

"""simple docstring"""





import argparse

import torch

from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()




def   _A  (  UpperCamelCase_     :       Union[str, Any],			UpperCamelCase_     :       str,			UpperCamelCase_     :       Optional[Any])	->						List[Any]:
   '''simple docstring'''


   if openai_config_file == "":
      __lowercase								=						OpenAIGPTConfig()
   else:
      __lowercase								=						OpenAIGPTConfig.from_json_file(UpperCamelCase_)
   __lowercase								=						OpenAIGPTModel(UpperCamelCase_)

   # Load weights from numpy
   load_tf_weights_in_openai_gpt(UpperCamelCase_,			UpperCamelCase_,			UpperCamelCase_)

   # Save pytorch-model
   __lowercase								=						pytorch_dump_folder_path + "/" + WEIGHTS_NAME
   __lowercase								=						pytorch_dump_folder_path + "/" + CONFIG_NAME
   print(F"""Save PyTorch model to {pytorch_weights_dump_path}""")
   torch.save(model.state_dict(),			UpperCamelCase_)
   print(F"""Save configuration file to {pytorch_config_dump_path}""")
   with open(UpperCamelCase_,			"w",			encoding="utf-8") as f:
      f.write(config.to_json_string())


if __name__ == "__main__":
   _a       =							argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--openai_checkpoint_folder_path',
       default=None,
       type=str,
       required=True,
       help='Path to the TensorFlow checkpoint path.',
   )
   parser.add_argument(
       '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
   )
   parser.add_argument(
       '--openai_config_file',
       default='',
       type=str,
       help=(
           'An optional config json file corresponding to the pre-trained OpenAI model. \n'
           'This specifies the model architecture.'
       ),
   )
   _a       =							parser.parse_args()
   convert_openai_checkpoint_to_pytorch(
       args.openai_checkpoint_folder_path, args.openai_config_file, args.pytorch_dump_folder_path
   )










def 					UpperCAmelCase__     (   lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase      ):
  # Return True if there is node that has not iterated.
  lowercase			:Union[str, Any]							=  [False] * len(lowerCamelCase      )
  lowercase			:Union[str, Any]							=  []
  queue.append(lowerCamelCase      )
  lowercase			:Optional[Any]							=  True

  while queue:
    lowercase			:Optional[Any]							=  queue.pop(0      )
    for ind in range(len(graph[u]      )      ):
      if visited[ind] is False and graph[u][ind] > 0:
        queue.append(lowerCamelCase      )
        lowercase			:Dict							=  True
        lowercase			:Dict							=  u

  return visited[t]



def 					UpperCAmelCase__     (   lowerCamelCase,	lowerCamelCase,	lowerCamelCase      ):
  # This array is filled by BFS and to store path
  lowercase			:Optional[int]							=  [-1] * (len(lowerCamelCase      ))
  lowercase			:int							=  0
  while bfs(lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase      ):
    lowercase			:int							=  float("Inf"      )
    lowercase			:Any							=  sink

    while s != source:
      # Find the minimum value in select path
      lowercase			:Any							=  min(lowerCamelCase,	graph[parent[s]][s]      )
      lowercase			:Dict							=  parent[s]

    max_flow += path_flow
    lowercase			:Union[str, Any]							=  sink

    while v != source:
      lowercase			:List[Any]							=  parent[v]
      graph[u][v] -= path_flow
      graph[v][u] += path_flow
      lowercase			:Any							=  parent[v]
  return max_flow


_UpperCAmelCase		:  Optional[Any]	=   [
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

_UpperCAmelCase	,	_UpperCAmelCase		:  Any	=   0, 5







print(ford_fulkerson(graph, source, sink))



