


from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand


def        _UpperCamelCase							(      lowercase__      ):
						return ConvertCommand(
						    args.model_type    ,       args.tf_checkpoint    ,       args.pytorch_dump_output    ,       args.config    ,       args.finetuning_task_name      )


__lowerCAmelCase	: Optional[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      (			A__       ):






				'''simple docstring'''



				@staticmethod
				def 		__magic_name__(       lowerCAmelCase__			:ArgumentParser	)		->			Any:
										__SCREAMING_SNAKE_CASE						:     List[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			:int			,			lowerCAmelCase__			:str			,			lowerCAmelCase__			:str			,			lowerCAmelCase__			:str			,			lowerCAmelCase__			:str			,			lowerCAmelCase__			:str			,			*lowerCAmelCase__			:List[Any]			,			)		->			Dict:
										__SCREAMING_SNAKE_CASE						:     str					=		logging.get_logger('''transformers-cli/converting'''	)

										self._logger.info(f'''Loading model {model_type}'''	)
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		model_type
										__SCREAMING_SNAKE_CASE						:     str					=		tf_checkpoint
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		pytorch_dump_output
										__SCREAMING_SNAKE_CASE						:     str					=		config
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		finetuning_task_name



				def 		__magic_name__(       self			:int	)		->			List[str]:
										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():
																						__SCREAMING_SNAKE_CASE						:     Dict					=		self._tf_checkpoint
																						__SCREAMING_SNAKE_CASE						:     int					=		''''''
																else:
																						__SCREAMING_SNAKE_CASE						:     int					=		self._tf_checkpoint
																						__SCREAMING_SNAKE_CASE						:     List[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]'''	)




from __future__ import annotations

A      :    Union[str, Any]		=					{
    "A": ["B", "C", "E"],
    "B": ["A", "D", "E"],
    "C": ["A", "F", "G"],
    "D": ["B"],
    "E": ["A", "B", "D"],
    "F": ["C"],
    "G": ["C"],
}







class 			_lowercase :


       """simple docstring"""




       def __init__(   self							:			Tuple					,      __lowerCamelCase							:			dict[str, list[str]]					,      __lowerCamelCase							:			str       ):


             '''simple docstring'''



             lowerCamelCase__		:				Union[str, Any]    =  graph
             # mapping node to its parent in resulting breadth first tree
             lowerCamelCase__		:				dict[str, str | None]    =  {}
             lowerCamelCase__		:				Dict    =  source_vertex





       def 						lowerCAmelCase     (   self							:			Any       ):


             '''simple docstring'''



             lowerCamelCase__		:				int    =  {self.source_vertex}
             lowerCamelCase__		:				Optional[int]    =  None
             lowerCamelCase__		:				Dict    =  [self.source_vertex]  # first in first out queue

             while queue:
                   lowerCamelCase__		:				Optional[int]    =  queue.pop(0       )
                   for adjacent_vertex in self.graph[vertex]:
                         if adjacent_vertex not in visited:
                               visited.add(__lowerCamelCase       )
                               lowerCamelCase__		:				List[str]    =  vertex
                               queue.append(__lowerCamelCase       )





       def 						lowerCAmelCase     (   self							:			Optional[Any]					,      __lowerCamelCase							:			str       ):


             '''simple docstring'''



             if target_vertex == self.source_vertex:
                   return self.source_vertex

             lowerCamelCase__		:				Tuple    =  self.parent.get(__lowerCamelCase       )
             if target_vertex_parent is None:
                   lowerCamelCase__		:				Tuple    =  (
                       f"No path from vertex: {self.source_vertex} to vertex: {target_vertex}"
                   )
                   raise ValueError(__lowerCamelCase       )

             return self.shortest_path(__lowerCamelCase       ) + f"->{target_vertex}"


if __name__ == "__main__":
    A      :    List[str]		=					Graph(graph, "G")
    g.breath_first_search()
    print(g.shortest_path("D"))
    print(g.shortest_path("G"))
    print(g.shortest_path("Foo"))


