput, TranslationParameters, TranslationTruncationStrategy
+from .video_classification import (
+    VideoClassificationInput,
+    VideoClassificationOutputElement,
+    VideoClassificationOutputTransform,
+    VideoClassificationParameters,
+)
+from .visual_question_answering import (
+    VisualQuestionAnsweringInput,
+    VisualQuestionAnsweringInputData,
+    VisualQuestionAnsweringOutputElement,
+    VisualQuestionAnsweringParameters,
+)
+from .zero_shot_classification import (
+    ZeroShotClassificationInput,
+    ZeroShotClassificationOutputElement,
+    ZeroShotClassificationParameters,
+)
+from .zero_shot_image_classification import (
+    ZeroShotImageClassificationInput,
+    ZeroShotImageClassificationOutputElement,
+    ZeroShotImageClassificationParameters,
+)
+from .zero_shot_object_detection import (
+    ZeroShotObjectDetectionBoundingBox,
+    ZeroShotObjectDetectionInput,
+    ZeroShotObjectDetectionOutputElement,
+    ZeroShotObjectDetectionParameters,
+)

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

@@ -0,0 +1,43 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import Literal, Optional
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+AudioClassificationOutputTransform = Literal["sigmoid", "softmax", "none"]
+
+
+@dataclass_with_extra
+class AudioClassificationParameters(BaseInferenceType):
+    """Additional inference parameters for Audio Classification"""
+
+    function_to_apply: Optional["AudioClassificationOutputTransform"] = None
+    """The function to apply to the model outputs in order to retrieve the scores."""
+    top_k: Optional[int] = None
+    """When specified, limits the output to the top K most probable classes."""
+
+
+@dataclass_with_extra
+class AudioClassificationInput(BaseInferenceType):
+    """Inputs for Audio Classification inference"""
+
+    inputs: str
+    """The input audio data as a base64-encoded string. If no `parameters` are provided, you can
+    also provide the audio data as a raw bytes payload.
+    """
+    parameters: Optional[AudioClassificationParameters] = None
+    """Additional inference parameters for Audio Classification"""
+
+
+@dataclass_with_extra
+class AudioClassificationOutputElement(BaseInferenceType):
+    """Outputs for Audio Classification inference"""
+
+    label: str
+    """The predicted class label."""
+    score: float
+    """The corresponding probability."""

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

@@ -0,0 +1,30 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import Any
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+@dataclass_with_extra
+class AudioToAudioInput(BaseInferenceType):
+    """Inputs for Audio to Audio inference"""
+
+    inputs: Any
+    """The input audio data"""
+
+
+@dataclass_with_extra
+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/automatic_speech_recognition.py

@@ -0,0 +1,114 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import List, Literal, Optional, Union
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+AutomaticSpeechRecognitionEarlyStoppingEnum = Literal["never"]
+
+
+@dataclass_with_extra
+class AutomaticSpeechRecognitionGenerationParameters(BaseInferenceType):
+    """Parametrization of the text generation process"""
+
+    do_sample: Optional[bool] = None
+    """Whether to use sampling instead of greedy decoding when generating new tokens."""
+    early_stopping: Optional[Union[bool, "AutomaticSpeechRecognitionEarlyStoppingEnum"]] = None
+    """Controls the stopping condition for beam-based methods."""
+    epsilon_cutoff: Optional[float] = None
+    """If set to float strictly between 0 and 1, only tokens with a conditional probability
+    greater than epsilon_cutoff will be sampled. In the paper, suggested values range from
+    3e-4 to 9e-4, depending on the size of the model. See [Truncation Sampling as Language
+    Model Desmoothing](https://hf.co/papers/2210.15191) for more details.
+    """
+    eta_cutoff: Optional[float] = None
+    """Eta sampling is a hybrid of locally typical sampling and epsilon sampling. If set to
+    float strictly between 0 and 1, a token is only considered if it is greater than either
+    eta_cutoff or sqrt(eta_cutoff) * exp(-entropy(softmax(next_token_logits))). The latter
+    term is intuitively the expected next token probability, scaled by sqrt(eta_cutoff). In
+    the paper, suggested values range from 3e-4 to 2e-3, depending on the size of the model.
+    See [Truncation Sampling as Language Model Desmoothing](https://hf.co/papers/2210.15191)
+    for more details.
+    """
+    max_length: Optional[int] = None
+    """The maximum length (in tokens) of the generated text, including the input."""
+    max_new_tokens: Optional[int] = None
+    """The maximum number of tokens to generate. Takes precedence over max_length."""
+    min_length: Optional[int] = None
+    """The minimum length (in tokens) of the generated text, including the input."""
+    min_new_tokens: Optional[int] = None
+    """The minimum number of tokens to generate. Takes precedence over min_length."""
+    num_beam_groups: Optional[int] = None
+    """Number of groups to divide num_beams into in order to ensure diversity among different
+    groups of beams. See [this paper](https://hf.co/papers/1610.02424) for more details.
+    """
+    num_beams: Optional[int] = None
+    """Number of beams to use for beam search."""
+    penalty_alpha: Optional[float] = None
+    """The value balances the model confidence and the degeneration penalty in contrastive
+    search decoding.
+    """
+    temperature: Optional[float] = None
+    """The value used to modulate the next token probabilities."""
+    top_k: Optional[int] = None
+    """The number of highest probability vocabulary tokens to keep for top-k-filtering."""
+    top_p: Optional[float] = None
+    """If set to float < 1, only the smallest set of most probable tokens with probabilities
+    that add up to top_p or higher are kept for generation.
+    """
+    typical_p: Optional[float] = None
+    """Local typicality measures how similar the conditional probability of predicting a target
+    token next is to the expected conditional probability of predicting a random token next,
+    given the partial text already generated. If set to float < 1, the smallest set of the
+    most locally typical tokens with probabilities that add up to typical_p or higher are
+    kept for generation. See [this paper](https://hf.co/papers/2202.00666) for more details.
+    """
+    use_cache: Optional[bool] = None
+    """Whether the model should use the past last key/values attentions to speed up decoding"""
+
+
+@dataclass_with_extra
+class AutomaticSpeechRecognitionParameters(BaseInferenceType):
+    """Additional inference parameters for Automatic Speech Recognition"""
+
+    return_timestamps: Optional[bool] = None
+    """Whether to output corresponding timestamps with the generated text"""
+    # Will be deprecated in the future when the renaming to `generation_parameters` is implemented in transformers
+    generate_kwargs: Optional[AutomaticSpeechRecognitionGenerationParameters] = None
+    """Parametrization of the text generation process"""
+
+
+@dataclass_with_extra
+class AutomaticSpeechRecognitionInput(BaseInferenceType):
+    """Inputs for Automatic Speech Recognition inference"""
+
+    inputs: str
+    """The input audio data as a base64-encoded string. If no `parameters` are provided, you can
+    also provide the audio data as a raw bytes payload.
+    """
+    parameters: Optional[AutomaticSpeechRecognitionParameters] = None
+    """Additional inference parameters for Automatic Speech Recognition"""
+
+
+@dataclass_with_extra
+class AutomaticSpeechRecognitionOutputChunk(BaseInferenceType):
+    text: str
+    """A chunk of text identified by the model"""
+    timestamp: List[float]
+    """The start and end timestamps corresponding with the text"""
+
+
+@dataclass_with_extra
+class AutomaticSpeechRecognitionOutput(BaseInferenceType):
+    """Outputs of inference for the Automatic Speech Recognition task"""
+
+    text: str
+    """The recognized text."""
+    chunks: Optional[List[AutomaticSpeechRecognitionOutputChunk]] = None
+    """When returnTimestamps is enabled, chunks contains a list of audio chunks identified by
+    the model.
+    """

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

@@ -0,0 +1,161 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Contains a base class for all inference types."""
+
+import inspect
+import json
+from dataclasses import asdict, dataclass
+from typing import Any, Dict, List, Type, TypeVar, Union, get_args
+
+
+T = TypeVar("T", bound="BaseInferenceType")
+
+
+def _repr_with_extra(self):
+    fields = list(self.__dataclass_fields__.keys())
+    other_fields = list(k for k in self.__dict__ if k not in fields)
+    return f"{self.__class__.__name__}({', '.join(f'{k}={self.__dict__[k]!r}' for k in fields + other_fields)})"
+
+
+def dataclass_with_extra(cls: Type[T]) -> Type[T]:
+    """Decorator to add a custom __repr__ method to a dataclass, showing all fields, including extra ones.
+
+    This decorator only works with dataclasses that inherit from `BaseInferenceType`.
+    """
+    cls = dataclass(cls)
+    cls.__repr__ = _repr_with_extra  # type: ignore[method-assign]
+    return cls
+
+
+@dataclass
+class BaseInferenceType(dict):
+    """Base class for all inference types.
+
+    Object is a dataclass and a dict for backward compatibility but plan is to remove the dict part in the future.
+
+    Handle parsing from dict, list and json strings in a permissive way to ensure future-compatibility (e.g. all fields
+    are made optional, and non-expected fields are added as dict attributes).
+    """
+
+    @classmethod
+    def parse_obj_as_list(cls: Type[T], data: Union[bytes, str, List, Dict]) -> List[T]:
+        """Alias to parse server response and return a single instance.
+
+        See `parse_obj` for more details.
+        """
+        output = cls.parse_obj(data)
+        if not isinstance(output, list):
+            raise ValueError(f"Invalid input data for {cls}. Expected a list, but got {type(output)}.")
+        return output
+
+    @classmethod
+    def parse_obj_as_instance(cls: Type[T], data: Union[bytes, str, List, Dict]) -> T:
+        """Alias to parse server response and return a single instance.
+
+        See `parse_obj` for more details.
+        """
+        output = cls.parse_obj(data)
+        if isinstance(output, list):
+            raise ValueError(f"Invalid input data for {cls}. Expected a single instance, but got a list.")
+        return output
+
+    @classmethod
+    def parse_obj(cls: Type[T], data: Union[bytes, str, List, Dict]) -> Union[List[T], T]:
+        """Parse server response as a dataclass or list of dataclasses.
+
+        To enable future-compatibility, we want to handle cases where the server return more fields than expected.
+        In such cases, we don't want to raise an error but still create the dataclass object. Remaining fields are
+        added as dict attributes.
+        """
+        # Parse server response (from bytes)
+        if isinstance(data, bytes):
+            data = data.decode()
+        if isinstance(data, str):
+            data = json.loads(data)
+
+        # If a list, parse each item individually
+        if isinstance(data, List):
+            return [cls.parse_obj(d) for d in data]  # type: ignore [misc]
+
+        # At this point, we expect a dict
+        if not isinstance(data, dict):
+            raise ValueError(f"Invalid data type: {type(data)}")
+
+        init_values = {}
+        other_values = {}
+        for key, value in data.items():
+            key = normalize_key(key)
+            if key in cls.__dataclass_fields__ and cls.__dataclass_fields__[key].init:
+                if isinstance(value, dict) or isinstance(value, list):
+                    field_type = cls.__dataclass_fields__[key].type
+
+                    # if `field_type` is a `BaseInferenceType`, parse it
+                    if inspect.isclass(field_type) and issubclass(field_type, BaseInferenceType):
+                        value = field_type.parse_obj(value)
+
+                    # otherwise, recursively parse nested dataclasses (if possible)
+                    # `get_args` returns handle Union and Optional for us
+                    else:
+                        expected_types = get_args(field_type)
+                        for expected_type in expected_types:
+                            if getattr(expected_type, "_name", None) == "List":
+                                expected_type = get_args(expected_type)[
+                                    0
+                                ]  # assume same type for all items in the list
+                            if inspect.isclass(expected_type) and issubclass(expected_type, BaseInferenceType):
+                                value = expected_type.parse_obj(value)
+                                break
+                init_values[key] = value
+            else:
+                other_values[key] = value
+
+        # Make all missing fields default to None
+        # => ensure that dataclass initialization will never fail even if the server does not return all fields.
+        for key in cls.__dataclass_fields__:
+            if key not in init_values:
+                init_values[key] = None
+
+        # Initialize dataclass with expected values
+        item = cls(**init_values)
+
+        # Add remaining fields as dict attributes
+        item.update(other_values)
+
+        # Add remaining fields as extra dataclass fields.
+        # They won't be part of the dataclass fields but will be accessible as attributes.
+        # Use @dataclass_with_extra to show them in __repr__.
+        item.__dict__.update(other_values)
+        return item
+
+    def __post_init__(self):
+        self.update(asdict(self))
+
+    def __setitem__(self, __key: Any, __value: Any) -> None:
+        # Hacky way to keep dataclass values in sync when dict is updated
+        super().__setitem__(__key, __value)
+        if __key in self.__dataclass_fields__ and getattr(self, __key, None) != __value:
+            self.__setattr__(__key, __value)
+        return
+
+    def __setattr__(self, __name: str, __value: Any) -> None:
+        # Hacky way to keep dict values is sync when dataclass is updated
+        super().__setattr__(__name, __value)
+        if self.get(__name) != __value:
+            self[__name] = __value
+        return
+
+
+def normalize_key(key: str) -> str:
+    # e.g "content-type" -> "content_type", "Accept" -> "accept"
+    return key.replace("-", "_").replace(" ", "_").lower()

