





import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


__A     =    get_tests_dir("fixtures/test_sentencepiece_no_bos.model")



@require_sentencepiece
@require_tokenizers
class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ,							unittest.TestCase   ):







 '''simple docstring'''
 lowercase_		=      PegasusTokenizer
 lowercase_		=      PegasusTokenizerFast
 lowercase_		=      True
 lowercase_		=      True

 def 							SCREAMING_SNAKE_CASE_ (self    :       str)	->List[str]:



   '''simple docstring'''



   super().setUp()

   # We have a SentencePiece fixture for testing
   lowerCamelCase__:				Optional[int]        =PegasusTokenizer(__A)
   tokenizer.save_pretrained(self.tmpdirname)

 @cached_property
 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[int])	->Optional[int]:



   '''simple docstring'''



   return PegasusTokenizer.from_pretrained("google/pegasus-large")

 def 							SCREAMING_SNAKE_CASE_ (self    :       Dict	,			**UpperCAmelCase_    :       str)	->PegasusTokenizer:



   '''simple docstring'''



   return PegasusTokenizer.from_pretrained(self.tmpdirname	,			**__A)

 def 							SCREAMING_SNAKE_CASE_ (self    :       int	,			UpperCAmelCase_    :       List[Any])	->str:



   '''simple docstring'''



   return ("This is a test", "This is a test")

 def 							SCREAMING_SNAKE_CASE_ (self    :       Any)	->Any:



   '''simple docstring'''



   lowerCamelCase__:				Tuple        ="</s>"
   lowerCamelCase__:				Union[str, Any]        =1

   self.assertEqual(self.get_tokenizer()._convert_token_to_id(__A)	,			__A)
   self.assertEqual(self.get_tokenizer()._convert_id_to_token(__A)	,			__A)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any])	->List[Any]:



   '''simple docstring'''



   lowerCamelCase__:				Any        =list(self.get_tokenizer().get_vocab().keys())

   self.assertEqual(vocab_keys[0]	,			"<pad>")
   self.assertEqual(vocab_keys[1]	,			"</s>")
   self.assertEqual(vocab_keys[-1]	,			"v")
   self.assertEqual(len(__A)	,			1_103)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[int])	->List[str]:



   '''simple docstring'''



   self.assertEqual(self.get_tokenizer().vocab_size	,			1_103)

 def 							SCREAMING_SNAKE_CASE_ (self    :       List[Any])	->List[Any]:



   '''simple docstring'''



   lowerCamelCase__:				Dict        =self.rust_tokenizer_class.from_pretrained(self.tmpdirname)
   lowerCamelCase__:				List[Any]        =self.tokenizer_class.from_pretrained(self.tmpdirname)
   lowerCamelCase__:				List[str]        =(
       "Let\'s see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important"
       " </s> <pad> <pad> <pad>"
   )
   lowerCamelCase__:				Union[str, Any]        =rust_tokenizer([raw_input_str]	,			return_tensors=__A	,			add_special_tokens=__A).input_ids[0]
   lowerCamelCase__:				Optional[int]        =py_tokenizer([raw_input_str]	,			return_tensors=__A	,			add_special_tokens=__A).input_ids[0]
   self.assertListEqual(__A	,			__A)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Dict)	->List[str]:



   '''simple docstring'''



   lowerCamelCase__:				Dict        =self._large_tokenizer
   # <mask_1> masks whole sentence while <mask_2> masks single word
   lowerCamelCase__:				List[Any]        ="<mask_1> To ensure a <mask_2> flow of bank resolutions."
   lowerCamelCase__:				int        =[2, 413, 615, 114, 3, 1_971, 113, 1_679, 10_710, 107, 1]
   lowerCamelCase__:				str        =tokenizer([raw_input_str]	,			return_tensors=__A).input_ids[0]
   self.assertListEqual(__A	,			__A)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any])	->Tuple:



