

def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      Tuple:

     """simple docstring"""







     _lowerCAmelCase   :					Optional[Any]        =    [False] * len(SCREAMING_SNAKE_CASE				)
     _lowerCAmelCase   :					Dict        =    []
     queue.append(SCREAMING_SNAKE_CASE				)
     _lowerCAmelCase   :					Dict        =    True

     while queue:
          _lowerCAmelCase   :					Optional[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				)
                    _lowerCAmelCase   :					Tuple        =    True
                    _lowerCAmelCase   :					str        =    u

     return visited[t]







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      Dict:

     """simple docstring"""







     _lowerCAmelCase   :					Optional[int]        =    [-1] * (len(SCREAMING_SNAKE_CASE				))
     _lowerCAmelCase   :					str        =    0
     while bfs(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				):
          _lowerCAmelCase   :					Union[str, Any]        =    float("Inf"				)
          _lowerCAmelCase   :					Optional[Any]        =    sink

          while s != source:
               # Find the minimum value in select path
               _lowerCAmelCase   :					Optional[int]        =    min(SCREAMING_SNAKE_CASE		,							graph[parent[s]][s]				)
               _lowerCAmelCase   :					int        =    parent[s]

          max_flow += path_flow
          _lowerCAmelCase   :					Union[str, Any]        =    sink

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


__UpperCAmelCase    				=     [
    [0, 1_6, 1_3, 0, 0, 0],
    [0, 0, 1_0, 1_2, 0, 0],
    [0, 4, 0, 0, 1_4, 0],
    [0, 0, 9, 0, 0, 2_0],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

__UpperCAmelCase				,__UpperCAmelCase    				=     0, 5



print(ford_fulkerson(graph, source, sink))









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCAmelCase    				=     {
    'configuration_lxmert': ['LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LxmertConfig'],
    'tokenization_lxmert': ['LxmertTokenizer'],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase    				=     ['LxmertTokenizerFast']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase    				=     [
      'LxmertEncoder',
      'LxmertForPreTraining',
      'LxmertForQuestionAnswering',
      'LxmertModel',
      'LxmertPreTrainedModel',
      'LxmertVisualFeatureEncoder',
      'LxmertXLayer',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase    				=     [
      'TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFLxmertForPreTraining',
      'TFLxmertMainLayer',
      'TFLxmertModel',
      'TFLxmertPreTrainedModel',
      'TFLxmertVisualFeatureEncoder',
  ]


if TYPE_CHECKING:
  from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
  from .tokenization_lxmert import LxmertTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_lxmert_fast import LxmertTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_lxmert import (
        LxmertEncoder,
        LxmertForPreTraining,
        LxmertForQuestionAnswering,
        LxmertModel,
        LxmertPreTrainedModel,
        LxmertVisualFeatureEncoder,
        LxmertXLayer,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_lxmert import (
        TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFLxmertForPreTraining,
        TFLxmertMainLayer,
        TFLxmertModel,
        TFLxmertPreTrainedModel,
        TFLxmertVisualFeatureEncoder,
    )

else:
  import sys

  __UpperCAmelCase    				=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






