

from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):
						return ConvertCommand(
						    args.model_type  ,		args.tf_checkpoint  ,		args.pytorch_dump_output  ,		args.config  ,		args.finetuning_task_name       )


lowerCAmelCase_       	=	'''
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 	snake_case_   (		__A					):







			'''simple docstring'''

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







			def __init__(     self :				Dict							,   _UpperCamelCase :				str							,   _UpperCamelCase :				str							,   _UpperCamelCase :				str							,   _UpperCamelCase :				str							,   _UpperCamelCase :				str							,   *_UpperCamelCase :				Any							,   )      ->str:
									snake_case_   =	logging.get_logger('''transformers-cli/converting'''							)

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







			def 				snake_case__(     self :				Tuple							)      ->Optional[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(_UpperCamelCase							)

															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(_UpperCamelCase							)

															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(_UpperCamelCase							)

															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(_UpperCamelCase							)

															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(_UpperCamelCase							)

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

															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(_UpperCamelCase							)

															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   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):
						snake_case_   =	len(SCREAMING_SNAKE_CASE__       )
						# We need to create solution object to save path.
						snake_case_   =	[[0 for _ in range(SCREAMING_SNAKE_CASE__       )] for _ in range(SCREAMING_SNAKE_CASE__       )]
						snake_case_   =	run_maze(SCREAMING_SNAKE_CASE__  ,		0  ,		0  ,		SCREAMING_SNAKE_CASE__       )
						if solved:
												print('''\n'''.join(str(SCREAMING_SNAKE_CASE__       ) for row in solutions       )       )
						else:
												print('''No solution exists!'''       )
						return solved


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
						snake_case_   =	len(SCREAMING_SNAKE_CASE__       )
						# Final check point.
						if i == j == (size - 1):
												snake_case_   =	1
												return True

						snake_case_   =	(not i < 0) and (not j < 0)  # Check lower bounds
						snake_case_   =	(i < size) and (j < size)  # Check upper bounds

						if lower_flag and upper_flag:
												# check for already visited and block points.
												snake_case_   =	(not solutions[i][j]) and (not maze[i][j])
												if block_flag:
																		# check visited
																		snake_case_   =	1

																		# check for directions
																		if (
																		    run_maze(SCREAMING_SNAKE_CASE__  ,		i + 1  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       )
																		    or run_maze(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		j + 1  ,		SCREAMING_SNAKE_CASE__       )
																		    or run_maze(SCREAMING_SNAKE_CASE__  ,		i - 1  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       )
																		    or run_maze(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		j - 1  ,		SCREAMING_SNAKE_CASE__       )
																		):
																								return True

																		snake_case_   =	0
																		return False
						return False


if __name__ == "__main__":
					import doctest

					doctest.testmod()