



from typing import List, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A				    =						logging.get_logger(__name__)

__A				    =						{
    '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/config.json''',
    # See all LED models at https://huggingface.co/models?filter=led
}






class 		lowercase						( snake_case__):






	"""simple docstring"""


	a__      :					str		   =							"led"
	a__      :					Optional[Any]		   =							{
	    "num_attention_heads": "encoder_attention_heads",
	    "hidden_size": "d_model",
	    "attention_probs_dropout_prob": "attention_dropout",
	    "initializer_range": "init_std",
	}




	def __init__(	self	:						Union[str, Any]						,  __UpperCAmelCase	:						Union[str, Any]=50_265						,  __UpperCAmelCase	:						Optional[int]=16_384						,  __UpperCAmelCase	:						int=1_024						,  __UpperCAmelCase	:						Tuple=12						,  __UpperCAmelCase	:						int=4_096						,  __UpperCAmelCase	:						List[str]=16						,  __UpperCAmelCase	:						Dict=12						,  __UpperCAmelCase	:						List[str]=4_096						,  __UpperCAmelCase	:						List[str]=16						,  __UpperCAmelCase	:						Optional[Any]=0.0						,  __UpperCAmelCase	:						Optional[int]=0.0						,  __UpperCAmelCase	:						Union[str, Any]=True						,  __UpperCAmelCase	:						Dict=True						,  __UpperCAmelCase	:						int="gelu"						,  __UpperCAmelCase	:						Any=1_024						,  __UpperCAmelCase	:						List[str]=0.1						,  __UpperCAmelCase	:						Union[str, Any]=0.0						,  __UpperCAmelCase	:						Tuple=0.0						,  __UpperCAmelCase	:						Optional[Any]=0.02						,  __UpperCAmelCase	:						Any=2						,  __UpperCAmelCase	:						Optional[Any]=0.0						,  __UpperCAmelCase	:						List[Any]=1						,  __UpperCAmelCase	:						Dict=0						,  __UpperCAmelCase	:						Optional[Any]=2						,  __UpperCAmelCase	:						Union[List[int], int] = 512						,  **__UpperCAmelCase	:						Dict						,  )			-> str:
							UpperCAmelCase_=   vocab_size
							UpperCAmelCase_=   max_encoder_position_embeddings
							UpperCAmelCase_=   max_decoder_position_embeddings
							UpperCAmelCase_=   d_model
							UpperCAmelCase_=   encoder_ffn_dim
							UpperCAmelCase_=   encoder_layers
							UpperCAmelCase_=   encoder_attention_heads
							UpperCAmelCase_=   decoder_ffn_dim
							UpperCAmelCase_=   decoder_layers
							UpperCAmelCase_=   decoder_attention_heads
							UpperCAmelCase_=   dropout
							UpperCAmelCase_=   attention_dropout
							UpperCAmelCase_=   activation_dropout
							UpperCAmelCase_=   activation_function
							UpperCAmelCase_=   init_std
							UpperCAmelCase_=   encoder_layerdrop
							UpperCAmelCase_=   decoder_layerdrop
							UpperCAmelCase_=   classifier_dropout
							UpperCAmelCase_=   use_cache
							UpperCAmelCase_=   encoder_layers
							UpperCAmelCase_=   attention_window

							super().__init__(
							    pad_token_id=__UpperCAmelCase						,  bos_token_id=__UpperCAmelCase						,  eos_token_id=__UpperCAmelCase						,  is_encoder_decoder=__UpperCAmelCase						,  decoder_start_token_id=__UpperCAmelCase						,  **__UpperCAmelCase						,  )









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     (    lowerCAmelCase_   :					dict[str, list[str]]				)      -> set[tuple[str, str]]:






						'''simple docstring'''



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

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

						while len(lowerCAmelCase_				) > 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(lowerCAmelCase_				)
												uv_neighbors.remove(lowerCAmelCase_				)
												UpperCAmelCase_=   uv_neighbors
												for neighbor in uv_neighbors:
																		graph_copy[neighbor].append(lowerCAmelCase_				)

												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(lowerCAmelCase_				)
																		if v in graph_copy[neighbor]:
																								graph_copy[neighbor].remove(lowerCAmelCase_				)

    # 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))