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

@@ -0,0 +1,311 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import Any, List, Literal, Optional, Union
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+@dataclass_with_extra
+class ChatCompletionInputURL(BaseInferenceType):
+    url: str
+
+
+ChatCompletionInputMessageChunkType = Literal["text", "image_url"]
+
+
+@dataclass_with_extra
+class ChatCompletionInputMessageChunk(BaseInferenceType):
+    type: "ChatCompletionInputMessageChunkType"
+    image_url: Optional[ChatCompletionInputURL] = None
+    text: Optional[str] = None
+
+
+@dataclass_with_extra
+class ChatCompletionInputFunctionDefinition(BaseInferenceType):
+    arguments: Any
+    name: str
+    description: Optional[str] = None
+
+
+@dataclass_with_extra
+class ChatCompletionInputToolCall(BaseInferenceType):
+    function: ChatCompletionInputFunctionDefinition
+    id: str
+    type: str
+
+
+@dataclass_with_extra
+class ChatCompletionInputMessage(BaseInferenceType):
+    role: str
+    content: Optional[Union[List[ChatCompletionInputMessageChunk], str]] = None
+    name: Optional[str] = None
+    tool_calls: Optional[List[ChatCompletionInputToolCall]] = None
+
+
+ChatCompletionInputGrammarTypeType = Literal["json", "regex"]
+
+
+@dataclass_with_extra
+class ChatCompletionInputGrammarType(BaseInferenceType):
+    type: "ChatCompletionInputGrammarTypeType"
+    value: Any
+    """A string that represents a [JSON Schema](https://json-schema.org/).
+    JSON Schema is a declarative language that allows to annotate JSON documents
+    with types and descriptions.
+    """
+
+
+@dataclass_with_extra
+class ChatCompletionInputStreamOptions(BaseInferenceType):
+    include_usage: Optional[bool] = None
+    """If set, an additional chunk will be streamed before the data: [DONE] message. The usage
+    field on this chunk shows the token usage statistics for the entire request, and the
+    choices field will always be an empty array. All other chunks will also include a usage
+    field, but with a null value.
+    """
+
+
+@dataclass_with_extra
+class ChatCompletionInputFunctionName(BaseInferenceType):
+    name: str
+
+
+@dataclass_with_extra
+class ChatCompletionInputToolChoiceClass(BaseInferenceType):
+    function: ChatCompletionInputFunctionName
+
+
+ChatCompletionInputToolChoiceEnum = Literal["auto", "none", "required"]
+
+
+@dataclass_with_extra
+class ChatCompletionInputTool(BaseInferenceType):
+    function: ChatCompletionInputFunctionDefinition
+    type: str
+
+
+@dataclass_with_extra
+class ChatCompletionInput(BaseInferenceType):
+    """Chat Completion Input.
+    Auto-generated from TGI specs.
+    For more details, check out
+    https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-tgi-import.ts.
+    """
+
+    messages: List[ChatCompletionInputMessage]
+    """A list of messages comprising the conversation so far."""
+    frequency_penalty: Optional[float] = None
+    """Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing
+    frequency in the text so far,
+    decreasing the model's likelihood to repeat the same line verbatim.
+    """
+    logit_bias: Optional[List[float]] = None
+    """UNUSED
+    Modify the likelihood of specified tokens appearing in the completion. Accepts a JSON
+    object that maps tokens
+    (specified by their token ID in the tokenizer) to an associated bias value from -100 to
+    100. Mathematically,
+    the bias is added to the logits generated by the model prior to sampling. The exact
+    effect will vary per model,
+    but values between -1 and 1 should decrease or increase likelihood of selection; values
+    like -100 or 100 should
+    result in a ban or exclusive selection of the relevant token.
+    """
+    logprobs: Optional[bool] = None
+    """Whether to return log probabilities of the output tokens or not. If true, returns the log
+    probabilities of each
+    output token returned in the content of message.
+    """
+    max_tokens: Optional[int] = None
+    """The maximum number of tokens that can be generated in the chat completion."""
+    model: Optional[str] = None
+    """[UNUSED] ID of the model to use. See the model endpoint compatibility table for details
+    on which models work with the Chat API.
+    """
+    n: Optional[int] = None
+    """UNUSED
+    How many chat completion choices to generate for each input message. Note that you will
+    be charged based on the
+    number of generated tokens across all of the choices. Keep n as 1 to minimize costs.
+    """
+    presence_penalty: Optional[float] = None
+    """Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they
+    appear in the text so far,
+    increasing the model's likelihood to talk about new topics
+    """
+    response_format: Optional[ChatCompletionInputGrammarType] = None
+    seed: Optional[int] = None
+    stop: Optional[List[str]] = None
+    """Up to 4 sequences where the API will stop generating further tokens."""
+    stream: Optional[bool] = None
+    stream_options: Optional[ChatCompletionInputStreamOptions] = None
+    temperature: Optional[float] = None
+    """What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the
+    output more random, while
+    lower values like 0.2 will make it more focused and deterministic.
+    We generally recommend altering this or `top_p` but not both.
+    """
+    tool_choice: Optional[Union[ChatCompletionInputToolChoiceClass, "ChatCompletionInputToolChoiceEnum"]] = None
+    tool_prompt: Optional[str] = None
+    """A prompt to be appended before the tools"""
+    tools: Optional[List[ChatCompletionInputTool]] = None
+    """A list of tools the model may call. Currently, only functions are supported as a tool.
+    Use this to provide a list of
+    functions the model may generate JSON inputs for.
+    """
+    top_logprobs: Optional[int] = None
+    """An integer between 0 and 5 specifying the number of most likely tokens to return at each
+    token position, each with
+    an associated log probability. logprobs must be set to true if this parameter is used.
+    """
+    top_p: Optional[float] = None
+    """An alternative to sampling with temperature, called nucleus sampling, where the model
+    considers the results of the
+    tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10%
+    probability mass are considered.
+    """
+
+
+@dataclass_with_extra
+class ChatCompletionOutputTopLogprob(BaseInferenceType):
+    logprob: float
+    token: str
+
+
+@dataclass_with_extra
+class ChatCompletionOutputLogprob(BaseInferenceType):
+    logprob: float
+    token: str
+    top_logprobs: List[ChatCompletionOutputTopLogprob]
+
+
+@dataclass_with_extra
+class ChatCompletionOutputLogprobs(BaseInferenceType):
+    content: List[ChatCompletionOutputLogprob]
+
+
+@dataclass_with_extra
+class ChatCompletionOutputFunctionDefinition(BaseInferenceType):
+    arguments: Any
+    name: str
+    description: Optional[str] = None
+
+
+@dataclass_with_extra
+class ChatCompletionOutputToolCall(BaseInferenceType):
+    function: ChatCompletionOutputFunctionDefinition
+    id: str
+    type: str
+
+
+@dataclass_with_extra
+class ChatCompletionOutputMessage(BaseInferenceType):
+    role: str
+    content: Optional[str] = None
+    tool_call_id: Optional[str] = None
+    tool_calls: Optional[List[ChatCompletionOutputToolCall]] = None
+
+
+@dataclass_with_extra
+class ChatCompletionOutputComplete(BaseInferenceType):
+    finish_reason: str
+    index: int
+    message: ChatCompletionOutputMessage
+    logprobs: Optional[ChatCompletionOutputLogprobs] = None
+
+
+@dataclass_with_extra
+class ChatCompletionOutputUsage(BaseInferenceType):
+    completion_tokens: int
+    prompt_tokens: int
+    total_tokens: int
+
+
+@dataclass_with_extra
+class ChatCompletionOutput(BaseInferenceType):
+    """Chat Completion Output.
+    Auto-generated from TGI specs.
+    For more details, check out
+    https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-tgi-import.ts.
+    """
+
+    choices: List[ChatCompletionOutputComplete]
+    created: int
+    id: str
+    model: str
+    system_fingerprint: str
+    usage: ChatCompletionOutputUsage
+
+
+@dataclass_with_extra
+class ChatCompletionStreamOutputFunction(BaseInferenceType):
+    arguments: str
+    name: Optional[str] = None
+
+
+@dataclass_with_extra
+class ChatCompletionStreamOutputDeltaToolCall(BaseInferenceType):
+    function: ChatCompletionStreamOutputFunction
+    id: str
+    index: int
+    type: str
+
+
+@dataclass_with_extra
+class ChatCompletionStreamOutputDelta(BaseInferenceType):
+    role: str
+    content: Optional[str] = None
+    tool_call_id: Optional[str] = None
+    tool_calls: Optional[List[ChatCompletionStreamOutputDeltaToolCall]] = None
+
+
+@dataclass_with_extra
+class ChatCompletionStreamOutputTopLogprob(BaseInferenceType):
+    logprob: float
+    token: str
+
+
+@dataclass_with_extra
+class ChatCompletionStreamOutputLogprob(BaseInferenceType):
+    logprob: float
+    token: str
+    top_logprobs: List[ChatCompletionStreamOutputTopLogprob]
+
+
+@dataclass_with_extra
+class ChatCompletionStreamOutputLogprobs(BaseInferenceType):
+    content: List[ChatCompletionStreamOutputLogprob]
+
+
+@dataclass_with_extra
+class ChatCompletionStreamOutputChoice(BaseInferenceType):
+    delta: ChatCompletionStreamOutputDelta
+    index: int
+    finish_reason: Optional[str] = None
+    logprobs: Optional[ChatCompletionStreamOutputLogprobs] = None
+
+
+@dataclass_with_extra
+class ChatCompletionStreamOutputUsage(BaseInferenceType):
+    completion_tokens: int
+    prompt_tokens: int
+    total_tokens: int
+
+
+@dataclass_with_extra
+class ChatCompletionStreamOutput(BaseInferenceType):
+    """Chat Completion Stream Output.
+    Auto-generated from TGI specs.
+    For more details, check out
+    https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-tgi-import.ts.
+    """
+
+    choices: List[ChatCompletionStreamOutputChoice]
+    created: int
+    id: str
+    model: str
+    system_fingerprint: str
+    usage: Optional[ChatCompletionStreamOutputUsage] = None

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

@@ -0,0 +1,28 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import Any, Dict, Optional
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+@dataclass_with_extra
+class DepthEstimationInput(BaseInferenceType):
+    """Inputs for Depth Estimation inference"""
+
+    inputs: Any
+    """The input image data"""
+    parameters: Optional[Dict[str, Any]] = None
+    """Additional inference parameters for Depth Estimation"""
+
+
+@dataclass_with_extra
+class DepthEstimationOutput(BaseInferenceType):
+    """Outputs of inference for the Depth Estimation task"""
+
+    depth: Any
+    """The predicted depth as an image"""
+    predicted_depth: Any
+    """The predicted depth as a tensor"""

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

@@ -0,0 +1,80 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import Any, List, Optional, Union
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+@dataclass_with_extra
+class DocumentQuestionAnsweringInputData(BaseInferenceType):
+    """One (document, question) pair to answer"""
+
+    image: Any
+    """The image on which the question is asked"""
+    question: str
+    """A question to ask of the document"""
+
+
+@dataclass_with_extra
+class DocumentQuestionAnsweringParameters(BaseInferenceType):
+    """Additional inference parameters for Document Question Answering"""
+
+    doc_stride: Optional[int] = None
+    """If the words in the document are too long to fit with the question for the model, it will
+    be split in several chunks with some overlap. This argument controls the size of that
+    overlap.
+    """
+    handle_impossible_answer: Optional[bool] = None
+    """Whether to accept impossible as an answer"""
+    lang: Optional[str] = None
+    """Language to use while running OCR. Defaults to english."""
+    max_answer_len: Optional[int] = None
+    """The maximum length of predicted answers (e.g., only answers with a shorter length are
+    considered).
+    """
+    max_question_len: Optional[int] = None
+    """The maximum length of the question after tokenization. It will be truncated if needed."""
+    max_seq_len: Optional[int] = None
+    """The maximum length of the total sentence (context + question) in tokens of each chunk
+    passed to the model. The context will be split in several chunks (using doc_stride as
+    overlap) if needed.
+    """
+    top_k: Optional[int] = None
+    """The number of answers to return (will be chosen by order of likelihood). Can return less
+    than top_k answers if there are not enough options available within the context.
+    """
+    word_boxes: Optional[List[Union[List[float], str]]] = None
+    """A list of words and bounding boxes (normalized 0->1000). If provided, the inference will
+    skip the OCR step and use the provided bounding boxes instead.
+    """
+
+
+@dataclass_with_extra
+class DocumentQuestionAnsweringInput(BaseInferenceType):
+    """Inputs for Document Question Answering inference"""
+
+    inputs: DocumentQuestionAnsweringInputData
+    """One (document, question) pair to answer"""
+    parameters: Optional[DocumentQuestionAnsweringParameters] = None
+    """Additional inference parameters for Document Question Answering"""
+
+
+@dataclass_with_extra
+class DocumentQuestionAnsweringOutputElement(BaseInferenceType):
+    """Outputs of inference for the Document Question Answering task"""
+
+    answer: str
+    """The answer to the question."""
+    end: int
+    """The end word index of the answer (in the OCR’d version of the input or provided word
+    boxes).
+    """
+    score: float
+    """The probability associated to the answer."""
+    start: int
+    """The start word index of the answer (in the OCR’d version of the input or provided word
+    boxes).
+    """

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

@@ -0,0 +1,36 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import List, Literal, Optional, Union
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+FeatureExtractionInputTruncationDirection = Literal["Left", "Right"]
+
+
+@dataclass_with_extra
+class FeatureExtractionInput(BaseInferenceType):
+    """Feature Extraction Input.
+    Auto-generated from TEI specs.
+    For more details, check out
+    https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-tei-import.ts.
+    """
+
+    inputs: Union[List[str], str]
+    """The text or list of texts to embed."""
+    normalize: Optional[bool] = None
+    prompt_name: Optional[str] = None
+    """The name of the prompt that should be used by for encoding. If not set, no prompt
+    will be applied.
+    Must be a key in the `sentence-transformers` configuration `prompts` dictionary.
+    For example if ``prompt_name`` is "query" and the ``prompts`` is {"query": "query: ",
+    ...},
+    then the sentence "What is the capital of France?" will be encoded as
+    "query: What is the capital of France?" because the prompt text will be prepended before
+    any text to encode.
+    """
+    truncate: Optional[bool] = None
+    truncation_direction: Optional["FeatureExtractionInputTruncationDirection"] = None

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

@@ -0,0 +1,47 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import Any, List, Optional
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+@dataclass_with_extra
+class FillMaskParameters(BaseInferenceType):
+    """Additional inference parameters for Fill Mask"""
+
+    targets: Optional[List[str]] = None
+    """When passed, the model will limit the scores to the passed targets instead of looking up
+    in the whole vocabulary. If the provided targets are not in the model vocab, they will be
+    tokenized and the first resulting token will be used (with a warning, and that might be
+    slower).
+    """
+    top_k: Optional[int] = None
+    """When passed, overrides the number of predictions to return."""
+
+
+@dataclass_with_extra
+class FillMaskInput(BaseInferenceType):
+    """Inputs for Fill Mask inference"""
+
+    inputs: str
+    """The text with masked tokens"""
+    parameters: Optional[FillMaskParameters] = None
+    """Additional inference parameters for Fill Mask"""
+
+
+@dataclass_with_extra
+class FillMaskOutputElement(BaseInferenceType):
+    """Outputs of inference for the Fill Mask task"""
+
+    score: float
+    """The corresponding probability"""
+    sequence: str
+    """The corresponding input with the mask token prediction."""
+    token: int
+    """The predicted token id (to replace the masked one)."""
+    token_str: Any
+    fill_mask_output_token_str: Optional[str] = None
+    """The predicted token (to replace the masked one)."""

