
'''simple docstring'''





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


a__						:				Tuple           =    logging.get_logger(__name__)

a__						:				int           =    {
    'facebook/opt-125m': 'https://huggingface.co/facebook/opt-125m/blob/main/config.json',
    'facebook/opt-350m': 'https://huggingface.co/facebook/opt-350m/blob/main/config.json',
    'facebook/opt-1.3b': 'https://huggingface.co/facebook/opt-1.3b/blob/main/config.json',
    'facebook/opt-2.7b': 'https://huggingface.co/facebook/opt-2.7b/blob/main/config.json',
    'facebook/opt-6.7b': 'https://huggingface.co/facebook/opt-6.7b/blob/main/config.json',
    'facebook/opt-13b': 'https://huggingface.co/facebook/opt-13b/blob/main/config.json',
}


class 	UpperCamelCase__					(      lowercase_):
 UpperCAmelCase__      :      Tuple  			=   'opt'
 UpperCAmelCase__      :      Union[str, Any]  			=   ['past_key_values']





 def __init__(     self					:Any		,			_A					:Union[str, Any]=50_272		,			_A					:List[Any]=768		,			_A					:int=12		,			_A					:List[str]=3_072		,			_A					:Optional[int]=2_048		,			_A					:Dict=True		,			_A					:List[str]=False		,			_A					:Union[str, Any]=None		,			_A					:Any=0.1		,			_A					:Dict=0.0		,			_A					:Optional[int]=12		,			_A					:str="relu"		,			_A					:Dict=0.0		,			_A					:Dict=0.02		,			_A					:Union[str, Any]=True		,			_A					:str=1		,			_A					:List[str]=2		,			_A					:Optional[Any]=2		,			_A					:Dict=True		,			_A					:int=True		,			**_A					:Dict		,			)			->     Tuple:







       '''simple docstring'''



       super().__init__(
           pad_token_id=__UpperCamelCase		,			bos_token_id=__UpperCamelCase		,			eos_token_id=__UpperCamelCase		,			**__UpperCamelCase		,			)
       __A    		=     vocab_size
       __A    		=     max_position_embeddings
       __A    		=     num_attention_heads
       __A    		=     word_embed_proj_dim if word_embed_proj_dim is not None else hidden_size
       __A    		=     ffn_dim
       __A    		=     hidden_size
       __A    		=     num_hidden_layers
       __A    		=     dropout
       __A    		=     attention_dropout
       __A    		=     activation_function
       __A    		=     init_std
       __A    		=     layerdrop
       __A    		=     use_cache
       __A    		=     do_layer_norm_before
       # We keep these variables at `True` for backward compatibility.
       __A    		=     enable_bias
       __A    		=     layer_norm_elementwise_affine

       # Note that the only purpose of `_remove_final_layer_norm` is to keep backward compatibility
       # with checkpoints that have been fine-tuned before transformers v4.20.1
       # see https://github.com/facebookresearch/metaseq/pull/164
       __A    		=     _remove_final_layer_norm








'''simple docstring'''





def     snake_case							(						UpperCAmelCase ,					UpperCAmelCase		)->     Union[str, Any]:







      """simple docstring"""
      print('\nVertex Distance'		)
      for i in range(UpperCAmelCase		):
            if dist[i] != float('inf'		):
                  print(UpperCAmelCase ,					'\t' ,					int(dist[i]		) ,					end='\t'		)
            else:
                  print(UpperCAmelCase ,					'\t' ,					'INF' ,					end='\t'		)
            print()





def     snake_case							(						UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase		)->     List[Any]:







      """simple docstring"""
      __A    		=     float('inf'		)
      __A    		=     -1
      for i in range(UpperCAmelCase		):
            if (not vset[i]) and mdist[i] < min_val:
                  __A    		=     i
                  __A    		=     mdist[i]
      return min_ind





def     snake_case							(						UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase		)->     Dict:







      """simple docstring"""
      __A    		=     [float('inf'		) for _ in range(UpperCAmelCase		)]
      __A    		=     [False for _ in range(UpperCAmelCase		)]
      __A    		=     0.0

      for _ in range(v - 1		):
            __A    		=     min_dist(UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase		)
            __A    		=     True

            for i in range(UpperCAmelCase		):
                  if (
                      (not vset[i])
                      and graph[u][i] != float('inf'		)
                      and mdist[u] + graph[u][i] < mdist[i]
                  ):
                        __A    		=     mdist[u] + graph[u][i]

      print_dist(UpperCAmelCase ,					UpperCAmelCase		)


if __name__ == "__main__":
 a__						:				Tuple           =    int(input("Enter number of vertices: ").strip())
 a__						:				List[str]           =    int(input("Enter number of edges: ").strip())

 a__						:				int           =    [[float("inf") for i in range(V)] for j in range(V)]

 for i in range(V):
  a__						:				Optional[int]           =    0.0

 for i in range(E):
  print("\nEdge ", i + 1)
  a__						:				Union[str, Any]           =    int(input("Enter source:").strip())
  a__						:				Tuple           =    int(input("Enter destination:").strip())
  a__						:				int           =    float(input("Enter weight:").strip())
  a__						:				Dict           =    weight

 a__						:				int           =    int(input("\nEnter shortest path source:").strip())
 dijkstra(graph, V, gsrc)






