


import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()



def   snake_case						(				_a:     Dict		,   _a:     Optional[Any]		,   _a:     str							)->		str:
      '''simple docstring'''


      lowerCamelCase__           =		RemBertConfig.from_json_file(lowerCAmelCase_							)
      print('Building PyTorch model from configuration: {}'.format(str(lowerCAmelCase_							)							)							)
      lowerCamelCase__           =		RemBertModel(lowerCAmelCase_							)

      # Load weights from tf checkpoint
      load_tf_weights_in_rembert(lowerCAmelCase_		,   lowerCAmelCase_		,   lowerCAmelCase_							)

      # Save pytorch-model
      print('Save PyTorch model to {}'.format(lowerCAmelCase_							)							)
      torch.save(model.state_dict()		,   lowerCAmelCase_							)


if __name__ == "__main__":
   _snake_case          =							argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
   )
   parser.add_argument(
       "--rembert_config_file",
       default=None,
       type=str,
       required=True,
       help=(
           "The config json file corresponding to the pre-trained RemBERT model. \n"
           "This specifies the model architecture."
       ),
   )
   parser.add_argument(
       "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
   )
   _snake_case          =							parser.parse_args()
   convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)







"""simple docstring"""


def   snake_case						(				_a:     List[Any]		,   _a:     Any		,   _a:     str		,   _a:     List[Any]							)->		List[Any]:
      '''simple docstring'''


      lowerCamelCase__           =		[False] * len(_a							)
      lowerCamelCase__           =		[]
      queue.append(_a							)
      lowerCamelCase__           =		True

      while queue:
            lowerCamelCase__           =		queue.pop(0							)
            for ind in range(len(graph[u]							)							):
                  if visited[ind] is False and graph[u][ind] > 0:
                        queue.append(_a							)
                        lowerCamelCase__           =		True
                        lowerCamelCase__           =		u

      return visited[t]



def   snake_case						(				_a:     List[Any]		,   _a:     str		,   _a:     List[str]							)->		Optional[int]:
      '''simple docstring'''


      lowerCamelCase__           =		[-1] * (len(_a							))
      lowerCamelCase__           =		0
      while bfs(_a		,   _a		,   _a		,   _a							):
            lowerCamelCase__           =		float('Inf'							)
            lowerCamelCase__           =		sink

            while s != source:
                  # Find the minimum value in select path
                  lowerCamelCase__           =		min(_a		,   graph[parent[s]][s]							)
                  lowerCamelCase__           =		parent[s]

            max_flow += path_flow
            lowerCamelCase__           =		sink

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


_snake_case          =							[
    [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],
]

_snake_case      ,					_snake_case          =							0, 5






print(ford_fulkerson(graph, source, sink))



