
from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand


def       A       (					lowercase )   ->  Any:
		'''simple docstring'''

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


_UpperCAmelCase					: List[Any]      =       "\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					(		_SCREAMING_SNAKE_CASE   ):



		@staticmethod
		def __UpperCamelCase ( A_       )  ->					Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase		=     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=A_				,      required=A_				,      help='Model\'s type.'       )
				train_parser.add_argument(
				    '--tf_checkpoint'				,      type=A_				,      required=A_				,      help='TensorFlow checkpoint path or folder.'       )
				train_parser.add_argument(
				    '--pytorch_dump_output'				,      type=A_				,      required=A_				,      help='Path to the PyTorch saved model output.'       )
				train_parser.add_argument('--config'				,      type=A_				,      default=''				,      help='Configuration file path or folder.'       )
				train_parser.add_argument(
				    '--finetuning_task_name'				,      type=A_				,      default=A_				,      help='Optional fine-tuning task name if the TF model was a finetuned model.'				,      )
				train_parser.set_defaults(func=A_       )



		def __init__( self				,      A_				,      A_				,      A_				,      A_				,      A_				,      *A_				,      )  ->					Optional[int]:
				"""simple docstring"""
				UpperCamelCase		=     logging.get_logger('transformers-cli/converting'       )

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



		def __UpperCamelCase ( self       )  ->					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(A_       )

						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(A_       )

						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(A_       )

						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(A_       )

						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(A_       )

						if "ckpt" in self._tf_checkpoint.lower():
								UpperCamelCase		=     self._tf_checkpoint
								UpperCamelCase		=     ''
						else:
								UpperCamelCase		=     self._tf_checkpoint
								UpperCamelCase		=     ''
						convert_transfo_xl_checkpoint_to_pytorch(
						    A_				,      self._config				,      self._pytorch_dump_output				,      A_       )
				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(A_       )

						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(A_       )

						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]'       )


def       A       (					lowercase    ,       lowercase    ,       lowercase    ,       lowercase    ,       lowercase )   ->  int:
		'''simple docstring'''

		if index == number_of_items:
				return 0
		UpperCamelCase		=     0
		UpperCamelCase		=     0
		UpperCamelCase		=     knapsack(lowercase    ,       lowercase    ,       lowercase    ,       lowercase    ,       index + 1 )
		if weights[index] <= max_weight:
				UpperCamelCase		=     values[index] + knapsack(
				    lowercase    ,       lowercase    ,       lowercase    ,       max_weight - weights[index]    ,       index + 1 )
		return max(lowercase    ,       lowercase )


if __name__ == "__main__":
					import doctest

					doctest.testmod()
