code: # 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 json
  import logging
  import os
  import pickle
  import sys
  from abc import ABC, abstractmethod
  from contextlib import contextmanager
  from os.path import abspath, exists
  from typing import Dict, List, Optional, Tuple, Union
  
  import numpy as np
  
  from .configuration_auto import ALL_PRETRAINED_CONFIG_ARCHIVE_MAP, AutoConfig
  from .configuration_bart import BartConfig
  from .configuration_distilbert import DistilBertConfig
  from .configuration_roberta import RobertaConfig
  from .configuration_t5 import T5Config
  from .configuration_utils import PretrainedConfig
  from .configuration_xlm import XLMConfig
  from .data import SquadExample, squad_convert_examples_to_features
  from .file_utils import is_tf_available, is_torch_available
  from .modelcard import ModelCard
  from .tokenization_auto import AutoTokenizer
  from .tokenization_bert import BasicTokenizer
  from .tokenization_utils import PreTrainedTokenizer
  
  
  if is_tf_available():
      import tensorflow as tf
      from .modeling_tf_auto import (
          TFAutoModel,
          TFAutoModelForSequenceClassification,
          TFAutoModelForQuestionAnswering,
          TFAutoModelForTokenClassification,
          TFAutoModelWithLMHead,
      )
  
  if is_torch_available():
      import torch
      from .modeling_auto import (
          AutoModel,
          AutoModelForSequenceClassification,
          AutoModelForQuestionAnswering,
          AutoModelForTokenClassification,
          AutoModelWithLMHead,
      )
  
  
  logger = logging.getLogger(__name__)
  
  
  def get_framework(model=None):
      """ Select framework (TensorFlow/PyTorch) to use.
          If both frameworks are installed and no specific model is provided, defaults to using PyTorch.
      """
      if is_tf_available() and is_torch_available() and model is not None and not isinstance(model, str):
          # Both framework are available but the user supplied a model class instance.
          # Try to guess which framework to use from the model classname
          framework = "tf" if model.__class__.__name__.startswith("TF") else "pt"
      elif 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/."
          )
      else:
          # framework = 'tf' if is_tf_available() else 'pt'
          framework = "pt" if is_torch_available() else "tf"
      return framework
  
  
  class ArgumentHandler(ABC):
      """
      Base interface for handling varargs for each Pipeline
      """
  
      @abstractmethod
      def __call__(self, *args, **kwargs):
          raise NotImplementedError()
  
  
  class DefaultArgumentHandler(ArgumentHandler):
      """
      Default varargs argument parser handling parameters for each Pipeline
      """
  
      def __call__(self, *args, **kwargs):
          if "X" in kwargs:
              return kwargs["X"]
          elif "data" in kwargs:
              return kwargs["data"]
          elif len(args) == 1:
              if isinstance(args[0], list):
                  return args[0]
              else:
                  return [args[0]]
          elif len(args) > 1:
              return list(args)
          raise ValueError("Unable to infer the format of the provided data (X=, data=, ...)")
  
  
  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.
      """
  
      SUPPORTED_FORMATS = ["json", "csv", "pipe"]
  
      def __init__(
          self, output_path: Optional[str], input_path: Optional[str], column: Optional[str], overwrite=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("{} already exists on disk".format(self.output_path))
  
          if input_path is not None:
              if not exists(abspath(self.input_path)):
                  raise OSError("{} doesnt exist on disk".format(self.input_path))
  
      @abstractmethod
      def __iter__(self):
          raise NotImplementedError()
  
      @abstractmethod
      def save(self, data: dict):
          """
          Save the provided data object with the representation for the current `DataFormat`.
          :param data: data to store
          :return:
          """
          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.
          :param data: data to store
          :return: (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,
      ):
          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("Unknown reader {} (Available reader are json/csv/pipe)".format(format))
  
  
  class CsvPipelineDataFormat(PipelineDataFormat):
      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]):
          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):
      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):
          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}
      """
  
      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(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()
  
  
  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. Users can specify
      device argument as an integer, -1 meaning "CPU", >= 0 referring the CUDA device ordinal.
  
      Some pipeline, like for instance FeatureExtractionPipeline ('feature-extraction') outputs 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.
  
      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`, defaults to :obj:`None`):
              Model card attributed to the model for this pipeline.
          framework (:obj:`str`, `optional`, defaults to :obj:`None`):
              The framework to use, either "pt" for PyTorch or "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 PyTorch.
          args_parser (:class:`~transformers.pipelines.ArgumentHandler`, `optional`, defaults to :obj:`None`):
              Reference to the object in charge of parsing supplied pipeline parameters.
          device (:obj:`int`, `optional`, defaults to :obj:`-1`):
              Device ordinal for CPU/GPU supports. Setting this to -1 will leverage CPU, >=0 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.
  
      Return:
          :obj:`List` or :obj:`Dict`:
          Pipeline returns list or dictionary depending on:
  
           - Whether the user supplied multiple samples
           - Whether the pipeline exposes multiple fields in the output object
      """
  
      default_input_names = None
  
      def __init__(
          self,
          model: Union["PreTrainedModel", "TFPreTrainedModel"],
          tokenizer: PreTrainedTokenizer,
          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 = get_framework()
  
          self.model = model
          self.tokenizer = tokenizer
          self.modelcard = modelcard
          self.framework = framework
          self.device = device if framework == "tf" else torch.device("cpu" if device < 0 else "cuda:{}".format(device))
          self.binary_output = binary_output
          self._args_parser = args_parser or DefaultArgumentHandler()
  
          # 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):
          """
          Save the pipeline's model and tokenizer to the specified save_directory
          """
          if not os.path.isdir(save_directory):
              logger.error("Provided path ({}) should be a directory".format(save_directory))
              return
  
          self.model.save_pretrained(save_directory)
          self.tokenizer.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.
          example:
              # Explicitly ask for tensor allocation on CUDA device :0
              nlp = pipeline(..., device=0)
              with nlp.device_placement():
                  # Every framework specific tensor allocation will be done on the request device
                  output = nlp(...)
          Returns:
              Context manager
          """
          if self.framework == "tf":
              with tf.device("/CPU:0" if self.device == -1 else "/device:GPU:{}".format(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.
          :param inputs:
          :return:
          """
          return {name: tensor.to(self.device) for name, tensor in inputs.items()}
  
      def inputs_for_model(self, features: Union[dict, List[dict]]) -> Dict:
          """
          Generates the input dictionary with model-specific parameters.
  
