image-match/python/py/Lib/site-packages/transformers/pipelines/base.py

# Copyright 2018 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations

import collections
import copy
import csv
import importlib
import json
import os
import pickle
import sys
import traceback
import types
from abc import ABC, abstractmethod
from collections import UserDict
from contextlib import contextmanager
from os.path import abspath, exists
from typing import TYPE_CHECKING, Any, Union

from ..dynamic_module_utils import custom_object_save
from ..feature_extraction_utils import PreTrainedFeatureExtractor
from ..generation import GenerationConfig
from ..image_processing_utils import BaseImageProcessor
from ..models.auto import AutoConfig, AutoTokenizer
from ..processing_utils import ProcessorMixin
from ..tokenization_python import PreTrainedTokenizer
from ..utils import (
    ModelOutput,
    PushToHubMixin,
    add_end_docstrings,
    copy_func,
    is_torch_available,
    is_torch_cuda_available,
    is_torch_hpu_available,
    is_torch_mlu_available,
    is_torch_mps_available,
    is_torch_musa_available,
    is_torch_npu_available,
    is_torch_xpu_available,
    logging,
)
from ..utils.chat_template_utils import Chat, is_valid_message


GenericTensor = Union[list["GenericTensor"], "torch.Tensor"]

if is_torch_available() or TYPE_CHECKING:
    import torch
    from torch.utils.data import DataLoader, Dataset

    from ..modeling_utils import PreTrainedModel
    from .pt_utils import KeyDataset
else:
    Dataset = None


logger = logging.get_logger(__name__)


def no_collate_fn(items):
    if len(items) != 1:
        raise ValueError("This collate_fn is meant to be used with batch_size=1")
    return items[0]


def _pad(items, key, padding_value, padding_side):
    batch_size = len(items)
    if isinstance(items[0][key], torch.Tensor):
        # Others include `attention_mask` etc...
        shape = items[0][key].shape
        dim = items[0][key].ndim
        if dim == 1:
            # We have a list of 1-dim torch tensors, which can be stacked without padding
            return torch.cat([item[key] for item in items], dim=0)
        if key in ["pixel_values", "image"]:
            # This is probable image so padding shouldn't be necessary
            # B, C, H, W
            return torch.cat([item[key] for item in items], dim=0)
        elif dim == 4 and key == "input_features":
            # this is probably a mel spectrogram batched
            return torch.cat([item[key] for item in items], dim=0)
        max_length = max(item[key].shape[1] for item in items)
        min_length = min(item[key].shape[1] for item in items)
        dtype = items[0][key].dtype

        if dim == 2 and max_length == min_length:
            # Bypass for `ImageGPT` which doesn't provide a padding value, yet
            # we can consistently pad since the size should be matching
            return torch.cat([item[key] for item in items], dim=0)
        else:
            tensor = torch.full([batch_size, max_length] + list(shape[2:]), fill_value=padding_value, dtype=dtype)

        for i, item in enumerate(items):
            if padding_side == "left":
                tensor[i, -len(item[key][0]) :] = item[key][0]
            else:
                tensor[i, : len(item[key][0])] = item[key][0]

        return tensor
    else:
        return [item[key] for item in items]


def pad_collate_fn(tokenizer, feature_extractor):
    # Tokenizer
    t_padding_side = None
    # Feature extractor
    f_padding_side = None
    if tokenizer is None and feature_extractor is None:
        raise ValueError("Pipeline without tokenizer or feature_extractor cannot do batching")
    if tokenizer is not None:
        if tokenizer.pad_token_id is None:
            raise ValueError(
                "Pipeline with tokenizer without pad_token cannot do batching. You can try to set it with "
                "`pipe.tokenizer.pad_token_id = model.config.eos_token_id`."
            )
        else:
            t_padding_value = tokenizer.pad_token_id
            t_padding_side = tokenizer.padding_side
    if feature_extractor is not None:
        # Feature extractor can be images, where no padding is expected
        f_padding_value = getattr(feature_extractor, "padding_value", None)
        f_padding_side = getattr(feature_extractor, "padding_side", None)

    if t_padding_side is not None and f_padding_side is not None and t_padding_side != f_padding_side:
        raise ValueError(
            f"The feature extractor, and tokenizer don't agree on padding side {t_padding_side} != {f_padding_side}"
        )
    padding_side = "right"
    if t_padding_side is not None:
        padding_side = t_padding_side
    if f_padding_side is not None:
        padding_side = f_padding_side

    def inner(items):
        keys = set(items[0].keys())
        for item in items:
            if set(item.keys()) != keys:
                raise ValueError(
                    f"The elements of the batch contain different keys. Cannot batch them ({set(item.keys())} !="
                    f" {keys})"
                )
        # input_values, input_pixels, input_ids, ...
        padded = {}
        for key in keys:
            if key == "input_ids":
                # ImageGPT uses a feature extractor
                if tokenizer is None and feature_extractor is not None:
                    _padding_value = f_padding_value
                else:
                    _padding_value = t_padding_value
            elif key in {"input_values", "pixel_values", "input_features"}:
                _padding_value = f_padding_value
            elif key in {"p_mask", "special_tokens_mask"}:
                _padding_value = 1
            elif key in {"attention_mask", "token_type_ids"}:
                _padding_value = 0
            else:
                # This is likely another random key maybe even user provided
                _padding_value = 0
            padded[key] = _pad(items, key, _padding_value, padding_side)
        return padded

    return inner


def load_model(
    model,
    config: AutoConfig,
    model_classes: tuple[type, ...] | None = None,
    task: str | None = None,
    **model_kwargs,
):
    """
    Load a model.

    If `model` is instantiated, this function will just return it. Otherwise `model` is
    actually a checkpoint name and this method will try to instantiate it using `model_classes`. Since we don't want to
    instantiate the model twice, this model is returned for use by the pipeline.

    Args:
        model (`str`, or [`PreTrainedModel`]):
            If `str`, a checkpoint name. The model to load.
        config ([`AutoConfig`]):
            The config associated with the model to help using the correct class
        model_classes (`tuple[type]`, *optional*):
            A tuple of model classes.
        task (`str`):
            The task defining which pipeline will be returned.
        model_kwargs:
            Additional dictionary of keyword arguments passed along to the model's `from_pretrained(...,
            **model_kwargs)` function.

