
"""simple docstring"""
def 				a__   (   a   :				Dict       ,     a   :				List[Any]		):

  """simple docstring"""







  _snake_case     :						Tuple						=   ""
  for word_or_phrase in separated:
    if not isinstance(UpperCAmelCase__       ,     UpperCAmelCase__		):
      raise Exception("join() accepts only strings to be joined"		)
    joined += word_or_phrase + separator
  return joined.strip(UpperCAmelCase__		)


if __name__ == "__main__":
    from doctest import testmod

    testmod()



"""simple docstring"""
from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand
def 				a__   (   a   :				Namespace		):

  """simple docstring"""







  return ConvertCommand(
      args.model_type       ,     args.tf_checkpoint       ,     args.pytorch_dump_output       ,     args.config       ,     args.finetuning_task_name		)


_a      :			int					=							"""
transformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires
TensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.
"""






class      _UpperCAmelCase				(					_snake_case):
    @staticmethod
    def 					lowerCamelCase__	(    snake_case_     ):
      _snake_case     :						Dict						=   parser.add_parser(
          "convert"						,    help="CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints."						,    )
      train_parser.add_argument("--model_type"						,    type=snake_case_						,    required=snake_case_						,    help="Model's type."     )
      train_parser.add_argument(
          "--tf_checkpoint"						,    type=snake_case_						,    required=snake_case_						,    help="TensorFlow checkpoint path or folder."     )
      train_parser.add_argument(
          "--pytorch_dump_output"						,    type=snake_case_						,    required=snake_case_						,    help="Path to the PyTorch saved model output."     )
      train_parser.add_argument("--config"						,    type=snake_case_						,    default=""						,    help="Configuration file path or folder."     )
      train_parser.add_argument(
          "--finetuning_task_name"						,    type=snake_case_						,    default=snake_case_						,    help="Optional fine-tuning task name if the TF model was a finetuned model."						,    )
      train_parser.set_defaults(func=snake_case_     )
    def __init__(    self						,    snake_case_						,    snake_case_						,    snake_case_						,    snake_case_						,    snake_case_						,    *snake_case_						,    ):
      _snake_case     :						str						=   logging.get_logger("transformers-cli/converting"     )

      self._logger.info(F'Loading model {model_type}'     )
      _snake_case     :						Optional[int]						=   model_type
      _snake_case     :						Any						=   tf_checkpoint
      _snake_case     :						Optional[int]						=   pytorch_dump_output
      _snake_case     :						Tuple						=   config
      _snake_case     :						Tuple						=   finetuning_task_name


    def 					lowerCamelCase__	(    self     ):
      if self._model_type == "albert":
        try:
          from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
              convert_tf_checkpoint_to_pytorch,
          )
        except ImportError:
          raise ImportError(snake_case_     )

        convert_tf_checkpoint_to_pytorch(self._tf_checkpoint						,    self._config						,    self._pytorch_dump_output     )
      elif self._model_type == "bert":
        try:
          from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
              convert_tf_checkpoint_to_pytorch,
          )
        except ImportError:
          raise ImportError(snake_case_     )

        convert_tf_checkpoint_to_pytorch(self._tf_checkpoint						,    self._config						,    self._pytorch_dump_output     )
      elif self._model_type == "funnel":
        try:
          from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
              convert_tf_checkpoint_to_pytorch,
          )
        except ImportError:
          raise ImportError(snake_case_     )

        convert_tf_checkpoint_to_pytorch(self._tf_checkpoint						,    self._config						,    self._pytorch_dump_output     )
      elif self._model_type == "t5":
        try:
          from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
        except ImportError:
          raise ImportError(snake_case_     )

        convert_tf_checkpoint_to_pytorch(self._tf_checkpoint						,    self._config						,    self._pytorch_dump_output     )
      elif self._model_type == "gpt":
        from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
            convert_openai_checkpoint_to_pytorch,
        )

        convert_openai_checkpoint_to_pytorch(self._tf_checkpoint						,    self._config						,    self._pytorch_dump_output     )
      elif self._model_type == "transfo_xl":
        try:
          from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
              convert_transfo_xl_checkpoint_to_pytorch,
          )
        except ImportError:
          raise ImportError(snake_case_     )

        if "ckpt" in self._tf_checkpoint.lower():
          _snake_case     :						int						=   self._tf_checkpoint
          _snake_case     :						Optional[Any]						=   ""
        else:
          _snake_case     :						Optional[int]						=   self._tf_checkpoint
          _snake_case     :						List[str]						=   ""
        convert_transfo_xl_checkpoint_to_pytorch(
            snake_case_						,    self._config						,    self._pytorch_dump_output						,    snake_case_     )
      elif self._model_type == "gpt2":
        try:
          from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
              convert_gpta_checkpoint_to_pytorch,
          )
        except ImportError:
          raise ImportError(snake_case_     )

        convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint						,    self._config						,    self._pytorch_dump_output     )
      elif self._model_type == "xlnet":
        try:
          from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
              convert_xlnet_checkpoint_to_pytorch,
          )
        except ImportError:
          raise ImportError(snake_case_     )

        convert_xlnet_checkpoint_to_pytorch(
            self._tf_checkpoint						,    self._config						,    self._pytorch_dump_output						,    self._finetuning_task_name     )
      elif self._model_type == "xlm":
        from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
            convert_xlm_checkpoint_to_pytorch,
        )

        convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint						,    self._pytorch_dump_output     )
      elif self._model_type == "lxmert":
        from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
            convert_lxmert_checkpoint_to_pytorch,
        )

        convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint						,    self._pytorch_dump_output     )
      elif self._model_type == "rembert":
        from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
            convert_rembert_tf_checkpoint_to_pytorch,
        )

        convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint						,    self._config						,    self._pytorch_dump_output     )
      else:
        raise ValueError(
            "--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]"     )

