# coding=utf-8
# 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.
import csv
import importlib
import json
import os
import pickle
import sys
import warnings
from abc import ABC, abstractmethod
from contextlib import contextmanager
from os.path import abspath, exists
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union

from ..feature_extraction_utils import PreTrainedFeatureExtractor
from ..file_utils import add_end_docstrings, is_tf_available, is_torch_available
from ..modelcard import ModelCard
from ..models.auto.configuration_auto import AutoConfig
from ..tokenization_utils import PreTrainedTokenizer, TruncationStrategy
from ..utils import logging


if is_tf_available():
    import tensorflow as tf

    from ..models.auto.modeling_tf_auto import TFAutoModel

if is_torch_available():
    import torch

    from ..models.auto.modeling_auto import AutoModel

if TYPE_CHECKING:
    from ..modeling_tf_utils import TFPreTrainedModel
    from ..modeling_utils import PreTrainedModel


logger = logging.get_logger(__name__)


def infer_framework_load_model(
    model,
    config: AutoConfig,
    model_classes: Optional[Dict[str, Tuple[type]]] = None,
    task: Optional[str] = None,
    framework: Optional[str] = None,
    **model_kwargs
):
    """
    Select framework (TensorFlow or PyTorch) to use from the :obj:`model` passed. Returns a tuple (framework, model).

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

    If both frameworks are installed and available for :obj:`model`, PyTorch is selected.

    Args:
        model (:obj:`str`, :class:`~transformers.PreTrainedModel` or :class:`~transformers.TFPreTrainedModel`):
            The model to infer the framework from. If :obj:`str`, a checkpoint name. The model to infer the framewrok
            from.
        config (:class:`~transformers.AutoConfig`):
            The config associated with the model to help using the correct class
        model_classes (dictionary :obj:`str` to :obj:`type`, `optional`):
            A mapping framework to class.
        task (:obj:`str`):
            The task defining which pipeline will be returned.
        model_kwargs:
            Additional dictionary of keyword arguments passed along to the model's :obj:`from_pretrained(...,
            **model_kwargs)` function.

    Returns:
        :obj:`Tuple`: A tuple framework, model.
    """
    if not is_tf_available() and not is_torch_available():
        raise RuntimeError(
            "At least one of TensorFlow 2.0 or PyTorch should be installed. "
            "To install TensorFlow 2.0, read the instructions at https://www.tensorflow.org/install/ "
            "To install PyTorch, read the instructions at https://pytorch.org/."
        )
    if isinstance(model, str):
        model_kwargs["_from_pipeline"] = task
        class_tuple = ()
        look_pt = is_torch_available() and framework in {"pt", None}
        look_tf = is_tf_available() and framework in {"tf", None}
        if model_classes:
            if look_pt:
                class_tuple = class_tuple + model_classes.get("pt", (AutoModel,))
            if look_tf:
                class_tuple = class_tuple + model_classes.get("tf", (TFAutoModel,))
        if config.architectures:
            classes = []
            for architecture in config.architectures:
                transformers_module = importlib.import_module("transformers")
                if look_pt:
                    _class = getattr(transformers_module, architecture, None)
                    if _class is not None:
                        classes.append(_class)
                if look_tf:
                    _class = getattr(transformers_module, f"TF{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}")

        for model_class in class_tuple:
            kwargs = model_kwargs.copy()
            if framework == "pt" and model.endswith(".h5"):
                kwargs["from_tf"] = True
                logger.warning(
                    "Model might be a TensorFlow model (ending with `.h5`) but TensorFlow is not available. "
                    "Trying to load the model with PyTorch."
                )
            elif framework == "tf" and model.endswith(".bin"):
                kwargs["from_pt"] = True
                logger.warning(
                    "Model might be a PyTorch model (ending with `.bin`) but PyTorch is not available. "
                    "Trying to load the model with Tensorflow."
                )

            try:
                model = model_class.from_pretrained(model, **kwargs)
                if hasattr(model, "eval"):
                    model = model.eval()
                # Stop loading on the first successful load.
                break
            except (OSError, ValueError):
                continue

        if isinstance(model, str):
            raise ValueError(f"Could not load model {model} with any of the following classes: {class_tuple}.")

    framework = "tf" if model.__class__.__name__.startswith("TF") else "pt"
    return framework, model


def infer_framework_from_model(
    model,
    model_classes: Optional[Dict[str, Tuple[type]]] = None,
    task: Optional[str] = None,
    framework: Optional[str] = None,
    **model_kwargs
):
    """
    Select framework (TensorFlow or PyTorch) to use from the :obj:`model` passed. Returns a tuple (framework, model).

