


"""simple docstring"""






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


_lowerCAmelCase							:    int					   =	logging.get_logger(__name__)

_lowerCAmelCase							:    Union[str, Any]					   =	{
    '''abeja/gpt-neox-japanese-2.7b''': '''https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json''',
}




class 							A_      (       _a ):
  lowerCAmelCase__      			=	'gpt_neox_japanese'


  def __init__(			self:						str							,__lowerCAmelCase:						List[str]=32_000							,__lowerCAmelCase:						Tuple=2_560							,__lowerCAmelCase:						Tuple=32							,__lowerCAmelCase:						Tuple=32							,__lowerCAmelCase:						Optional[int]=4							,__lowerCAmelCase:						int="gelu"							,__lowerCAmelCase:						Optional[Any]=1.00							,__lowerCAmelCase:						int=10_000							,__lowerCAmelCase:						List[str]=2_048							,__lowerCAmelCase:						Union[str, Any]=0.02							,__lowerCAmelCase:						Tuple=1e-5							,__lowerCAmelCase:						Tuple=True							,__lowerCAmelCase:						List[str]=31_996							,__lowerCAmelCase:						Union[str, Any]=31_999							,__lowerCAmelCase:						Union[str, Any]=0.1							,__lowerCAmelCase:						int=0.0							,**__lowerCAmelCase:						int							,):







     '''simple docstring'''







     super().__init__(bos_token_id=__lowerCAmelCase							,eos_token_id=__lowerCAmelCase							,**__lowerCAmelCase    )
     _lowerCamelCase   :				Union[str, Any]							=     vocab_size
     _lowerCamelCase   :				Any							=     max_position_embeddings
     _lowerCamelCase   :				Tuple							=     hidden_size
     _lowerCamelCase   :				List[Any]							=     num_hidden_layers
     _lowerCamelCase   :				int							=     num_attention_heads
     _lowerCamelCase   :				Tuple							=     intermediate_multiple_size
     _lowerCamelCase   :				List[str]							=     hidden_act
     _lowerCamelCase   :				str							=     rotary_pct
     _lowerCamelCase   :				Any							=     rotary_emb_base
     _lowerCamelCase   :				str							=     initializer_range
     _lowerCamelCase   :				Optional[int]							=     layer_norm_eps
     _lowerCamelCase   :				Optional[Any]							=     use_cache
     _lowerCamelCase   :				List[str]							=     attention_dropout
     _lowerCamelCase   :				int							=     hidden_dropout

"""simple docstring"""





import heapq



def  lowerCamelCase__						(     __snake_case      )				->						set[int]:


					"""simple docstring"""





					_UpperCamelCase									=    []

					# 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(__snake_case,	[-1 * len(__snake_case      ), (key, value)]      )

					# chosen_vertices = set of chosen vertices
					_UpperCamelCase									=    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
										_UpperCamelCase									=    heapq.heappop(__snake_case      )[1][0]
										chosen_vertices.add(__snake_case      )

										# 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]:
																				_UpperCamelCase									=    elem[1][1].index(__snake_case      )
																				del elem[1][1][index]
																				elem[0] += 1
        # re-order the queue
										heapq.heapify(__snake_case      )
					return chosen_vertices


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				_a								=						{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)}""")