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

@@ -0,0 +1,43 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import Literal, Optional
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+ImageClassificationOutputTransform = Literal["sigmoid", "softmax", "none"]
+
+
+@dataclass_with_extra
+class ImageClassificationParameters(BaseInferenceType):
+    """Additional inference parameters for Image Classification"""
+
+    function_to_apply: Optional["ImageClassificationOutputTransform"] = None
+    """The function to apply to the model outputs in order to retrieve the scores."""
+    top_k: Optional[int] = None
+    """When specified, limits the output to the top K most probable classes."""
+
+
+@dataclass_with_extra
+class ImageClassificationInput(BaseInferenceType):
+    """Inputs for Image Classification inference"""
+
+    inputs: str
+    """The input image data as a base64-encoded string. If no `parameters` are provided, you can
+    also provide the image data as a raw bytes payload.
+    """
+    parameters: Optional[ImageClassificationParameters] = None
+    """Additional inference parameters for Image Classification"""
+
+
+@dataclass_with_extra
+class ImageClassificationOutputElement(BaseInferenceType):
+    """Outputs of inference for the Image Classification task"""
+
+    label: str
+    """The predicted class label."""
+    score: float
+    """The corresponding probability."""

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

@@ -0,0 +1,51 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import Literal, Optional
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+ImageSegmentationSubtask = Literal["instance", "panoptic", "semantic"]
+
+
+@dataclass_with_extra
+class ImageSegmentationParameters(BaseInferenceType):
+    """Additional inference parameters for Image Segmentation"""
+
+    mask_threshold: Optional[float] = None
+    """Threshold to use when turning the predicted masks into binary values."""
+    overlap_mask_area_threshold: Optional[float] = None
+    """Mask overlap threshold to eliminate small, disconnected segments."""
+    subtask: Optional["ImageSegmentationSubtask"] = None
+    """Segmentation task to be performed, depending on model capabilities."""
+    threshold: Optional[float] = None
+    """Probability threshold to filter out predicted masks."""
+
+
+@dataclass_with_extra
+class ImageSegmentationInput(BaseInferenceType):
+    """Inputs for Image Segmentation inference"""
+
+    inputs: str
+    """The input image data as a base64-encoded string. If no `parameters` are provided, you can
+    also provide the image data as a raw bytes payload.
+    """
+    parameters: Optional[ImageSegmentationParameters] = None
+    """Additional inference parameters for Image Segmentation"""
+
+
+@dataclass_with_extra
+class ImageSegmentationOutputElement(BaseInferenceType):
+    """Outputs of inference for the Image Segmentation task
+    A predicted mask / segment
+    """
+
+    label: str
+    """The label of the predicted segment."""
+    mask: str
+    """The corresponding mask as a black-and-white image (base64-encoded)."""
+    score: Optional[float] = None
+    """The score or confidence degree the model has."""

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

@@ -0,0 +1,56 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import Any, Optional
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+@dataclass_with_extra
+class ImageToImageTargetSize(BaseInferenceType):
+    """The size in pixel of the output image."""
+
+    height: int
+    width: int
+
+
+@dataclass_with_extra
+class ImageToImageParameters(BaseInferenceType):
+    """Additional inference parameters for Image To Image"""
+
+    guidance_scale: Optional[float] = None
+    """For diffusion models. A higher guidance scale value encourages the model to generate
+    images closely linked to the text prompt at the expense of lower image quality.
+    """
+    negative_prompt: Optional[str] = None
+    """One prompt to guide what NOT to include in image generation."""
+    num_inference_steps: Optional[int] = None
+    """For diffusion models. The number of denoising steps. More denoising steps usually lead to
+    a higher quality image at the expense of slower inference.
+    """
+    prompt: Optional[str] = None
+    """The text prompt to guide the image generation."""
+    target_size: Optional[ImageToImageTargetSize] = None
+    """The size in pixel of the output image."""
+
+
+@dataclass_with_extra
+class ImageToImageInput(BaseInferenceType):
+    """Inputs for Image To Image inference"""
+
+    inputs: str
+    """The input image data as a base64-encoded string. If no `parameters` are provided, you can
+    also provide the image data as a raw bytes payload.
+    """
+    parameters: Optional[ImageToImageParameters] = None
+    """Additional inference parameters for Image To Image"""
+
+
+@dataclass_with_extra
+class ImageToImageOutput(BaseInferenceType):
+    """Outputs of inference for the Image To Image task"""
+
+    image: Any
+    """The output image returned as raw bytes in the payload."""

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

@@ -0,0 +1,101 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import Any, Literal, Optional, Union
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+ImageToTextEarlyStoppingEnum = Literal["never"]
+
+
+@dataclass_with_extra
+class ImageToTextGenerationParameters(BaseInferenceType):
+    """Parametrization of the text generation process"""
+
+    do_sample: Optional[bool] = None
+    """Whether to use sampling instead of greedy decoding when generating new tokens."""
+    early_stopping: Optional[Union[bool, "ImageToTextEarlyStoppingEnum"]] = None
+    """Controls the stopping condition for beam-based methods."""
+    epsilon_cutoff: Optional[float] = None
+    """If set to float strictly between 0 and 1, only tokens with a conditional probability
+    greater than epsilon_cutoff will be sampled. In the paper, suggested values range from
+    3e-4 to 9e-4, depending on the size of the model. See [Truncation Sampling as Language
+    Model Desmoothing](https://hf.co/papers/2210.15191) for more details.
+    """
+    eta_cutoff: Optional[float] = None
+    """Eta sampling is a hybrid of locally typical sampling and epsilon sampling. If set to
+    float strictly between 0 and 1, a token is only considered if it is greater than either
+    eta_cutoff or sqrt(eta_cutoff) * exp(-entropy(softmax(next_token_logits))). The latter
+    term is intuitively the expected next token probability, scaled by sqrt(eta_cutoff). In
+    the paper, suggested values range from 3e-4 to 2e-3, depending on the size of the model.
+    See [Truncation Sampling as Language Model Desmoothing](https://hf.co/papers/2210.15191)
+    for more details.
+    """
+    max_length: Optional[int] = None
+    """The maximum length (in tokens) of the generated text, including the input."""
+    max_new_tokens: Optional[int] = None
+    """The maximum number of tokens to generate. Takes precedence over max_length."""
+    min_length: Optional[int] = None
+    """The minimum length (in tokens) of the generated text, including the input."""
+    min_new_tokens: Optional[int] = None
+    """The minimum number of tokens to generate. Takes precedence over min_length."""
+    num_beam_groups: Optional[int] = None
+    """Number of groups to divide num_beams into in order to ensure diversity among different
+    groups of beams. See [this paper](https://hf.co/papers/1610.02424) for more details.
+    """
+    num_beams: Optional[int] = None
+    """Number of beams to use for beam search."""
+    penalty_alpha: Optional[float] = None
+    """The value balances the model confidence and the degeneration penalty in contrastive
+    search decoding.
+    """
+    temperature: Optional[float] = None
+    """The value used to modulate the next token probabilities."""
+    top_k: Optional[int] = None
+    """The number of highest probability vocabulary tokens to keep for top-k-filtering."""
+    top_p: Optional[float] = None
+    """If set to float < 1, only the smallest set of most probable tokens with probabilities
+    that add up to top_p or higher are kept for generation.
+    """
+    typical_p: Optional[float] = None
+    """Local typicality measures how similar the conditional probability of predicting a target
+    token next is to the expected conditional probability of predicting a random token next,
+    given the partial text already generated. If set to float < 1, the smallest set of the
+    most locally typical tokens with probabilities that add up to typical_p or higher are
+    kept for generation. See [this paper](https://hf.co/papers/2202.00666) for more details.
+    """
+    use_cache: Optional[bool] = None
+    """Whether the model should use the past last key/values attentions to speed up decoding"""
+
+
+@dataclass_with_extra
+class ImageToTextParameters(BaseInferenceType):
+    """Additional inference parameters for Image To Text"""
+
+    max_new_tokens: Optional[int] = None
+    """The amount of maximum tokens to generate."""
+    # Will be deprecated in the future when the renaming to `generation_parameters` is implemented in transformers
+    generate_kwargs: Optional[ImageToTextGenerationParameters] = None
+    """Parametrization of the text generation process"""
+
+
+@dataclass_with_extra
+class ImageToTextInput(BaseInferenceType):
+    """Inputs for Image To Text inference"""
+
+    inputs: Any
+    """The input image data"""
+    parameters: Optional[ImageToTextParameters] = None
+    """Additional inference parameters for Image To Text"""
+
+
+@dataclass_with_extra
+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 +1,58 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import Optional
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+@dataclass_with_extra
+class ObjectDetectionParameters(BaseInferenceType):
+    """Additional inference parameters for Object Detection"""
+
+    threshold: Optional[float] = None
+    """The probability necessary to make a prediction."""
+
+
+@dataclass_with_extra
+class ObjectDetectionInput(BaseInferenceType):
+    """Inputs for Object Detection inference"""
+
+    inputs: str
+    """The input image data as a base64-encoded string. If no `parameters` are provided, you can
+    also provide the image data as a raw bytes payload.
+    """
+    parameters: Optional[ObjectDetectionParameters] = None
+    """Additional inference parameters for Object Detection"""
+
+
+@dataclass_with_extra
+class ObjectDetectionBoundingBox(BaseInferenceType):
+    """The predicted bounding box. Coordinates are relative to the top left corner of the input
+    image.
+    """
+
+    xmax: int
+    """The x-coordinate of the bottom-right corner of the bounding box."""
+    xmin: int
+    """The x-coordinate of the top-left corner of the bounding box."""
+    ymax: int
+    """The y-coordinate of the bottom-right corner of the bounding box."""
+    ymin: int
+    """The y-coordinate of the top-left corner of the bounding box."""
+
+
+@dataclass_with_extra
+class ObjectDetectionOutputElement(BaseInferenceType):
+    """Outputs of inference for the Object Detection task"""
+
+    box: ObjectDetectionBoundingBox
+    """The predicted bounding box. Coordinates are relative to the top left corner of the input
+    image.
+    """
+    label: str
+    """The predicted label for the bounding box."""
+    score: float
+    """The associated score / probability."""

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

@@ -0,0 +1,74 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import Optional
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+@dataclass_with_extra
+class QuestionAnsweringInputData(BaseInferenceType):
+    """One (context, question) pair to answer"""
+
+    context: str
+    """The context to be used for answering the question"""
+    question: str
+    """The question to be answered"""
+
+
+@dataclass_with_extra
+class QuestionAnsweringParameters(BaseInferenceType):
+    """Additional inference parameters for Question Answering"""
+
+    align_to_words: Optional[bool] = None
+    """Attempts to align the answer to real words. Improves quality on space separated
+    languages. Might hurt on non-space-separated languages (like Japanese or Chinese)
+    """
+    doc_stride: Optional[int] = None
+    """If the context is too long to fit with the question for the model, it will be split in
+    several chunks with some overlap. This argument controls the size of that overlap.
+    """
+    handle_impossible_answer: Optional[bool] = None
+    """Whether to accept impossible as an answer."""
+    max_answer_len: Optional[int] = None
+    """The maximum length of predicted answers (e.g., only answers with a shorter length are
+    considered).
+    """
+    max_question_len: Optional[int] = None
+    """The maximum length of the question after tokenization. It will be truncated if needed."""
+    max_seq_len: Optional[int] = None
+    """The maximum length of the total sentence (context + question) in tokens of each chunk
+    passed to the model. The context will be split in several chunks (using docStride as
+    overlap) if needed.
+    """
+    top_k: Optional[int] = None
+    """The number of answers to return (will be chosen by order of likelihood). Note that we
+    return less than topk answers if there are not enough options available within the
+    context.
+    """
+
+
+@dataclass_with_extra
+class QuestionAnsweringInput(BaseInferenceType):
+    """Inputs for Question Answering inference"""
+
+    inputs: QuestionAnsweringInputData
+    """One (context, question) pair to answer"""
+    parameters: Optional[QuestionAnsweringParameters] = None
+    """Additional inference parameters for Question Answering"""
+
+
+@dataclass_with_extra
+class QuestionAnsweringOutputElement(BaseInferenceType):
+    """Outputs of inference for the Question Answering task"""
+
+    answer: str
+    """The answer to the question."""
+    end: int
+    """The character position in the input where the answer ends."""
+    score: float
+    """The probability associated to the answer."""
+    start: int
+    """The character position in the input where the answer begins."""

