





from __future__ import annotations

from typing import Dict

from ...configuration_utils import PretrainedConfig


__A							   =							{
    "susnato/ernie-m-base_pytorch": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json",
    "susnato/ernie-m-large_pytorch": "https://huggingface.co/susnato/ernie-m-large_pytorch/blob/main/config.json",
}



class   _SCREAMING_SNAKE_CASE		(      __SCREAMING_SNAKE_CASE       ):







 '''simple docstring'''
 lowercase_						=     "ernie_m"
 lowercase_						=     {"dropout": "classifier_dropout", "num_classes": "num_labels"}

 def __init__(self							:  Any			,					UpperCAmelCase_							:  int = 250_002			,					UpperCAmelCase_							:  int = 768			,					UpperCAmelCase_							:  int = 12			,					UpperCAmelCase_							:  int = 12			,					UpperCAmelCase_							:  int = 3_072			,					UpperCAmelCase_							:  str = "gelu"			,					UpperCAmelCase_							:  float = 0.1			,					UpperCAmelCase_							:  float = 0.1			,					UpperCAmelCase_							:  int = 514			,					UpperCAmelCase_							:  float = 0.02			,					UpperCAmelCase_							:  int = 1			,					UpperCAmelCase_							:  float = 1E-0_5			,					UpperCAmelCase_							:  Optional[Any]=None			,					UpperCAmelCase_							:  Optional[int]=False			,					UpperCAmelCase_							:  List[Any]=0.0			,					**UpperCAmelCase_							:  str			,					)			->List[str]:



   '''simple docstring'''



   super().__init__(pad_token_id=UpperCAmelCase_			,					**UpperCAmelCase_)
   lowerCamelCase__: List[Any]         =vocab_size
   lowerCamelCase__: Tuple         =hidden_size
   lowerCamelCase__: Optional[Any]         =num_hidden_layers
   lowerCamelCase__: Optional[Any]         =num_attention_heads
   lowerCamelCase__: List[str]         =intermediate_size
   lowerCamelCase__: Optional[int]         =hidden_act
   lowerCamelCase__: Optional[Any]         =hidden_dropout_prob
   lowerCamelCase__: Union[str, Any]         =attention_probs_dropout_prob
   lowerCamelCase__: Optional[Any]         =max_position_embeddings
   lowerCamelCase__: Union[str, Any]         =initializer_range
   lowerCamelCase__: Dict         =layer_norm_eps
   lowerCamelCase__: List[str]         =classifier_dropout
   lowerCamelCase__: Union[str, Any]         =is_decoder
   lowerCamelCase__: str         =act_dropout







import heapq




def 		lowerCAmelCase_ (						lowerCamelCase ):
				__magic_name__						:						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
				__magic_name__						:						Tuple										=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
								__magic_name__						:						Tuple										=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]:
																__magic_name__						:						Tuple										=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()

							UpperCAmelCase_		:					Optional[int]          =  {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)}""")