    Returns:
        The model.
    """
    if not is_torch_available():
        raise RuntimeError("PyTorch should be installed. Please follow the instructions at https://pytorch.org/.")

    if isinstance(model, str):
        model_kwargs["_from_pipeline"] = task
        class_tuple = model_classes if model_classes is not None else ()
        if config.architectures:
            classes = []
            for architecture in config.architectures:
                transformers_module = importlib.import_module("transformers")
                _class = getattr(transformers_module, architecture, None)
                if _class is not None:
                    classes.append(_class)
            class_tuple = class_tuple + tuple(classes)

        if len(class_tuple) == 0:
            raise ValueError(f"Pipeline cannot infer suitable model classes from {model}")

        all_traceback = {}
        for model_class in class_tuple:
            kwargs = model_kwargs.copy()

            try:
                model = model_class.from_pretrained(model, **kwargs)
                # Stop loading on the first successful load.
                break
            except (OSError, ValueError, TypeError, RuntimeError):
                # `from_pretrained` may raise a `TypeError` or `RuntimeError` when the requested `dtype`
                # is not supported on the execution device (e.g. bf16 on a consumer GPU). We capture those so
                # we can transparently retry the load in float32 before surfacing an error to the user.
                fallback_tried = False
                if "dtype" in kwargs:
                    import torch

                    fallback_tried = True
                    fp32_kwargs = kwargs.copy()
                    fp32_kwargs["dtype"] = torch.float32

                    try:
                        model = model_class.from_pretrained(model, **fp32_kwargs)
                        logger.warning(
                            "Falling back to torch.float32 because loading with the original dtype failed on the"
                            " target device."
                        )
                        break
                    except Exception:
                        # If it still fails, capture the traceback and continue to the next class.
                        all_traceback[model_class.__name__] = traceback.format_exc()
                        continue

                # If no fallback was attempted or it also failed, record the original traceback.
                if not fallback_tried:
                    all_traceback[model_class.__name__] = traceback.format_exc()
                continue

        if isinstance(model, str):
            error = ""
            for class_name, trace in all_traceback.items():
                error += f"while loading with {class_name}, an error is thrown:\n{trace}\n"
            raise ValueError(
                f"Could not load model {model} with any of the following classes: {class_tuple}. See the original errors:\n\n{error}\n"
            )

    return model


def get_default_model_and_revision(targeted_task: dict, task_options: Any | None) -> tuple[str, str]:
    """
    Select a default model to use for a given task.

    Args:
        targeted_task (`Dict`):
           Dictionary representing the given task, that should contain default models

        task_options (`Any`, None)
           Any further value required by the task to get fully specified.

    Returns

        Tuple:
            - `str` The model string representing the default model for this pipeline.
            - `str` The revision of the model.
    """
    defaults = targeted_task["default"]
    if task_options:
        if task_options not in defaults:
            raise ValueError(f"The task does not provide any default models for options {task_options}")
        default_models = defaults[task_options]["model"]
    elif "model" in defaults:
        default_models = targeted_task["default"]["model"]
    else:
        raise ValueError("The task defaults can't be correctly selected.")

    return default_models


def load_assistant_model(
    model: PreTrainedModel,
    assistant_model: str | PreTrainedModel | None,
    assistant_tokenizer: PreTrainedTokenizer | None,
) -> tuple[PreTrainedModel | None, PreTrainedTokenizer | None]:
    """
    Prepares the assistant model and the assistant tokenizer for a pipeline whose model that can call `generate`.

    Args:
        model ([`PreTrainedModel`]):
            The main model that will be used by the pipeline to make predictions.
        assistant_model (`str` or [`PreTrainedModel`], *optional*):
            The assistant model that will be used by the pipeline to make predictions.
        assistant_tokenizer ([`PreTrainedTokenizer`], *optional*):
            The assistant tokenizer that will be used by the pipeline to encode data for the model.

    Returns:
        Tuple: The loaded assistant model and (optionally) the loaded tokenizer.
    """
    if not model.can_generate() or assistant_model is None:
        return None, None

    # If the model is passed as a string, load the model and the corresponding tokenizer
    if isinstance(assistant_model, str):
        assistant_config = AutoConfig.from_pretrained(assistant_model)
        loaded_assistant_model = load_model(assistant_model, config=assistant_config)
        loaded_assistant_model = loaded_assistant_model.to(device=model.device, dtype=model.dtype)
        loaded_assistant_tokenizer = AutoTokenizer.from_pretrained(assistant_model)
    else:
        loaded_assistant_model = assistant_model
        loaded_assistant_tokenizer = assistant_tokenizer

    # Finally, let's check the tokenizers: if the two models have different tokenizers, we need to keep the assistant
    # tokenizer
    same_vocab_size = model.config.vocab_size == loaded_assistant_model.config.vocab_size
    same_special_tokens = all(
        getattr(model.config, token) == getattr(loaded_assistant_model.config, token)
        for token in ("eos_token_id", "pad_token_id", "bos_token_id")
    )
    if same_vocab_size and same_special_tokens:
        loaded_assistant_tokenizer = None
    elif loaded_assistant_tokenizer is None:
        raise ValueError(
            "The assistant model has a different tokenizer than the main model. You should pass the assistant "
            "tokenizer."
        )

    return loaded_assistant_model, loaded_assistant_tokenizer


class PipelineException(Exception):
    """
    Raised by a [`Pipeline`] when handling __call__.

    Args:
        task (`str`): The task of the pipeline.
        model (`str`): The model used by the pipeline.
        reason (`str`): The error message to display.
    """

    def __init__(self, task: str, model: str, reason: str):
        super().__init__(reason)

        self.task = task
        self.model = model


class ArgumentHandler(ABC):
    """
    Base interface for handling arguments for each [`~pipelines.Pipeline`].
    """

    @abstractmethod
    def __call__(self, *args, **kwargs):
        raise NotImplementedError()


class PipelineDataFormat:
    """
    Base class for all the pipeline supported data format both for reading and writing. Supported data formats
    currently includes:

    - JSON
    - CSV
    - stdin/stdout (pipe)

    `PipelineDataFormat` also includes some utilities to work with multi-columns like mapping from datasets columns to
    pipelines keyword arguments through the `dataset_kwarg_1=dataset_column_1` format.

