



'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__lowerCAmelCase						      =       logging.get_logger(__name__)

__lowerCAmelCase						      =       {
    '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/config.json''',
    '''umberto-commoncrawl-cased-v1''': (
        '''https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json'''
    ),
    '''umberto-wikipedia-uncased-v1''': (
        '''https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json'''
    ),
}

class      __magic_name__						(   _UpperCamelCase       ):
							lowerCAmelCase	:   Any           = 'camembert'







							def __init__(							self :	int     ,_UpperCAmelCase :	Tuple=30522     ,_UpperCAmelCase :	Union[str, Any]=768     ,_UpperCAmelCase :	str=12     ,_UpperCAmelCase :	Any=12     ,_UpperCAmelCase :	str=3072     ,_UpperCAmelCase :	Any="gelu"     ,_UpperCAmelCase :	int=0.1     ,_UpperCAmelCase :	Union[str, Any]=0.1     ,_UpperCAmelCase :	Dict=512     ,_UpperCAmelCase :	Any=2     ,_UpperCAmelCase :	Any=0.02     ,_UpperCAmelCase :	int=1E-12     ,_UpperCAmelCase :	str=1     ,_UpperCAmelCase :	int=0     ,_UpperCAmelCase :	List[Any]=2     ,_UpperCAmelCase :	int="absolute"     ,_UpperCAmelCase :	List[str]=True     ,_UpperCAmelCase :	Tuple=None     ,**_UpperCAmelCase :	Dict     ,):
									super().__init__(pad_token_id=_UpperCAmelCase     ,bos_token_id=_UpperCAmelCase     ,eos_token_id=_UpperCAmelCase     ,**_UpperCAmelCase		)

									_a					:				Dict				=							vocab_size
									_a					:				Dict				=							hidden_size
									_a					:				Union[str, Any]				=							num_hidden_layers
									_a					:				Optional[int]				=							num_attention_heads
									_a					:				Dict				=							hidden_act
									_a					:				Dict				=							intermediate_size
									_a					:				Tuple				=							hidden_dropout_prob
									_a					:				Tuple				=							attention_probs_dropout_prob
									_a					:				Tuple				=							max_position_embeddings
									_a					:				Tuple				=							type_vocab_size
									_a					:				int				=							initializer_range
									_a					:				int				=							layer_norm_eps
									_a					:				List[str]				=							position_embedding_type
									_a					:				str				=							use_cache
									_a					:				int				=							classifier_dropout

class      __magic_name__						(   _UpperCamelCase       ):







							@property
							def 							__lowercase      (							self :	Any		):
									if self.task == "multiple-choice":
											_a					:				Union[str, Any]				=							{0: 'batch', 1: 'choice', 2: 'sequence'}
									else:
											_a					:				Dict				=							{0: 'batch', 1: 'sequence'}
									return OrderedDict(
									    [
									        ('input_ids', dynamic_axis),
									        ('attention_mask', dynamic_axis),
									    ]		)









'''simple docstring'''

import heapq



def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				set[int]:
		_a					:				list[list]				=							[]

		# for each node and his adjacency list add them and the rank of the node to queue
		# using heapq module the queue will be filled like a Priority Queue
		# heapq works with a min priority queue, so I used -1*len(v) to build it
		for key, value in graph.items():
				# O(log(n))
				heapq.heappush(lowerCAmelCase_						,      [-1 * len(lowerCAmelCase_  ), (key, value)]  )

		# chosen_vertices = set of chosen vertices
		_a					:				str				=							set()

		# while queue isn't empty and there are still edges
		#   (queue[0][0] is the rank of the node with max rank)
		while queue and queue[0][0] != 0:
				# extract vertex with max rank from queue and add it to chosen_vertices
				_a					:				Optional[int]				=							heapq.heappop(lowerCAmelCase_  )[1][0]
				chosen_vertices.add(lowerCAmelCase_  )

				# Remove all arcs adjacent to argmax
				for elem in queue:
						# if v haven't adjacent node, skip
						if elem[0] == 0:
								continue
						# if argmax is reachable from elem
						# remove argmax from elem's adjacent list and update his rank
						if argmax in elem[1][1]:
								_a					:				Dict				=							elem[1][1].index(lowerCAmelCase_  )
								del elem[1][1][index]
								elem[0] += 1
        # re-order the queue
				heapq.heapify(lowerCAmelCase_  )
		return chosen_vertices


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							__lowerCAmelCase						      =       {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
							print(f"""Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}""")




