






from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand

def   snake_case (UpperCAmelCase__  )	->    Optional[int]:
  return ConvertCommand(
      args.model_type     ,   args.tf_checkpoint     ,   args.pytorch_dump_output     ,   args.config     ,   args.finetuning_task_name  )


A_		:		Optional[int]		     =			'\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(	UpperCAmelCase_ ):





       """simple docstring"""







       @staticmethod
       def       _a   ( _lowerCamelCase							):
         UpperCamelCase_: Union[str, 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  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      *_lowerCamelCase  ,      ):
         UpperCamelCase_: int   				=    logging.get_logger('transformers-cli/converting'							)

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





       def       _a   ( self							):
         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():
             UpperCamelCase_: Optional[int]   				=    self._tf_checkpoint
             UpperCamelCase_: str   				=    ''
           else:
             UpperCamelCase_: int   				=    self._tf_checkpoint
             UpperCamelCase_: List[str]   				=    ''
           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]'							)







# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.

def   snake_case (UpperCAmelCase__  )	->    List[str]:
  UpperCamelCase_: int   				=    [False] * len(UpperCAmelCase__  )
  UpperCamelCase_: Any   				=    [-1] * len(UpperCAmelCase__  )

  def dfs(UpperCAmelCase__     ,   UpperCAmelCase__  ):
    UpperCamelCase_: Tuple   				=    True
    UpperCamelCase_: Optional[int]   				=    c
    for u in graph[v]:
      if not visited[u]:
        dfs(UpperCAmelCase__     ,   1 - c  )

  for i in range(len(UpperCAmelCase__  )  ):
    if not visited[i]:
      dfs(UpperCAmelCase__     ,   0  )

  for i in range(len(UpperCAmelCase__  )  ):
    for j in graph[i]:
      if color[i] == color[j]:
        return False

  return True


# Adjacency list of graph
A_		:		Dict		     =			{0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}
print(check_bipartite_dfs(graph))