    Args:
        output_path (`str`): Where to save the outgoing data.
        input_path (`str`): Where to look for the input data.
        column (`str`): The column to read.
        overwrite (`bool`, *optional*, defaults to `False`):
            Whether or not to overwrite the `output_path`.
    """

    SUPPORTED_FORMATS = ["json", "csv", "pipe"]

    def __init__(
        self,
        output_path: str | None,
        input_path: str | None,
        column: str | None,
        overwrite: bool = False,
    ):
        self.output_path = output_path
        self.input_path = input_path
        self.column = column.split(",") if column is not None else [""]
        self.is_multi_columns = len(self.column) > 1

        if self.is_multi_columns:
            self.column = [tuple(c.split("=")) if "=" in c else (c, c) for c in self.column]

        if output_path is not None and not overwrite:
            if exists(abspath(self.output_path)):
                raise OSError(f"{self.output_path} already exists on disk")

        if input_path is not None:
            if not exists(abspath(self.input_path)):
                raise OSError(f"{self.input_path} doesn't exist on disk")

    @abstractmethod
    def __iter__(self):
        raise NotImplementedError()

    @abstractmethod
    def save(self, data: dict | list[dict]):
        """
        Save the provided data object with the representation for the current [`~pipelines.PipelineDataFormat`].

        Args:
            data (`dict` or list of `dict`): The data to store.
        """
        raise NotImplementedError()

    def save_binary(self, data: dict | list[dict]) -> str:
        """
        Save the provided data object as a pickle-formatted binary data on the disk.

        Args:
            data (`dict` or list of `dict`): The data to store.

        Returns:
            `str`: Path where the data has been saved.
        """
        path, _ = os.path.splitext(self.output_path)
        binary_path = os.path.extsep.join((path, "pickle"))

        with open(binary_path, "wb+") as f_output:
            pickle.dump(data, f_output)

        return binary_path

    @staticmethod
    def from_str(
        format: str,
        output_path: str | None,
        input_path: str | None,
        column: str | None,
        overwrite=False,
    ) -> PipelineDataFormat:
        """
        Creates an instance of the right subclass of [`~pipelines.PipelineDataFormat`] depending on `format`.

        Args:
            format (`str`):
                The format of the desired pipeline. Acceptable values are `"json"`, `"csv"` or `"pipe"`.
            output_path (`str`, *optional*):
                Where to save the outgoing data.
            input_path (`str`, *optional*):
                Where to look for the input data.
            column (`str`, *optional*):
                The column to read.
            overwrite (`bool`, *optional*, defaults to `False`):
                Whether or not to overwrite the `output_path`.

        Returns:
            [`~pipelines.PipelineDataFormat`]: The proper data format.
        """
        if format == "json":
            return JsonPipelineDataFormat(output_path, input_path, column, overwrite=overwrite)
        elif format == "csv":
            return CsvPipelineDataFormat(output_path, input_path, column, overwrite=overwrite)
        elif format == "pipe":
            return PipedPipelineDataFormat(output_path, input_path, column, overwrite=overwrite)
        else:
            raise KeyError(f"Unknown reader {format} (Available reader are json/csv/pipe)")


class CsvPipelineDataFormat(PipelineDataFormat):
    """
    Support for pipelines using CSV data format.

    Args:
        output_path (`str`): Where to save the outgoing data.
        input_path (`str`): Where to look for the input data.
        column (`str`): The column to read.
        overwrite (`bool`, *optional*, defaults to `False`):
            Whether or not to overwrite the `output_path`.
    """

    def __init__(
        self,
        output_path: str | None,
        input_path: str | None,
        column: str | None,
        overwrite=False,
    ):
        super().__init__(output_path, input_path, column, overwrite=overwrite)

    def __iter__(self):
        with open(self.input_path, "r") as f:
            reader = csv.DictReader(f)
            for row in reader:
                if self.is_multi_columns:
                    yield {k: row[c] for k, c in self.column}
                else:
                    yield row[self.column[0]]

    def save(self, data: list[dict]):
        """
        Save the provided data object with the representation for the current [`~pipelines.PipelineDataFormat`].

        Args:
            data (`list[dict]`): The data to store.
        """
        with open(self.output_path, "w") as f:
            if len(data) > 0:
                writer = csv.DictWriter(f, list(data[0].keys()))
                writer.writeheader()
                writer.writerows(data)


class JsonPipelineDataFormat(PipelineDataFormat):
    """
    Support for pipelines using JSON file format.

    Args:
        output_path (`str`): Where to save the outgoing data.
        input_path (`str`): Where to look for the input data.
        column (`str`): The column to read.
        overwrite (`bool`, *optional*, defaults to `False`):
            Whether or not to overwrite the `output_path`.
    """

    def __init__(
        self,
        output_path: str | None,
        input_path: str | None,
        column: str | None,
        overwrite=False,
    ):
        super().__init__(output_path, input_path, column, overwrite=overwrite)

        with open(input_path, "r") as f:
            self._entries = json.load(f)

    def __iter__(self):
        for entry in self._entries:
            if self.is_multi_columns:
                yield {k: entry[c] for k, c in self.column}
            else:
                yield entry[self.column[0]]

    def save(self, data: dict):
        """
        Save the provided data object in a json file.

        Args:
            data (`dict`): The data to store.
        """
        with open(self.output_path, "w") as f:
            json.dump(data, f)


class PipedPipelineDataFormat(PipelineDataFormat):
    """
    Read data from piped input to the python process. For multi columns data, columns should separated by \t

    If columns are provided, then the output will be a dictionary with {column_x: value_x}

    Args:
        output_path (`str`): Where to save the outgoing data.
        input_path (`str`): Where to look for the input data.
        column (`str`): The column to read.
        overwrite (`bool`, *optional*, defaults to `False`):
            Whether or not to overwrite the `output_path`.
    """

    def __iter__(self):
        for line in sys.stdin:
            # Split for multi-columns
            if "\t" in line:
                line = line.split("\t")
                if self.column:
                    # Dictionary to map arguments
                    yield {kwargs: l for (kwargs, _), l in zip(self.column, line)}
                else:
                    yield tuple(line)

            # No dictionary to map arguments
            else:
                yield line

    def save(self, data: dict):
        """
        Print the data.

