


def     UpperCamelCase    (      _a       ,		_a       ,		_a       ,		_a		)					->							List[Any]:
     '''simple docstring'''





     lowercase_       :Dict     =       [False] * len(_a		)
     lowercase_       :Optional[int]     =       []
     queue.append(_a		)
     lowercase_       :Tuple     =       True

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

     return visited[t]




def     UpperCamelCase    (      _a       ,		_a       ,		_a		)					->							Optional[Any]:
     '''simple docstring'''





     lowercase_       :Optional[int]     =       [-1] * (len(_a		))
     lowercase_       :int     =       0
     while bfs(_a       ,		_a       ,		_a       ,		_a		):
          lowercase_       :str     =       float('''Inf'''		)
          lowercase_       :Dict     =       sink

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

          max_flow += path_flow
          lowercase_       :Tuple     =       sink

          while v != source:
               lowercase_       :str     =       parent[v]
               graph[u][v] -= path_flow
               graph[v][u] += path_flow
               lowercase_       :Optional[Any]     =       parent[v]
     return max_flow


SCREAMING_SNAKE_CASE       :		List[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],
]

SCREAMING_SNAKE_CASE     ,		SCREAMING_SNAKE_CASE       :		List[Any]         =				0, 5
print(ford_fulkerson(graph, source, sink))





from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE       :		str         =				{
    "configuration_tapas": ["TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP", "TapasConfig"],
    "tokenization_tapas": ["TapasTokenizer"],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE       :		Dict         =				[
       "TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
       "TapasForMaskedLM",
       "TapasForQuestionAnswering",
       "TapasForSequenceClassification",
       "TapasModel",
       "TapasPreTrainedModel",
       "load_tf_weights_in_tapas",
   ]
try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE       :		str         =				[
       "TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
       "TFTapasForMaskedLM",
       "TFTapasForQuestionAnswering",
       "TFTapasForSequenceClassification",
       "TFTapasModel",
       "TFTapasPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
   from .tokenization_tapas import TapasTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tapas import (
          TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
          TapasForMaskedLM,
          TapasForQuestionAnswering,
          TapasForSequenceClassification,
          TapasModel,
          TapasPreTrainedModel,
          load_tf_weights_in_tapas,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_tapas import (
          TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFTapasForMaskedLM,
          TFTapasForQuestionAnswering,
          TFTapasForSequenceClassification,
          TFTapasModel,
          TFTapasPreTrainedModel,
      )


else:
   import sys

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

