






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


a_							=      {
    """configuration_flava""": [
        """FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """FlavaConfig""",
        """FlavaImageCodebookConfig""",
        """FlavaImageConfig""",
        """FlavaMultimodalConfig""",
        """FlavaTextConfig""",
    ],
}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				a_							=      ["""FlavaFeatureExtractor"""]
				a_							=      ["""FlavaImageProcessor"""]
				a_							=      ["""FlavaProcessor"""]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				a_							=      [
				    """FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """FlavaForPreTraining""",
				    """FlavaImageCodebook""",
				    """FlavaImageModel""",
				    """FlavaModel""",
				    """FlavaMultimodalModel""",
				    """FlavaPreTrainedModel""",
				    """FlavaTextModel""",
				]

if TYPE_CHECKING:
				from .configuration_flava import (
				    FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    FlavaConfig,
				    FlavaImageCodebookConfig,
				    FlavaImageConfig,
				    FlavaMultimodalConfig,
				    FlavaTextConfig,
				)

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_flava import FlavaFeatureExtractor
								from .image_processing_flava import FlavaImageProcessor
								from .processing_flava import FlavaProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flava import (
								    FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST,
								    FlavaForPreTraining,
								    FlavaImageCodebook,
								    FlavaImageModel,
								    FlavaModel,
								    FlavaMultimodalModel,
								    FlavaPreTrainedModel,
								    FlavaTextModel,
								)

else:
				import sys

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














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
a_							=      {
    '''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					(       UpperCamelCase_   :			dict[str, list[str]]				)	->				set[tuple[str, str]]:
						"""simple docstring"""







						lowerCAmelCase__	 =  {node: {node} for node in graph}

						lowerCAmelCase__	 =  {node: graph[node][:] for node in graph}

						while len(UpperCamelCase_				) > 2:
												# Choose a random edge.
												lowerCAmelCase__	 =  random.choice(list(graph_copy.keys()				)				)
												lowerCAmelCase__	 =  random.choice(graph_copy[u]				)

												# Contract edge (u, v) to new node uv
												lowerCAmelCase__	 =  u + v
												lowerCAmelCase__	 =  list(set(graph_copy[u] + graph_copy[v]				)				)
												uv_neighbors.remove(UpperCamelCase_				)
												uv_neighbors.remove(UpperCamelCase_				)
												lowerCAmelCase__	 =  uv_neighbors
												for neighbor in uv_neighbors:
																		graph_copy[neighbor].append(UpperCamelCase_				)

												lowerCAmelCase__	 =  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(UpperCamelCase_				)
																		if v in graph_copy[neighbor]:
																								graph_copy[neighbor].remove(UpperCamelCase_				)

    # Find cutset.
						lowerCAmelCase__	 =  [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))