          Returns:
              dict holding all the required parameters for model's forward
          """
          args = ["input_ids", "attention_mask"]
  
          if not isinstance(self.model.config, (DistilBertConfig, XLMConfig, RobertaConfig, BartConfig, T5Config)):
              args += ["token_type_ids"]
  
          # PR #1548 (CLI) There is an issue with attention_mask
          # if 'xlnet' in model_type or 'xlm' in model_type:
          #     args += ['cls_index', 'p_mask']
  
          if isinstance(features, dict):
              return {k: features[k] for k in args}
          else:
              return {k: [feature[k] for feature in features] for k in args}
  
      def _parse_and_tokenize(self, *texts, pad_to_max_length=False, **kwargs):
          """
          Parse arguments and tokenize
          """
          # Parse arguments
          inputs = self._args_parser(*texts, **kwargs)
          inputs = self.tokenizer.batch_encode_plus(
              inputs,
              add_special_tokens=True,
              return_tensors=self.framework,
              max_length=self.tokenizer.max_len,
              pad_to_max_length=pad_to_max_length,
          )
  
          # Filter out features not available on specific models
          inputs = self.inputs_for_model(inputs)
  
          return inputs
  
      def __call__(self, *texts, **kwargs):
          inputs = self._parse_and_tokenize(*texts, **kwargs)
          return self._forward(inputs)
  
      def _forward(self, inputs, return_tensors=False):
          """
          Internal framework specific forward dispatching.
          Args:
              inputs: dict holding all the keyworded 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, 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()
  
  
  class FeatureExtractionPipeline(Pipeline):
      """
      Feature extraction pipeline using Model head. This pipeline extracts the hidden states from the base transformer,
      which can be used as features in a downstream tasks.
  
      This feature extraction pipeline can currently be loaded from the :func:`~transformers.pipeline` method using
      the following task identifier(s):
  
      - "feature-extraction", for extracting features of a sequence.
  
      All models may be used for this pipeline. See a list of all models, including community-contributed models on
      `huggingface.co/models <https://huggingface.co/models>`__.
  
      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`, defaults to :obj:`None`):
              Model card attributed to the model for this pipeline.
          framework (:obj:`str`, `optional`, defaults to :obj:`None`):
              The framework to use, either "pt" for PyTorch or "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 PyTorch.
          args_parser (:class:`~transformers.pipelines.ArgumentHandler`, `optional`, defaults to :obj:`None`):
              Reference to the object in charge of parsing supplied pipeline parameters.
          device (:obj:`int`, `optional`, defaults to :obj:`-1`):
              Device ordinal for CPU/GPU supports. Setting this to -1 will leverage CPU, >=0 will run the model
              on the associated CUDA device id.
      """
  
      def __init__(
          self,
          model: Union["PreTrainedModel", "TFPreTrainedModel"],
          tokenizer: PreTrainedTokenizer,
          modelcard: Optional[ModelCard] = None,
          framework: Optional[str] = None,
          args_parser: ArgumentHandler = None,
          device: int = -1,
          task: str = "",
      ):
          super().__init__(
              model=model,
              tokenizer=tokenizer,
              modelcard=modelcard,
              framework=framework,
              args_parser=args_parser,
              device=device,
              binary_output=True,
              task=task,
          )
  
      def __call__(self, *args, **kwargs):
          return super().__call__(*args, **kwargs).tolist()
  
  
  class TextClassificationPipeline(Pipeline):
      """
      Text classification pipeline using ModelForSequenceClassification head. See the
      `sequence classification usage <../usage.html#sequence-classification>`__ examples for more information.
  
      This text classification pipeline can currently be loaded from the :func:`~transformers.pipeline` method using
      the following task identifier(s):
  
      - "sentiment-analysis", for classifying sequences according to positive or negative sentiments.
  
      The models that this pipeline can use are models that have been fine-tuned on a sequence classification task.
      See the list of available community models fine-tuned on such a task on
      `huggingface.co/models <https://huggingface.co/models?search=&filter=text-classification>`__.
  
      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`, defaults to :obj:`None`):
              Model card attributed to the model for this pipeline.
          framework (:obj:`str`, `optional`, defaults to :obj:`None`):
              The framework to use, either "pt" for PyTorch or "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 PyTorch.
          args_parser (:class:`~transformers.pipelines.ArgumentHandler`, `optional`, defaults to :obj:`None`):
              Reference to the object in charge of parsing supplied pipeline parameters.
          device (:obj:`int`, `optional`, defaults to :obj:`-1`):
              Device ordinal for CPU/GPU supports. Setting this to -1 will leverage CPU, >=0 will run the model
              on the associated CUDA device id.
      """
  
      def __call__(self, *args, **kwargs):
          outputs = super().__call__(*args, **kwargs)
          scores = np.exp(outputs) / np.exp(outputs).sum(-1)
          return [{"label": self.model.config.id2label[item.argmax()], "score": item.max()} for item in scores]
  
  
  class FillMaskPipeline(Pipeline):
      """
      Masked language modeling prediction pipeline using ModelWithLMHead head. See the
      `masked language modeling usage <../usage.html#masked-language-modeling>`__ examples for more information.
  
      This mask filling pipeline can currently be loaded from the :func:`~transformers.pipeline` method using
      the following task identifier(s):
  
      - "fill-mask", for predicting masked tokens in a sequence.
  
      The models that this pipeline can use are models that have been trained with a masked language modeling objective,
      which includes the bi-directional models in the library.
      See the list of available community models on
      `huggingface.co/models <https://huggingface.co/models?search=&filter=lm-head>`__.
  
