


def  _a			(						__UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase ):
     # Return True if there is node that has not iterated.
     a_    :     Union[str, Any]										=    [False] * len(__UpperCamelCase )
     a_    :     Optional[Any]										=    []
     queue.append(__UpperCamelCase )
     a_    :     List[Any]										=    True

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

     return visited[t]



def  _a			(						__UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase ):
     # This array is filled by BFS and to store path
     a_    :     int										=    [-1] * (len(__UpperCamelCase ))
     a_    :     List[Any]										=    0
     while bfs(__UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase ):
          a_    :     Optional[int]										=    float("""Inf""" )
          a_    :     Optional[int]										=    sink

          while s != source:
               # Find the minimum value in select path
               a_    :     str										=    min(__UpperCamelCase  ,  graph[parent[s]][s] )
               a_    :     int										=    parent[s]

          max_flow += path_flow
          a_    :     Optional[int]										=    sink

          while v != source:
               a_    :     Union[str, Any]										=    parent[v]
               graph[u][v] -= path_flow
               graph[v][u] += path_flow
               a_    :     int										=    parent[v]
     return max_flow


__lowerCamelCase        =     [
    [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],
]

__lowerCamelCase  ,       __lowerCamelCase        =     0, 5
print(ford_fulkerson(graph, source, sink))






from typing import TYPE_CHECKING

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


__lowerCamelCase        =     {'''configuration_vit''': ['''VIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTConfig''', '''ViTOnnxConfig''']}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCamelCase        =     ['''ViTFeatureExtractor''']
       __lowerCamelCase        =     ['''ViTImageProcessor''']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCamelCase        =     [
           '''VIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''ViTForImageClassification''',
           '''ViTForMaskedImageModeling''',
           '''ViTModel''',
           '''ViTPreTrainedModel''',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCamelCase        =     [
           '''TFViTForImageClassification''',
           '''TFViTModel''',
           '''TFViTPreTrainedModel''',
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCamelCase        =     [
           '''FlaxViTForImageClassification''',
           '''FlaxViTModel''',
           '''FlaxViTPreTrainedModel''',
       ]

if TYPE_CHECKING:
       from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_vit import ViTFeatureExtractor
              from .image_processing_vit import ViTImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_vit import (
                  VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  ViTForImageClassification,
                  ViTForMaskedImageModeling,
                  ViTModel,
                  ViTPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel


else:
       import sys

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



