
'''simple docstring'''





import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import (
    TaBlock,
    TaConfig,
    TaLayerNorm,
)

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin


class 	UpperCamelCase__					(      __A ,       __A ,       __A):





 @register_to_config
 def __init__(     self					:Optional[Any]		,			_A					:int		,			_A					:int		,			_A					:int		,			_A					:float		,			_A					:int		,			_A					:int		,			_A					:int		,			_A					:int		,			_A					:str		,			_A					:bool = False		,			)			->     Dict:







       '''simple docstring'''



       super().__init__()

       __A    		=     nn.Linear(_A		,			_A		,			bias=_A     )

       __A    		=     nn.Embedding(_A		,			_A     )
       __A    		=     False

       __A    		=     nn.Dropout(p=_A     )

       __A    		=     TaConfig(
           d_model=_A		,			num_heads=_A		,			d_kv=_A		,			d_ff=_A		,			feed_forward_proj=_A		,			dropout_rate=_A		,			is_decoder=_A		,			is_encoder_decoder=_A		,			)
       __A    		=     nn.ModuleList()
       for lyr_num in range(_A     ):
             __A    		=     TaBlock(_A     )
             self.encoders.append(_A     )

       __A    		=     TaLayerNorm(_A     )
       __A    		=     nn.Dropout(p=_A     )





 def 		lowercase_  (     self					:Optional[Any]		,			_A					:Union[str, Any]		,			_A					:str     )			->     Dict:







       '''simple docstring'''



       __A    		=     self.input_proj(_A     )

       # terminal relative positional encodings
       __A    		=     encoder_inputs.shape[1]
       __A    		=     torch.arange(_A		,			device=encoder_inputs.device     )

       __A    		=     encoder_inputs_mask.sum(-1     )
       __A    		=     torch.roll(input_positions.unsqueeze(0     )		,			tuple(seq_lens.tolist()     )		,			dims=0     )
       x += self.position_encoding(_A     )

       __A    		=     self.dropout_pre(_A     )

       # inverted the attention mask
       __A    		=     encoder_inputs.size()
       __A    		=     self.get_extended_attention_mask(_A		,			_A     )

       for lyr in self.encoders:
             __A    		=     lyr(_A		,			_A     )[0]
       __A    		=     self.layer_norm(_A     )

       return self.dropout_post(_A     ), encoder_inputs_mask








'''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)






