






from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Namespace    )						->      str:





    """simple docstring"""

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


__a      :       List[str]				  =   "\ntransformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires\nTensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.\n"






class       __lowercase      (	lowercase_  ):







     '''simple docstring'''




     @staticmethod
     def 		lowerCAmelCase_     (				UpperCamelCase_       :			ArgumentParser				):


         """simple docstring"""



         __A         =	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=UpperCamelCase_ ,	required=UpperCamelCase_ ,	help="""Model's type."""				)
         train_parser.add_argument(
             """--tf_checkpoint""" ,	type=UpperCamelCase_ ,	required=UpperCamelCase_ ,	help="""TensorFlow checkpoint path or folder."""				)
         train_parser.add_argument(
             """--pytorch_dump_output""" ,	type=UpperCamelCase_ ,	required=UpperCamelCase_ ,	help="""Path to the PyTorch saved model output."""				)
         train_parser.add_argument("""--config""" ,	type=UpperCamelCase_ ,	default="""""" ,	help="""Configuration file path or folder."""				)
         train_parser.add_argument(
             """--finetuning_task_name""" ,	type=UpperCamelCase_ ,	default=UpperCamelCase_ ,	help="""Optional fine-tuning task name if the TF model was a finetuned model.""" ,	)
         train_parser.set_defaults(func=UpperCamelCase_				)




     def __init__(				self       :			Optional[int] ,	UpperCamelCase_       :			str ,	UpperCamelCase_       :			str ,	UpperCamelCase_       :			str ,	UpperCamelCase_       :			str ,	UpperCamelCase_       :			str ,	*UpperCamelCase_       :			Optional[int] ,	):


         """simple docstring"""



         __A         =	logging.get_logger("""transformers-cli/converting"""				)

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





     def 		lowerCAmelCase_     (				self       :			Optional[Any]				):


         """simple docstring"""



         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(UpperCamelCase_				)

             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(UpperCamelCase_				)

             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(UpperCamelCase_				)

             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(UpperCamelCase_				)

             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(UpperCamelCase_				)

             if "ckpt" in self._tf_checkpoint.lower():
                 __A         =	self._tf_checkpoint
                 __A         =	""""""
             else:
                 __A         =	self._tf_checkpoint
                 __A         =	""""""
             convert_transfo_xl_checkpoint_to_pytorch(
                 UpperCamelCase_ ,	self._config ,	self._pytorch_dump_output ,	UpperCamelCase_				)
         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(UpperCamelCase_				)

             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(UpperCamelCase_				)

             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]"""				)












from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments



def 			_SCREAMING_SNAKE_CASE						( )						->      Tuple:





    """simple docstring"""

    __A         =	HfArgumentParser(__lowercase    )
    __A         =	parser.parse_args_into_dataclasses()[0]
    __A         =	TensorFlowBenchmark(args=__lowercase    )
    try:
        __A         =	parser.parse_args_into_dataclasses()[0]
    except ValueError as e:
        __A         =	"""Arg --no_{0} is no longer used, please use --no-{0} instead."""
        __A         =	""" """.join(str(__lowercase    ).split(""" """    )[:-1]    )
        __A         =	""""""
        __A         =	eval(str(__lowercase    ).split(""" """    )[-1]    )
        __A         =	[]
        for arg in depreciated_args:
            # arg[2:] removes '--'
            if arg[2:] in TensorFlowBenchmark.deprecated_args:
                # arg[5:] removes '--no_'
                full_error_msg += arg_error_msg.format(arg[5:]    )
            else:
                wrong_args.append(__lowercase    )
        if len(__lowercase    ) > 0:
            __A         =	full_error_msg + begin_error_msg + str(__lowercase    )
        raise ValueError(__lowercase    )
    benchmark.run()


if __name__ == "__main__":
  main()




