from typing import TYPE_CHECKING

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


lowerCAmelCase__					:       Union[str, Any]		=       {
    '''configuration_esm''': ['''ESM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''EsmConfig'''],
    '''tokenization_esm''': ['''EsmTokenizer'''],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__					:       Union[str, Any]		=       [
				    '''ESM_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''EsmForMaskedLM''',
				    '''EsmForSequenceClassification''',
				    '''EsmForTokenClassification''',
				    '''EsmModel''',
				    '''EsmPreTrainedModel''',
				]
				lowerCAmelCase__					:       Union[str, Any]		=       ['''EsmForProteinFolding''', '''EsmFoldPreTrainedModel''']

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__					:       int		=       [
				    '''TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''TFEsmForMaskedLM''',
				    '''TFEsmForSequenceClassification''',
				    '''TFEsmForTokenClassification''',
				    '''TFEsmModel''',
				    '''TFEsmPreTrainedModel''',
				]

if TYPE_CHECKING:
				from .configuration_esm import ESM_PRETRAINED_CONFIG_ARCHIVE_MAP, EsmConfig
				from .tokenization_esm import EsmTokenizer

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_esm import (
								    ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
								    EsmForMaskedLM,
								    EsmForSequenceClassification,
								    EsmForTokenClassification,
								    EsmModel,
								    EsmPreTrainedModel,
								)
								from .modeling_esmfold import EsmFoldPreTrainedModel, EsmForProteinFolding

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_esm import (
								    TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFEsmForMaskedLM,
								    TFEsmForSequenceClassification,
								    TFEsmForTokenClassification,
								    TFEsmModel,
								    TFEsmPreTrainedModel,
								)


else:
				import sys

				lowerCAmelCase__					:       Any		=       _LazyModule(__name__, globals()['''__file__'''], _import_structure)



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))


