




from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	Namespace							)					-> Optional[Any]:
      return ConvertCommand(
          args.model_type			,			args.tf_checkpoint			,			args.pytorch_dump_output			,			args.config			,			args.finetuning_task_name							)


lowerCamelCase__ :   Union[str, 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        lowerCamelCase_							(      _SCREAMING_SNAKE_CASE					):


 '''simple docstring'''







 @staticmethod
 def       lowerCAmelCase_  (							_lowerCAmelCase  :		ArgumentParser						):
       SCREAMING_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=_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  :		Optional[int]				,				_lowerCAmelCase  :		str				,				_lowerCAmelCase  :		str				,				_lowerCAmelCase  :		str				,				_lowerCAmelCase  :		str				,				_lowerCAmelCase  :		str				,				*_lowerCAmelCase  :		int				,				):
       SCREAMING_SNAKE_CASE_							=			logging.get_logger('transformers-cli/converting'						)

       self._logger.info(F"Loading model {model_type}"						)
       SCREAMING_SNAKE_CASE_							=			model_type
       SCREAMING_SNAKE_CASE_							=			tf_checkpoint
       SCREAMING_SNAKE_CASE_							=			pytorch_dump_output
       SCREAMING_SNAKE_CASE_							=			config
       SCREAMING_SNAKE_CASE_							=			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():
                   SCREAMING_SNAKE_CASE_							=			self._tf_checkpoint
                   SCREAMING_SNAKE_CASE_							=			''
             else:
                   SCREAMING_SNAKE_CASE_							=			self._tf_checkpoint
                   SCREAMING_SNAKE_CASE_							=			''
             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]'						)





class        lowerCamelCase_							:


 '''simple docstring'''







 def __init__(							self  :		List[Any]						):
       SCREAMING_SNAKE_CASE_							=			{}



 def       lowerCAmelCase_  (							self  :		List[str]						):
       print(self.vertex						)
       for i in self.vertex:
             print(_lowerCAmelCase				,				' -> '				,				' -> '.join([str(_lowerCAmelCase						) for j in self.vertex[i]]						)						)



 def       lowerCAmelCase_  (							self  :		List[Any]				,				_lowerCAmelCase  :		int				,				_lowerCAmelCase  :		int						):
       # check if vertex is already present,
       if from_vertex in self.vertex:
             self.vertex[from_vertex].append(_lowerCAmelCase						)
       else:
             # else make a new vertex
             SCREAMING_SNAKE_CASE_							=			[to_vertex]



 def       lowerCAmelCase_  (							self  :		Dict						):
       # visited array for storing already visited nodes
       SCREAMING_SNAKE_CASE_							=			[False] * len(self.vertex						)

       # call the recursive helper function
       for i in range(len(self.vertex						)						):
             if not visited[i]:
                   self.dfs_recursive(_lowerCAmelCase				,				_lowerCAmelCase						)




 def       lowerCAmelCase_  (							self  :		Union[str, Any]				,				_lowerCAmelCase  :		int				,				_lowerCAmelCase  :		list						):
       # mark start vertex as visited
       SCREAMING_SNAKE_CASE_							=			True

       print(_lowerCAmelCase				,				end=' '						)

       # Recur for all the vertices that are adjacent to this node
       for i in self.vertex:
             if not visited[i]:
                   self.dfs_recursive(_lowerCAmelCase				,				_lowerCAmelCase						)


if __name__ == "__main__":
   lowerCamelCase__ :   List[Any]					=							Graph()
   g.add_edge(0, 1)
   g.add_edge(0, 2)
   g.add_edge(1, 2)
   g.add_edge(2, 0)
   g.add_edge(2, 3)
   g.add_edge(3, 3)

   g.print_graph()
   print('DFS:')
   g.dfs()

   # OUTPUT:
   # 0  ->  1 -> 2
   # 1  ->  2
   # 2  ->  0 -> 3
   # 3  ->  3
   # DFS:
   #  0 1 2 3