




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
SCREAMING_SNAKE_CASE__	:				Tuple  =       {
    '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				(				_SCREAMING_SNAKE_CASE   )     ->			set[tuple[str, str]]:
       lowerCamelCase       :				int      			= {node: {node} for node in graph}

       lowerCamelCase       :				Any      			= {node: graph[node][:] for node in graph}

       while len(_SCREAMING_SNAKE_CASE   ) > 2:
              # Choose a random edge.
              lowerCamelCase       :				int      			= random.choice(list(graph_copy.keys()   )   )
              lowerCamelCase       :				Any      			= random.choice(graph_copy[u]   )

              # Contract edge (u, v) to new node uv
              lowerCamelCase       :				Any      			= u + v
              lowerCamelCase       :				List[str]      			= list(set(graph_copy[u] + graph_copy[v]   )   )
              uv_neighbors.remove(_SCREAMING_SNAKE_CASE   )
              uv_neighbors.remove(_SCREAMING_SNAKE_CASE   )
              lowerCamelCase       :				Tuple      			= uv_neighbors
              for neighbor in uv_neighbors:
                     graph_copy[neighbor].append(_SCREAMING_SNAKE_CASE   )

              lowerCamelCase       :				str      			= 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(_SCREAMING_SNAKE_CASE   )
                     if v in graph_copy[neighbor]:
                            graph_copy[neighbor].remove(_SCREAMING_SNAKE_CASE   )

    # Find cutset.
       lowerCamelCase       :				Union[str, Any]      			= [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))












import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


SCREAMING_SNAKE_CASE__	:				int  =       logging.get_logger(__name__)




class 		UpperCamelCase__     (lowerCAmelCase__					):

    '''simple docstring'''



    lowerCamelCase_				:     str     					=				"""speech-encoder-decoder"""
    lowerCamelCase_				:     List[str]     					=				True







    def __init__(   self					,  **UpperCamelCase__					)					->				Dict:
           super().__init__(**UpperCamelCase__					)
           if "encoder" not in kwargs or "decoder" not in kwargs:
                  raise ValueError(
                      F'''A configuraton of type {self.model_type} cannot be instantiated because not both `encoder` and'''
                      F''' `decoder` sub-configurations are passed, but only {kwargs}'''					)

           lowerCamelCase       :				Dict      			= kwargs.pop("encoder"					)
           lowerCamelCase       :				Tuple      			= encoder_config.pop("model_type"					)
           lowerCamelCase       :				int      			= kwargs.pop("decoder"					)
           lowerCamelCase       :				List[Any]      			= decoder_config.pop("model_type"					)

           lowerCamelCase       :				str      			= AutoConfig.for_model(UpperCamelCase__					,  **UpperCamelCase__					)
           lowerCamelCase       :				str      			= AutoConfig.for_model(UpperCamelCase__					,  **UpperCamelCase__					)
           lowerCamelCase       :				Tuple      			= True







    @classmethod
    def    _lowercase					(   cls					,  UpperCamelCase__					,  UpperCamelCase__					,  **UpperCamelCase__					)					->				PretrainedConfig:
           logger.info("Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config"					)
           lowerCamelCase       :				Dict      			= True
           lowerCamelCase       :				Union[str, Any]      			= True

           return cls(encoder=encoder_config.to_dict()					,  decoder=decoder_config.to_dict()					,  **UpperCamelCase__					)







    def    _lowercase					(   self					)					->				Tuple:
           lowerCamelCase       :				Tuple      			= copy.deepcopy(self.__dict__					)
           lowerCamelCase       :				Optional[int]      			= self.encoder.to_dict()
           lowerCamelCase       :				List[str]      			= self.decoder.to_dict()
           lowerCamelCase       :				Dict      			= self.__class__.model_type
           return output