        Args:
            data (`dict`): The data to store.
        """
        print(data)

    def save_binary(self, data: dict | list[dict]) -> str:
        if self.output_path is None:
            raise KeyError(
                "When using piped input on pipeline outputting large object requires an output file path. "
                "Please provide such output path through --output argument."
            )

        return super().save_binary(data)


class _ScikitCompat(ABC):
    """
    Interface layer for the Scikit and Keras compatibility.
    """

    @abstractmethod
    def transform(self, X):
        raise NotImplementedError()

    @abstractmethod
    def predict(self, X):
        raise NotImplementedError()


def build_pipeline_init_args(
    has_tokenizer: bool = False,
    has_feature_extractor: bool = False,
    has_image_processor: bool = False,
    has_processor: bool = False,
    supports_binary_output: bool = True,
) -> str:
    docstring = r"""
    Arguments:
        model ([`PreTrainedModel`]):
            The model that will be used by the pipeline to make predictions. This needs to be a model inheriting from
            [`PreTrainedModel`]."""
    if has_tokenizer:
        docstring += r"""
        tokenizer ([`PreTrainedTokenizer`]):
            The tokenizer that will be used by the pipeline to encode data for the model. This object inherits from
            [`PreTrainedTokenizer`]."""
    if has_feature_extractor:
        docstring += r"""
        feature_extractor ([`SequenceFeatureExtractor`]):
            The feature extractor that will be used by the pipeline to encode data for the model. This object inherits from
            [`SequenceFeatureExtractor`]."""
    if has_image_processor:
        docstring += r"""
        image_processor ([`BaseImageProcessor`]):
            The image processor that will be used by the pipeline to encode data for the model. This object inherits from
            [`BaseImageProcessor`]."""
    if has_processor:
        docstring += r"""
        processor ([`ProcessorMixin`]):
            The processor that will be used by the pipeline to encode data for the model. This object inherits from
            [`ProcessorMixin`]. Processor is a composite object that might contain `tokenizer`, `feature_extractor`, and
            `image_processor`."""
    docstring += r"""
        task (`str`, defaults to `""`):
            A task-identifier for the pipeline.
        num_workers (`int`, *optional*, defaults to 8):
            When the pipeline will use *DataLoader* (when passing a dataset, on GPU for a Pytorch model), the number of
            workers to be used.
        batch_size (`int`, *optional*, defaults to 1):
            When the pipeline will use *DataLoader* (when passing a dataset, on GPU for a Pytorch model), the size of
            the batch to use, for inference this is not always beneficial, please read [Batching with
            pipelines](https://huggingface.co/transformers/main_classes/pipelines.html#pipeline-batching) .
        args_parser ([`~pipelines.ArgumentHandler`], *optional*):
            Reference to the object in charge of parsing supplied pipeline parameters.
        device (`int`, *optional*, defaults to -1):
            Device ordinal for CPU/GPU supports. Setting this to -1 will leverage CPU, a positive will run the model on
            the associated CUDA device id. You can pass native `torch.device` or a `str` too
        dtype (`str` or `torch.dtype`, *optional*):
            Sent directly as `model_kwargs` (just a simpler shortcut) to use the available precision for this model
            (`torch.float16`, `torch.bfloat16`, ... or `"auto"`)"""
    if supports_binary_output:
        docstring += r"""
        binary_output (`bool`, *optional*, defaults to `False`):
            Flag indicating if the output the pipeline should happen in a serialized format (i.e., pickle) or as
            the raw output data e.g. text."""
    return docstring


PIPELINE_INIT_ARGS = build_pipeline_init_args(
    has_tokenizer=True,
    has_feature_extractor=True,
    has_image_processor=True,
    has_processor=True,
    supports_binary_output=True,
)

SUPPORTED_PEFT_TASKS = {
    "document-question-answering": ["PeftModelForQuestionAnswering"],
    "feature-extraction": ["PeftModelForFeatureExtraction", "PeftModel"],
    "summarization": ["PeftModelForSeq2SeqLM"],
    "table-question-answering": ["PeftModelForQuestionAnswering"],
    "text-classification": ["PeftModelForSequenceClassification"],
    "sentiment-analysis": ["PeftModelForSequenceClassification"],
    "text-generation": ["PeftModelForCausalLM"],
    "token-classification": ["PeftModelForTokenClassification"],
    "ner": ["PeftModelForTokenClassification"],
    "zero-shot-classification": ["PeftModelForSequenceClassification"],
}

if is_torch_available():
    from transformers.pipelines.pt_utils import (
        PipelineChunkIterator,
        PipelineDataset,
        PipelineIterator,
        PipelinePackIterator,
    )


@add_end_docstrings(
    build_pipeline_init_args(
        has_tokenizer=True, has_feature_extractor=True, has_image_processor=True, has_processor=True
    )
)
class Pipeline(_ScikitCompat, PushToHubMixin):
    """
    The Pipeline class is the class from which all pipelines inherit. Refer to this class for methods shared across
    different pipelines.

    Base class implementing pipelined operations. Pipeline workflow is defined as a sequence of the following
    operations:

        Input -> Tokenization -> Model Inference -> Post-Processing (task dependent) -> Output

    Pipeline supports running on CPU or GPU through the device argument (see below).

