




from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowerCamelCase      :						Optional[Any]							=  logging.get_logger(__name__)

_lowerCamelCase      :						Union[str, Any]							=  {
    """google/mobilenet_v1_1.0_224""": """https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json""",
    """google/mobilenet_v1_0.75_192""": """https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json""",
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}


class 							UpperCamelCase_       (					UpperCAmelCase__	):

    '''simple docstring'''






    UpperCAmelCase__				    = '''mobilenet_v1'''


    def __init__(     self :      Optional[int]   ,					UpperCAmelCase__ :      Optional[int]=3   ,					UpperCAmelCase__ :      Optional[Any]=224   ,					UpperCAmelCase__ :      Optional[int]=1.0   ,					UpperCAmelCase__ :      Optional[int]=8   ,					UpperCAmelCase__ :      Tuple="relu6"   ,					UpperCAmelCase__ :      List[Any]=True   ,					UpperCAmelCase__ :      Dict=0.999   ,					UpperCAmelCase__ :      str=0.02   ,					UpperCAmelCase__ :      Optional[int]=0.001   ,					**UpperCAmelCase__ :      Dict   ,					)		->List[str]:





           '''simple docstring'''

           super().__init__(**UpperCAmelCase__)

           if depth_multiplier <= 0:
                  raise ValueError('''depth_multiplier must be greater than zero.''')

           A__ 					=       num_channels
           A__ 					=       image_size
           A__ 					=       depth_multiplier
           A__ 					=       min_depth
           A__ 					=       hidden_act
           A__ 					=       tf_padding
           A__ 					=       classifier_dropout_prob
           A__ 					=       initializer_range
           A__ 					=       layer_norm_eps


class 							UpperCamelCase_       (					UpperCAmelCase__	):

    '''simple docstring'''






    UpperCAmelCase__				    = version.parse('''1.11'''	)


    @property
    def 			SCREAMING_SNAKE_CASE				(     self :      Any)		->Mapping[str, Mapping[int, str]]:





           '''simple docstring'''

           return OrderedDict([('''pixel_values''', {0: '''batch'''})])


    @property
    def 			SCREAMING_SNAKE_CASE				(     self :      List[str])		->Mapping[str, Mapping[int, str]]:





           '''simple docstring'''

           if self.task == "image-classification":
                  return OrderedDict([('''logits''', {0: '''batch'''})])
           else:
                  return OrderedDict([('''last_hidden_state''', {0: '''batch'''}), ('''pooler_output''', {0: '''batch'''})])





    @property
    def 			SCREAMING_SNAKE_CASE				(     self :      int)		->float:





           '''simple docstring'''

           return 1e-4





"""simple docstring"""


UpperCAmelCase													=[
    [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   _A					(	_a	:						Dict     ,   _a	:						int     ,   _a	:						Dict     ,   _a	:						List[Any]				):



      """simple docstring"""






      A				  =		[False] * len(_a				)
      A				  =		[s]
      A				  =		True

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

      return visited[t]



def   _A					(	_a	:						str     ,   _a	:						Optional[Any]     ,   _a	:						Optional[int]				):



      """simple docstring"""






      A				  =		[-1] * (len(_a				))
      A				  =		0
      A				  =		[]
      A				  =		[i[:] for i in graph]  # Record original cut, copy.
      while bfs(_a     ,   _a     ,   _a     ,   _a				):
            A				  =		float("""Inf"""				)
            A				  =		sink

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

            max_flow += path_flow
            A				  =		sink

            while v != source:
                  A				  =		parent[v]
                  graph[u][v] -= path_flow
                  graph[v][u] += path_flow
                  A				  =		parent[v]

      for i in range(len(_a				)				):
            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))




