




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[int]		=       logging.get_logger(__name__)

lowerCAmelCase__					:       List[str]		=       {
    '''apple/mobilevitv2-1.0''': '''https://huggingface.co/apple/mobilevitv2-1.0/resolve/main/config.json''',
}





class 			__snake_case  (      UpperCAmelCase__     ):
					__lowerCamelCase	 =       'mobilevitv2'




					def __init__(       self   ,	__UpperCamelCase=3   ,	__UpperCamelCase=256   ,	__UpperCamelCase=2   ,	__UpperCamelCase=2.0   ,	__UpperCamelCase="swish"   ,	__UpperCamelCase=3   ,	__UpperCamelCase=32   ,	__UpperCamelCase=0.1   ,	__UpperCamelCase=0.0_2   ,	__UpperCamelCase=1E-5   ,	__UpperCamelCase=512   ,	__UpperCamelCase=[6, 12, 18]   ,	__UpperCamelCase=0.1   ,	__UpperCamelCase=255   ,	__UpperCamelCase=[2, 4, 3]   ,	__UpperCamelCase=[128, 192, 256]   ,	__UpperCamelCase=1.0   ,	__UpperCamelCase=2   ,	__UpperCamelCase=0.0   ,	__UpperCamelCase=0.0   ,	**__UpperCamelCase   ,	)					->  Any:



									'''simple docstring'''







									super().__init__(**__UpperCamelCase		)

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

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





class 			__snake_case  (      UpperCAmelCase__     ):
					__lowerCamelCase	 =       version.parse("""1.11"""     )




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



									'''simple docstring'''







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




					@property
					def       __a      (       self		)					->  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       __a      (       self		)					->  float:



									'''simple docstring'''







									return 1E-4



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 			UpperCamelCase__						(    A__   )       -> set[tuple[str, str]]:
				snake_case__ :     Tuple 							=				{node: {node} for node in graph}

				snake_case__ :     Tuple 							=				{node: graph[node][:] for node in graph}

				while len(A__   ) > 2:
								# Choose a random edge.
								snake_case__ :     Union[str, Any] 							=				random.choice(list(graph_copy.keys()   )   )
								snake_case__ :     Any 							=				random.choice(graph_copy[u]   )

								# Contract edge (u, v) to new node uv
								snake_case__ :     Tuple 							=				u + v
								snake_case__ :     Dict 							=				list(set(graph_copy[u] + graph_copy[v]   )   )
								uv_neighbors.remove(A__   )
								uv_neighbors.remove(A__   )
								snake_case__ :     Optional[Any] 							=				uv_neighbors
								for neighbor in uv_neighbors:
												graph_copy[neighbor].append(A__   )

								snake_case__ :     Tuple 							=				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(A__   )
												if v in graph_copy[neighbor]:
																graph_copy[neighbor].remove(A__   )

    # Find cutset.
				snake_case__ :     Tuple 							=				[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))


