





def 					lowerCAmelCase_			(	__a		)					->		Optional[Any]:







  """simple docstring"""





  lowerCamelCase__:				Union[str, Any]        =[0] * len(a__		)
  lowerCamelCase__:				List[str]        =[]
  lowerCamelCase__:				Any        =[1] * len(a__		)

  for values in graph.values():
    for i in values:
      indegree[i] += 1

  for i in range(len(a__		)		):
    if indegree[i] == 0:
      queue.append(a__		)

  while queue:
    lowerCamelCase__:				List[Any]        =queue.pop(0		)
    for x in graph[vertex]:
      indegree[x] -= 1

      if long_dist[vertex] + 1 > long_dist[x]:
        lowerCamelCase__:				Optional[Any]        =long_dist[vertex] + 1

      if indegree[x] == 0:
        queue.append(a__		)

  print(max(a__		)		)


# Adjacency list of Graph
__A     =    {0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}






longest_distance(graph)








from argparse import ArgumentParser, Namespace

from ..utils import logging
from . import BaseTransformersCLICommand





def 					lowerCAmelCase_			(	__a		)					->		int:







  """simple docstring"""





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


__A     =    "\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        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''

 @staticmethod
 def 							SCREAMING_SNAKE_CASE_ (UpperCAmelCase_    :       ArgumentParser)	->str:



   '''simple docstring'''



   lowerCamelCase__:				Dict        =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    :       List[Any]	,			UpperCAmelCase_    :       str	,			UpperCAmelCase_    :       str	,			UpperCAmelCase_    :       str	,			UpperCAmelCase_    :       str	,			UpperCAmelCase_    :       str	,			*UpperCAmelCase_    :       Optional[int]	,			)	->List[str]:



   '''simple docstring'''



   lowerCamelCase__:				Dict        =logging.get_logger("transformers-cli/converting")

   self._logger.info(F"""Loading model {model_type}""")
   lowerCamelCase__:				Any        =model_type
   lowerCamelCase__:				Optional[int]        =tf_checkpoint
   lowerCamelCase__:				Any        =pytorch_dump_output
   lowerCamelCase__:				Union[str, Any]        =config
   lowerCamelCase__:				str        =finetuning_task_name







 def 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any])	->Tuple:



   '''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(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():
       lowerCamelCase__:				Tuple        =self._tf_checkpoint
       lowerCamelCase__:				List[str]        =""
     else:
       lowerCamelCase__:				Any        =self._tf_checkpoint
       lowerCamelCase__:				Dict        =""
     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]")

