def    __lowercase     (						lowerCamelCase					:     List[Any]  ,  lowerCamelCase					:     Any  ,  lowerCamelCase					:     int  ,  lowerCamelCase					:     List[str]	):
 # Return True if there is node that has not iterated.
 UpperCamelCase_					:						List[str] =					[False] * len(lowerCamelCase	)
 UpperCamelCase_					:						List[str] =					[]
 queue.append(lowerCamelCase	)
 UpperCamelCase_					:						str =					True

 while queue:
  UpperCamelCase_					:						List[Any] =					queue.pop(0	)
  for ind in range(len(graph[u]	)	):
   if visited[ind] is False and graph[u][ind] > 0:
    queue.append(lowerCamelCase	)
    UpperCamelCase_					:						Union[str, Any] =					True
    UpperCamelCase_					:						Tuple =					u

 return visited[t]





def    __lowercase     (						lowerCamelCase					:     str  ,  lowerCamelCase					:     Optional[Any]  ,  lowerCamelCase					:     List[Any]	):
 # This array is filled by BFS and to store path
 UpperCamelCase_					:						int =					[-1] * (len(lowerCamelCase	))
 UpperCamelCase_					:						int =					0
 while bfs(lowerCamelCase  ,  lowerCamelCase  ,  lowerCamelCase  ,  lowerCamelCase	):
  UpperCamelCase_					:						int =					float('Inf'	)
  UpperCamelCase_					:						Any =					sink

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

  max_flow += path_flow
  UpperCamelCase_					:						Any =					sink

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


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],
]

a_					, a_    					=       0, 5






print(ford_fulkerson(graph, source, sink))




from typing import TYPE_CHECKING

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


a_    					=       {
    'configuration_efficientformer': [
        'EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'EfficientFormerConfig',
    ]
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a_    					=       ['EfficientFormerImageProcessor']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a_    					=       [
       'EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'EfficientFormerForImageClassification',
       'EfficientFormerForImageClassificationWithTeacher',
       'EfficientFormerModel',
       'EfficientFormerPreTrainedModel',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a_    					=       [
       'TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFEfficientFormerForImageClassification',
       'TFEfficientFormerForImageClassificationWithTeacher',
       'TFEfficientFormerModel',
       'TFEfficientFormerPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_efficientformer import EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, EfficientFormerConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .image_processing_efficientformer import EfficientFormerImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_efficientformer import (
          EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          EfficientFormerForImageClassification,
          EfficientFormerForImageClassificationWithTeacher,
          EfficientFormerModel,
          EfficientFormerPreTrainedModel,
      )
   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_efficientformer import (
          TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFEfficientFormerForImageClassification,
          TFEfficientFormerForImageClassificationWithTeacher,
          TFEfficientFormerModel,
          TFEfficientFormerPreTrainedModel,
      )

else:
   import sys

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



