
"""simple docstring"""





def 							A_     (__a = 10				):

					'''simple docstring'''





					if not isinstance(__a				,   __a				) or n < 0:
										raise ValueError("Invalid input"				)
					A_				 =						10**n
					A_				 =						2_8433 * (pow(2				,   783_0457				,   __a				)) + 1
					return str(number % modulus				)


if __name__ == "__main__":
		from doctest import testmod

		testmod()
		print(F"""{solution(10) = }""")






"""simple docstring"""





from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand
def 							A_     (__a				):

					'''simple docstring'''





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


UpperCamelCase_					:						Tuple				= '''
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				(					_lowercase      ):




			"""simple docstring"""







			@staticmethod
			def      lowerCamelCase__     (				_snake_case						:					ArgumentParser	)      -> Union[str, Any]:







								"""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=_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						:					str       ,   _snake_case						:					str       ,   _snake_case						:					str       ,   _snake_case						:					str       ,   _snake_case						:					str       ,   _snake_case						:					str       ,   *_snake_case						:					Optional[int]       ,   )      -> Union[str, Any]:







								"""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						:					Tuple	)      -> Dict:







								"""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(_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():
																		A_				 =						self._tf_checkpoint
																		A_				 =						""
													else:
																		A_				 =						self._tf_checkpoint
																		A_				 =						""
													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]"	)




