






"""simple docstring"""



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


_UpperCamelCase						:    Tuple		=						logging.get_logger(__name__)

_UpperCamelCase						:    List[str]		=						{
    'google/pegasus-x-base': 'https://huggingface.co/google/pegasus-x-base/resolve/main/config.json',
    'google/pegasus-x-large': 'https://huggingface.co/google/pegasus-x-large/resolve/main/config.json',
    # See all PEGASUS-X models at https://huggingface.co/models?filter=pegasus-x
}

class 			a	(  a_      ):
    UpperCAmelCase_					:	int      	="pegasus_x"
    UpperCAmelCase_					:	List[Any]      	=["past_key_values"]
    UpperCAmelCase_					:	Optional[int]      	={"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}







    def __init__(     self				,						_lowerCamelCase=9_6_1_0_3				,						_lowerCamelCase=1_6_3_8_4				,						_lowerCamelCase=1_6				,						_lowerCamelCase=4_0_9_6				,						_lowerCamelCase=1_6				,						_lowerCamelCase=1_6				,						_lowerCamelCase=4_0_9_6				,						_lowerCamelCase=1_6				,						_lowerCamelCase=0.0				,						_lowerCamelCase=0.0				,						_lowerCamelCase=True				,						_lowerCamelCase=True				,						_lowerCamelCase="gelu"				,						_lowerCamelCase=1_0_2_4				,						_lowerCamelCase=0.1				,						_lowerCamelCase=0.0				,						_lowerCamelCase=0.0				,						_lowerCamelCase=0.0_2				,						_lowerCamelCase=0				,						_lowerCamelCase=True				,						_lowerCamelCase=0				,						_lowerCamelCase=1				,						_lowerCamelCase=1				,						_lowerCamelCase=3_2				,						_lowerCamelCase=5_1_2				,						_lowerCamelCase=True				,						**_lowerCamelCase				,						):
      lowercase				       =			vocab_size
      lowercase				       =			max_position_embeddings
      lowercase				       =			d_model
      lowercase				       =			encoder_ffn_dim
      lowercase				       =			encoder_layers
      lowercase				       =			encoder_attention_heads
      lowercase				       =			decoder_ffn_dim
      lowercase				       =			decoder_layers
      lowercase				       =			decoder_attention_heads
      lowercase				       =			dropout
      lowercase				       =			attention_dropout
      lowercase				       =			activation_dropout
      lowercase				       =			activation_function
      lowercase				       =			init_std
      lowercase				       =			encoder_layerdrop
      lowercase				       =			decoder_layerdrop
      lowercase				       =			use_cache
      lowercase				       =			encoder_layers
      lowercase				       =			scale_embedding  # scale factor will be sqrt(d_model) if True

      lowercase				       =			num_global_tokens
      lowercase				       =			block_size
      lowercase				       =			stagger_local_blocks

      super().__init__(
          pad_token_id=_lowerCamelCase				,						eos_token_id=_lowerCamelCase				,						is_encoder_decoder=_lowerCamelCase				,						decoder_start_token_id=_lowerCamelCase				,						forced_eos_token_id=_lowerCamelCase				,						**_lowerCamelCase				,						)







    @property
    def      UpperCamelCase_ (     self			):
      return self.encoder_attention_heads







    @property
    def      UpperCamelCase_ (     self			):
      return self.d_model







def    UpperCAmelCase					(  lowercase__						:					List[Any]				,					lowercase__						:					Optional[int]	):



			'''simple docstring'''





			print("""\nVertex Distance"""	)
			for i in range(lowercase__	):
						if dist[i] != float("""inf"""	):
									print(lowercase__				,					"""\t"""				,					int(dist[i]	)				,					end="""\t"""	)
						else:
									print(lowercase__				,					"""\t"""				,					"""INF"""				,					end="""\t"""	)
						print()




def    UpperCAmelCase					(  lowercase__						:					Optional[int]				,					lowercase__						:					Any				,					lowercase__						:					Tuple	):



			'''simple docstring'''





			a__					=  float("""inf"""	)
			a__					=  -1
			for i in range(lowercase__	):
						if (not vset[i]) and mdist[i] < min_val:
									a__					=  i
									a__					=  mdist[i]
			return min_ind




def    UpperCAmelCase					(  lowercase__						:					Optional[Any]				,					lowercase__						:					Tuple				,					lowercase__						:					List[Any]	):



			'''simple docstring'''





			a__					=  [float("""inf"""	) for _ in range(lowercase__	)]
			a__					=  [False for _ in range(lowercase__	)]
			a__					=  0.0

			for _ in range(v - 1	):
						a__					=  min_dist(lowercase__				,					lowercase__				,					lowercase__	)
						a__					=  True

						for i in range(lowercase__	):
									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(lowercase__				,					lowercase__	)


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

	_lowercase				:					List[str]     					=[[float("""inf""") for i in range(V)] for j in range(V)]

	for i in range(V):
		_lowercase				:					Dict     					=0.0

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

	_lowercase				:					List[Any]     					=int(input("""\nEnter shortest path source:""").strip())
	dijkstra(graph, V, gsrc)