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

@@ -0,0 +1,27 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import Any, Dict, List, Optional
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+@dataclass_with_extra
+class SentenceSimilarityInputData(BaseInferenceType):
+    sentences: List[str]
+    """A list of strings which will be compared against the source_sentence."""
+    source_sentence: str
+    """The string that you wish to compare the other strings with. This can be a phrase,
+    sentence, or longer passage, depending on the model being used.
+    """
+
+
+@dataclass_with_extra
+class SentenceSimilarityInput(BaseInferenceType):
+    """Inputs for Sentence similarity inference"""
+
+    inputs: SentenceSimilarityInputData
+    parameters: Optional[Dict[str, Any]] = None
+    """Additional inference parameters for Sentence Similarity"""

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

@@ -0,0 +1,41 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import Any, Dict, Literal, Optional
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+SummarizationTruncationStrategy = Literal["do_not_truncate", "longest_first", "only_first", "only_second"]
+
+
+@dataclass_with_extra
+class SummarizationParameters(BaseInferenceType):
+    """Additional inference parameters for summarization."""
+
+    clean_up_tokenization_spaces: Optional[bool] = None
+    """Whether to clean up the potential extra spaces in the text output."""
+    generate_parameters: Optional[Dict[str, Any]] = None
+    """Additional parametrization of the text generation algorithm."""
+    truncation: Optional["SummarizationTruncationStrategy"] = None
+    """The truncation strategy to use."""
+
+
+@dataclass_with_extra
+class SummarizationInput(BaseInferenceType):
+    """Inputs for Summarization inference"""
+
+    inputs: str
+    """The input text to summarize."""
+    parameters: Optional[SummarizationParameters] = None
+    """Additional inference parameters for summarization."""
+
+
+@dataclass_with_extra
+class SummarizationOutput(BaseInferenceType):
+    """Outputs of inference for the Summarization task"""
+
+    summary_text: str
+    """The summarized text."""

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

@@ -0,0 +1,62 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import Dict, List, Literal, Optional
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+@dataclass_with_extra
+class TableQuestionAnsweringInputData(BaseInferenceType):
+    """One (table, question) pair to answer"""
+
+    question: str
+    """The question to be answered about the table"""
+    table: Dict[str, List[str]]
+    """The table to serve as context for the questions"""
+
+
+Padding = Literal["do_not_pad", "longest", "max_length"]
+
+
+@dataclass_with_extra
+class TableQuestionAnsweringParameters(BaseInferenceType):
+    """Additional inference parameters for Table Question Answering"""
+
+    padding: Optional["Padding"] = None
+    """Activates and controls padding."""
+    sequential: Optional[bool] = None
+    """Whether to do inference sequentially or as a batch. Batching is faster, but models like
+    SQA require the inference to be done sequentially to extract relations within sequences,
+    given their conversational nature.
+    """
+    truncation: Optional[bool] = None
+    """Activates and controls truncation."""
+
+
+@dataclass_with_extra
+class TableQuestionAnsweringInput(BaseInferenceType):
+    """Inputs for Table Question Answering inference"""
+
+    inputs: TableQuestionAnsweringInputData
+    """One (table, question) pair to answer"""
+    parameters: Optional[TableQuestionAnsweringParameters] = None
+    """Additional inference parameters for Table Question Answering"""
+
+
+@dataclass_with_extra
+class TableQuestionAnsweringOutputElement(BaseInferenceType):
+    """Outputs of inference for the Table Question Answering task"""
+
+    answer: str
+    """The answer of the question given the table. If there is an aggregator, the answer will be
+    preceded by `AGGREGATOR >`.
+    """
+    cells: List[str]
+    """List of strings made up of the answer cell values."""
+    coordinates: List[List[int]]
+    """Coordinates of the cells of the answers."""
+    aggregator: Optional[str] = None
+    """If the model has an aggregator, this returns the aggregator."""

.venv/lib/python3.10/site-packages/huggingface_hub/inference/_generated/types/text2text_generation.py

@@ -0,0 +1,42 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import Any, Dict, Literal, Optional
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+Text2TextGenerationTruncationStrategy = Literal["do_not_truncate", "longest_first", "only_first", "only_second"]
+
+
+@dataclass_with_extra
+class Text2TextGenerationParameters(BaseInferenceType):
+    """Additional inference parameters for Text2text Generation"""
+
+    clean_up_tokenization_spaces: Optional[bool] = None
+    """Whether to clean up the potential extra spaces in the text output."""
+    generate_parameters: Optional[Dict[str, Any]] = None
+    """Additional parametrization of the text generation algorithm"""
+    truncation: Optional["Text2TextGenerationTruncationStrategy"] = None
+    """The truncation strategy to use"""
+
+
+@dataclass_with_extra
+class Text2TextGenerationInput(BaseInferenceType):
+    """Inputs for Text2text Generation inference"""
+
+    inputs: str
+    """The input text data"""
+    parameters: Optional[Text2TextGenerationParameters] = None
+    """Additional inference parameters for Text2text Generation"""
+
+
+@dataclass_with_extra
+class Text2TextGenerationOutput(BaseInferenceType):
+    """Outputs of inference for the Text2text Generation task"""
+
+    generated_text: Any
+    text2_text_generation_output_generated_text: Optional[str] = None
+    """The generated text."""

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

@@ -0,0 +1,41 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import Literal, Optional
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+TextClassificationOutputTransform = Literal["sigmoid", "softmax", "none"]
+
+
+@dataclass_with_extra
+class TextClassificationParameters(BaseInferenceType):
+    """Additional inference parameters for Text Classification"""
+
+    function_to_apply: Optional["TextClassificationOutputTransform"] = None
+    """The function to apply to the model outputs in order to retrieve the scores."""
+    top_k: Optional[int] = None
+    """When specified, limits the output to the top K most probable classes."""
+
+
+@dataclass_with_extra
+class TextClassificationInput(BaseInferenceType):
+    """Inputs for Text Classification inference"""
+
+    inputs: str
+    """The text to classify"""
+    parameters: Optional[TextClassificationParameters] = None
+    """Additional inference parameters for Text Classification"""
+
+
+@dataclass_with_extra
+class TextClassificationOutputElement(BaseInferenceType):
+    """Outputs of inference for the Text Classification task"""
+
+    label: str
+    """The predicted class label."""
+    score: float
+    """The corresponding probability."""

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

@@ -0,0 +1,168 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import Any, List, Literal, Optional
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+TypeEnum = Literal["json", "regex"]
+
+
+@dataclass_with_extra
+class TextGenerationInputGrammarType(BaseInferenceType):
+    type: "TypeEnum"
+    value: Any
+    """A string that represents a [JSON Schema](https://json-schema.org/).
+    JSON Schema is a declarative language that allows to annotate JSON documents
+    with types and descriptions.
+    """
+
+
+@dataclass_with_extra
+class TextGenerationInputGenerateParameters(BaseInferenceType):
+    adapter_id: Optional[str] = None
+    """Lora adapter id"""
+    best_of: Optional[int] = None
+    """Generate best_of sequences and return the one if the highest token logprobs."""
+    decoder_input_details: Optional[bool] = None
+    """Whether to return decoder input token logprobs and ids."""
+    details: Optional[bool] = None
+    """Whether to return generation details."""
+    do_sample: Optional[bool] = None
+    """Activate logits sampling."""
+    frequency_penalty: Optional[float] = None
+    """The parameter for frequency penalty. 1.0 means no penalty
+    Penalize new tokens based on their existing frequency in the text so far,
+    decreasing the model's likelihood to repeat the same line verbatim.
+    """
+    grammar: Optional[TextGenerationInputGrammarType] = None
+    max_new_tokens: Optional[int] = None
+    """Maximum number of tokens to generate."""
+    repetition_penalty: Optional[float] = None
+    """The parameter for repetition penalty. 1.0 means no penalty.
+    See [this paper](https://arxiv.org/pdf/1909.05858.pdf) for more details.
+    """
+    return_full_text: Optional[bool] = None
+    """Whether to prepend the prompt to the generated text"""
+    seed: Optional[int] = None
+    """Random sampling seed."""
+    stop: Optional[List[str]] = None
+    """Stop generating tokens if a member of `stop` is generated."""
+    temperature: Optional[float] = None
+    """The value used to module the logits distribution."""
+    top_k: Optional[int] = None
+    """The number of highest probability vocabulary tokens to keep for top-k-filtering."""
+    top_n_tokens: Optional[int] = None
+    """The number of highest probability vocabulary tokens to keep for top-n-filtering."""
+    top_p: Optional[float] = None
+    """Top-p value for nucleus sampling."""
+    truncate: Optional[int] = None
+    """Truncate inputs tokens to the given size."""
+    typical_p: Optional[float] = None
+    """Typical Decoding mass
+    See [Typical Decoding for Natural Language Generation](https://arxiv.org/abs/2202.00666)
+    for more information.
+    """
+    watermark: Optional[bool] = None
+    """Watermarking with [A Watermark for Large Language
+    Models](https://arxiv.org/abs/2301.10226).
+    """
+
+
+@dataclass_with_extra
+class TextGenerationInput(BaseInferenceType):
+    """Text Generation Input.
+    Auto-generated from TGI specs.
+    For more details, check out
+    https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-tgi-import.ts.
+    """
+
+    inputs: str
+    parameters: Optional[TextGenerationInputGenerateParameters] = None
+    stream: Optional[bool] = None
+
+
+TextGenerationOutputFinishReason = Literal["length", "eos_token", "stop_sequence"]
+
+
+@dataclass_with_extra
+class TextGenerationOutputPrefillToken(BaseInferenceType):
+    id: int
+    logprob: float
+    text: str
+
+
+@dataclass_with_extra
+class TextGenerationOutputToken(BaseInferenceType):
+    id: int
+    logprob: float
+    special: bool
+    text: str
+
+
+@dataclass_with_extra
+class TextGenerationOutputBestOfSequence(BaseInferenceType):
+    finish_reason: "TextGenerationOutputFinishReason"
+    generated_text: str
+    generated_tokens: int
+    prefill: List[TextGenerationOutputPrefillToken]
+    tokens: List[TextGenerationOutputToken]
+    seed: Optional[int] = None
+    top_tokens: Optional[List[List[TextGenerationOutputToken]]] = None
+
+
+@dataclass_with_extra
+class TextGenerationOutputDetails(BaseInferenceType):
+    finish_reason: "TextGenerationOutputFinishReason"
+    generated_tokens: int
+    prefill: List[TextGenerationOutputPrefillToken]
+    tokens: List[TextGenerationOutputToken]
+    best_of_sequences: Optional[List[TextGenerationOutputBestOfSequence]] = None
+    seed: Optional[int] = None
+    top_tokens: Optional[List[List[TextGenerationOutputToken]]] = None
+
+
+@dataclass_with_extra
+class TextGenerationOutput(BaseInferenceType):
+    """Text Generation Output.
+    Auto-generated from TGI specs.
+    For more details, check out
+    https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-tgi-import.ts.
+    """
+
+    generated_text: str
+    details: Optional[TextGenerationOutputDetails] = None
+
+
+@dataclass_with_extra
+class TextGenerationStreamOutputStreamDetails(BaseInferenceType):
+    finish_reason: "TextGenerationOutputFinishReason"
+    generated_tokens: int
+    input_length: int
+    seed: Optional[int] = None
+
+
+@dataclass_with_extra
+class TextGenerationStreamOutputToken(BaseInferenceType):
+    id: int
+    logprob: float
+    special: bool
+    text: str
+
+
+@dataclass_with_extra
+class TextGenerationStreamOutput(BaseInferenceType):
+    """Text Generation Stream Output.
+    Auto-generated from TGI specs.
+    For more details, check out
+    https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-tgi-import.ts.
+    """
+
+    index: int
+    token: TextGenerationStreamOutputToken
+    details: Optional[TextGenerationStreamOutputStreamDetails] = None
+    generated_text: Optional[str] = None
+    top_tokens: Optional[List[TextGenerationStreamOutputToken]] = None

