






"""simple docstring"""


_UpperCamelCase							:       Tuple       					=		float('inf')






class 		snake_case    :





    def __init__(				self      :   Optional[Any]							,						A      :   Any     ):


      '''simple docstring'''




      a	:     Tuple							=  [0] * n
      a	:     List[Any]							=  [0] * n
      a	:     List[Any]							=  [0] * n
      a	:     Dict							=  [[] for _ in range(A     )]





    def 							lowerCamelCase__ (				self      :   List[str]							,						A      :   Union[str, Any]							,						A      :   Dict							,						A      :   Tuple							,						A      :   Tuple=0     ):


      '''simple docstring'''




      self.adj[a].append([b, len(self.adj[b]     ), c, 0]     )
      self.adj[b].append([a, len(self.adj[a]     ) - 1, rcap, 0]     )





    def 							lowerCamelCase__ (				self      :   Any							,						A      :   Tuple							,						A      :   Tuple							,						A      :   Any     ):


      '''simple docstring'''




      if vertex == sink or not flow:
        return flow

      for i in range(self.ptr[vertex]							,						len(self.adj[vertex]     )     ):
        a	:     List[Any]							=  self.adj[vertex][i]
        if self.lvl[e[0]] == self.lvl[vertex] + 1:
          a	:     str							=  self.depth_first_search(e[0]							,						A							,						min(A							,						e[2] - e[3]     )     )
          if p:
            self.adj[vertex][i][3] += p
            self.adj[e[0]][e[1]][3] -= p
            return p
        a	:     Optional[int]							=  self.ptr[vertex] + 1
      return 0





    def 							lowerCamelCase__ (				self      :   Union[str, Any]							,						A      :   Any							,						A      :   Tuple     ):


      '''simple docstring'''




      a,	a	:     Any							=  0, source
      for l in range(3_1     ):  # noqa: E741  l = 30 maybe faster for random data
        while True:
          a,	a	:     Optional[Any]							=  [0] * len(self.q     ), [0] * len(self.q     )
          a,	a,	a	:     Optional[int]							=  0, 1, 1
          while qi < qe and not self.lvl[sink]:
            a	:     List[Any]							=  self.q[qi]
            qi += 1
            for e in self.adj[v]:
              if not self.lvl[e[0]] and (e[2] - e[3]) >> (3_0 - l):
                a	:     List[Any]							=  e[0]
                qe += 1
                a	:     Dict							=  self.lvl[v] + 1

          a	:     str							=  self.depth_first_search(A							,						A							,						A     )
          while p:
            flow += p
            a	:     Optional[Any]							=  self.depth_first_search(A							,						A							,						A     )

          if not self.lvl[sink]:
            break

      return flow
_UpperCamelCase							:       List[Any]       					=		Dinic(10)
_UpperCamelCase							:       List[str]       					=		0
_UpperCamelCase							:       str       					=		9
for vertex in range(1, 5):
   graph.add_edge(source, vertex, 1)
for vertex in range(5, 9):
   graph.add_edge(vertex, sink, 1)
for vertex in range(1, 5):
   graph.add_edge(vertex, vertex + 4, 1)

# Now we can know that is the maximum flow(source -> sink)
print(graph.max_flow(source, sink))












"""simple docstring"""


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


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

_UpperCamelCase							:       int       					=		{
    'apple/mobilevitv2-1.0': 'https://huggingface.co/apple/mobilevitv2-1.0/resolve/main/config.json',
}






class 		snake_case    (    UpperCAmelCase			):
    __magic_name__       	=	'''mobilevitv2'''





    def __init__(				self      :   Any							,						A      :   Any=3							,						A      :   Any=2_5_6							,						A      :   Optional[int]=2							,						A      :   str=2.0							,						A      :   Union[str, Any]="swish"							,						A      :   str=3							,						A      :   Tuple=3_2							,						A      :   Optional[int]=0.1							,						A      :   str=0.02							,						A      :   int=1E-5							,						A      :   List[Any]=5_1_2							,						A      :   str=[6, 1_2, 1_8]							,						A      :   Any=0.1							,						A      :   Union[str, Any]=2_5_5							,						A      :   Optional[Any]=[2, 4, 3]							,						A      :   List[Any]=[1_2_8, 1_9_2, 2_5_6]							,						A      :   Tuple=1.0							,						A      :   Union[str, Any]=2							,						A      :   List[Any]=0.0							,						A      :   int=0.0							,						**A      :   Optional[Any]							,						):


      '''simple docstring'''




      super().__init__(**A     )

      a	:     Optional[Any]							=  num_channels
      a	:     List[Any]							=  image_size
      a	:     Tuple							=  patch_size
      a	:     Optional[Any]							=  expand_ratio
      a	:     Any							=  hidden_act
      a	:     Dict							=  conv_kernel_size
      a	:     Optional[int]							=  output_stride
      a	:     Optional[Any]							=  initializer_range
      a	:     Tuple							=  layer_norm_eps
      a	:     Optional[int]							=  n_attn_blocks
      a	:     Tuple							=  base_attn_unit_dims
      a	:     List[str]							=  width_multiplier
      a	:     Dict							=  ffn_multiplier
      a	:     Dict							=  ffn_dropout
      a	:     Optional[int]							=  attn_dropout
      a	:     Tuple							=  classifier_dropout_prob

      # decode head attributes for semantic segmentation
      a	:     str							=  aspp_out_channels
      a	:     List[Any]							=  atrous_rates
      a	:     Optional[int]							=  aspp_dropout_prob
      a	:     Optional[Any]							=  semantic_loss_ignore_index






class 		snake_case    (    UpperCAmelCase			):
    __magic_name__       	=	version.parse('''1.11'''			)





    @property
    def 							lowerCamelCase__ (				self      :   List[str]     ):


      '''simple docstring'''




      return OrderedDict([('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'})]     )





    @property
    def 							lowerCamelCase__ (				self      :   int     ):


      '''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 							lowerCamelCase__ (				self      :   Union[str, Any]     ):


      '''simple docstring'''




      return 1E-4




