



"""simple docstring"""






lowerCAmelCase__              =							[
    [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],
]






def 			snake_case_    ( A_   :						Tuple,							A_   :						List[str],							A_   :						Dict,							A_   :						Tuple		):


       '''simple docstring'''





       _lowerCamelCase		:							List[str]            =					[False] * len(A_		)
       _lowerCamelCase		:							Tuple            =					[s]
       _lowerCamelCase		:							Any            =					True

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

       return visited[t]






def 			snake_case_    ( A_   :						Tuple,							A_   :						str,							A_   :						Any		):


       '''simple docstring'''





       _lowerCamelCase		:							Any            =					[-1] * (len(A_		))
       _lowerCamelCase		:							Any            =					0
       _lowerCamelCase		:							Optional[int]            =					[]
       _lowerCamelCase		:							Tuple            =					[i[:] for i in graph]  # Record original cut, copy.
       while bfs(A_,							A_,							A_,							A_		):
              _lowerCamelCase		:							int            =					float('''Inf'''		)
              _lowerCamelCase		:							List[str]            =					sink

              while s != source:
                     # Find the minimum value in select path
                     _lowerCamelCase		:							List[Any]            =					min(A_,							graph[parent[s]][s]		)
                     _lowerCamelCase		:							List[str]            =					parent[s]

              max_flow += path_flow
              _lowerCamelCase		:							Optional[Any]            =					sink

              while v != source:
                     _lowerCamelCase		:							Tuple            =					parent[v]
                     graph[u][v] -= path_flow
                     graph[v][u] += path_flow
                     _lowerCamelCase		:							Optional[int]            =					parent[v]

       for i in range(len(A_		)		):
              for j in range(len(graph[0]		)		):
                     if graph[i][j] == 0 and temp[i][j] > 0:
                            res.append((i, j)		)

       return res


if __name__ == "__main__":
   print(mincut(test_graph, source=0, sink=5))










'''simple docstring'''



import argparse

import torch

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


logging.set_verbosity_info()



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						Optional[Any],       SCREAMING_SNAKE_CASE__:						Tuple,       SCREAMING_SNAKE_CASE__:						Dict     )	->      List[Any]:

   """simple docstring"""
   # Initialise PyTorch model
   __a					  =     RemBertConfig.from_json_file(SCREAMING_SNAKE_CASE__     )
   print('Building PyTorch model from configuration: {}'.format(str(SCREAMING_SNAKE_CASE__     )     )     )
   __a					  =     RemBertModel(SCREAMING_SNAKE_CASE__     )

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

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


if __name__ == "__main__":
 __UpperCamelCase   :							List[str]         =   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."""
 )
 __UpperCamelCase   :							List[str]         =   parser.parse_args()
 convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)