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

@@ -0,0 +1,100 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import Any, Literal, Optional, Union
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+TextToAudioEarlyStoppingEnum = Literal["never"]
+
+
+@dataclass_with_extra
+class TextToAudioGenerationParameters(BaseInferenceType):
+    """Parametrization of the text generation process"""
+
+    do_sample: Optional[bool] = None
+    """Whether to use sampling instead of greedy decoding when generating new tokens."""
+    early_stopping: Optional[Union[bool, "TextToAudioEarlyStoppingEnum"]] = None
+    """Controls the stopping condition for beam-based methods."""
+    epsilon_cutoff: Optional[float] = None
+    """If set to float strictly between 0 and 1, only tokens with a conditional probability
+    greater than epsilon_cutoff will be sampled. In the paper, suggested values range from
+    3e-4 to 9e-4, depending on the size of the model. See [Truncation Sampling as Language
+    Model Desmoothing](https://hf.co/papers/2210.15191) for more details.
+    """
+    eta_cutoff: Optional[float] = None
+    """Eta sampling is a hybrid of locally typical sampling and epsilon sampling. If set to
+    float strictly between 0 and 1, a token is only considered if it is greater than either
+    eta_cutoff or sqrt(eta_cutoff) * exp(-entropy(softmax(next_token_logits))). The latter
+    term is intuitively the expected next token probability, scaled by sqrt(eta_cutoff). In
+    the paper, suggested values range from 3e-4 to 2e-3, depending on the size of the model.
+    See [Truncation Sampling as Language Model Desmoothing](https://hf.co/papers/2210.15191)
+    for more details.
+    """
+    max_length: Optional[int] = None
+    """The maximum length (in tokens) of the generated text, including the input."""
+    max_new_tokens: Optional[int] = None
+    """The maximum number of tokens to generate. Takes precedence over max_length."""
+    min_length: Optional[int] = None
+    """The minimum length (in tokens) of the generated text, including the input."""
+    min_new_tokens: Optional[int] = None
+    """The minimum number of tokens to generate. Takes precedence over min_length."""
+    num_beam_groups: Optional[int] = None
+    """Number of groups to divide num_beams into in order to ensure diversity among different
+    groups of beams. See [this paper](https://hf.co/papers/1610.02424) for more details.
+    """
+    num_beams: Optional[int] = None
+    """Number of beams to use for beam search."""
+    penalty_alpha: Optional[float] = None
+    """The value balances the model confidence and the degeneration penalty in contrastive
+    search decoding.
+    """
+    temperature: Optional[float] = None
+    """The value used to modulate the next token probabilities."""
+    top_k: Optional[int] = None
+    """The number of highest probability vocabulary tokens to keep for top-k-filtering."""
+    top_p: Optional[float] = None
+    """If set to float < 1, only the smallest set of most probable tokens with probabilities
+    that add up to top_p or higher are kept for generation.
+    """
+    typical_p: Optional[float] = None
+    """Local typicality measures how similar the conditional probability of predicting a target
+    token next is to the expected conditional probability of predicting a random token next,
+    given the partial text already generated. If set to float < 1, the smallest set of the
+    most locally typical tokens with probabilities that add up to typical_p or higher are
+    kept for generation. See [this paper](https://hf.co/papers/2202.00666) for more details.
+    """
+    use_cache: Optional[bool] = None
+    """Whether the model should use the past last key/values attentions to speed up decoding"""
+
+
+@dataclass_with_extra
+class TextToAudioParameters(BaseInferenceType):
+    """Additional inference parameters for Text To Audio"""
+
+    # Will be deprecated in the future when the renaming to `generation_parameters` is implemented in transformers
+    generate_kwargs: Optional[TextToAudioGenerationParameters] = None
+    """Parametrization of the text generation process"""
+
+
+@dataclass_with_extra
+class TextToAudioInput(BaseInferenceType):
+    """Inputs for Text To Audio inference"""
+
+    inputs: str
+    """The input text data"""
+    parameters: Optional[TextToAudioParameters] = None
+    """Additional inference parameters for Text To Audio"""
+
+
+@dataclass_with_extra
+class TextToAudioOutput(BaseInferenceType):
+    """Outputs of inference for the Text To Audio task"""
+
+    audio: Any
+    """The generated audio waveform."""
+    sampling_rate: float
+    """The sampling rate of the generated audio waveform."""

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

@@ -0,0 +1,50 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import Any, Optional
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+@dataclass_with_extra
+class TextToImageParameters(BaseInferenceType):
+    """Additional inference parameters for Text To Image"""
+
+    guidance_scale: Optional[float] = None
+    """A higher guidance scale value encourages the model to generate images closely linked to
+    the text prompt, but values too high may cause saturation and other artifacts.
+    """
+    height: Optional[int] = None
+    """The height in pixels of the output image"""
+    negative_prompt: Optional[str] = None
+    """One prompt to guide what NOT to include in image generation."""
+    num_inference_steps: Optional[int] = None
+    """The number of denoising steps. More denoising steps usually lead to a higher quality
+    image at the expense of slower inference.
+    """
+    scheduler: Optional[str] = None
+    """Override the scheduler with a compatible one."""
+    seed: Optional[int] = None
+    """Seed for the random number generator."""
+    width: Optional[int] = None
+    """The width in pixels of the output image"""
+
+
+@dataclass_with_extra
+class TextToImageInput(BaseInferenceType):
+    """Inputs for Text To Image inference"""
+
+    inputs: str
+    """The input text data (sometimes called "prompt")"""
+    parameters: Optional[TextToImageParameters] = None
+    """Additional inference parameters for Text To Image"""
+
+
+@dataclass_with_extra
+class TextToImageOutput(BaseInferenceType):
+    """Outputs of inference for the Text To Image task"""
+
+    image: Any
+    """The generated image returned as raw bytes in the payload."""

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

@@ -0,0 +1,100 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import Any, Literal, Optional, Union
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+TextToSpeechEarlyStoppingEnum = Literal["never"]
+
+
+@dataclass_with_extra
+class TextToSpeechGenerationParameters(BaseInferenceType):
+    """Parametrization of the text generation process"""
+
+    do_sample: Optional[bool] = None
+    """Whether to use sampling instead of greedy decoding when generating new tokens."""
+    early_stopping: Optional[Union[bool, "TextToSpeechEarlyStoppingEnum"]] = None
+    """Controls the stopping condition for beam-based methods."""
+    epsilon_cutoff: Optional[float] = None
+    """If set to float strictly between 0 and 1, only tokens with a conditional probability
+    greater than epsilon_cutoff will be sampled. In the paper, suggested values range from
+    3e-4 to 9e-4, depending on the size of the model. See [Truncation Sampling as Language
+    Model Desmoothing](https://hf.co/papers/2210.15191) for more details.
+    """
+    eta_cutoff: Optional[float] = None
+    """Eta sampling is a hybrid of locally typical sampling and epsilon sampling. If set to
+    float strictly between 0 and 1, a token is only considered if it is greater than either
+    eta_cutoff or sqrt(eta_cutoff) * exp(-entropy(softmax(next_token_logits))). The latter
+    term is intuitively the expected next token probability, scaled by sqrt(eta_cutoff). In
+    the paper, suggested values range from 3e-4 to 2e-3, depending on the size of the model.
+    See [Truncation Sampling as Language Model Desmoothing](https://hf.co/papers/2210.15191)
+    for more details.
+    """
+    max_length: Optional[int] = None
+    """The maximum length (in tokens) of the generated text, including the input."""
+    max_new_tokens: Optional[int] = None
+    """The maximum number of tokens to generate. Takes precedence over max_length."""
+    min_length: Optional[int] = None
+    """The minimum length (in tokens) of the generated text, including the input."""
+    min_new_tokens: Optional[int] = None
+    """The minimum number of tokens to generate. Takes precedence over min_length."""
+    num_beam_groups: Optional[int] = None
+    """Number of groups to divide num_beams into in order to ensure diversity among different
+    groups of beams. See [this paper](https://hf.co/papers/1610.02424) for more details.
+    """
+    num_beams: Optional[int] = None
+    """Number of beams to use for beam search."""
+    penalty_alpha: Optional[float] = None
+    """The value balances the model confidence and the degeneration penalty in contrastive
+    search decoding.
+    """
+    temperature: Optional[float] = None
+    """The value used to modulate the next token probabilities."""
+    top_k: Optional[int] = None
+    """The number of highest probability vocabulary tokens to keep for top-k-filtering."""
+    top_p: Optional[float] = None
+    """If set to float < 1, only the smallest set of most probable tokens with probabilities
+    that add up to top_p or higher are kept for generation.
+    """
+    typical_p: Optional[float] = None
+    """Local typicality measures how similar the conditional probability of predicting a target
+    token next is to the expected conditional probability of predicting a random token next,
+    given the partial text already generated. If set to float < 1, the smallest set of the
+    most locally typical tokens with probabilities that add up to typical_p or higher are
+    kept for generation. See [this paper](https://hf.co/papers/2202.00666) for more details.
+    """
+    use_cache: Optional[bool] = None
+    """Whether the model should use the past last key/values attentions to speed up decoding"""
+
+
+@dataclass_with_extra
+class TextToSpeechParameters(BaseInferenceType):
+    """Additional inference parameters for Text To Speech"""
+
+    # Will be deprecated in the future when the renaming to `generation_parameters` is implemented in transformers
+    generate_kwargs: Optional[TextToSpeechGenerationParameters] = None
+    """Parametrization of the text generation process"""
+
+
+@dataclass_with_extra
+class TextToSpeechInput(BaseInferenceType):
+    """Inputs for Text To Speech inference"""
+
+    inputs: str
+    """The input text data"""
+    parameters: Optional[TextToSpeechParameters] = None
+    """Additional inference parameters for Text To Speech"""
+
+
+@dataclass_with_extra
+class TextToSpeechOutput(BaseInferenceType):
+    """Outputs of inference for the Text To Speech task"""
+
+    audio: Any
+    """The generated audio"""
+    sampling_rate: Optional[float] = None
+    """The sampling rate of the generated audio waveform."""

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

@@ -0,0 +1,46 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import Any, List, Optional
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+@dataclass_with_extra
+class TextToVideoParameters(BaseInferenceType):
+    """Additional inference parameters for Text To Video"""
+
+    guidance_scale: Optional[float] = None
+    """A higher guidance scale value encourages the model to generate videos closely linked to
+    the text prompt, but values too high may cause saturation and other artifacts.
+    """
+    negative_prompt: Optional[List[str]] = None
+    """One or several prompt to guide what NOT to include in video generation."""
+    num_frames: Optional[float] = None
+    """The num_frames parameter determines how many video frames are generated."""
+    num_inference_steps: Optional[int] = None
+    """The number of denoising steps. More denoising steps usually lead to a higher quality
+    video at the expense of slower inference.
+    """
+    seed: Optional[int] = None
+    """Seed for the random number generator."""
+
+
+@dataclass_with_extra
+class TextToVideoInput(BaseInferenceType):
+    """Inputs for Text To Video inference"""
+
+    inputs: str
+    """The input text data (sometimes called "prompt")"""
+    parameters: Optional[TextToVideoParameters] = None
+    """Additional inference parameters for Text To Video"""
+
+
+@dataclass_with_extra
+class TextToVideoOutput(BaseInferenceType):
+    """Outputs of inference for the Text To Video task"""
+
+    video: Any
+    """The generated video returned as raw bytes in the payload."""

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

@@ -0,0 +1,51 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import List, Literal, Optional
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+TokenClassificationAggregationStrategy = Literal["none", "simple", "first", "average", "max"]
+
+
+@dataclass_with_extra
+class TokenClassificationParameters(BaseInferenceType):
+    """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_with_extra
+class TokenClassificationInput(BaseInferenceType):
+    """Inputs for Token Classification inference"""
+
+    inputs: str
+    """The input text data"""
+    parameters: Optional[TokenClassificationParameters] = None
+    """Additional inference parameters for Token Classification"""
+
+
+@dataclass_with_extra
+class TokenClassificationOutputElement(BaseInferenceType):
+    """Outputs of inference for the Token Classification task"""
+
+    end: int
+    """The character position in the input where this group ends."""
+    score: float
+    """The associated score / probability"""
+    start: int
+    """The character position in the input where this group begins."""
+    word: str
+    """The corresponding text"""
+    entity: Optional[str] = None
+    """The predicted label for a single token"""
+    entity_group: Optional[str] = None
+    """The predicted label for a group of one or more tokens"""

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

@@ -0,0 +1,49 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import Any, Dict, Literal, Optional
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+TranslationTruncationStrategy = Literal["do_not_truncate", "longest_first", "only_first", "only_second"]
+
+
+@dataclass_with_extra
+class TranslationParameters(BaseInferenceType):
+    """Additional inference parameters for Translation"""
+
+    clean_up_tokenization_spaces: Optional[bool] = None
+    """Whether to clean up the potential extra spaces in the text output."""
+    generate_parameters: Optional[Dict[str, Any]] = None
+    """Additional parametrization of the text generation algorithm."""
+    src_lang: Optional[str] = None
+    """The source language of the text. Required for models that can translate from multiple
+    languages.
+    """
+    tgt_lang: Optional[str] = None
+    """Target language to translate to. Required for models that can translate to multiple
+    languages.
+    """
+    truncation: Optional["TranslationTruncationStrategy"] = None
+    """The truncation strategy to use."""
+
+
+@dataclass_with_extra
+class TranslationInput(BaseInferenceType):
+    """Inputs for Translation inference"""
+
+    inputs: str
+    """The text to translate."""
+    parameters: Optional[TranslationParameters] = None
+    """Additional inference parameters for Translation"""
+
+
+@dataclass_with_extra
+class TranslationOutput(BaseInferenceType):
+    """Outputs of inference for the Translation task"""
+
+    translation_text: str
+    """The translated text."""