      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`, defaults to :obj:`None`):
              Model card attributed to the model for this pipeline.
          framework (:obj:`str`, `optional`, defaults to :obj:`None`):
              The framework to use, either "pt" for PyTorch or "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 PyTorch.
          args_parser (:class:`~transformers.pipelines.ArgumentHandler`, `optional`, defaults to :obj:`None`):
              Reference to the object in charge of parsing supplied pipeline parameters.
          device (:obj:`int`, `optional`, defaults to :obj:`-1`):
              Device ordinal for CPU/GPU supports. Setting this to -1 will leverage CPU, >=0 will run the model
              on the associated CUDA device id.
      """
  
      def __init__(
          self,
          model: Union["PreTrainedModel", "TFPreTrainedModel"],
          tokenizer: PreTrainedTokenizer,
          modelcard: Optional[ModelCard] = None,
          framework: Optional[str] = None,
          args_parser: ArgumentHandler = None,
          device: int = -1,
          topk=5,
          task: str = "",
      ):
          super().__init__(
              model=model,
              tokenizer=tokenizer,
              modelcard=modelcard,
              framework=framework,
              args_parser=args_parser,
              device=device,
              binary_output=True,
              task=task,
          )
  
          self.topk = topk
  
      def __call__(self, *args, **kwargs):
          inputs = self._parse_and_tokenize(*args, **kwargs)
          outputs = self._forward(inputs, return_tensors=True)
  
          results = []
          batch_size = outputs.shape[0] if self.framework == "tf" else outputs.size(0)
  
          for i in range(batch_size):
              input_ids = inputs["input_ids"][i]
              result = []
  
              if self.framework == "tf":
                  masked_index = tf.where(input_ids == self.tokenizer.mask_token_id).numpy().item()
                  logits = outputs[i, masked_index, :]
                  probs = tf.nn.softmax(logits)
                  topk = tf.math.top_k(probs, k=self.topk)
                  values, predictions = topk.values.numpy(), topk.indices.numpy()
              else:
                  masked_index = (input_ids == self.tokenizer.mask_token_id).nonzero().item()
                  logits = outputs[i, masked_index, :]
                  probs = logits.softmax(dim=0)
                  values, predictions = probs.topk(self.topk)
  
              for v, p in zip(values.tolist(), predictions.tolist()):
                  tokens = input_ids.numpy()
                  tokens[masked_index] = p
                  # Filter padding out:
                  tokens = tokens[np.where(tokens != self.tokenizer.pad_token_id)]
                  result.append({"sequence": self.tokenizer.decode(tokens), "score": v, "token": p})
  
              # Append
              results += [result]
  
          if len(results) == 1:
              return results[0]
          return results
  
  
  class NerPipeline(Pipeline):
      """
      Named Entity Recognition pipeline using ModelForTokenClassification head. See the
      `named entity recognition usage <../usage.html#named-entity-recognition>`__ examples for more information.
  
      This token recognition pipeline can currently be loaded from the :func:`~transformers.pipeline` method using
      the following task identifier(s):
  
      - "ner", for predicting the classes of tokens in a sequence: person, organisation, location or miscellaneous.
  
      The models that this pipeline can use are models that have been fine-tuned on a token classification task.
      See the list of available community models fine-tuned on such a task on
      `huggingface.co/models <https://huggingface.co/models?search=&filter=token-classification>`__.
  
      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`, defaults to :obj:`None`):
              Model card attributed to the model for this pipeline.
          framework (:obj:`str`, `optional`, defaults to :obj:`None`):
              The framework to use, either "pt" for PyTorch or "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 PyTorch.
          args_parser (:class:`~transformers.pipelines.ArgumentHandler`, `optional`, defaults to :obj:`None`):
              Reference to the object in charge of parsing supplied pipeline parameters.
          device (:obj:`int`, `optional`, defaults to :obj:`-1`):
              Device ordinal for CPU/GPU supports. Setting this to -1 will leverage CPU, >=0 will run the model
              on the associated CUDA device id.
      """
  
      default_input_names = "sequences"
  
      def __init__(
          self,
          model: Union["PreTrainedModel", "TFPreTrainedModel"],
          tokenizer: PreTrainedTokenizer,
          modelcard: Optional[ModelCard] = None,
          framework: Optional[str] = None,
          args_parser: ArgumentHandler = None,
          device: int = -1,
          binary_output: bool = False,
          ignore_labels=["O"],
          task: str = "",
      ):
          super().__init__(
              model=model,
              tokenizer=tokenizer,
              modelcard=modelcard,
              framework=framework,
              args_parser=args_parser,
              device=device,
              binary_output=binary_output,
              task=task,
          )
  
          self._basic_tokenizer = BasicTokenizer(do_lower_case=False)
          self.ignore_labels = ignore_labels
  
      def __call__(self, *texts, **kwargs):
          inputs = self._args_parser(*texts, **kwargs)
          answers = []
          for sentence in inputs:
  
              # Manage correct placement of the tensors
              with self.device_placement():
  
                  tokens = self.tokenizer.encode_plus(
                      sentence,
                      return_attention_mask=False,
                      return_tensors=self.framework,
                      max_length=self.tokenizer.max_len,
                  )
  
                  # Forward
                  if self.framework == "tf":
                      entities = self.model(tokens)[0][0].numpy()
                      input_ids = tokens["input_ids"].numpy()[0]
                  else:
                      with torch.no_grad():
                          tokens = self.ensure_tensor_on_device(**tokens)
                          entities = self.model(**tokens)[0][0].cpu().numpy()
                          input_ids = tokens["input_ids"].cpu().numpy()[0]
  
              score = np.exp(entities) / np.exp(entities).sum(-1, keepdims=True)
              labels_idx = score.argmax(axis=-1)
  
              answer = []
              for idx, label_idx in enumerate(labels_idx):
                  if self.model.config.id2label[label_idx] not in self.ignore_labels:
                      answer += [
                          {
                              "word": self.tokenizer.convert_ids_to_tokens(int(input_ids[idx])),
                              "score": score[idx][label_idx].item(),
                              "entity": self.model.config.id2label[label_idx],
                          }
                      ]
  
              # Append
              answers += [answer]
          if len(answers) == 1:
              return answers[0]
          return answers
  
  
  TokenClassificationPipeline = NerPipeline
  
  
  class QuestionAnsweringArgumentHandler(ArgumentHandler):
      """
      QuestionAnsweringPipeline requires the user to provide multiple arguments (i.e. question & context) to be mapped
      to internal SquadExample / SquadFeature structures.
  
