code: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  from __future__ import annotations
  
  import random
  
  # Adjacency list representation of this graph:
  # https://en.wikipedia.org/wiki/File:Single_run_of_Karger%E2%80%99s_Mincut_algorithm.svg
  lowerCamelCase_     :     Dict    		=						{
      """1""": ["""2""", """3""", """4""", """5"""],
      """2""": ["""1""", """3""", """4""", """5"""],
      """3""": ["""1""", """2""", """4""", """5""", """10"""],
      """4""": ["""1""", """2""", """3""", """5""", """6"""],
      """5""": ["""1""", """2""", """3""", """4""", """7"""],
      """6""": ["""7""", """8""", """9""", """10""", """4"""],
      """7""": ["""6""", """8""", """9""", """10""", """5"""],
      """8""": ["""6""", """7""", """9""", """10"""],
      """9""": ["""6""", """7""", """8""", """10"""],
      """10""": ["""6""", """7""", """8""", """9""", """3"""],
  }
  def _A		(		lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      a					={node: {node} for node in graph}
  
      a					={node: graph[node][:] for node in graph}
  
      while len(lowercase	) > 2:
          # Choose a random edge.
          a					=random.choice(list(graph_copy.keys()	)	)
          a					=random.choice(graph_copy[u]	)
  
          # Contract edge (u, v) to new node uv
          a					=u + v
          a					=list(set(graph_copy[u] + graph_copy[v]	)	)
          uv_neighbors.remove(lowercase	)
          uv_neighbors.remove(lowercase	)
          a					=uv_neighbors
          for neighbor in uv_neighbors:
              graph_copy[neighbor].append(lowercase	)
  
          a					=set(contracted_nodes[u].union(contracted_nodes[v]	)	)
  
          # Remove nodes u and v.
          del graph_copy[u]
          del graph_copy[v]
          for neighbor in uv_neighbors:
              if u in graph_copy[neighbor]:
                  graph_copy[neighbor].remove(lowercase	)
              if v in graph_copy[neighbor]:
                  graph_copy[neighbor].remove(lowercase	)
  
      # Find cutset.
      a					=[contracted_nodes[node] for node in graph_copy]
      return {
          (node, neighbor)
          for node in groups[0]
          for neighbor in graph[node]
          if neighbor in groups[1]
      }
  
  
  if __name__ == "__main__":
         print(partition_graph(TEST_GRAPH))
  
  
  
  
  
  
style_context: 
  
  
  """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
  
  
  lowerCamelCase_     :     Any    		=						logging.get_logger(__name__)
  
  lowerCamelCase_     :     Tuple    		=						{
      """apple/mobilevitv2-1.0""": """https://huggingface.co/apple/mobilevitv2-1.0/resolve/main/config.json""",
  }
  
  
  
  
  
  
  class    __A       ( _SCREAMING_SNAKE_CASE    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   __lowerCAmelCase				      =						"mobilevitv2"
  
  
   def __init__(	self				,					__A=3				,					__A=256				,					__A=2				,					__A=2.0				,					__A="swish"				,					__A=3				,					__A=32				,					__A=0.1				,					__A=0.02				,					__A=1E-5				,					__A=512				,					__A=[6, 12, 18]				,					__A=0.1				,					__A=255				,					__A=[2, 4, 3]				,					__A=[128, 192, 256]				,					__A=1.0				,					__A=2				,					__A=0.0				,					__A=0.0				,					**__A				,					)      ->  int:
       super().__init__(**__A		)
  
       a					=num_channels
       a					=image_size
       a					=patch_size
       a					=expand_ratio
       a					=hidden_act
       a					=conv_kernel_size
       a					=output_stride
       a					=initializer_range
       a					=layer_norm_eps
       a					=n_attn_blocks
       a					=base_attn_unit_dims
       a					=width_multiplier
       a					=ffn_multiplier
       a					=ffn_dropout
       a					=attn_dropout
       a					=classifier_dropout_prob
  
       # decode head attributes for semantic segmentation
       a					=aspp_out_channels
       a					=atrous_rates
       a					=aspp_dropout_prob
       a					=semantic_loss_ignore_index
  
  
  
  
  
  
  class    __A       ( _SCREAMING_SNAKE_CASE    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   __lowerCAmelCase				      =						version.parse("1.11"    )
  
  
   @property
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Mapping[str, Mapping[int, str]]:
       return OrderedDict([('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''})]		)
  
  
   @property
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Mapping[str, Mapping[int, str]]:
       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		)      ->  float:
       return 1E-4