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

@@ -0,0 +1,45 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import Any, Literal, Optional
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+VideoClassificationOutputTransform = Literal["sigmoid", "softmax", "none"]
+
+
+@dataclass_with_extra
+class VideoClassificationParameters(BaseInferenceType):
+    """Additional inference parameters for Video Classification"""
+
+    frame_sampling_rate: Optional[int] = None
+    """The sampling rate used to select frames from the video."""
+    function_to_apply: Optional["VideoClassificationOutputTransform"] = None
+    """The function to apply to the model outputs in order to retrieve the scores."""
+    num_frames: Optional[int] = None
+    """The number of sampled frames to consider for classification."""
+    top_k: Optional[int] = None
+    """When specified, limits the output to the top K most probable classes."""
+
+
+@dataclass_with_extra
+class VideoClassificationInput(BaseInferenceType):
+    """Inputs for Video Classification inference"""
+
+    inputs: Any
+    """The input video data"""
+    parameters: Optional[VideoClassificationParameters] = None
+    """Additional inference parameters for Video Classification"""
+
+
+@dataclass_with_extra
+class VideoClassificationOutputElement(BaseInferenceType):
+    """Outputs of inference for the Video Classification task"""
+
+    label: str
+    """The predicted class label."""
+    score: float
+    """The corresponding probability."""

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

@@ -0,0 +1,49 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import Any, Optional
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+@dataclass_with_extra
+class VisualQuestionAnsweringInputData(BaseInferenceType):
+    """One (image, question) pair to answer"""
+
+    image: Any
+    """The image."""
+    question: str
+    """The question to answer based on the image."""
+
+
+@dataclass_with_extra
+class VisualQuestionAnsweringParameters(BaseInferenceType):
+    """Additional inference parameters for Visual Question Answering"""
+
+    top_k: Optional[int] = None
+    """The number of answers to return (will be chosen by order of likelihood). Note that we
+    return less than topk answers if there are not enough options available within the
+    context.
+    """
+
+
+@dataclass_with_extra
+class VisualQuestionAnsweringInput(BaseInferenceType):
+    """Inputs for Visual Question Answering inference"""
+
+    inputs: VisualQuestionAnsweringInputData
+    """One (image, question) pair to answer"""
+    parameters: Optional[VisualQuestionAnsweringParameters] = None
+    """Additional inference parameters for Visual Question Answering"""
+
+
+@dataclass_with_extra
+class VisualQuestionAnsweringOutputElement(BaseInferenceType):
+    """Outputs of inference for the Visual Question Answering task"""
+
+    score: float
+    """The associated score / probability"""
+    answer: Optional[str] = None
+    """The answer to the question"""

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

@@ -0,0 +1,45 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import List, Optional
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+@dataclass_with_extra
+class ZeroShotClassificationParameters(BaseInferenceType):
+    """Additional inference parameters for Zero Shot Classification"""
+
+    candidate_labels: List[str]
+    """The set of possible class labels to classify the text into."""
+    hypothesis_template: Optional[str] = None
+    """The sentence used in conjunction with `candidate_labels` to attempt the text
+    classification by replacing the placeholder with the candidate labels.
+    """
+    multi_label: Optional[bool] = None
+    """Whether multiple candidate labels can be true. If false, the scores are normalized such
+    that the sum of the label likelihoods for each sequence is 1. If true, the labels are
+    considered independent and probabilities are normalized for each candidate.
+    """
+
+
+@dataclass_with_extra
+class ZeroShotClassificationInput(BaseInferenceType):
+    """Inputs for Zero Shot Classification inference"""
+
+    inputs: str
+    """The text to classify"""
+    parameters: ZeroShotClassificationParameters
+    """Additional inference parameters for Zero Shot Classification"""
+
+
+@dataclass_with_extra
+class ZeroShotClassificationOutputElement(BaseInferenceType):
+    """Outputs of inference for the Zero Shot Classification task"""
+
+    label: str
+    """The predicted class label."""
+    score: float
+    """The corresponding probability."""

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

@@ -0,0 +1,40 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import List, Optional
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+@dataclass_with_extra
+class ZeroShotImageClassificationParameters(BaseInferenceType):
+    """Additional inference parameters for Zero Shot Image Classification"""
+
+    candidate_labels: List[str]
+    """The candidate labels for this image"""
+    hypothesis_template: Optional[str] = None
+    """The sentence used in conjunction with `candidate_labels` to attempt the image
+    classification by replacing the placeholder with the candidate labels.
+    """
+
+
+@dataclass_with_extra
+class ZeroShotImageClassificationInput(BaseInferenceType):
+    """Inputs for Zero Shot Image Classification inference"""
+
+    inputs: str
+    """The input image data to classify as a base64-encoded string."""
+    parameters: ZeroShotImageClassificationParameters
+    """Additional inference parameters for Zero Shot Image Classification"""
+
+
+@dataclass_with_extra
+class ZeroShotImageClassificationOutputElement(BaseInferenceType):
+    """Outputs of inference for the Zero Shot Image Classification task"""
+
+    label: str
+    """The predicted class label."""
+    score: float
+    """The corresponding probability."""

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

@@ -0,0 +1,52 @@
+# Inference code generated from the JSON schema spec in @huggingface/tasks.
+#
+# See:
+#   - script: https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/scripts/inference-codegen.ts
+#   - specs:  https://github.com/huggingface/huggingface.js/tree/main/packages/tasks/src/tasks.
+from typing import List
+
+from .base import BaseInferenceType, dataclass_with_extra
+
+
+@dataclass_with_extra
+class ZeroShotObjectDetectionParameters(BaseInferenceType):
+    """Additional inference parameters for Zero Shot Object Detection"""
+
+    candidate_labels: List[str]
+    """The candidate labels for this image"""
+
+
+@dataclass_with_extra
+class ZeroShotObjectDetectionInput(BaseInferenceType):
+    """Inputs for Zero Shot Object Detection inference"""
+
+    inputs: str
+    """The input image data as a base64-encoded string."""
+    parameters: ZeroShotObjectDetectionParameters
+    """Additional inference parameters for Zero Shot Object Detection"""
+
+
+@dataclass_with_extra
+class ZeroShotObjectDetectionBoundingBox(BaseInferenceType):
+    """The predicted bounding box. Coordinates are relative to the top left corner of the input
+    image.
+    """
+
+    xmax: int
+    xmin: int
+    ymax: int
+    ymin: int
+
+
+@dataclass_with_extra
+class ZeroShotObjectDetectionOutputElement(BaseInferenceType):
+    """Outputs of inference for the Zero Shot Object Detection task"""
+
+    box: ZeroShotObjectDetectionBoundingBox
+    """The predicted bounding box. Coordinates are relative to the top left corner of the input
+    image.
+    """
+    label: str
+    """A candidate label"""
+    score: float
+    """The associated score / probability"""

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

@@ -0,0 +1,141 @@
+from typing import Dict, Literal
+
+from ._common import TaskProviderHelper
+from .black_forest_labs import BlackForestLabsTextToImageTask
+from .cerebras import CerebrasConversationalTask
+from .cohere import CohereConversationalTask
+from .fal_ai import (
+    FalAIAutomaticSpeechRecognitionTask,
+    FalAITextToImageTask,
+    FalAITextToSpeechTask,
+    FalAITextToVideoTask,
+)
+from .fireworks_ai import FireworksAIConversationalTask
+from .hf_inference import HFInferenceBinaryInputTask, HFInferenceConversational, HFInferenceTask
+from .hyperbolic import HyperbolicTextGenerationTask, HyperbolicTextToImageTask
+from .nebius import NebiusConversationalTask, NebiusTextGenerationTask, NebiusTextToImageTask
+from .novita import NovitaConversationalTask, NovitaTextGenerationTask, NovitaTextToVideoTask
+from .openai import OpenAIConversationalTask
+from .replicate import ReplicateTask, ReplicateTextToSpeechTask
+from .sambanova import SambanovaConversationalTask
+from .together import TogetherConversationalTask, TogetherTextGenerationTask, TogetherTextToImageTask
+
+
+PROVIDER_T = Literal[
+    "black-forest-labs",
+    "cerebras",
+    "cohere",
+    "fal-ai",
+    "fireworks-ai",
+    "hf-inference",
+    "hyperbolic",
+    "nebius",
+    "novita",
+    "openai",
+    "replicate",
+    "sambanova",
+    "together",
+]
+
+PROVIDERS: Dict[PROVIDER_T, Dict[str, TaskProviderHelper]] = {
+    "black-forest-labs": {
+        "text-to-image": BlackForestLabsTextToImageTask(),
+    },
+    "cerebras": {
+        "conversational": CerebrasConversationalTask(),
+    },
+    "cohere": {
+        "conversational": CohereConversationalTask(),
+    },
+    "fal-ai": {
+        "automatic-speech-recognition": FalAIAutomaticSpeechRecognitionTask(),
+        "text-to-image": FalAITextToImageTask(),
+        "text-to-speech": FalAITextToSpeechTask(),
+        "text-to-video": FalAITextToVideoTask(),
+    },
+    "fireworks-ai": {
+        "conversational": FireworksAIConversationalTask(),
+    },
+    "hf-inference": {
+        "text-to-image": HFInferenceTask("text-to-image"),
+        "conversational": HFInferenceConversational(),
+        "text-generation": HFInferenceTask("text-generation"),
+        "text-classification": HFInferenceTask("text-classification"),
+        "question-answering": HFInferenceTask("question-answering"),
+        "audio-classification": HFInferenceBinaryInputTask("audio-classification"),
+        "automatic-speech-recognition": HFInferenceBinaryInputTask("automatic-speech-recognition"),
+        "fill-mask": HFInferenceTask("fill-mask"),
+        "feature-extraction": HFInferenceTask("feature-extraction"),
+        "image-classification": HFInferenceBinaryInputTask("image-classification"),
+        "image-segmentation": HFInferenceBinaryInputTask("image-segmentation"),
+        "document-question-answering": HFInferenceTask("document-question-answering"),
+        "image-to-text": HFInferenceBinaryInputTask("image-to-text"),
+        "object-detection": HFInferenceBinaryInputTask("object-detection"),
+        "audio-to-audio": HFInferenceBinaryInputTask("audio-to-audio"),
+        "zero-shot-image-classification": HFInferenceBinaryInputTask("zero-shot-image-classification"),
+        "zero-shot-classification": HFInferenceTask("zero-shot-classification"),
+        "image-to-image": HFInferenceBinaryInputTask("image-to-image"),
+        "sentence-similarity": HFInferenceTask("sentence-similarity"),
+        "table-question-answering": HFInferenceTask("table-question-answering"),
+        "tabular-classification": HFInferenceTask("tabular-classification"),
+        "text-to-speech": HFInferenceTask("text-to-speech"),
+        "token-classification": HFInferenceTask("token-classification"),
+        "translation": HFInferenceTask("translation"),
+        "summarization": HFInferenceTask("summarization"),
+        "visual-question-answering": HFInferenceBinaryInputTask("visual-question-answering"),
+    },
+    "hyperbolic": {
+        "text-to-image": HyperbolicTextToImageTask(),
+        "conversational": HyperbolicTextGenerationTask("conversational"),
+        "text-generation": HyperbolicTextGenerationTask("text-generation"),
+    },
+    "nebius": {
+        "text-to-image": NebiusTextToImageTask(),
+        "conversational": NebiusConversationalTask(),
+        "text-generation": NebiusTextGenerationTask(),
+    },
+    "novita": {
+        "text-generation": NovitaTextGenerationTask(),
+        "conversational": NovitaConversationalTask(),
+        "text-to-video": NovitaTextToVideoTask(),
+    },
+    "openai": {
+        "conversational": OpenAIConversationalTask(),
+    },
+    "replicate": {
+        "text-to-image": ReplicateTask("text-to-image"),
+        "text-to-speech": ReplicateTextToSpeechTask(),
+        "text-to-video": ReplicateTask("text-to-video"),
+    },
+    "sambanova": {
+        "conversational": SambanovaConversationalTask(),
+    },
+    "together": {
+        "text-to-image": TogetherTextToImageTask(),
+        "conversational": TogetherConversationalTask(),
+        "text-generation": TogetherTextGenerationTask(),
+    },
+}
+
+
+def get_provider_helper(provider: PROVIDER_T, task: str) -> TaskProviderHelper:
+    """Get provider helper instance by name and task.
+
+    Args:
+        provider (str): Name of the provider
+        task (str): Name of the task
+
+    Returns:
+        TaskProviderHelper: Helper instance for the specified provider and task
+
+    Raises:
+        ValueError: If provider or task is not supported
+    """
+    if provider not in PROVIDERS:
+        raise ValueError(f"Provider '{provider}' not supported. Available providers: {list(PROVIDERS.keys())}")
+    if task not in PROVIDERS[provider]:
+        raise ValueError(
+            f"Task '{task}' not supported for provider '{provider}'. "
+            f"Available tasks: {list(PROVIDERS[provider].keys())}"
+        )
+    return PROVIDERS[provider][task]

