






"""simple docstring"""

from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand

def 		__lowerCamelCase	(  __UpperCamelCase       )						->    int:

		"""simple docstring"""







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


lowercase__          = '''
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 		__lowerCamelCase				( a__			):





		'''simple docstring'''






		@staticmethod
		def    lowerCamelCase       (   a_							:	List[Any]	):
				lowerCAmelCase_   :							Optional[Any]									=			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=lowerCAmelCase__						,			required=lowerCAmelCase__						,			help="Model's type."	)
				train_parser.add_argument(
				    "--tf_checkpoint"						,			type=lowerCAmelCase__						,			required=lowerCAmelCase__						,			help="TensorFlow checkpoint path or folder."	)
				train_parser.add_argument(
				    "--pytorch_dump_output"						,			type=lowerCAmelCase__						,			required=lowerCAmelCase__						,			help="Path to the PyTorch saved model output."	)
				train_parser.add_argument("--config"						,			type=lowerCAmelCase__						,			default=""						,			help="Configuration file path or folder."	)
				train_parser.add_argument(
				    "--finetuning_task_name"						,			type=lowerCAmelCase__						,			default=lowerCAmelCase__						,			help="Optional fine-tuning task name if the TF model was a finetuned model."						,			)
				train_parser.set_defaults(func=lowerCAmelCase__	)





		def __init__(   self							:	Tuple						,			a_							:	Tuple						,			a_							:	List[Any]						,			a_							:	Optional[int]						,			a_							:	List[Any]						,			a_							:	Optional[int]						,			*a_							:	Optional[Any]						,			):
				lowerCAmelCase_   :							Optional[Any]									=			logging.get_logger("transformers-cli/converting"	)

				self._logger.info(f'''Loading model {model_type}'''	)
				lowerCAmelCase_   :							int									=			model_type
				lowerCAmelCase_   :							str									=			tf_checkpoint
				lowerCAmelCase_   :							Union[str, Any]									=			pytorch_dump_output
				lowerCAmelCase_   :							Dict									=			config
				lowerCAmelCase_   :							Tuple									=			finetuning_task_name





		def    lowerCamelCase       (   self							:	List[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(lowerCAmelCase__	)

						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(lowerCAmelCase__	)

						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(lowerCAmelCase__	)

						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(lowerCAmelCase__	)

						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(lowerCAmelCase__	)

						if "ckpt" in self._tf_checkpoint.lower():
								lowerCAmelCase_   :							List[Any]									=			self._tf_checkpoint
								lowerCAmelCase_   :							str									=			""
						else:
								lowerCAmelCase_   :							Optional[Any]									=			self._tf_checkpoint
								lowerCAmelCase_   :							Optional[Any]									=			""
						convert_transfo_xl_checkpoint_to_pytorch(
						    lowerCAmelCase__						,			self._config						,			self._pytorch_dump_output						,			lowerCAmelCase__	)
				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(lowerCAmelCase__	)

						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(lowerCAmelCase__	)

						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       _SCREAMING_SNAKE_CASE		(	_lowercase						:    float    ,       _lowercase						:    float					) ->float:






	'''simple docstring'''






	return price * (1 + tax_rate)


if __name__ == "__main__":
		print(F'''{price_plus_tax(100, 0.25) = }''')
		print(F'''{price_plus_tax(125.50, 0.05) = }''')