    Some pipeline, like for instance [`FeatureExtractionPipeline`] (`'feature-extraction'`) output large tensor object
    as nested-lists. In order to avoid dumping such large structure as textual data we provide the `binary_output`
    constructor argument. If set to `True`, the output will be stored in the pickle format.
    """

    # These flags should be overridden for downstream pipelines. They indicate which preprocessing classes are
    # used by each pipeline. The possible values are:
    # - True (the class is mandatory, raise an error if it's not present in the repo)
    # - None (the class is optional; it should be loaded if present in the repo but the pipeline can work without it)
    # - False (the class is never used by the pipeline and should not be loaded even if present)
    _load_processor = None
    _load_image_processor = None
    _load_feature_extractor = None
    _load_tokenizer = None

    # Pipelines that call `generate` have shared logic, e.g. preparing the generation config.
    _pipeline_calls_generate = False

    default_input_names = None

    def __init__(
        self,
        model: PreTrainedModel,
        tokenizer: PreTrainedTokenizer | None = None,
        feature_extractor: PreTrainedFeatureExtractor | None = None,
        image_processor: BaseImageProcessor | None = None,
        processor: ProcessorMixin | None = None,
        task: str = "",
        device: int | torch.device | None = None,
        binary_output: bool = False,
        **kwargs,
    ):
        # We need to pop them for _sanitize_parameters call later
        _, _, _ = kwargs.pop("args_parser", None), kwargs.pop("torch_dtype", None), kwargs.pop("dtype", None)

        self.task = task
        self.model = model
        self.tokenizer = tokenizer
        self.feature_extractor = feature_extractor
        self.image_processor = image_processor
        self.processor = processor

        # `accelerate` device map
        hf_device_map = getattr(self.model, "hf_device_map", None)

        if hf_device_map is not None and device is not None:
            raise ValueError(
                "The model has been loaded with `accelerate` and therefore cannot be moved to a specific device. Please "
                "discard the `device` argument when creating your pipeline object."
            )

        if device is None:
            if hf_device_map is not None:
                # Take the first device used by `accelerate`.
                device = next(iter(hf_device_map.values()))
            else:
                device = 0

        if device == -1 and self.model.device is not None:
            device = self.model.device
        if isinstance(device, torch.device):
            if (device.type == "xpu" and not is_torch_xpu_available(check_device=True)) or (
                device.type == "hpu" and not is_torch_hpu_available()
            ):
                raise ValueError(f'{device} is not available, you should use device="cpu" instead')

            self.device = device
        elif isinstance(device, str):
            if ("xpu" in device and not is_torch_xpu_available(check_device=True)) or (
                "hpu" in device and not is_torch_hpu_available()
            ):
                raise ValueError(f'{device} is not available, you should use device="cpu" instead')

            self.device = torch.device(device)
        elif device < 0:
            self.device = torch.device("cpu")
        elif is_torch_mlu_available():
            self.device = torch.device(f"mlu:{device}")
        elif is_torch_musa_available():
            self.device = torch.device(f"musa:{device}")
        elif is_torch_cuda_available():
            self.device = torch.device(f"cuda:{device}")
        elif is_torch_npu_available():
            self.device = torch.device(f"npu:{device}")
        elif is_torch_hpu_available():
            self.device = torch.device(f"hpu:{device}")
        elif is_torch_xpu_available(check_device=True):
            self.device = torch.device(f"xpu:{device}")
        elif is_torch_mps_available():
            self.device = torch.device(f"mps:{device}")
        else:
            self.device = torch.device("cpu")

        if torch.distributed.is_available() and torch.distributed.is_initialized():
            self.device = self.model.device
        logger.debug(f"Device set to use {self.device}")

        self.binary_output = binary_output

        # We shouldn't call `model.to()` for models loaded with accelerate as well as the case that model is already on device
        if (
            self.model.device != self.device
            and not (isinstance(self.device, int) and self.device < 0)
            and hf_device_map is None
        ):
            self.model.to(self.device)

        # If it's a generation pipeline and the model can generate:
        # 1 - create a local generation config. This is done to avoid side-effects on the model as we apply local
        # tweaks to the generation config.
        # 2 - load the assistant model if it is passed.
        if self._pipeline_calls_generate and self.model.can_generate():
            self.assistant_model, self.assistant_tokenizer = load_assistant_model(
                self.model, kwargs.pop("assistant_model", None), kwargs.pop("assistant_tokenizer", None)
            )
            self.prefix = self.model.config.prefix if hasattr(self.model.config, "prefix") else None
            # each pipeline with text generation capabilities should define its own default generation in a
            # `_default_generation_config` class attribute
            default_pipeline_generation_config = getattr(self, "_default_generation_config", GenerationConfig())
            if hasattr(self.model, "_prepare_generation_config"):
                # Uses `generate`'s logic to enforce the following priority of arguments:
                # 1. user-defined config options in `**kwargs`
                # 2. model's generation config values
                # 3. pipeline's default generation config values
                # NOTE: _prepare_generation_config creates a deep copy of the generation config before updating it,
                # and returns all kwargs that were not used to update the generation config
                prepared_generation_config, kwargs = self.model._prepare_generation_config(
                    generation_config=default_pipeline_generation_config, **kwargs
                )
                self.generation_config = prepared_generation_config
                # if the `max_new_tokens` is set to the pipeline default, but `max_length` is set to a non-default
                # value: let's honor `max_length`. E.g. we want Whisper's default `max_length=448` take precedence
                # over over the pipeline's length default.
                if (
                    default_pipeline_generation_config.max_new_tokens is not None  # there's a pipeline default
                    and self.generation_config.max_new_tokens == default_pipeline_generation_config.max_new_tokens
                    and self.generation_config.max_length is not None
                    and self.generation_config.max_length != 20  # global default
                ):
                    self.generation_config.max_new_tokens = None
            else:
                # TODO (joao): no PT model should reach this line. However, some audio models with complex
                # inheritance patterns do. Streamline those models such that this line is no longer needed.
                # In those models, the default generation config is not (yet) used.
                self.generation_config = copy.deepcopy(self.model.generation_config)
            # Update the generation config with task specific params if they exist.
            # NOTE: 1. `prefix` is pipeline-specific and doesn't exist in the generation config.
            #       2. `task_specific_params` is a legacy feature and should be removed in a future version.
            task_specific_params = getattr(self.model.config, "task_specific_params", None)
            if task_specific_params is not None and task in task_specific_params:
                this_task_params = task_specific_params.get(task)
                if "prefix" in this_task_params:
                    self.prefix = this_task_params.pop("prefix")
                self.generation_config.update(**this_task_params)
            # If the tokenizer has a pad token but the model doesn't, set it so that `generate` is aware of it.
            if (
                self.tokenizer is not None
                and self.tokenizer.pad_token_id is not None
                and self.generation_config.pad_token_id is None
            ):
                self.generation_config.pad_token_id = self.tokenizer.pad_token_id

        self.call_count = 0
        self._batch_size = kwargs.pop("batch_size", None)
        self._num_workers = kwargs.pop("num_workers", None)
        self._preprocess_params, self._forward_params, self._postprocess_params = self._sanitize_parameters(**kwargs)

