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


_lowercase							:      int      							=logging.get_logger(__name__)

_lowercase							:      Dict      							={
    '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/config.json''',
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}



class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_			):
  '''simple docstring'''


  lowercase		:     Optional[int]            =				"xglm"
  lowercase		:     Any            =				["past_key_values"]

  lowercase		:     Dict            =				{
      "num_attention_heads": "attention_heads",
      "hidden_size": "d_model",
      "num_hidden_layers": "num_layers",
  }







  def __init__(				self    :					int      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=25_60_08      ,	SCREAMING_SNAKE_CASE__    :					Dict=20_48      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=10_24      ,	SCREAMING_SNAKE_CASE__    :					str=40_96      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]=24      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]=16      ,	SCREAMING_SNAKE_CASE__    :					Any="gelu"      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]=0.1      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]=0.1      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=0.0      ,	SCREAMING_SNAKE_CASE__    :					Tuple=0.0      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=0.0_2      ,	SCREAMING_SNAKE_CASE__    :					int=True      ,	SCREAMING_SNAKE_CASE__    :					Dict=True      ,	SCREAMING_SNAKE_CASE__    :					Any=2      ,	SCREAMING_SNAKE_CASE__    :					List[Any]=1      ,	SCREAMING_SNAKE_CASE__    :					str=0      ,	SCREAMING_SNAKE_CASE__    :					List[str]=2      ,	**SCREAMING_SNAKE_CASE__    :					Dict      ,	)    ->				int:
         A						:     str         =vocab_size
         A						:     Union[str, Any]         =max_position_embeddings
         A						:     Optional[Any]         =d_model
         A						:     Optional[int]         =ffn_dim
         A						:     int         =num_layers
         A						:     Any         =attention_heads
         A						:     Dict         =activation_function
         A						:     List[Any]         =dropout
         A						:     str         =attention_dropout
         A						:     List[Any]         =activation_dropout
         A						:     List[Any]         =layerdrop
         A						:     List[Any]         =init_std
         A						:     Union[str, Any]         =scale_embedding  # scale factor will be sqrt(d_model) if True
         A						:     List[str]         =use_cache

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







import heapq



def A__  (			lowercase:     dict					)				->		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(lowercase,		[-1 * len(lowercase					), (key, value)]					)

       # chosen_vertices = set of chosen vertices
       A						:     Dict         =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						:     List[str]         =heapq.heappop(lowercase					)[1][0]
              chosen_vertices.add(lowercase					)

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


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    _lowercase							:      List[Any]      							={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)}''')






