
"""simple docstring"""





def 	__a     (		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase  ):
   # Return True if there is node that has not iterated.
   UpperCAmelCase_					:					str			     =    [False] * len(_a  )
   UpperCAmelCase_					:					Tuple			     =    []
   queue.append(_a  )
   UpperCAmelCase_					:					Optional[Any]			     =    True

   while queue:
      UpperCAmelCase_					:					Tuple			     =    queue.pop(0  )
      for ind in range(len(graph[u]  )  ):
         if visited[ind] is False and graph[u][ind] > 0:
            queue.append(_a  )
            UpperCAmelCase_					:					Union[str, Any]			     =    True
            UpperCAmelCase_					:					List[str]			     =    u

   return visited[t]







def 	__a     (		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase  ):
   # This array is filled by BFS and to store path
   UpperCAmelCase_					:					List[Any]			     =    [-1] * (len(_a  ))
   UpperCAmelCase_					:					Dict			     =    0
   while bfs(_a,		_a,		_a,		_a  ):
      UpperCAmelCase_					:					Tuple			     =    float("Inf"  )
      UpperCAmelCase_					:					Optional[int]			     =    sink

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

      max_flow += path_flow
      UpperCAmelCase_					:					Tuple			     =    sink

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


_a		       =							[
    [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],
]

_a		       =							0, 5
print(ford_fulkerson(graph, source, sink))







"""simple docstring"""

import argparse
import json

import numpy
import torch

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()




def __lowercase			(							_a		,			_a    ):
    # Load checkpoint
    snake_case_ :     Optional[Any]				=	torch.load(_a		,			map_location='''cpu'''    )

    snake_case_ :     Union[str, Any]				=	chkpt['''model''']

    # We have the base model one level deeper than the original XLM repository
    snake_case_ :     Dict				=	{}
    for k, v in state_dict.items():
        if "pred_layer" in k:
            snake_case_ :     Union[str, Any]				=	v
        else:
            snake_case_ :     Dict				=	v

    snake_case_ :     Union[str, Any]				=	chkpt['''params''']
    snake_case_ :     int				=	{n: v for n, v in config.items() if not isinstance(_a		,			(torch.FloatTensor, numpy.ndarray)    )}

    snake_case_ :     int				=	chkpt['''dico_word2id''']
    snake_case_ :     str				=	{s + '''</w>''' if s.find('''@@'''    ) == -1 and i > 13 else s.replace('''@@'''		,			''''''    ): i for s, i in vocab.items()}

    # Save pytorch-model
    snake_case_ :     Union[str, Any]				=	pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
    snake_case_ :     Union[str, Any]				=	pytorch_dump_folder_path + '''/''' + CONFIG_NAME
    snake_case_ :     Any				=	pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''vocab_file''']

    print(f"Save PyTorch model to {pytorch_weights_dump_path}"    )
    torch.save(_a		,			_a    )

    print(f"Save configuration file to {pytorch_config_dump_path}"    )
    with open(_a		,			'''w'''		,			encoding='''utf-8'''    ) as f:
        f.write(json.dumps(_a		,			indent=2    ) + '''\n'''    )

    print(f"Save vocab file to {pytorch_config_dump_path}"    )
    with open(_a		,			'''w'''		,			encoding='''utf-8'''    ) as f:
        f.write(json.dumps(_a		,			indent=2    ) + '''\n'''    )


if __name__ == "__main__":
     lowercase__    :  Optional[Any]							    =	argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--xlm_checkpoint_path''', default=None, type=str, required=True, help='''Path the official PyTorch dump.'''
     )
     parser.add_argument(
         '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
     )
     lowercase__    :  List[str]							    =	parser.parse_args()
     convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)