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

@@ -0,0 +1,245 @@
+from functools import lru_cache
+from typing import Any, Dict, Optional, Union
+
+from huggingface_hub import constants
+from huggingface_hub.inference._common import RequestParameters
+from huggingface_hub.utils import build_hf_headers, get_token, logging
+
+
+logger = logging.get_logger(__name__)
+
+
+# Dev purposes only.
+# If you want to try to run inference for a new model locally before it's registered on huggingface.co
+# for a given Inference Provider, you can add it to the following dictionary.
+HARDCODED_MODEL_ID_MAPPING: Dict[str, Dict[str, str]] = {
+    # "HF model ID" => "Model ID on Inference Provider's side"
+    #
+    # Example:
+    # "Qwen/Qwen2.5-Coder-32B-Instruct": "Qwen2.5-Coder-32B-Instruct",
+    "cerebras": {},
+    "cohere": {},
+    "fal-ai": {},
+    "fireworks-ai": {},
+    "hf-inference": {},
+    "hyperbolic": {},
+    "nebius": {},
+    "replicate": {},
+    "sambanova": {},
+    "together": {},
+}
+
+
+def filter_none(d: Dict[str, Any]) -> Dict[str, Any]:
+    return {k: v for k, v in d.items() if v is not None}
+
+
+class TaskProviderHelper:
+    """Base class for task-specific provider helpers."""
+
+    def __init__(self, provider: str, base_url: str, task: str) -> None:
+        self.provider = provider
+        self.task = task
+        self.base_url = base_url
+
+    def prepare_request(
+        self,
+        *,
+        inputs: Any,
+        parameters: Dict[str, Any],
+        headers: Dict,
+        model: Optional[str],
+        api_key: Optional[str],
+        extra_payload: Optional[Dict[str, Any]] = None,
+    ) -> RequestParameters:
+        """
+        Prepare the request to be sent to the provider.
+
+        Each step (api_key, model, headers, url, payload) can be customized in subclasses.
+        """
+        # api_key from user, or local token, or raise error
+        api_key = self._prepare_api_key(api_key)
+
+        # mapped model from HF model ID
+        mapped_model = self._prepare_mapped_model(model)
+
+        # default HF headers + user headers (to customize in subclasses)
+        headers = self._prepare_headers(headers, api_key)
+
+        # routed URL if HF token, or direct URL (to customize in '_prepare_route' in subclasses)
+        url = self._prepare_url(api_key, mapped_model)
+
+        # prepare payload (to customize in subclasses)
+        payload = self._prepare_payload_as_dict(inputs, parameters, mapped_model=mapped_model)
+        if payload is not None:
+            payload = recursive_merge(payload, extra_payload or {})
+
+        # body data (to customize in subclasses)
+        data = self._prepare_payload_as_bytes(inputs, parameters, mapped_model, extra_payload)
+
+        # check if both payload and data are set and return
+        if payload is not None and data is not None:
+            raise ValueError("Both payload and data cannot be set in the same request.")
+        if payload is None and data is None:
+            raise ValueError("Either payload or data must be set in the request.")
+        return RequestParameters(url=url, task=self.task, model=mapped_model, json=payload, data=data, headers=headers)
+
+    def get_response(
+        self,
+        response: Union[bytes, Dict],
+        request_params: Optional[RequestParameters] = None,
+    ) -> Any:
+        """
+        Return the response in the expected format.
+
+        Override this method in subclasses for customized response handling."""
+        return response
+
+    def _prepare_api_key(self, api_key: Optional[str]) -> str:
+        """Return the API key to use for the request.
+
+        Usually not overwritten in subclasses."""
+        if api_key is None:
+            api_key = get_token()
+        if api_key is None:
+            raise ValueError(
+                f"You must provide an api_key to work with {self.provider} API or log in with `huggingface-cli login`."
+            )
+        return api_key
+
+    def _prepare_mapped_model(self, model: Optional[str]) -> str:
+        """Return the mapped model ID to use for the request.
+
+        Usually not overwritten in subclasses."""
+        if model is None:
+            raise ValueError(f"Please provide an HF model ID supported by {self.provider}.")
+
+        # hardcoded mapping for local testing
+        if HARDCODED_MODEL_ID_MAPPING.get(self.provider, {}).get(model):
+            return HARDCODED_MODEL_ID_MAPPING[self.provider][model]
+
+        provider_mapping = _fetch_inference_provider_mapping(model).get(self.provider)
+        if provider_mapping is None:
+            raise ValueError(f"Model {model} is not supported by provider {self.provider}.")
+
+        if provider_mapping.task != self.task:
+            raise ValueError(
+                f"Model {model} is not supported for task {self.task} and provider {self.provider}. "
+                f"Supported task: {provider_mapping.task}."
+            )
+
+        if provider_mapping.status == "staging":
+            logger.warning(
+                f"Model {model} is in staging mode for provider {self.provider}. Meant for test purposes only."
+            )
+        return provider_mapping.provider_id
+
+    def _prepare_headers(self, headers: Dict, api_key: str) -> Dict:
+        """Return the headers to use for the request.
+
+        Override this method in subclasses for customized headers.
+        """
+        return {**build_hf_headers(token=api_key), **headers}
+
+    def _prepare_url(self, api_key: str, mapped_model: str) -> str:
+        """Return the URL to use for the request.
+
+        Usually not overwritten in subclasses."""
+        base_url = self._prepare_base_url(api_key)
+        route = self._prepare_route(mapped_model, api_key)
+        return f"{base_url.rstrip('/')}/{route.lstrip('/')}"
+
+    def _prepare_base_url(self, api_key: str) -> str:
+        """Return the base URL to use for the request.
+
+        Usually not overwritten in subclasses."""
+        # Route to the proxy if the api_key is a HF TOKEN
+        if api_key.startswith("hf_"):
+            logger.info(f"Calling '{self.provider}' provider through Hugging Face router.")
+            return constants.INFERENCE_PROXY_TEMPLATE.format(provider=self.provider)
+        else:
+            logger.info(f"Calling '{self.provider}' provider directly.")
+            return self.base_url
+
+    def _prepare_route(self, mapped_model: str, api_key: str) -> str:
+        """Return the route to use for the request.
+
+        Override this method in subclasses for customized routes.
+        """
+        return ""
+
+    def _prepare_payload_as_dict(self, inputs: Any, parameters: Dict, mapped_model: str) -> Optional[Dict]:
+        """Return the payload to use for the request, as a dict.
+
+        Override this method in subclasses for customized payloads.
+        Only one of `_prepare_payload_as_dict` and `_prepare_payload_as_bytes` should return a value.
+        """
+        return None
+
+    def _prepare_payload_as_bytes(
+        self, inputs: Any, parameters: Dict, mapped_model: str, extra_payload: Optional[Dict]
+    ) -> Optional[bytes]:
+        """Return the body to use for the request, as bytes.
+
+        Override this method in subclasses for customized body data.
+        Only one of `_prepare_payload_as_dict` and `_prepare_payload_as_bytes` should return a value.
+        """
+        return None
+
+
+class BaseConversationalTask(TaskProviderHelper):
+    """
+    Base class for conversational (chat completion) tasks.
+    The schema follows the OpenAI API format defined here: https://platform.openai.com/docs/api-reference/chat
+    """
+
+    def __init__(self, provider: str, base_url: str):
+        super().__init__(provider=provider, base_url=base_url, task="conversational")
+
+    def _prepare_route(self, mapped_model: str, api_key: str) -> str:
+        return "/v1/chat/completions"
+
+    def _prepare_payload_as_dict(self, inputs: Any, parameters: Dict, mapped_model: str) -> Optional[Dict]:
+        return {"messages": inputs, **filter_none(parameters), "model": mapped_model}
+
+
+class BaseTextGenerationTask(TaskProviderHelper):
+    """
+    Base class for text-generation (completion) tasks.
+    The schema follows the OpenAI API format defined here: https://platform.openai.com/docs/api-reference/completions
+    """
+
+    def __init__(self, provider: str, base_url: str):
+        super().__init__(provider=provider, base_url=base_url, task="text-generation")
+
+    def _prepare_route(self, mapped_model: str, api_key: str) -> str:
+        return "/v1/completions"
+
+    def _prepare_payload_as_dict(self, inputs: Any, parameters: Dict, mapped_model: str) -> Optional[Dict]:
+        return {"prompt": inputs, **filter_none(parameters), "model": mapped_model}
+
+
+@lru_cache(maxsize=None)
+def _fetch_inference_provider_mapping(model: str) -> Dict:
+    """
+    Fetch provider mappings for a model from the Hub.
+    """
+    from huggingface_hub.hf_api import HfApi
+
+    info = HfApi().model_info(model, expand=["inferenceProviderMapping"])
+    provider_mapping = info.inference_provider_mapping
+    if provider_mapping is None:
+        raise ValueError(f"No provider mapping found for model {model}")
+    return provider_mapping
+
+
+def recursive_merge(dict1: Dict, dict2: Dict) -> Dict:
+    return {
+        **dict1,
+        **{
+            key: recursive_merge(dict1[key], value)
+            if (key in dict1 and isinstance(dict1[key], dict) and isinstance(value, dict))
+            else value
+            for key, value in dict2.items()
+        },
+    }

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

@@ -0,0 +1,66 @@
+import time
+from typing import Any, Dict, Optional, Union
+
+from huggingface_hub.inference._common import RequestParameters, _as_dict
+from huggingface_hub.inference._providers._common import TaskProviderHelper, filter_none
+from huggingface_hub.utils import logging
+from huggingface_hub.utils._http import get_session
+
+
+logger = logging.get_logger(__name__)
+
+MAX_POLLING_ATTEMPTS = 6
+POLLING_INTERVAL = 1.0
+
+
+class BlackForestLabsTextToImageTask(TaskProviderHelper):
+    def __init__(self):
+        super().__init__(provider="black-forest-labs", base_url="https://api.us1.bfl.ai", task="text-to-image")
+
+    def _prepare_headers(self, headers: Dict, api_key: str) -> Dict:
+        headers = super()._prepare_headers(headers, api_key)
+        if not api_key.startswith("hf_"):
+            _ = headers.pop("authorization")
+            headers["X-Key"] = api_key
+        return headers
+
+    def _prepare_route(self, mapped_model: str, api_key: str) -> str:
+        return f"/v1/{mapped_model}"
+
+    def _prepare_payload_as_dict(self, inputs: Any, parameters: Dict, mapped_model: str) -> Optional[Dict]:
+        parameters = filter_none(parameters)
+        if "num_inference_steps" in parameters:
+            parameters["steps"] = parameters.pop("num_inference_steps")
+        if "guidance_scale" in parameters:
+            parameters["guidance"] = parameters.pop("guidance_scale")
+
+        return {"prompt": inputs, **parameters}
+
+    def get_response(self, response: Union[bytes, Dict], request_params: Optional[RequestParameters] = None) -> Any:
+        """
+        Polling mechanism for Black Forest Labs since the API is asynchronous.
+        """
+        url = _as_dict(response).get("polling_url")
+        session = get_session()
+        for _ in range(MAX_POLLING_ATTEMPTS):
+            time.sleep(POLLING_INTERVAL)
+
+            response = session.get(url, headers={"Content-Type": "application/json"})  # type: ignore
+            response.raise_for_status()  # type: ignore
+            response_json: Dict = response.json()  # type: ignore
+            status = response_json.get("status")
+            logger.info(
+                f"Polling generation result from {url}. Current status: {status}. "
+                f"Will retry after {POLLING_INTERVAL} seconds if not ready."
+            )
+
+            if (
+                status == "Ready"
+                and isinstance(response_json.get("result"), dict)
+                and (sample_url := response_json["result"].get("sample"))
+            ):
+                image_resp = session.get(sample_url)
+                image_resp.raise_for_status()
+                return image_resp.content
+
+        raise TimeoutError(f"Failed to get the image URL after {MAX_POLLING_ATTEMPTS} attempts.")

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

@@ -0,0 +1,6 @@
+from huggingface_hub.inference._providers._common import BaseConversationalTask
+
+
+class CerebrasConversationalTask(BaseConversationalTask):
+    def __init__(self):
+        super().__init__(provider="cerebras", base_url="https://api.cerebras.ai")

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

@@ -0,0 +1,15 @@
+from huggingface_hub.inference._providers._common import (
+    BaseConversationalTask,
+)
+
+
+_PROVIDER = "cohere"
+_BASE_URL = "https://api.cohere.com"
+
+
+class CohereConversationalTask(BaseConversationalTask):
+    def __init__(self):
+        super().__init__(provider=_PROVIDER, base_url=_BASE_URL)
+
+    def _prepare_route(self, mapped_model: str, api_key: str) -> str:
+        return "/compatibility/v1/chat/completions"