        # In processor only mode, we can get the modality processors from the processor
        if self.processor is not None and all(
            [self.tokenizer is None, self.feature_extractor is None, self.image_processor is None]
        ):
            self.tokenizer = getattr(self.processor, "tokenizer", None)
            self.feature_extractor = getattr(self.processor, "feature_extractor", None)
            self.image_processor = getattr(self.processor, "image_processor", None)

        if self.image_processor is None and self.feature_extractor is not None:
            if isinstance(self.feature_extractor, BaseImageProcessor):
                # Backward compatible change, if users called
                # ImageSegmentationPipeline(.., feature_extractor=MyFeatureExtractor())
                # then we should keep working
                self.image_processor = self.feature_extractor

    def __repr__(self):
        pipe_information = {
            "model": self.model.__class__.__name__,
            "dtype": str(self.dtype).split(".")[-1],
            "device": self.device.type,
            "input_modalities": self.model.input_modalities,
        }
        if self.model.can_generate():
            pipe_information["output_modalities"] = self.model.output_modalities
        return f"{self.__class__.__name__}: {pipe_information}"

    def save_pretrained(self, save_directory: str | os.PathLike, **kwargs: Any):
        """
        Save the pipeline's model and tokenizer.

        Args:
            save_directory (`str` or `os.PathLike`):
                A path to the directory where to saved. It will be created if it doesn't exist.
            kwargs (`dict[str, Any]`, *optional*):
                Additional key word arguments passed along to the [`~utils.PushToHubMixin.push_to_hub`] method.
        """
        if os.path.isfile(save_directory):
            logger.error(f"Provided path ({save_directory}) should be a directory, not a file")
            return
        os.makedirs(save_directory, exist_ok=True)

        if hasattr(self, "_registered_impl"):
            # Add info to the config
            pipeline_info = self._registered_impl.copy()
            custom_pipelines = {}
            for task, info in pipeline_info.items():
                if info["impl"] != self.__class__:
                    continue

                info = info.copy()
                module_name = info["impl"].__module__
                last_module = module_name.split(".")[-1]
                # Change classes into their names/full names
                info["impl"] = f"{last_module}.{info['impl'].__name__}"
                info["pt"] = tuple(c.__name__ for c in info["pt"])

                custom_pipelines[task] = info
            self.model.config.custom_pipelines = custom_pipelines
            # Save the pipeline custom code
            custom_object_save(self, save_directory)

        self.model.save_pretrained(save_directory, **kwargs)

        if self.tokenizer is not None:
            self.tokenizer.save_pretrained(save_directory, **kwargs)

        if self.feature_extractor is not None:
            self.feature_extractor.save_pretrained(save_directory, **kwargs)

        if self.image_processor is not None:
            self.image_processor.save_pretrained(save_directory, **kwargs)

    def transform(self, X):
        """
        Scikit / Keras interface to transformers' pipelines. This method will forward to __call__().
        """
        return self(X)

    def predict(self, X):
        """
        Scikit / Keras interface to transformers' pipelines. This method will forward to __call__().
        """
        return self(X)

    @property
    def dtype(self) -> torch.dtype | None:
        """
        Dtype of the model (if it's Pytorch model), `None` otherwise.
        """
        return getattr(self.model, "dtype", None)

    @property
    def torch_dtype(self) -> torch.dtype | None:
        """
        Torch dtype of the model (if it's Pytorch model), `None` otherwise.
        """
        logger.warning_once("`torch_dtype` attribute is deprecated. Use `dtype` instead!")
        return getattr(self.model, "dtype", None)

    @contextmanager
    def device_placement(self):
        """
        Context Manager allowing tensor allocation on the user-specified device.

        Returns:
            Context manager

        Examples:

        ```python
        # Explicitly ask for tensor allocation on CUDA device :0
        pipe = pipeline(..., device=0)
        with pipe.device_placement():
            # Every tensor allocation will be done on the request device
            output = pipe(...)
        ```"""
        if self.device.type == "cuda":
            with torch.cuda.device(self.device):
                yield
        elif self.device.type == "mlu":
            with torch.mlu.device(self.device):
                yield
        elif self.device.type == "musa":
            with torch.musa.device(self.device):
                yield
        elif self.device.type == "xpu":
            with torch.xpu.device(self.device):
                yield
        else:
            yield

    def ensure_tensor_on_device(self, **inputs):
        """
        Ensure PyTorch tensors are on the specified device.

        Args:
            inputs (keyword arguments that should be `torch.Tensor`, the rest is ignored):
                The tensors to place on `self.device`.
            Recursive on lists **only**.

        Return:
            `dict[str, torch.Tensor]`: The same as `inputs` but on the proper device.
        """
        return self._ensure_tensor_on_device(inputs, self.device)

    def _ensure_tensor_on_device(self, inputs, device):
        if isinstance(inputs, ModelOutput):
            return ModelOutput(
                {name: self._ensure_tensor_on_device(tensor, device) for name, tensor in inputs.items()}
            )
        elif isinstance(inputs, dict):
            return {name: self._ensure_tensor_on_device(tensor, device) for name, tensor in inputs.items()}
        elif isinstance(inputs, UserDict):
            return UserDict({name: self._ensure_tensor_on_device(tensor, device) for name, tensor in inputs.items()})
        elif isinstance(inputs, list):
            return [self._ensure_tensor_on_device(item, device) for item in inputs]
        elif isinstance(inputs, tuple):
            return tuple(self._ensure_tensor_on_device(item, device) for item in inputs)
        elif isinstance(inputs, torch.Tensor):
            return inputs.to(device)
        else:
            return inputs

    def check_model_type(self, supported_models: list[str] | dict):
        """
        Check if the model class is in supported by the pipeline.

