


"""simple docstring"""






from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput


@dataclass
class 			__lowerCamelCase     (    snake_case_ ):

     '''simple docstring'''
     A_				:		Optional[torch.FloatTensor]			=						None
     A_				:		torch.FloatTensor			=						None
     A_				:		Optional[Tuple[torch.FloatTensor]]			=						None
     A_				:		Optional[Tuple[torch.FloatTensor]]			=						None







class 			__lowerCamelCase     (    snake_case_ ):

     '''simple docstring'''


     def __init__(					self			, __UpperCAmelCase=1			, __UpperCAmelCase=0			, __UpperCAmelCase=2			, __UpperCAmelCase=512			, __UpperCAmelCase="cls"			, __UpperCAmelCase=False			, __UpperCAmelCase=True			, **__UpperCAmelCase			, )				->							List[Any]:
          super().__init__(pad_token_id=UpperCAmelCase__			, bos_token_id=UpperCAmelCase__			, eos_token_id=UpperCAmelCase__			, **UpperCAmelCase__							)
          _a					= project_dim
          _a					= pooler_fn
          _a					= learn_encoder
          _a					= use_attention_mask







class 			__lowerCamelCase     (    snake_case_ ):

     '''simple docstring'''
     A_				:		Dict			=						[R'''pooler''', R'''logit_scale''']
     A_				:		Optional[Any]			=						[R'''position_ids''', R'''predictions.decoder.bias''']
     A_				:		List[str]			=						'''roberta'''
     A_				:		str			=						RobertaSeriesConfig


     def __init__(					self			, __UpperCAmelCase							)				->							Union[str, Any]:
          super().__init__(UpperCAmelCase__							)
          _a					= XLMRobertaModel(UpperCAmelCase__							)
          _a					= nn.Linear(config.hidden_size			, config.project_dim							)
          _a					= getattr(UpperCAmelCase__			, '''has_pre_transformation'''			, UpperCAmelCase__							)
          if self.has_pre_transformation:
               _a					= nn.Linear(config.hidden_size			, config.project_dim							)
               _a					= nn.LayerNorm(config.hidden_size			, eps=config.layer_norm_eps							)
          self.post_init()





     def 							_UpperCAmelCase					(					self			, __UpperCAmelCase = None			, __UpperCAmelCase = None			, __UpperCAmelCase = None			, __UpperCAmelCase = None			, __UpperCAmelCase = None			, __UpperCAmelCase = None			, __UpperCAmelCase = None			, __UpperCAmelCase = None			, __UpperCAmelCase = None			, __UpperCAmelCase = None			, __UpperCAmelCase = None			, )				->							Optional[Any]:
          _a					= return_dict if return_dict is not None else self.config.use_return_dict

          _a					= self.base_model(
              input_ids=UpperCAmelCase__			, attention_mask=UpperCAmelCase__			, token_type_ids=UpperCAmelCase__			, position_ids=UpperCAmelCase__			, head_mask=UpperCAmelCase__			, inputs_embeds=UpperCAmelCase__			, encoder_hidden_states=UpperCAmelCase__			, encoder_attention_mask=UpperCAmelCase__			, output_attentions=UpperCAmelCase__			, output_hidden_states=True if self.has_pre_transformation else output_hidden_states			, return_dict=UpperCAmelCase__			, )

          if self.has_pre_transformation:
               _a					= outputs["""hidden_states"""][-2]
               _a					= self.pre_LN(UpperCAmelCase__							)
               _a					= self.transformation_pre(UpperCAmelCase__							)

               return TransformationModelOutput(
                   projection_state=UpperCAmelCase__			, last_hidden_state=outputs.last_hidden_state			, hidden_states=outputs.hidden_states			, attentions=outputs.attentions			, )
          else:
               _a					= self.transformation(outputs.last_hidden_state							)
               return TransformationModelOutput(
                   projection_state=UpperCAmelCase__			, last_hidden_state=outputs.last_hidden_state			, hidden_states=outputs.hidden_states			, attentions=outputs.attentions			, )







"""simple docstring"""




from __future__ import annotations

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__				):



  '''simple docstring'''
  print(F"""Vertex\tShortest Distance from vertex {src}"""				)
  for i, d in enumerate(UpperCamelCase__				):
    print(F"""{i}\t\t{d}"""				)

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__				):



  '''simple docstring'''
  for j in range(UpperCamelCase__				):
    _a      ,  _a      ,  _a  :       List[str]             =	(graph[j][k] for k in ["""src""", """dst""", """weight"""])
    if distance[u] != float("""inf"""				) and distance[u] + w < distance[v]:
      return True
  return False

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__				):



  '''simple docstring'''
  _a  :       Dict             =	[float("""inf"""				)] * vertex_count
  _a  :       Any             =	0.0

  for _ in range(vertex_count - 1				):
    for j in range(UpperCamelCase__				):
      _a      ,  _a      ,  _a  :       List[Any]             =	(graph[j][k] for k in ["""src""", """dst""", """weight"""])

      if distance[u] != float("""inf"""				) and distance[u] + w < distance[v]:
        _a  :       Any             =	distance[u] + w

  _a  :       Union[str, Any]             =	check_negative_cycle(UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__				)
  if negative_cycle_exists:
    raise Exception("""Negative cycle found"""				)

  return distance


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 _snake_case          =   int(input('Enter number of vertices: ').strip())
 _snake_case          =   int(input('Enter number of edges: ').strip())

 _snake_case          =   [{} for _ in range(E)]

 for i in range(E):
  print('Edge ', i + 1)
  _snake_case      , _snake_case      , _snake_case          =   (
      int(x)
      for x in input('Enter source, destination, weight: ').strip().split(' ')
  )
  _snake_case          =   {'src': src, 'dst': dest, 'weight': weight}

 _snake_case          =   int(input('\nEnter shortest path source:').strip())
 _snake_case          =   bellman_ford(graph, V, E, source)
 print_distance(shortest_distance, 0)