      QuestionAnsweringArgumentHandler manages all the possible to create SquadExample from the command-line supplied
      arguments.
      """
  
      def __call__(self, *args, **kwargs):
          # Position args, handling is sensibly the same as X and data, so forwarding to avoid duplicating
          if args is not None and len(args) > 0:
              if len(args) == 1:
                  kwargs["X"] = args[0]
              else:
                  kwargs["X"] = list(args)
  
          # Generic compatibility with sklearn and Keras
          # Batched data
          if "X" in kwargs or "data" in kwargs:
              inputs = kwargs["X"] if "X" in kwargs else kwargs["data"]
  
              if isinstance(inputs, dict):
                  inputs = [inputs]
              else:
                  # Copy to avoid overriding arguments
                  inputs = [i for i in inputs]
  
              for i, item in enumerate(inputs):
                  if isinstance(item, dict):
                      if any(k not in item for k in ["question", "context"]):
                          raise KeyError("You need to provide a dictionary with keys {question:..., context:...}")
  
                      inputs[i] = QuestionAnsweringPipeline.create_sample(**item)
  
                  elif not isinstance(item, SquadExample):
                      raise ValueError(
                          "{} argument needs to be of type (list[SquadExample | dict], SquadExample, dict)".format(
                              "X" if "X" in kwargs else "data"
                          )
                      )
  
              # Tabular input
          elif "question" in kwargs and "context" in kwargs:
              if isinstance(kwargs["question"], str):
                  kwargs["question"] = [kwargs["question"]]
  
              if isinstance(kwargs["context"], str):
                  kwargs["context"] = [kwargs["context"]]
  
              inputs = [
                  QuestionAnsweringPipeline.create_sample(q, c) for q, c in zip(kwargs["question"], kwargs["context"])
              ]
          else:
              raise ValueError("Unknown arguments {}".format(kwargs))
  
          if not isinstance(inputs, list):
              inputs = [inputs]
  
          return inputs
  
  
  class QuestionAnsweringPipeline(Pipeline):
      """
      Question Answering pipeline using ModelForQuestionAnswering head. See the
      `question answering usage <../usage.html#question-answering>`__ examples for more information.
  
      This question answering can currently be loaded from the :func:`~transformers.pipeline` method using
      the following task identifier(s):
  
      - "question-answering", for answering questions given a context.
  
      The models that this pipeline can use are models that have been fine-tuned on a question answering task.
      See the list of available community models fine-tuned on such a task on
      `huggingface.co/models <https://huggingface.co/models?search=&filter=question-answering>`__.
  
      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`, defaults to :obj:`None`):
              Model card attributed to the model for this pipeline.
          framework (:obj:`str`, `optional`, defaults to :obj:`None`):
              The framework to use, either "pt" for PyTorch or "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 PyTorch.
          args_parser (:class:`~transformers.pipelines.ArgumentHandler`, `optional`, defaults to :obj:`None`):
              Reference to the object in charge of parsing supplied pipeline parameters.
          device (:obj:`int`, `optional`, defaults to :obj:`-1`):
              Device ordinal for CPU/GPU supports. Setting this to -1 will leverage CPU, >=0 will run the model
              on the associated CUDA device id.
      """
  
      default_input_names = "question,context"
  
      def __init__(
          self,
          model: Union["PreTrainedModel", "TFPreTrainedModel"],
          tokenizer: PreTrainedTokenizer,
          modelcard: Optional[ModelCard] = None,
          framework: Optional[str] = None,
          device: int = -1,
          task: str = "",
          **kwargs
      ):
          super().__init__(
              model=model,
              tokenizer=tokenizer,
              modelcard=modelcard,
              framework=framework,
              args_parser=QuestionAnsweringArgumentHandler(),
              device=device,
              task=task,
              **kwargs,
          )
  
      @staticmethod
      def create_sample(
          question: Union[str, List[str]], context: Union[str, List[str]]
      ) -> Union[SquadExample, List[SquadExample]]:
          """
          QuestionAnsweringPipeline leverages the SquadExample/SquadFeatures internally.
          This helper method encapsulate all the logic for converting question(s) and context(s) to SquadExample(s).
          We currently support extractive question answering.
          Arguments:
               question: (str, List[str]) The question to be ask for the associated context
               context: (str, List[str]) The context in which we will look for the answer.
  
          Returns:
              SquadExample initialized with the corresponding question and context.
          """
          if isinstance(question, list):
              return [SquadExample(None, q, c, None, None, None) for q, c in zip(question, context)]
          else:
              return SquadExample(None, question, context, None, None, None)
  
      def __call__(self, *texts, **kwargs):
          """
          Args:
              We support multiple use-cases, the following are exclusive:
              X: sequence of SquadExample
              data: sequence of SquadExample
              question: (str, List[str]), batch of question(s) to map along with context
              context: (str, List[str]), batch of context(s) associated with the provided question keyword argument
          Returns:
              dict: {'answer': str, 'score": float, 'start": int, "end": int}
              answer: the textual answer in the intial context
              score: the score the current answer scored for the model
              start: the character index in the original string corresponding to the beginning of the answer' span
              end: the character index in the original string corresponding to the ending of the answer' span
          """
          # Set defaults values
          kwargs.setdefault("topk", 1)
          kwargs.setdefault("doc_stride", 128)
          kwargs.setdefault("max_answer_len", 15)
          kwargs.setdefault("max_seq_len", 384)
          kwargs.setdefault("max_question_len", 64)
  
          if kwargs["topk"] < 1:
              raise ValueError("topk parameter should be >= 1 (got {})".format(kwargs["topk"]))
  
          if kwargs["max_answer_len"] < 1:
              raise ValueError("max_answer_len parameter should be >= 1 (got {})".format(kwargs["max_answer_len"]))
  
          # Convert inputs to features
          examples = self._args_parser(*texts, **kwargs)
          features_list = [
              squad_convert_examples_to_features(
                  [example],
                  self.tokenizer,
                  kwargs["max_seq_len"],
                  kwargs["doc_stride"],
                  kwargs["max_question_len"],
                  False,
              )
              for example in examples
          ]
          all_answers = []
          for features, example in zip(features_list, examples):
              fw_args = self.inputs_for_model([f.__dict__ for f in features])
  
              # Manage tensor allocation on correct device
              with self.device_placement():
                  if self.framework == "tf":
                      fw_args = {k: tf.constant(v) for (k, v) in fw_args.items()}
                      start, end = self.model(fw_args)
                      start, end = start.numpy(), end.numpy()
                  else:
                      with torch.no_grad():
                          # Retrieve the score for the context tokens only (removing question tokens)
                          fw_args = {k: torch.tensor(v, device=self.device) for (k, v) in fw_args.items()}
                          start, end = self.model(**fw_args)
                          start, end = start.cpu().numpy(), end.cpu().numpy()
  
              answers = []
              for (feature, start_, end_) in zip(features, start, end):
                  # Normalize logits and spans to retrieve the answer
                  start_ = np.exp(start_) / np.sum(np.exp(start_))
                  end_ = np.exp(end_) / np.sum(np.exp(end_))
  