        Args:
            supported_models (`list[str]` or `dict`):
                The list of models supported by the pipeline, or a dictionary with model class values.
        """
        if not isinstance(supported_models, list):  # Create from a model mapping
            supported_models_names = []
            if self.task in SUPPORTED_PEFT_TASKS:
                supported_models_names.extend(SUPPORTED_PEFT_TASKS[self.task])

            model_name = None
            for model_name in supported_models.values():
                # Mapping can now contain tuples of models for the same configuration.
                if isinstance(model_name, tuple):
                    supported_models_names.extend(list(model_name))
                else:
                    supported_models_names.append(model_name)
            if hasattr(supported_models, "_model_mapping"):
                for model in supported_models._model_mapping._extra_content.values():
                    if isinstance(model_name, tuple):
                        supported_models_names.extend([m.__name__ for m in model])
                    else:
                        supported_models_names.append(model.__name__)
            supported_models = supported_models_names
        if self.model.__class__.__name__ not in supported_models:
            logger.error(
                f"The model '{self.model.__class__.__name__}' is not supported for {self.task}. Supported models are"
                f" {supported_models}."
            )

    @abstractmethod
    def _sanitize_parameters(self, **pipeline_parameters):
        """
        _sanitize_parameters will be called with any excessive named arguments from either `__init__` or `__call__`
        methods. It should return 3 dictionaries of the resolved parameters used by the various `preprocess`,
        `forward` and `postprocess` methods. Do not fill dictionaries if the caller didn't specify a kwargs. This
        lets you keep defaults in function signatures, which is more "natural".

        It is not meant to be called directly, it will be automatically called and the final parameters resolved by
        `__init__` and `__call__`
        """
        raise NotImplementedError("_sanitize_parameters not implemented")

    @abstractmethod
    def preprocess(self, input_: Any, **preprocess_parameters: dict) -> dict[str, GenericTensor]:
        """
        Preprocess will take the `input_` of a specific pipeline and return a dictionary of everything necessary for
        `_forward` to run properly. It should contain at least one tensor, but might have arbitrary other items.
        """
        raise NotImplementedError("preprocess not implemented")

    @abstractmethod
    def _forward(self, input_tensors: dict[str, GenericTensor], **forward_parameters: dict) -> ModelOutput:
        """
        _forward will receive the prepared dictionary from `preprocess` and run it on the model. This method might
        involve the GPU or the CPU and should be agnostic to it. Isolating this function is the reason for `preprocess`
        and `postprocess` to exist, so that the hot path, this method generally can run as fast as possible.

        It is not meant to be called directly, `forward` is preferred. It is basically the same but contains additional
        code surrounding `_forward` making sure tensors and models are on the same device, disabling the training part
        of the code (leading to faster inference).
        """
        raise NotImplementedError("_forward not implemented")

    @abstractmethod
    def postprocess(self, model_outputs: ModelOutput, **postprocess_parameters: dict) -> Any:
        """
        Postprocess will receive the raw outputs of the `_forward` method, generally tensors, and reformat them into
        something more friendly. Generally it will output a list or a dict or results (containing just strings and
        numbers).
        """
        raise NotImplementedError("postprocess not implemented")

    def get_inference_context(self):
        return torch.no_grad

    def forward(self, model_inputs, **forward_params):
        with self.device_placement():
            inference_context = self.get_inference_context()
            with inference_context():
                model_inputs = self._ensure_tensor_on_device(model_inputs, device=self.device)
                model_outputs = self._forward(model_inputs, **forward_params)
                model_outputs = self._ensure_tensor_on_device(model_outputs, device=torch.device("cpu"))
        return model_outputs

    def get_iterator(
        self, inputs, num_workers: int, batch_size: int, preprocess_params, forward_params, postprocess_params
    ):
        if isinstance(inputs, collections.abc.Sized):
            dataset = PipelineDataset(inputs, self.preprocess, preprocess_params)
        else:
            if num_workers > 1:
                logger.warning(
                    "For iterable dataset using num_workers>1 is likely to result"
                    " in errors since everything is iterable, setting `num_workers=1`"
                    " to guarantee correctness."
                )
                num_workers = 1
            dataset = PipelineIterator(inputs, self.preprocess, preprocess_params)
        if "TOKENIZERS_PARALLELISM" not in os.environ:
            logger.info("Disabling tokenizer parallelism, we're using DataLoader multithreading already")
            os.environ["TOKENIZERS_PARALLELISM"] = "false"
        # TODO hack by collating feature_extractor and image_processor
        feature_extractor = self.feature_extractor if self.feature_extractor is not None else self.image_processor
        collate_fn = no_collate_fn if batch_size == 1 else pad_collate_fn(self.tokenizer, feature_extractor)
        dataloader = DataLoader(dataset, num_workers=num_workers, batch_size=batch_size, collate_fn=collate_fn)
        model_iterator = PipelineIterator(dataloader, self.forward, forward_params, loader_batch_size=batch_size)
        final_iterator = PipelineIterator(model_iterator, self.postprocess, postprocess_params)
        return final_iterator

    def __call__(self, inputs, *args, num_workers=None, batch_size=None, **kwargs):
        if args:
            logger.warning(f"Ignoring args : {args}")

        # Detect if inputs are a chat-style input(s) and cast as `Chat` or list of `Chat`
        container_types = (list, tuple, types.GeneratorType)
        if is_torch_available():
            container_types = (*container_types, KeyDataset)
        if isinstance(inputs, container_types):
            if isinstance(inputs, types.GeneratorType):
                inputs = list(inputs)
            if is_valid_message(inputs[0]):
                inputs = Chat(inputs)
            elif isinstance(inputs[0], (list, tuple)) and all(chat and is_valid_message(chat[0]) for chat in inputs):
                inputs = [Chat(chat) for chat in inputs]

        if num_workers is None:
            if self._num_workers is None:
                num_workers = 0
            else:
                num_workers = self._num_workers
        if batch_size is None:
            if self._batch_size is None:
                batch_size = 1
            else:
                batch_size = self._batch_size

        preprocess_params, forward_params, postprocess_params = self._sanitize_parameters(**kwargs)

