from typing import TYPE_CHECKING

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


lowerCamelCase_				:		Union[str, Any]       						=       {
    """configuration_flaubert""": ["""FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FlaubertConfig""", """FlaubertOnnxConfig"""],
    """tokenization_flaubert""": ["""FlaubertTokenizer"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_				:		List[str]       						=       [
	    """FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """FlaubertForMultipleChoice""",
	    """FlaubertForQuestionAnswering""",
	    """FlaubertForQuestionAnsweringSimple""",
	    """FlaubertForSequenceClassification""",
	    """FlaubertForTokenClassification""",
	    """FlaubertModel""",
	    """FlaubertWithLMHeadModel""",
	    """FlaubertPreTrainedModel""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_				:		str       						=       [
	    """TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFFlaubertForMultipleChoice""",
	    """TFFlaubertForQuestionAnsweringSimple""",
	    """TFFlaubertForSequenceClassification""",
	    """TFFlaubertForTokenClassification""",
	    """TFFlaubertModel""",
	    """TFFlaubertPreTrainedModel""",
	    """TFFlaubertWithLMHeadModel""",
	]


if TYPE_CHECKING:
	from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
	from .tokenization_flaubert import FlaubertTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flaubert import (
		    FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    FlaubertForMultipleChoice,
		    FlaubertForQuestionAnswering,
		    FlaubertForQuestionAnsweringSimple,
		    FlaubertForSequenceClassification,
		    FlaubertForTokenClassification,
		    FlaubertModel,
		    FlaubertPreTrainedModel,
		    FlaubertWithLMHeadModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_flaubert import (
		    TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFFlaubertForMultipleChoice,
		    TFFlaubertForQuestionAnsweringSimple,
		    TFFlaubertForSequenceClassification,
		    TFFlaubertForTokenClassification,
		    TFFlaubertModel,
		    TFFlaubertPreTrainedModel,
		    TFFlaubertWithLMHeadModel,
		)

else:
	import sys

	lowerCamelCase_				:		Any       						=       _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
_UpperCamelCase			       =			{
    '''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_(			snake_case__:  dict[str, list[str]]   )       -> set[tuple[str, str]]:
 UpperCAmelCase__							=				{node: {node} for node in graph}

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

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

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

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

    # Find cutset.
 UpperCAmelCase__							=				[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))



