


'''simple docstring'''



A__						:   str				=    [
    [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      UpperCAmelCase__   (      UpperCAmelCase_       :							Dict ,       UpperCAmelCase_       :							Any ,       UpperCAmelCase_       :							str ,       UpperCAmelCase_       :							Dict				)	->						int:
     # Return True if there is node that has not iterated.
     __lowerCamelCase  :      Optional[int]														=						[False] * len(UpperCAmelCase_				)
     __lowerCamelCase  :      List[Any]														=						[s]
     __lowerCamelCase  :      str														=						True

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

     return visited[t]



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

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

          max_flow += path_flow
          __lowerCamelCase  :      List[Any]														=						sink

          while v != source:
               __lowerCamelCase  :      Tuple														=						parent[v]
               graph[u][v] -= path_flow
               graph[v][u] += path_flow
               __lowerCamelCase  :      List[Any]														=						parent[v]

     for i in range(len(UpperCAmelCase_				)				):
          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,
    is_vision_available,
)


__magic_name__  :  Any					=       {
    """configuration_layoutlmv2""": ["""LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LayoutLMv2Config"""],
    """processing_layoutlmv2""": ["""LayoutLMv2Processor"""],
    """tokenization_layoutlmv2""": ["""LayoutLMv2Tokenizer"""],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __magic_name__  :  List[str]					=       ["""LayoutLMv2TokenizerFast"""]

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __magic_name__  :  Any					=       ["""LayoutLMv2FeatureExtractor"""]
      __magic_name__  :  Any					=       ["""LayoutLMv2ImageProcessor"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __magic_name__  :  Any					=       [
          """LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """LayoutLMv2ForQuestionAnswering""",
          """LayoutLMv2ForSequenceClassification""",
          """LayoutLMv2ForTokenClassification""",
          """LayoutLMv2Layer""",
          """LayoutLMv2Model""",
          """LayoutLMv2PreTrainedModel""",
      ]

if TYPE_CHECKING:
      from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
      from .processing_layoutlmva import LayoutLMvaProcessor
      from .tokenization_layoutlmva import LayoutLMvaTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_layoutlmva import (
                LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
                LayoutLMvaForQuestionAnswering,
                LayoutLMvaForSequenceClassification,
                LayoutLMvaForTokenClassification,
                LayoutLMvaLayer,
                LayoutLMvaModel,
                LayoutLMvaPreTrainedModel,
            )
else:
      import sys

      __magic_name__  :  Union[str, Any]					=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