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

    If both frameworks are installed and available for :obj:`model`, PyTorch is selected.

    Args:
        model (:obj:`str`, :class:`~transformers.PreTrainedModel` or :class:`~transformers.TFPreTrainedModel`):
            The model to infer the framework from. If :obj:`str`, a checkpoint name. The model to infer the framewrok
            from.
        model_classes (dictionary :obj:`str` to :obj:`type`, `optional`):
            A mapping framework to class.
        task (:obj:`str`):
            The task defining which pipeline will be returned.
        model_kwargs:
            Additional dictionary of keyword arguments passed along to the model's :obj:`from_pretrained(...,
            **model_kwargs)` function.

    Returns:
        :obj:`Tuple`: A tuple framework, model.
    """
    if isinstance(model, str):
        config = AutoConfig.from_pretrained(model, _from_pipeline=task, **model_kwargs)
    else:
        config = model.config
    return infer_framework_load_model(
        model, config, model_classes=model_classes, _from_pipeline=task, task=task, framework=framework, **model_kwargs
    )


def get_framework(model, revision: Optional[str] = None):
    """
    Select framework (TensorFlow or PyTorch) to use.

    Args:
        model (:obj:`str`, :class:`~transformers.PreTrainedModel` or :class:`~transformers.TFPreTrainedModel`):
            If both frameworks are installed, picks the one corresponding to the model passed (either a model class or
            the model name). If no specific model is provided, defaults to using PyTorch.
    """
    warnings.warn(
        "`get_framework` is deprecated and will be removed in v5, use `infer_framework_from_model` instead.",
        FutureWarning,
    )
    if not is_tf_available() and not is_torch_available():
        raise RuntimeError(
            "At least one of TensorFlow 2.0 or PyTorch should be installed. "
            "To install TensorFlow 2.0, read the instructions at https://www.tensorflow.org/install/ "
            "To install PyTorch, read the instructions at https://pytorch.org/."
        )
    if isinstance(model, str):
        if is_torch_available() and not is_tf_available():
            model = AutoModel.from_pretrained(model, revision=revision)
        elif is_tf_available() and not is_torch_available():
            model = TFAutoModel.from_pretrained(model, revision=revision)
        else:
            try:
                model = AutoModel.from_pretrained(model, revision=revision)
            except OSError:
                model = TFAutoModel.from_pretrained(model, revision=revision)

    framework = "tf" if model.__class__.__name__.startswith("TF") else "pt"
    return framework


def get_default_model(targeted_task: Dict, framework: Optional[str], task_options: Optional[Any]) -> str:
    """
    Select a default model to use for a given task. Defaults to pytorch if ambiguous.

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

        framework (:obj:`str`, None)
           "pt", "tf" or None, representing a specific framework if it was specified, or None if we don't know yet.

        task_options (:obj:`Any`, None)
           Any further value required by the task to get fully specified, for instance (SRC, TGT) languages for
           translation task.

    Returns

        :obj:`str` The model string representing the default model for this pipeline
    """
    if is_torch_available() and not is_tf_available():
        framework = "pt"
    elif is_tf_available() and not is_torch_available():
        framework = "tf"

    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:
        # XXX This error message needs to be updated to be more generic if more tasks are going to become
        # parametrized
        raise ValueError('The task defaults can\'t be correctly selected. You probably meant "translation_XX_to_YY"')

    if framework is None:
        framework = "pt"

    return default_models[framework]


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

    Args:
        task (:obj:`str`): The task of the pipeline.
        model (:obj:`str`): The model used by the pipeline.
        reason (:obj:`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 :class:`~transformers.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)

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

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

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

    def __init__(
        self,
        output_path: Optional[str],
        input_path: Optional[str],
        column: Optional[str],
        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} doesnt exist on disk")

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

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

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

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

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

        Returns:
            :obj:`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: Optional[str],
        input_path: Optional[str],
        column: Optional[str],
        overwrite=False,
    ) -> "PipelineDataFormat":
        """
        Creates an instance of the right subclass of :class:`~transformers.pipelines.PipelineDataFormat` depending on
        :obj:`format`.