                  # Mask padding and question
                  start_, end_ = (
                      start_ * np.abs(np.array(feature.p_mask) - 1),
                      end_ * np.abs(np.array(feature.p_mask) - 1),
                  )
  
                  # TODO : What happens if not possible
                  # Mask CLS
                  start_[0] = end_[0] = 0
  
                  starts, ends, scores = self.decode(start_, end_, kwargs["topk"], kwargs["max_answer_len"])
                  char_to_word = np.array(example.char_to_word_offset)
  
                  # Convert the answer (tokens) back to the original text
                  answers += [
                      {
                          "score": score.item(),
                          "start": np.where(char_to_word == feature.token_to_orig_map[s])[0][0].item(),
                          "end": np.where(char_to_word == feature.token_to_orig_map[e])[0][-1].item(),
                          "answer": " ".join(
                              example.doc_tokens[feature.token_to_orig_map[s] : feature.token_to_orig_map[e] + 1]
                          ),
                      }
                      for s, e, score in zip(starts, ends, scores)
                  ]
              answers = sorted(answers, key=lambda x: x["score"], reverse=True)[: kwargs["topk"]]
              all_answers += answers
  
          if len(all_answers) == 1:
              return all_answers[0]
          return all_answers
  
      def decode(self, start: np.ndarray, end: np.ndarray, topk: int, max_answer_len: int) -> Tuple:
          """
          Take the output of any QuestionAnswering head and will generate probalities for each span to be
          the actual answer.
          In addition, it filters out some unwanted/impossible cases like answer len being greater than
          max_answer_len or answer end position being before the starting position.
          The method supports output the k-best answer through the topk argument.
  
          Args:
              start: numpy array, holding individual start probabilities for each token
              end: numpy array, holding individual end probabilities for each token
              topk: int, indicates how many possible answer span(s) to extract from the model's output
              max_answer_len: int, maximum size of the answer to extract from the model's output
          """
          # Ensure we have batch axis
          if start.ndim == 1:
              start = start[None]
  
          if end.ndim == 1:
              end = end[None]
  
          # Compute the score of each tuple(start, end) to be the real answer
          outer = np.matmul(np.expand_dims(start, -1), np.expand_dims(end, 1))
  
          # Remove candidate with end < start and end - start > max_answer_len
          candidates = np.tril(np.triu(outer), max_answer_len - 1)
  
          #  Inspired by Chen & al. (https://github.com/facebookresearch/DrQA)
          scores_flat = candidates.flatten()
          if topk == 1:
              idx_sort = [np.argmax(scores_flat)]
          elif len(scores_flat) < topk:
              idx_sort = np.argsort(-scores_flat)
          else:
              idx = np.argpartition(-scores_flat, topk)[0:topk]
              idx_sort = idx[np.argsort(-scores_flat[idx])]
  
          start, end = np.unravel_index(idx_sort, candidates.shape)[1:]
          return start, end, candidates[0, start, end]
  
      def span_to_answer(self, text: str, start: int, end: int):
          """
          When decoding from token probalities, this method maps token indexes to actual word in
          the initial context.
  
          Args:
              text: str, the actual context to extract the answer from
              start: int, starting answer token index
              end: int, ending answer token index
  
          Returns:
              dict: {'answer': str, 'start': int, 'end': int}
          """
          words = []
          token_idx = char_start_idx = char_end_idx = chars_idx = 0
  
          for i, word in enumerate(text.split(" ")):
              token = self.tokenizer.tokenize(word)
  
              # Append words if they are in the span
              if start <= token_idx <= end:
                  if token_idx == start:
                      char_start_idx = chars_idx
  
                  if token_idx == end:
                      char_end_idx = chars_idx + len(word)
  
                  words += [word]
  
              # Stop if we went over the end of the answer
              if token_idx > end:
                  break
  
              # Append the subtokenization length to the running index
              token_idx += len(token)
              chars_idx += len(word) + 1
  
          # Join text with spaces
          return {
              "answer": " ".join(words),
              "start": max(0, char_start_idx),
              "end": min(len(text), char_end_idx),
          }
  
  
  class SummarizationPipeline(Pipeline):
      """
      Summarize news articles and other documents
  
      Usage::
  
          # use bart in pytorch
          summarizer = pipeline("summarization")
          summarizer("Sam Shleifer writes the best docstring examples in the whole world.", min_length=5, max_length=20)
  
          # use t5 in tf
          summarizer = pipeline("summarization", model="t5-base", tokenizer="t5-base", framework="tf")
          summarizer("Sam Shleifer writes the best docstring examples in the whole world.", min_length=5, max_length=20)
  
      Supported Models:
          The models that this pipeline can use are models that have been fine-tuned on a summarization task, which is currently, '`bart-large-cnn`', '`t5-small`', '`t5-base`', '`t5-large`', '`t5-3b`', '`t5-11b`'.
  
      Arguments:
          model (:obj:`str` or :obj:`~transformers.PreTrainedModel` or :obj:`~transformers.TFPreTrainedModel`, `optional`, defaults to :obj:`None`):
              The model that will be used by the pipeline to make predictions. This can be :obj:`None`, a string
              checkpoint identifier or an actual pre-trained model inheriting from
              :class:`~transformers.PreTrainedModel` for PyTorch and :class:`~transformers.TFPreTrainedModel` for
              TensorFlow.
  
              If :obj:`None`, the default of the pipeline will be loaded.
          tokenizer (:obj:`str` or :obj:`~transformers.PreTrainedTokenizer`, `optional`, defaults to :obj:`None`):
              The tokenizer that will be used by the pipeline to encode data for the model. This can be :obj:`None`,
              a string checkpoint identifier or an actual pre-trained tokenizer inheriting from
              :class:`~transformers.PreTrainedTokenizer`.
  
