





import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
     import torch

     from transformers import XLMRobertaModel



@require_sentencepiece
@require_tokenizers
@require_torch
class        _SCREAMING_SNAKE_CASE						(     unittest.TestCase   ):







 '''simple docstring'''

 @slow
 def 							SCREAMING_SNAKE_CASE_ (self    :       str)	->Dict:



   '''simple docstring'''



   lowerCamelCase__:				Union[str, Any]        =XLMRobertaModel.from_pretrained("xlm-roberta-base")
   lowerCamelCase__:				Optional[Any]        =torch.tensor([[0, 581, 10_269, 83, 99_942, 136, 60_742, 23, 70, 80_583, 18_276, 2]])
   # The dog is cute and lives in the garden house

   lowerCamelCase__:				Any        =torch.Size((1, 12, 768))  # batch_size, sequence_length, embedding_vector_dim
   lowerCamelCase__:				List[Any]        =torch.tensor(
       [[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]])
   #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
   #  xlmr.eval()
   #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
   with torch.no_grad():
     lowerCamelCase__:				Optional[int]        =model(UpperCAmelCase_)["last_hidden_state"].detach()
   self.assertEqual(output.shape	,			UpperCAmelCase_)
   # compare the actual values for a slice of last dim
   self.assertTrue(torch.allclose(output[:, :, -1]	,			UpperCAmelCase_	,			atol=1E-3))







 @slow
 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[Any])	->Optional[Any]:



   '''simple docstring'''



   lowerCamelCase__:				List[Any]        =XLMRobertaModel.from_pretrained("xlm-roberta-large")
   lowerCamelCase__:				List[Any]        =torch.tensor([[0, 581, 10_269, 83, 99_942, 136, 60_742, 23, 70, 80_583, 18_276, 2]])
   # The dog is cute and lives in the garden house

   lowerCamelCase__:				Dict        =torch.Size((1, 12, 1_024))  # batch_size, sequence_length, embedding_vector_dim
   lowerCamelCase__:				Dict        =torch.tensor(
       [[-0.0699, -0.0318, 0.0705, -0.1241, 0.0999, -0.0520, 0.1004, -0.1838, -0.4704, 0.1437, 0.0821, 0.0126]])
   #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
   #  xlmr.eval()
   #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
   with torch.no_grad():
     lowerCamelCase__:				Any        =model(UpperCAmelCase_)["last_hidden_state"].detach()
   self.assertEqual(output.shape	,			UpperCAmelCase_)
   # compare the actual values for a slice of last dim
   self.assertTrue(torch.allclose(output[:, :, -1]	,			UpperCAmelCase_	,			atol=1E-3))








from __future__ import annotations





def 					lowerCAmelCase_			(	__a     ,   __a		)					->		List[Any]:







  """simple docstring"""





  print(F"""Vertex\tShortest Distance from vertex {src}"""		)
  for i, d in enumerate(__a		):
    print(F"""{i}\t\t{d}"""		)





def 					lowerCAmelCase_			(	__a     ,   __a     ,   __a		)					->		Tuple:







  """simple docstring"""





  for j in range(__a		):
    lowerCamelCase__  , lowerCamelCase__  , lowerCamelCase__:				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_			(	__a     ,   __a     ,   __a     ,   __a		)					->		list[float]:







  """simple docstring"""





  lowerCamelCase__:				List[str]        =[float("inf"		)] * vertex_count
  lowerCamelCase__:				List[str]        =0.0

  for _ in range(vertex_count - 1		):
    for j in range(__a		):
      lowerCamelCase__  , lowerCamelCase__  , lowerCamelCase__:				Union[str, Any]        =(graph[j][k] for k in ["src", "dst", "weight"])

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

  lowerCamelCase__:				Tuple        =check_negative_cycle(__a     ,   __a     ,   __a		)
  if negative_cycle_exists:
    raise Exception("Negative cycle found"		)

  return distance


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     __A     =    int(input("Enter number of vertices: ").strip())
     __A     =    int(input("Enter number of edges: ").strip())

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

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

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