        # Fuse __init__ params and __call__ params without modifying the __init__ ones.
        preprocess_params = {**self._preprocess_params, **preprocess_params}
        forward_params = {**self._forward_params, **forward_params}
        postprocess_params = {**self._postprocess_params, **postprocess_params}

        self.call_count += 1
        if self.call_count > 10 and self.device.type == "cuda":
            logger.warning_once(
                "You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a"
                " dataset",
            )

        is_dataset = Dataset is not None and isinstance(inputs, Dataset)
        is_generator = isinstance(inputs, types.GeneratorType)
        is_list = isinstance(inputs, list)

        is_iterable = is_dataset or is_generator or is_list
        can_use_iterator = is_dataset or is_generator or is_list

        if is_list:
            if can_use_iterator:
                final_iterator = self.get_iterator(
                    inputs, num_workers, batch_size, preprocess_params, forward_params, postprocess_params
                )
                outputs = list(final_iterator)
                return outputs
            else:
                return self.run_multi(inputs, preprocess_params, forward_params, postprocess_params)
        elif can_use_iterator:
            return self.get_iterator(
                inputs, num_workers, batch_size, preprocess_params, forward_params, postprocess_params
            )
        elif is_iterable:
            return self.iterate(inputs, preprocess_params, forward_params, postprocess_params)
        elif isinstance(self, ChunkPipeline):
            return next(
                iter(
                    self.get_iterator(
                        [inputs], num_workers, batch_size, preprocess_params, forward_params, postprocess_params
                    )
                )
            )
        else:
            return self.run_single(inputs, preprocess_params, forward_params, postprocess_params)

    def run_multi(self, inputs, preprocess_params, forward_params, postprocess_params):
        return [self.run_single(item, preprocess_params, forward_params, postprocess_params) for item in inputs]

    def run_single(self, inputs, preprocess_params, forward_params, postprocess_params):
        model_inputs = self.preprocess(inputs, **preprocess_params)
        model_outputs = self.forward(model_inputs, **forward_params)
        outputs = self.postprocess(model_outputs, **postprocess_params)
        return outputs

    def iterate(self, inputs, preprocess_params, forward_params, postprocess_params):
        # This function should become `get_iterator` again, this is a temporary
        # easy solution.
        for input_ in inputs:
            yield self.run_single(input_, preprocess_params, forward_params, postprocess_params)


Pipeline.push_to_hub = copy_func(Pipeline.push_to_hub)
if Pipeline.push_to_hub.__doc__ is not None:
    Pipeline.push_to_hub.__doc__ = Pipeline.push_to_hub.__doc__.format(
        object="pipe", object_class="pipeline", object_files="pipeline file"
    ).replace(".from_pretrained", "")


class ChunkPipeline(Pipeline):
    def run_single(self, inputs, preprocess_params, forward_params, postprocess_params):
        all_outputs = []
        for model_inputs in self.preprocess(inputs, **preprocess_params):
            model_outputs = self.forward(model_inputs, **forward_params)
            all_outputs.append(model_outputs)
        outputs = self.postprocess(all_outputs, **postprocess_params)
        return outputs

    def get_iterator(
        self, inputs, num_workers: int, batch_size: int, preprocess_params, forward_params, postprocess_params
    ):
        if "TOKENIZERS_PARALLELISM" not in os.environ:
            logger.info("Disabling tokenizer parallelism, we're using DataLoader multithreading already")
            os.environ["TOKENIZERS_PARALLELISM"] = "false"
        if num_workers > 1:
            logger.warning(
                "For ChunkPipeline using num_workers>0 is likely to result in errors since everything is iterable,"
                " setting `num_workers=1` to guarantee correctness."
            )
            num_workers = 1
        dataset = PipelineChunkIterator(inputs, self.preprocess, preprocess_params)

        # TODO hack by collating feature_extractor and image_processor
        feature_extractor = self.feature_extractor if self.feature_extractor is not None else self.image_processor
        collate_fn = no_collate_fn if batch_size == 1 else pad_collate_fn(self.tokenizer, feature_extractor)
        dataloader = DataLoader(dataset, num_workers=num_workers, batch_size=batch_size, collate_fn=collate_fn)
        model_iterator = PipelinePackIterator(dataloader, self.forward, forward_params, loader_batch_size=batch_size)
        final_iterator = PipelineIterator(model_iterator, self.postprocess, postprocess_params)
        return final_iterator


class PipelineRegistry:
    def __init__(self, supported_tasks: dict[str, Any], task_aliases: dict[str, str]) -> None:
        self.supported_tasks = supported_tasks
        self.task_aliases = task_aliases

    def get_supported_tasks(self) -> list[str]:
        supported_task = list(self.supported_tasks.keys()) + list(self.task_aliases.keys())
        supported_task.sort()
        return supported_task

    def check_task(self, task: str) -> tuple[str, dict, Any]:
        if task in self.task_aliases:
            task = self.task_aliases[task]
        if task in self.supported_tasks:
            targeted_task = self.supported_tasks[task]
            return task, targeted_task, None

        raise KeyError(f"Unknown task {task}, available tasks are {self.get_supported_tasks()}")

    def register_pipeline(
        self,
        task: str,
        pipeline_class: type,
        pt_model: type | tuple[type] | None = None,
        default: dict | None = None,
        type: str | None = None,
    ) -> None:
        if task in self.supported_tasks:
            logger.warning(f"{task} is already registered. Overwriting pipeline for task {task}...")

        if pt_model is None:
            pt_model = ()
        elif not isinstance(pt_model, tuple):
            pt_model = (pt_model,)

        task_impl = {"impl": pipeline_class, "pt": pt_model}

        if default is not None:
            if "model" not in default:
                default = {"model": default}
            task_impl["default"] = default

        if type is not None:
            task_impl["type"] = type

        self.supported_tasks[task] = task_impl
        pipeline_class._registered_impl = {task: task_impl}

    def to_dict(self):
        return self.supported_tasks