              If :obj:`None`, the default of the pipeline will be loaded.
          modelcard (:obj:`str` or :class:`~transformers.ModelCard`, `optional`, defaults to :obj:`None`):
              Model card attributed to the model for this pipeline.
          framework (:obj:`str`, `optional`, defaults to :obj:`None`):
              The framework to use, either "pt" for PyTorch or "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 PyTorch.
          args_parser (:class:`~transformers.pipelines.ArgumentHandler`, `optional`, defaults to :obj:`None`):
              Reference to the object in charge of parsing supplied pipeline parameters.
          device (:obj:`int`, `optional`, defaults to :obj:`-1`):
              Device ordinal for CPU/GPU supports. Setting this to -1 will leverage CPU, >=0 will run the model
              on the associated CUDA device id.
      """
  
      def __call__(
          self, *documents, return_tensors=False, return_text=True, clean_up_tokenization_spaces=False, **generate_kwargs
      ):
          r"""
          Args:
              *documents: (list of strings) articles to be summarized
              return_text: (bool, default=True) whether to add a decoded "summary_text" to each result
              return_tensors: (bool, default=False) whether to return the raw "summary_token_ids" to each result
  
              clean_up_tokenization_spaces: (`optional`) bool whether to include extra spaces in the output
              **generate_kwargs: extra kwargs passed to `self.model.generate`_
  
          Returns:
              list of dicts with 'summary_text' and/or 'summary_token_ids' for each document_to_summarize
  
          .. _`self.model.generate`:
              https://huggingface.co/transformers/model_doc/bart.html#transformers.BartForConditionalGeneration.generate
  
          """
          assert return_tensors or return_text, "You must specify return_tensors=True or return_text=True"
          assert len(documents) > 0, "Please provide a document to summarize"
  
          if self.framework == "tf" and "BartForConditionalGeneration" in self.model.__class__.__name__:
              raise NotImplementedError(
                  "Tensorflow is not yet supported for Bart. Please consider using T5, e.g. `t5-base`"
              )
  
          prefix = self.model.config.prefix if self.model.config.prefix is not None else ""
  
          if isinstance(documents[0], list):
              assert (
                  self.tokenizer.pad_token_id is not None
              ), "Please make sure that the tokenizer has a pad_token_id when using a batch input"
  
              documents = ([prefix + document for document in documents[0]],)
              pad_to_max_length = True
  
          elif isinstance(documents[0], str):
              documents = (prefix + documents[0],)
              pad_to_max_length = False
          else:
              raise ValueError(
                  " `documents[0]`: {} have the wrong format. The should be either of type `str` or type `list`".format(
                      documents[0]
                  )
              )
  
          with self.device_placement():
              inputs = self._parse_and_tokenize(*documents, pad_to_max_length=pad_to_max_length)
  
              if self.framework == "pt":
                  inputs = self.ensure_tensor_on_device(**inputs)
                  input_length = inputs["input_ids"].shape[-1]
              elif self.framework == "tf":
                  input_length = tf.shape(inputs["input_ids"])[-1].numpy()
  
              if input_length < self.model.config.min_length // 2:
                  logger.warning(
                      "Your min_length is set to {}, but you input_length is only {}. You might consider decreasing min_length manually, e.g. summarizer('...', min_length=10)".format(
                          self.model.config.min_length, input_length
                      )
                  )
  
              if input_length < self.model.config.max_length:
                  logger.warning(
                      "Your max_length is set to {}, but you input_length is only {}. You might consider decreasing max_length manually, e.g. summarizer('...', max_length=50)".format(
                          self.model.config.max_length, input_length
                      )
                  )
  
              summaries = self.model.generate(
                  inputs["input_ids"], attention_mask=inputs["attention_mask"], **generate_kwargs,
              )
  
              results = []
              for summary in summaries:
                  record = {}
                  if return_tensors:
                      record["summary_token_ids"] = summary
                  if return_text:
                      record["summary_text"] = self.tokenizer.decode(
                          summary, skip_special_tokens=True, clean_up_tokenization_spaces=clean_up_tokenization_spaces,
                      )
                  results.append(record)
              return results
  
  
  class TranslationPipeline(Pipeline):
      """
      Translates from one language to another.
  
      Usage::
          en_fr_translator = pipeline("translation_en_to_fr")
          en_fr_translator("How old are you?")
  
      Supported Models: "t5-small", "t5-base", "t5-large", "t5-3b", "t5-11b"
  
      Arguments:
          model (:obj:`str` or :obj:`~transformers.PreTrainedModel` or :obj:`~transformers.TFPreTrainedModel`, `optional`, defaults to :obj:`None`):
              The model that will be used by the pipeline to make predictions. This can be :obj:`None`, a string
              checkpoint identifier or an actual pre-trained model inheriting from
              :class:`~transformers.PreTrainedModel` for PyTorch and :class:`~transformers.TFPreTrainedModel` for
              TensorFlow.
              If :obj:`None`, the default of the pipeline will be loaded.
          tokenizer (:obj:`str` or :obj:`~transformers.PreTrainedTokenizer`, `optional`, defaults to :obj:`None`):
              The tokenizer that will be used by the pipeline to encode data for the model. This can be :obj:`None`,
              a string checkpoint identifier or an actual pre-trained tokenizer inheriting from
              :class:`~transformers.PreTrainedTokenizer`.
              If :obj:`None`, the default of the pipeline will be loaded.
          modelcard (:obj:`str` or :class:`~transformers.ModelCard`, `optional`, defaults to :obj:`None`):
              Model card attributed to the model for this pipeline.
          framework (:obj:`str`, `optional`, defaults to :obj:`None`):
              The framework to use, either "pt" for PyTorch or "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 PyTorch.
          args_parser (:class:`~transformers.pipelines.ArgumentHandler`, `optional`, defaults to :obj:`None`):
              Reference to the object in charge of parsing supplied pipeline parameters.
          device (:obj:`int`, `optional`, defaults to :obj:`-1`):
              Device ordinal for CPU/GPU supports. Setting this to -1 will leverage CPU, >=0 will run the model
              on the associated CUDA device id.
      """
  
      def __call__(
          self, *texts, return_tensors=False, return_text=True, clean_up_tokenization_spaces=False, **generate_kwargs
      ):
          r"""
          Args:
              *texts: (list of strings) texts to be translated
              return_text: (bool, default=True) whether to add a decoded "translation_text" to each result
              return_tensors: (bool, default=False) whether to return the raw "translation_token_ids" to each result
  
