
from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand


def     UpperCAmelCase_  (						__lowerCAmelCase	) ->						Any:
    return ConvertCommand(
        args.model_type	,					args.tf_checkpoint	,					args.pytorch_dump_output	,					args.config	,					args.finetuning_task_name	)


__lowerCAmelCase					:			int			      =		"\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 						__lowerCAmelCase	(	lowerCAmelCase_      ):



      """simple docstring"""







      @staticmethod
      def 		snake_case_			(      _snake_case		:       ArgumentParser				):
          __lowercase		:			str	    =       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		:       Dict				,       _snake_case		:       str				,       _snake_case		:       str				,       _snake_case		:       str				,       _snake_case		:       str				,       _snake_case		:       str				,       *_snake_case		:       Optional[int]				,       ):
          __lowercase		:			Union[str, Any]	    =       logging.get_logger('''transformers-cli/converting'''				)

          self._logger.info(F'Loading model {model_type}'				)
          __lowercase		:			Union[str, Any]	    =       model_type
          __lowercase		:			List[Any]	    =       tf_checkpoint
          __lowercase		:			str	    =       pytorch_dump_output
          __lowercase		:			Any	    =       config
          __lowercase		:			List[str]	    =       finetuning_task_name




      def 		snake_case_			(      self		:       Optional[int]				):
          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():
                  __lowercase		:			Union[str, Any]	    =       self._tf_checkpoint
                  __lowercase		:			Tuple	    =       ''''''
              else:
                  __lowercase		:			int	    =       self._tf_checkpoint
                  __lowercase		:			Dict	    =       ''''''
              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]'''				)




from __future__ import annotations


def     UpperCAmelCase_  (						__lowerCAmelCase	) ->						int:
    if not nums:
        return 0
    __lowercase		:			List[Any]	    =       nums[0]
    __lowercase		:			Union[str, Any]	    =       0
    for num in nums[1:]:
        __lowercase    ,      __lowercase		:			Dict	    =       (
            max_excluding + num,
            max(__lowerCAmelCase	,					__lowerCAmelCase	),
        )
    return max(__lowerCAmelCase	,					__lowerCAmelCase	)


if __name__ == "__main__":
 import doctest

 doctest.testmod()


