
'''simple docstring'''
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 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    Optional[Any]     			=						[False] * len(__SCREAMING_SNAKE_CASE       )
    snake_case_ :    Tuple     			=						[s]
    snake_case_ :    Tuple     			=						True

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

    return visited[t]

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    List[Any]     			=						[-1] * (len(__SCREAMING_SNAKE_CASE       ))
    snake_case_ :    Any     			=						0
    snake_case_ :    Any     			=						[]
    snake_case_ :    Tuple     			=						[i[:] for i in graph]  # Record original cut, copy.
    while bfs(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
        snake_case_ :    Dict     			=						float("Inf"       )
        snake_case_ :    str     			=						sink

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

        max_flow += path_flow
        snake_case_ :    int     			=						sink

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

    for i in range(len(__SCREAMING_SNAKE_CASE       )       ):
        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'''
from typing import TYPE_CHECKING

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


a_											=						{
    "configuration_biogpt": ["BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BioGptConfig"],
    "tokenization_biogpt": ["BioGptTokenizer"],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_											=						[
      "BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "BioGptForCausalLM",
      "BioGptForTokenClassification",
      "BioGptForSequenceClassification",
      "BioGptModel",
      "BioGptPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
  from .tokenization_biogpt import BioGptTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_biogpt import (
        BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
        BioGptForCausalLM,
        BioGptForSequenceClassification,
        BioGptForTokenClassification,
        BioGptModel,
        BioGptPreTrainedModel,
    )


else:
  import sys

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