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

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

    def __init__(
        self,
        output_path: Optional[str],
        input_path: Optional[str],
        column: Optional[str],
        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
        :class:`~transformers.pipelines.PipelineDataFormat`.

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

    def __init__(
        self,
        output_path: Optional[str],
        input_path: Optional[str],
        column: Optional[str],
        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 (:obj:`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 (:obj:`str`, `optional`): Where to save the outgoing data.
        input_path (:obj:`str`, `optional`): Where to look for the input data.
        column (:obj:`str`, `optional`): The column to read.
        overwrite (:obj:`bool`, `optional`, defaults to :obj:`False`):
            Whether or not to overwrite the :obj:`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 (:obj:`dict`): The data to store.
        """
        print(data)

    def save_binary(self, data: Union[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()


PIPELINE_INIT_ARGS = r"""
    Arguments:
        model (:obj:`~transformers.PreTrainedModel` or :obj:`~transformers.TFPreTrainedModel`):
            The model that will be used by the pipeline to make predictions. This needs to be a model inheriting from
            :class:`~transformers.PreTrainedModel` for PyTorch and :class:`~transformers.TFPreTrainedModel` for
            TensorFlow.
        tokenizer (:obj:`~transformers.PreTrainedTokenizer`):
            The tokenizer that will be used by the pipeline to encode data for the model. This object inherits from
            :class:`~transformers.PreTrainedTokenizer`.
        modelcard (:obj:`str` or :class:`~transformers.ModelCard`, `optional`):
            Model card attributed to the model for this pipeline.
        framework (:obj:`str`, `optional`):
            The framework to use, either :obj:`"pt"` for PyTorch or :obj:`"tf"` for TensorFlow. The specified framework
            must be installed.

            If no framework is specified, will default to the one currently installed. If no framework is specified and
            both frameworks are installed, will default to the framework of the :obj:`model`, or to PyTorch if no model
            is provided.
        task (:obj:`str`, defaults to :obj:`""`):
            A task-identifier for the pipeline.
        args_parser (:class:`~transformers.pipelines.ArgumentHandler`, `optional`):
            Reference to the object in charge of parsing supplied pipeline parameters.
        device (:obj:`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.
        binary_output (:obj:`bool`, `optional`, defaults to :obj:`False`):
            Flag indicating if the output the pipeline should happen in a binary format (i.e., pickle) or as raw text.
"""


@add_end_docstrings(PIPELINE_INIT_ARGS)
class Pipeline(_ScikitCompat):
    """
    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 :class:`~transformers.FeatureExtractionPipeline` (:obj:`'feature-extraction'` )
    output large tensor object as nested-lists. In order to avoid dumping such large structure as textual data we
    provide the :obj:`binary_output` constructor argument. If set to :obj:`True`, the output will be stored in the
    pickle format.
    """

    default_input_names = None

    def __init__(
        self,
        model: Union["PreTrainedModel", "TFPreTrainedModel"],
        tokenizer: Optional[PreTrainedTokenizer] = None,
        feature_extractor: Optional[PreTrainedFeatureExtractor] = None,
        modelcard: Optional[ModelCard] = None,
        framework: Optional[str] = None,
        task: str = "",
        args_parser: ArgumentHandler = None,
        device: int = -1,
        binary_output: bool = False,
    ):

        if framework is None:
            framework, model = infer_framework_load_model(model, config=model.config)

        self.task = task
        self.model = model
        self.tokenizer = tokenizer
        self.feature_extractor = feature_extractor
        self.modelcard = modelcard
        self.framework = framework
        self.device = device if framework == "tf" else torch.device("cpu" if device < 0 else f"cuda:{device}")
        self.binary_output = binary_output

        # Special handling
        if self.framework == "pt" and self.device.type == "cuda":
            self.model = self.model.to(self.device)

        # Update config with task specific parameters
        task_specific_params = self.model.config.task_specific_params
        if task_specific_params is not None and task in task_specific_params:
            self.model.config.update(task_specific_params.get(task))

    def save_pretrained(self, save_directory: str):
        """
        Save the pipeline's model and tokenizer.