              **generate_kwargs: extra kwargs passed to `self.model.generate`_
  
          Returns:
              list of dicts with 'translation_text' and/or 'translation_token_ids' for each text_to_translate
          .. _`self.model.generate`:
              https://huggingface.co/transformers/model_doc/bart.html#transformers.BartForConditionalGeneration.generate
          """
          assert return_tensors or return_text, "You must specify return_tensors=True or return_text=True"
  
          prefix = self.model.config.prefix if self.model.config.prefix is not None else ""
  
          if isinstance(texts[0], list):
              assert (
                  self.tokenizer.pad_token_id is not None
              ), "Please make sure that the tokenizer has a pad_token_id when using a batch input"
              texts = ([prefix + text for text in texts[0]],)
              pad_to_max_length = True
  
          elif isinstance(texts[0], str):
              texts = (prefix + texts[0],)
              pad_to_max_length = False
          else:
              raise ValueError(
                  " `documents[0]`: {} have the wrong format. The should be either of type `str` or type `list`".format(
                      texts[0]
                  )
              )
  
          with self.device_placement():
              inputs = self._parse_and_tokenize(*texts, pad_to_max_length=pad_to_max_length)
  
              if self.framework == "pt":
                  inputs = self.ensure_tensor_on_device(**inputs)
                  input_length = inputs["input_ids"].shape[-1]
  
              elif self.framework == "tf":
                  input_length = tf.shape(inputs["input_ids"])[-1].numpy()
  
              if input_length > 0.9 * self.model.config.max_length:
                  logger.warning(
                      "Your input_length: {} is bigger than 0.9 * max_length: {}. You might consider increasing your max_length manually, e.g. translator('...', max_length=400)".format(
                          input_length, self.model.config.max_length
                      )
                  )
  
              translations = self.model.generate(
                  inputs["input_ids"], attention_mask=inputs["attention_mask"], **generate_kwargs,
              )
              results = []
              for translation in translations:
                  record = {}
                  if return_tensors:
                      record["translation_token_ids"] = translation
                  if return_text:
                      record["translation_text"] = self.tokenizer.decode(
                          translation,
                          skip_special_tokens=True,
                          clean_up_tokenization_spaces=clean_up_tokenization_spaces,
                      )
                  results.append(record)
              return results
  
  
  # Register all the supported task here
  SUPPORTED_TASKS = {
      "feature-extraction": {
          "impl": FeatureExtractionPipeline,
          "tf": TFAutoModel if is_tf_available() else None,
          "pt": AutoModel if is_torch_available() else None,
          "default": {
              "model": {"pt": "distilbert-base-cased", "tf": "distilbert-base-cased"},
              "config": None,
              "tokenizer": "distilbert-base-cased",
          },
      },
      "sentiment-analysis": {
          "impl": TextClassificationPipeline,
          "tf": TFAutoModelForSequenceClassification if is_tf_available() else None,
          "pt": AutoModelForSequenceClassification if is_torch_available() else None,
          "default": {
              "model": {
                  "pt": "distilbert-base-uncased-finetuned-sst-2-english",
                  "tf": "distilbert-base-uncased-finetuned-sst-2-english",
              },
              "config": "distilbert-base-uncased-finetuned-sst-2-english",
              "tokenizer": "distilbert-base-uncased",
          },
      },
      "ner": {
          "impl": NerPipeline,
          "tf": TFAutoModelForTokenClassification if is_tf_available() else None,
          "pt": AutoModelForTokenClassification if is_torch_available() else None,
          "default": {
              "model": {
                  "pt": "dbmdz/bert-large-cased-finetuned-conll03-english",
                  "tf": "dbmdz/bert-large-cased-finetuned-conll03-english",
              },
              "config": "dbmdz/bert-large-cased-finetuned-conll03-english",
              "tokenizer": "bert-large-cased",
          },
      },
      "question-answering": {
          "impl": QuestionAnsweringPipeline,
          "tf": TFAutoModelForQuestionAnswering if is_tf_available() else None,
          "pt": AutoModelForQuestionAnswering if is_torch_available() else None,
          "default": {
              "model": {"pt": "distilbert-base-cased-distilled-squad", "tf": "distilbert-base-cased-distilled-squad"},
              "config": None,
              "tokenizer": ("distilbert-base-cased", {"use_fast": False}),
          },
      },
      "fill-mask": {
          "impl": FillMaskPipeline,
          "tf": TFAutoModelWithLMHead if is_tf_available() else None,
          "pt": AutoModelWithLMHead if is_torch_available() else None,
          "default": {
              "model": {"pt": "distilroberta-base", "tf": "distilroberta-base"},
              "config": None,
              "tokenizer": ("distilroberta-base", {"use_fast": False}),
          },
      },
      "summarization": {
          "impl": SummarizationPipeline,
          "tf": TFAutoModelWithLMHead if is_tf_available() else None,
          "pt": AutoModelWithLMHead if is_torch_available() else None,
          "default": {
              "model": {"pt": "bart-large-cnn", "tf": None},
              "config": None,
              "tokenizer": ("bart-large-cnn", {"use_fast": False}),
          },
      },
      "translation_en_to_fr": {
          "impl": TranslationPipeline,
          "tf": TFAutoModelWithLMHead if is_tf_available() else None,
          "pt": AutoModelWithLMHead if is_torch_available() else None,
          "default": {
              "model": {"pt": "t5-base", "tf": "t5-base"},
              "config": None,
              "tokenizer": ("t5-base", {"use_fast": False}),
          },
      },
      "translation_en_to_de": {
          "impl": TranslationPipeline,
          "tf": TFAutoModelWithLMHead if is_tf_available() else None,
          "pt": AutoModelWithLMHead if is_torch_available() else None,
          "default": {
              "model": {"pt": "t5-base", "tf": "t5-base"},
              "config": None,
              "tokenizer": ("t5-base", {"use_fast": False}),
          },
      },
      "translation_en_to_ro": {
          "impl": TranslationPipeline,
          "tf": TFAutoModelWithLMHead if is_tf_available() else None,
          "pt": AutoModelWithLMHead if is_torch_available() else None,
          "default": {
              "model": {"pt": "t5-base", "tf": "t5-base"},
              "config": None,
              "tokenizer": ("t5-base", {"use_fast": False}),
          },
      },
  }
  
  
  def pipeline(
      task: str,
      model: Optional = None,
      config: Optional[Union[str, PretrainedConfig]] = None,
      tokenizer: Optional[Union[str, PreTrainedTokenizer]] = None,
      framework: Optional[str] = None,
      **kwargs
  ) -> Pipeline:
      """
      Utility factory method to build a pipeline.
  
