





from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__ )  ->				List[str]:







			'''simple docstring'''

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


__A				:    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 	A_       (a_			):
	@staticmethod
	def      _lowercase				(    _A		):






				'''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							,					):






				'''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      _lowercase				(    self		):






				'''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]'''		)










from __future__ import annotations

from collections import namedtuple





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__ )  ->				tuple:







			'''simple docstring'''

			UpperCAmelCase   		=				namedtuple('''result'''			,			'''name value''' )
			if (voltage, current, power).count(0 ) != 1:
						raise ValueError('''Only one argument must be 0''' )
			elif power < 0:
						raise ValueError(
						    '''Power cannot be negative in any electrical/electronics system''' )
			elif voltage == 0:
						return result('''voltage'''			,			power / current )
			elif current == 0:
						return result('''current'''			,			power / voltage )
			elif power == 0:
						return result('''power'''			,			float(round(abs(voltage * current )			,			2 ) ) )
			else:
						raise ValueError('''Exactly one argument must be 0''' )


if __name__ == "__main__":
				import doctest

				doctest.testmod()



