'''simple docstring'''

from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand







def      UpperCamelCase_						(					snake_case_       :       Namespace      )					->     Any:






 '''simple docstring'''

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


_A			:      Optional[Any]					      =				'''
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 				_lowercase     (				UpperCAmelCase__			):


 '''simple docstring'''






 @staticmethod
 def a					(      SCREAMING_SNAKE_CASE__   :	ArgumentParser							)  ->    Optional[int]:
  __lowerCAmelCase											=						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=SCREAMING_SNAKE_CASE__      ,       required=SCREAMING_SNAKE_CASE__      ,       help="""Model's type."""							)
  train_parser.add_argument(
      """--tf_checkpoint"""      ,       type=SCREAMING_SNAKE_CASE__      ,       required=SCREAMING_SNAKE_CASE__      ,       help="""TensorFlow checkpoint path or folder."""							)
  train_parser.add_argument(
      """--pytorch_dump_output"""      ,       type=SCREAMING_SNAKE_CASE__      ,       required=SCREAMING_SNAKE_CASE__      ,       help="""Path to the PyTorch saved model output."""							)
  train_parser.add_argument("""--config"""      ,       type=SCREAMING_SNAKE_CASE__      ,       default=""""""      ,       help="""Configuration file path or folder."""							)
  train_parser.add_argument(
      """--finetuning_task_name"""      ,       type=SCREAMING_SNAKE_CASE__      ,       default=SCREAMING_SNAKE_CASE__      ,       help="""Optional fine-tuning task name if the TF model was a finetuned model."""      ,       )
  train_parser.set_defaults(func=SCREAMING_SNAKE_CASE__							)
 def __init__(      self   :	int      ,       SCREAMING_SNAKE_CASE__   :	str      ,       SCREAMING_SNAKE_CASE__   :	str      ,       SCREAMING_SNAKE_CASE__   :	str      ,       SCREAMING_SNAKE_CASE__   :	str      ,       SCREAMING_SNAKE_CASE__   :	str      ,       *SCREAMING_SNAKE_CASE__   :	Any      ,       )  ->    Any:
  __lowerCAmelCase											=						logging.get_logger("""transformers-cli/converting"""							)

  self._logger.info(f"""Loading model {model_type}"""							)
  __lowerCAmelCase											=						model_type
  __lowerCAmelCase											=						tf_checkpoint
  __lowerCAmelCase											=						pytorch_dump_output
  __lowerCAmelCase											=						config
  __lowerCAmelCase											=						finetuning_task_name





 def a					(      self   :	Optional[int]							)  ->    Any:
  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(SCREAMING_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(SCREAMING_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(SCREAMING_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(SCREAMING_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(SCREAMING_SNAKE_CASE__							)

   if "ckpt" in self._tf_checkpoint.lower():
    __lowerCAmelCase											=						self._tf_checkpoint
    __lowerCAmelCase											=						""""""
   else:
    __lowerCAmelCase											=						self._tf_checkpoint
    __lowerCAmelCase											=						""""""
   convert_transfo_xl_checkpoint_to_pytorch(
       SCREAMING_SNAKE_CASE__      ,       self._config      ,       self._pytorch_dump_output      ,       SCREAMING_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(SCREAMING_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(SCREAMING_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]"""							)







'''simple docstring'''

def      UpperCamelCase_						(					snake_case_       :       int						,		snake_case_       :       int      )					->     str:






 '''simple docstring'''

 if a < 0 or b < 0:
  raise ValueError("""the value of both inputs must be positive"""      )

 __lowerCAmelCase											=						str(bin(snake_case_      )      )[2:]  # remove the leading "0b"
 __lowerCAmelCase											=						str(bin(snake_case_      )      )[2:]  # remove the leading "0b"

 __lowerCAmelCase											=						max(len(snake_case_      )						,		len(snake_case_      )      )

 return "0b" + "".join(
     str(int(char_a == """1""" and char_b == """1"""      )      )
     for char_a, char_b in zip(a_binary.zfill(snake_case_      )						,		b_binary.zfill(snake_case_      )      )      )


if __name__ == "__main__":
 import doctest

 doctest.testmod()