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

@@ -0,0 +1,147 @@
+import base64
+import time
+from abc import ABC
+from typing import Any, Dict, Optional, Union
+from urllib.parse import urlparse
+
+from huggingface_hub.inference._common import RequestParameters, _as_dict
+from huggingface_hub.inference._providers._common import TaskProviderHelper, filter_none
+from huggingface_hub.utils import get_session, hf_raise_for_status
+from huggingface_hub.utils.logging import get_logger
+
+
+logger = get_logger(__name__)
+
+# Arbitrary polling interval
+_POLLING_INTERVAL = 0.5
+
+
+class FalAITask(TaskProviderHelper, ABC):
+    def __init__(self, task: str):
+        super().__init__(provider="fal-ai", base_url="https://fal.run", task=task)
+
+    def _prepare_headers(self, headers: Dict, api_key: str) -> Dict:
+        headers = super()._prepare_headers(headers, api_key)
+        if not api_key.startswith("hf_"):
+            headers["authorization"] = f"Key {api_key}"
+        return headers
+
+    def _prepare_route(self, mapped_model: str, api_key: str) -> str:
+        return f"/{mapped_model}"
+
+
+class FalAIAutomaticSpeechRecognitionTask(FalAITask):
+    def __init__(self):
+        super().__init__("automatic-speech-recognition")
+
+    def _prepare_payload_as_dict(self, inputs: Any, parameters: Dict, mapped_model: str) -> Optional[Dict]:
+        if isinstance(inputs, str) and inputs.startswith(("http://", "https://")):
+            # If input is a URL, pass it directly
+            audio_url = inputs
+        else:
+            # If input is a file path, read it first
+            if isinstance(inputs, str):
+                with open(inputs, "rb") as f:
+                    inputs = f.read()
+
+            audio_b64 = base64.b64encode(inputs).decode()
+            content_type = "audio/mpeg"
+            audio_url = f"data:{content_type};base64,{audio_b64}"
+
+        return {"audio_url": audio_url, **filter_none(parameters)}
+
+    def get_response(self, response: Union[bytes, Dict], request_params: Optional[RequestParameters] = None) -> Any:
+        text = _as_dict(response)["text"]
+        if not isinstance(text, str):
+            raise ValueError(f"Unexpected output format from FalAI API. Expected string, got {type(text)}.")
+        return text
+
+
+class FalAITextToImageTask(FalAITask):
+    def __init__(self):
+        super().__init__("text-to-image")
+
+    def _prepare_payload_as_dict(self, inputs: Any, parameters: Dict, mapped_model: str) -> Optional[Dict]:
+        parameters = filter_none(parameters)
+        if "width" in parameters and "height" in parameters:
+            parameters["image_size"] = {
+                "width": parameters.pop("width"),
+                "height": parameters.pop("height"),
+            }
+        return {"prompt": inputs, **parameters}
+
+    def get_response(self, response: Union[bytes, Dict], request_params: Optional[RequestParameters] = None) -> Any:
+        url = _as_dict(response)["images"][0]["url"]
+        return get_session().get(url).content
+
+
+class FalAITextToSpeechTask(FalAITask):
+    def __init__(self):
+        super().__init__("text-to-speech")
+
+    def _prepare_payload_as_dict(self, inputs: Any, parameters: Dict, mapped_model: str) -> Optional[Dict]:
+        return {"lyrics": inputs, **filter_none(parameters)}
+
+    def get_response(self, response: Union[bytes, Dict], request_params: Optional[RequestParameters] = None) -> Any:
+        url = _as_dict(response)["audio"]["url"]
+        return get_session().get(url).content
+
+
+class FalAITextToVideoTask(FalAITask):
+    def __init__(self):
+        super().__init__("text-to-video")
+
+    def _prepare_base_url(self, api_key: str) -> str:
+        if api_key.startswith("hf_"):
+            return super()._prepare_base_url(api_key)
+        else:
+            logger.info(f"Calling '{self.provider}' provider directly.")
+            return "https://queue.fal.run"
+
+    def _prepare_route(self, mapped_model: str, api_key: str) -> str:
+        if api_key.startswith("hf_"):
+            # Use the queue subdomain for HF routing
+            return f"/{mapped_model}?_subdomain=queue"
+        return f"/{mapped_model}"
+
+    def _prepare_payload_as_dict(self, inputs: Any, parameters: Dict, mapped_model: str) -> Optional[Dict]:
+        return {"prompt": inputs, **filter_none(parameters)}
+
+    def get_response(
+        self,
+        response: Union[bytes, Dict],
+        request_params: Optional[RequestParameters] = None,
+    ) -> Any:
+        response_dict = _as_dict(response)
+
+        request_id = response_dict.get("request_id")
+        if not request_id:
+            raise ValueError("No request ID found in the response")
+        if request_params is None:
+            raise ValueError(
+                "A `RequestParameters` object should be provided to get text-to-video responses with Fal AI."
+            )
+
+        # extract the base url and query params
+        parsed_url = urlparse(request_params.url)
+        # a bit hacky way to concatenate the provider name without parsing `parsed_url.path`
+        base_url = f"{parsed_url.scheme}://{parsed_url.netloc}{'/fal-ai' if parsed_url.netloc == 'router.huggingface.co' else ''}"
+        query_param = f"?{parsed_url.query}" if parsed_url.query else ""
+
+        # extracting the provider model id for status and result urls
+        # from the response as it might be different from the mapped model in `request_params.url`
+        model_id = urlparse(response_dict.get("response_url")).path
+        status_url = f"{base_url}{str(model_id)}/status{query_param}"
+        result_url = f"{base_url}{str(model_id)}{query_param}"
+
+        status = response_dict.get("status")
+        logger.info("Generating the video.. this can take several minutes.")
+        while status != "COMPLETED":
+            time.sleep(_POLLING_INTERVAL)
+            status_response = get_session().get(status_url, headers=request_params.headers)
+            hf_raise_for_status(status_response)
+            status = status_response.json().get("status")
+
+        response = get_session().get(result_url, headers=request_params.headers).json()
+        url = _as_dict(response)["video"]["url"]
+        return get_session().get(url).content

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

@@ -0,0 +1,9 @@
+from ._common import BaseConversationalTask
+
+
+class FireworksAIConversationalTask(BaseConversationalTask):
+    def __init__(self):
+        super().__init__(provider="fireworks-ai", base_url="https://api.fireworks.ai")
+
+    def _prepare_route(self, mapped_model: str, api_key: str) -> str:
+        return "/inference/v1/chat/completions"

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

@@ -0,0 +1,167 @@
+import json
+from functools import lru_cache
+from pathlib import Path
+from typing import Any, Dict, Optional
+
+from huggingface_hub import constants
+from huggingface_hub.inference._common import _b64_encode, _open_as_binary
+from huggingface_hub.inference._providers._common import TaskProviderHelper, filter_none
+from huggingface_hub.utils import build_hf_headers, get_session, get_token, hf_raise_for_status
+
+
+class HFInferenceTask(TaskProviderHelper):
+    """Base class for HF Inference API tasks."""
+
+    def __init__(self, task: str):
+        super().__init__(
+            provider="hf-inference",
+            base_url=constants.INFERENCE_PROXY_TEMPLATE.format(provider="hf-inference"),
+            task=task,
+        )
+
+    def _prepare_api_key(self, api_key: Optional[str]) -> str:
+        # special case: for HF Inference we allow not providing an API key
+        return api_key or get_token()  # type: ignore[return-value]
+
+    def _prepare_mapped_model(self, model: Optional[str]) -> str:
+        if model is not None and model.startswith(("http://", "https://")):
+            return model
+        model_id = model if model is not None else _fetch_recommended_models().get(self.task)
+        if model_id is None:
+            raise ValueError(
+                f"Task {self.task} has no recommended model for HF Inference. Please specify a model"
+                " explicitly. Visit https://huggingface.co/tasks for more info."
+            )
+        _check_supported_task(model_id, self.task)
+        return model_id
+
+    def _prepare_url(self, api_key: str, mapped_model: str) -> str:
+        # hf-inference provider can handle URLs (e.g. Inference Endpoints or TGI deployment)
+        if mapped_model.startswith(("http://", "https://")):
+            return mapped_model
+        return (
+            # Feature-extraction and sentence-similarity are the only cases where we handle models with several tasks.
+            f"{self.base_url}/pipeline/{self.task}/{mapped_model}"
+            if self.task in ("feature-extraction", "sentence-similarity")
+            # Otherwise, we use the default endpoint
+            else f"{self.base_url}/models/{mapped_model}"
+        )
+
+    def _prepare_payload_as_dict(self, inputs: Any, parameters: Dict, mapped_model: str) -> Optional[Dict]:
+        if isinstance(inputs, bytes):
+            raise ValueError(f"Unexpected binary input for task {self.task}.")
+        if isinstance(inputs, Path):
+            raise ValueError(f"Unexpected path input for task {self.task} (got {inputs})")
+        return {"inputs": inputs, "parameters": filter_none(parameters)}
+
+
+class HFInferenceBinaryInputTask(HFInferenceTask):
+    def _prepare_payload_as_dict(self, inputs: Any, parameters: Dict, mapped_model: str) -> Optional[Dict]:
+        return None
+
+    def _prepare_payload_as_bytes(
+        self, inputs: Any, parameters: Dict, mapped_model: str, extra_payload: Optional[Dict]
+    ) -> Optional[bytes]:
+        parameters = filter_none({k: v for k, v in parameters.items() if v is not None})
+        extra_payload = extra_payload or {}
+        has_parameters = len(parameters) > 0 or len(extra_payload) > 0
+
+        # Raise if not a binary object or a local path or a URL.
+        if not isinstance(inputs, (bytes, Path)) and not isinstance(inputs, str):
+            raise ValueError(f"Expected binary inputs or a local path or a URL. Got {inputs}")
+
+        # Send inputs as raw content when no parameters are provided
+        if not has_parameters:
+            with _open_as_binary(inputs) as data:
+                data_as_bytes = data if isinstance(data, bytes) else data.read()
+                return data_as_bytes
+
+        # Otherwise encode as b64
+        return json.dumps({"inputs": _b64_encode(inputs), "parameters": parameters, **extra_payload}).encode("utf-8")
+
+
+class HFInferenceConversational(HFInferenceTask):
+    def __init__(self):
+        super().__init__("conversational")
+
+    def _prepare_payload_as_dict(self, inputs: Any, parameters: Dict, mapped_model: str) -> Optional[Dict]:
+        payload_model = parameters.get("model") or mapped_model
+
+        if payload_model is None or payload_model.startswith(("http://", "https://")):
+            payload_model = "dummy"
+
+        return {**filter_none(parameters), "model": payload_model, "messages": inputs}
+
+    def _prepare_url(self, api_key: str, mapped_model: str) -> str:
+        base_url = (
+            mapped_model
+            if mapped_model.startswith(("http://", "https://"))
+            else f"{constants.INFERENCE_PROXY_TEMPLATE.format(provider='hf-inference')}/models/{mapped_model}"
+        )
+        return _build_chat_completion_url(base_url)
+
+
+def _build_chat_completion_url(model_url: str) -> str:
+    # Strip trailing /
+    model_url = model_url.rstrip("/")
+
+    # Append /chat/completions if not already present
+    if model_url.endswith("/v1"):
+        model_url += "/chat/completions"
+
+    # Append /v1/chat/completions if not already present
+    if not model_url.endswith("/chat/completions"):
+        model_url += "/v1/chat/completions"
+
+    return model_url
+
+
+@lru_cache(maxsize=1)
+def _fetch_recommended_models() -> Dict[str, Optional[str]]:
+    response = get_session().get(f"{constants.ENDPOINT}/api/tasks", headers=build_hf_headers())
+    hf_raise_for_status(response)
+    return {task: next(iter(details["widgetModels"]), None) for task, details in response.json().items()}
+
+
+@lru_cache(maxsize=None)
+def _check_supported_task(model: str, task: str) -> None:
+    from huggingface_hub.hf_api import HfApi
+
+    model_info = HfApi().model_info(model)
+    pipeline_tag = model_info.pipeline_tag
+    tags = model_info.tags or []
+    is_conversational = "conversational" in tags
+    if task in ("text-generation", "conversational"):
+        if pipeline_tag == "text-generation":
+            # text-generation + conversational tag -> both tasks allowed
+            if is_conversational:
+                return
+            # text-generation without conversational tag -> only text-generation allowed
+            if task == "text-generation":
+                return
+            raise ValueError(f"Model '{model}' doesn't support task '{task}'.")
+
+    if pipeline_tag == "text2text-generation":
+        if task == "text-generation":
+            return
+        raise ValueError(f"Model '{model}' doesn't support task '{task}'.")
+
+    if pipeline_tag == "image-text-to-text":
+        if is_conversational and task == "conversational":
+            return  # Only conversational allowed if tagged as conversational
+        raise ValueError("Non-conversational image-text-to-text task is not supported.")
+
+    if (
+        task in ("feature-extraction", "sentence-similarity")
+        and pipeline_tag in ("feature-extraction", "sentence-similarity")
+        and task in tags
+    ):
+        # feature-extraction and sentence-similarity are interchangeable for HF Inference
+        return
+
+    # For all other tasks, just check pipeline tag
+    if pipeline_tag != task:
+        raise ValueError(
+            f"Model '{model}' doesn't support task '{task}'. Supported tasks: '{pipeline_tag}', got: '{task}'"
+        )
+    return

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

@@ -0,0 +1,43 @@
+import base64
+from typing import Any, Dict, Optional, Union
+
+from huggingface_hub.inference._common import RequestParameters, _as_dict
+from huggingface_hub.inference._providers._common import BaseConversationalTask, TaskProviderHelper, filter_none
+
+
+class HyperbolicTextToImageTask(TaskProviderHelper):
+    def __init__(self):
+        super().__init__(provider="hyperbolic", base_url="https://api.hyperbolic.xyz", task="text-to-image")
+
+    def _prepare_route(self, mapped_model: str, api_key: str) -> str:
+        return "/v1/images/generations"
+
+    def _prepare_payload_as_dict(self, inputs: Any, parameters: Dict, mapped_model: str) -> Optional[Dict]:
+        parameters = filter_none(parameters)
+        if "num_inference_steps" in parameters:
+            parameters["steps"] = parameters.pop("num_inference_steps")
+        if "guidance_scale" in parameters:
+            parameters["cfg_scale"] = parameters.pop("guidance_scale")
+        # For Hyperbolic, the width and height are required parameters
+        if "width" not in parameters:
+            parameters["width"] = 512
+        if "height" not in parameters:
+            parameters["height"] = 512
+        return {"prompt": inputs, "model_name": mapped_model, **parameters}
+
+    def get_response(self, response: Union[bytes, Dict], request_params: Optional[RequestParameters] = None) -> Any:
+        response_dict = _as_dict(response)
+        return base64.b64decode(response_dict["images"][0]["image"])
+
+
+class HyperbolicTextGenerationTask(BaseConversationalTask):
+    """
+    Special case for Hyperbolic, where text-generation task is handled as a conversational task.
+    """
+
+    def __init__(self, task: str):
+        super().__init__(
+            provider="hyperbolic",
+            base_url="https://api.hyperbolic.xyz",
+        )
+        self.task = task