        Args:
            save_directory (:obj:`str`):
                A path to the directory where to saved. It will be created if it doesn't exist.
        """
        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)

        self.model.save_pretrained(save_directory)

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

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

        if self.modelcard is not None:
            self.modelcard.save_pretrained(save_directory)

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

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

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

        Returns:
            Context manager

        Examples::

            # Explicitly ask for tensor allocation on CUDA device :0
            pipe = pipeline(..., device=0)
            with pipe.device_placement():
                # Every framework specific tensor allocation will be done on the request device
                output = pipe(...)
        """
        if self.framework == "tf":
            with tf.device("/CPU:0" if self.device == -1 else f"/device:GPU:{self.device}"):
                yield
        else:
            if self.device.type == "cuda":
                torch.cuda.set_device(self.device)

            yield

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

        Args:
            inputs (keyword arguments that should be :obj:`torch.Tensor`): The tensors to place on :obj:`self.device`.

        Return:
            :obj:`Dict[str, torch.Tensor]`: The same as :obj:`inputs` but on the proper device.
        """
        return {
            name: tensor.to(self.device) if isinstance(tensor, torch.Tensor) else tensor
            for name, tensor in inputs.items()
        }

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

        Args:
            supported_models (:obj:`List[str]` or :obj:`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 = []
            for config, model in supported_models.items():
                # Mapping can now contain tuples of models for the same configuration.
                if isinstance(model, tuple):
                    supported_models_names.extend([_model.__name__ for _model in model])
                else:
                    supported_models_names.append(model.__name__)
            supported_models = supported_models_names
        if self.model.__class__.__name__ not in supported_models:
            raise PipelineException(
                self.task,
                self.model.base_model_prefix,
                f"The model '{self.model.__class__.__name__}' is not supported for {self.task}. Supported models are {supported_models}",
            )

    def _parse_and_tokenize(
        self, inputs, padding=True, add_special_tokens=True, truncation=TruncationStrategy.DO_NOT_TRUNCATE, **kwargs
    ):
        """
        Parse arguments and tokenize
        """
        # Parse arguments
        inputs = self.tokenizer(
            inputs,
            add_special_tokens=add_special_tokens,
            return_tensors=self.framework,
            padding=padding,
            truncation=truncation,
        )
        return inputs

    def __call__(self, inputs, *args, **kwargs):
        try:
            model_inputs = self._parse_and_tokenize(inputs, *args, **kwargs)
            outputs = self._forward(model_inputs)
            return outputs
        except ValueError:
            # XXX: Some tokenizer do NOT have a pad token, hence we cannot run the inference
            # in a batch, instead we run everything sequentially
            if isinstance(inputs, list):
                values = []
                for input_ in inputs:
                    model_input = self._parse_and_tokenize(input_, padding=False, *args, **kwargs)
                    value = self._forward(model_input)
                    values.append(value.squeeze(0))
            else:
                model_input = self._parse_and_tokenize(inputs, padding=False, *args, **kwargs)
                values = self._forward(model_input)
            return values

    def _forward(self, inputs, return_tensors=False):
        """
        Internal framework specific forward dispatching

        Args:
            inputs: dict holding all the keyword arguments for required by the model forward method.
            return_tensors: Whether to return native framework (pt/tf) tensors rather than numpy array

        Returns:
            Numpy array
        """
        # Encode for forward
        with self.device_placement():
            if self.framework == "tf":
                # TODO trace model
                predictions = self.model(inputs.data, training=False)[0]
            else:
                with torch.no_grad():
                    inputs = self.ensure_tensor_on_device(**inputs)
                    predictions = self.model(**inputs)[0].cpu()

        if return_tensors:
            return predictions
        else:
            return predictions.numpy()
