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],
]




def       __lowerCAmelCase      (						A_   : List[Any]						,	A_   : Optional[int]						,	A_   : str						,	A_   : Optional[int]					):
  # Return True if there is node that has not iterated.
  __UpperCAmelCase						    =       [False] * len(A_					)
  __UpperCAmelCase						    =       [s]
  __UpperCAmelCase						    =       True

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

  return visited[t]




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

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

    max_flow += path_flow
    __UpperCAmelCase						    =       sink

    while v != source:
      __UpperCAmelCase						    =       parent[v]
      graph[u][v] -= path_flow
      graph[v][u] += path_flow
      __UpperCAmelCase						    =       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))




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a_							=      {
    """configuration_squeezebert""": [
        """SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """SqueezeBertConfig""",
        """SqueezeBertOnnxConfig""",
    ],
    """tokenization_squeezebert""": ["""SqueezeBertTokenizer"""],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_							=      ["""SqueezeBertTokenizerFast"""]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_							=      [
           """SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """SqueezeBertForMaskedLM""",
           """SqueezeBertForMultipleChoice""",
           """SqueezeBertForQuestionAnswering""",
           """SqueezeBertForSequenceClassification""",
           """SqueezeBertForTokenClassification""",
           """SqueezeBertModel""",
           """SqueezeBertModule""",
           """SqueezeBertPreTrainedModel""",
       ]


if TYPE_CHECKING:
       from .configuration_squeezebert import (
           SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
           SqueezeBertConfig,
           SqueezeBertOnnxConfig,
       )
       from .tokenization_squeezebert import SqueezeBertTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_squeezebert import (
                  SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  SqueezeBertForMaskedLM,
                  SqueezeBertForMultipleChoice,
                  SqueezeBertForQuestionAnswering,
                  SqueezeBertForSequenceClassification,
                  SqueezeBertForTokenClassification,
                  SqueezeBertModel,
                  SqueezeBertModule,
                  SqueezeBertPreTrainedModel,
              )

else:
       import sys

       a_							=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