      Pipeline are made of:
  
          - A Tokenizer instance in charge of mapping raw textual input to token
          - A Model instance
          - Some (optional) post processing for enhancing model's output
  
  
      Args:
          task (:obj:`str`):
              The task defining which pipeline will be returned. Currently accepted tasks are:
  
              - "feature-extraction": will return a :class:`~transformers.FeatureExtractionPipeline`
              - "sentiment-analysis": will return a :class:`~transformers.TextClassificationPipeline`
              - "ner": will return a :class:`~transformers.NerPipeline`
              - "question-answering": will return a :class:`~transformers.QuestionAnsweringPipeline`
              - "fill-mask": will return a :class:`~transformers.FillMaskPipeline`
          model (:obj:`str` or :obj:`~transformers.PreTrainedModel` or :obj:`~transformers.TFPreTrainedModel`, `optional`, defaults to :obj:`None`):
              The model that will be used by the pipeline to make predictions. This can be :obj:`None`, a string
              checkpoint identifier or an actual pre-trained model inheriting from
              :class:`~transformers.PreTrainedModel` for PyTorch and :class:`~transformers.TFPreTrainedModel` for
              TensorFlow.
  
              If :obj:`None`, the default of the pipeline will be loaded.
          config (:obj:`str` or :obj:`~transformers.PretrainedConfig`, `optional`, defaults to :obj:`None`):
              The configuration that will be used by the pipeline to instantiate the model. This can be :obj:`None`,
              a string checkpoint identifier or an actual pre-trained model configuration inheriting from
              :class:`~transformers.PretrainedConfig`.
  
              If :obj:`None`, the default of the pipeline will be loaded.
          tokenizer (:obj:`str` or :obj:`~transformers.PreTrainedTokenizer`, `optional`, defaults to :obj:`None`):
              The tokenizer that will be used by the pipeline to encode data for the model. This can be :obj:`None`,
              a string checkpoint identifier or an actual pre-trained tokenizer inheriting from
              :class:`~transformers.PreTrainedTokenizer`.
  
              If :obj:`None`, the default of the pipeline will be loaded.
          framework (:obj:`str`, `optional`, defaults to :obj:`None`):
              The framework to use, either "pt" for PyTorch or "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 PyTorch.
  
      Returns:
          :class:`~transformers.Pipeline`: Class inheriting from :class:`~transformers.Pipeline`, according to
          the task.
  
      Examples::
  
          from transformers import pipeline, AutoModelForTokenClassification, AutoTokenizer
  
          # Sentiment analysis pipeline
          pipeline('sentiment-analysis')
  
          # Question answering pipeline, specifying the checkpoint identifier
          pipeline('question-answering', model='distilbert-base-cased-distilled-squad', tokenizer='bert-base-cased')
  
          # Named entity recognition pipeline, passing in a specific model and tokenizer
          model = AutoModelForTokenClassification.from_pretrained("dbmdz/bert-large-cased-finetuned-conll03-english")
          tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
          pipeline('ner', model=model, tokenizer=tokenizer)
  
          # Named entity recognition pipeline, passing a model and configuration with a HTTPS URL.
          model_url = "https://s3.amazonaws.com/models.huggingface.co/bert/dbmdz/bert-large-cased-finetuned-conll03-english/pytorch_model.bin"
          config_url = "https://s3.amazonaws.com/models.huggingface.co/bert/dbmdz/bert-large-cased-finetuned-conll03-english/config.json"
          pipeline('ner', model=model_url, config=config_url, tokenizer='bert-base-cased')
      """
      # Retrieve the task
      if task not in SUPPORTED_TASKS:
          raise KeyError("Unknown task {}, available tasks are {}".format(task, list(SUPPORTED_TASKS.keys())))
  
      framework = framework or get_framework(model)
  
      targeted_task = SUPPORTED_TASKS[task]
      task_class, model_class = targeted_task["impl"], targeted_task[framework]
  
      # Use default model/config/tokenizer for the task if no model is provided
      if model is None:
          models, config, tokenizer = [targeted_task["default"][k] for k in ["model", "config", "tokenizer"]]
          model = models[framework]
  
      # Try to infer tokenizer from model or config name (if provided as str)
      if tokenizer is None:
          if isinstance(model, str) and model in ALL_PRETRAINED_CONFIG_ARCHIVE_MAP:
              tokenizer = model
          elif isinstance(config, str) and config in ALL_PRETRAINED_CONFIG_ARCHIVE_MAP:
              tokenizer = config
          else:
              # Impossible to guest what is the right tokenizer here
              raise Exception(
                  "Impossible to guess which tokenizer to use. "
                  "Please provided a PretrainedTokenizer class or a path/url/shortcut name to a pretrained tokenizer."
              )
  
      modelcard = None
      # Try to infer modelcard from model or config name (if provided as str)
      if isinstance(model, str):
          modelcard = model
      elif isinstance(config, str):
          modelcard = config
  
      # Instantiate tokenizer if needed
      if isinstance(tokenizer, (str, tuple)):
          if isinstance(tokenizer, tuple):
              # For tuple we have (tokenizer name, {kwargs})
              tokenizer = AutoTokenizer.from_pretrained(tokenizer[0], **tokenizer[1])
          else:
              tokenizer = AutoTokenizer.from_pretrained(tokenizer)
  
      # Instantiate config if needed
      if isinstance(config, str):
          config = AutoConfig.from_pretrained(config)
  
      # Instantiate modelcard if needed
      if isinstance(modelcard, str):
          modelcard = ModelCard.from_pretrained(modelcard)
  
      # Instantiate model if needed
      if isinstance(model, str):
          # Handle transparent TF/PT model conversion
          model_kwargs = {}
          if framework == "pt" and model.endswith(".h5"):
              model_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"):
              model_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."
              )
          model = model_class.from_pretrained(model, config=config, **model_kwargs)
  
      return task_class(model=model, tokenizer=tokenizer, modelcard=modelcard, framework=framework, task=task, **kwargs,)
  