   '''simple docstring'''



   lowerCamelCase__:				Any        =self._large_tokenizer
   # The tracebacks for the following asserts are **better** without messages or self.assertEqual
   assert tokenizer.vocab_size == 96_103
   assert tokenizer.pad_token_id == 0
   assert tokenizer.eos_token_id == 1
   assert tokenizer.offset == 103
   assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
   assert tokenizer.unk_token == "<unk>"
   assert tokenizer.model_max_length == 1_024
   lowerCamelCase__:				Any        ="To ensure a smooth flow of bank resolutions."
   lowerCamelCase__:				int        =[413, 615, 114, 2_291, 1_971, 113, 1_679, 10_710, 107, 1]
   lowerCamelCase__:				Union[str, Any]        =tokenizer([raw_input_str]	,			return_tensors=__A).input_ids[0]
   self.assertListEqual(__A	,			__A)
   assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]

 @require_torch
 def 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any])	->Any:



   '''simple docstring'''



   lowerCamelCase__:				List[str]        =["This is going to be way too long." * 150, "short example"]
   lowerCamelCase__:				List[Any]        =["not super long but more than 5 tokens", "tiny"]
   lowerCamelCase__:				List[Any]        =self._large_tokenizer(__A	,			padding=__A	,			truncation=__A	,			return_tensors="pt")
   lowerCamelCase__:				Optional[int]        =self._large_tokenizer(
       text_target=__A	,			max_length=5	,			padding=__A	,			truncation=__A	,			return_tensors="pt")

   assert batch.input_ids.shape == (2, 1_024)
   assert batch.attention_mask.shape == (2, 1_024)
   assert targets["input_ids"].shape == (2, 5)
   assert len(__A) == 2  # input_ids, attention_mask.







 @slow
 def 							SCREAMING_SNAKE_CASE_ (self    :       List[str])	->Union[str, Any]:



   '''simple docstring'''



   lowerCamelCase__:				Union[str, Any]        ={"input_ids": [[38_979, 143, 18_485, 606, 130, 26_669, 87_686, 121, 54_189, 1_129, 111, 26_669, 87_686, 121, 9_114, 14_787, 121, 13_249, 158, 592, 956, 121, 14_621, 31_576, 143, 62_613, 108, 9_688, 930, 43_430, 11_562, 62_613, 304, 108, 11_443, 897, 108, 9_314, 17_415, 63_399, 108, 11_443, 7_614, 18_316, 118, 4_284, 7_148, 12_430, 143, 1_400, 25_703, 158, 111, 4_284, 7_148, 11_772, 143, 21_297, 1_064, 158, 122, 204, 3_506, 1_754, 1_133, 14_787, 1_581, 115, 33_224, 4_482, 111, 1_355, 110, 29_173, 317, 50_833, 108, 20_147, 94_665, 111, 77_198, 107, 1], [110, 62_613, 117, 638, 112, 1_133, 121, 20_098, 1_355, 79_050, 13_872, 135, 1_596, 53_541, 1_352, 141, 13_039, 5_542, 124, 302, 518, 111, 268, 2_956, 115, 149, 4_427, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [139, 1_235, 2_799, 18_289, 17_780, 204, 109, 9_474, 1_296, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
   # fmt: on

   self.tokenizer_integration_test_util(
       expected_encoding=__A	,			model_name="google/bigbird-pegasus-large-arxiv"	,			revision="ba85d0851d708441f91440d509690f1ab6353415"	,			)



@require_sentencepiece
@require_tokenizers
class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ,							unittest.TestCase   ):







 '''simple docstring'''
 lowercase_		=      PegasusTokenizer
 lowercase_		=      PegasusTokenizerFast
 lowercase_		=      True
 lowercase_		=      True

 def 							SCREAMING_SNAKE_CASE_ (self    :       List[str])	->Tuple:



   '''simple docstring'''



   super().setUp()

   # We have a SentencePiece fixture for testing
   lowerCamelCase__:				str        =PegasusTokenizer(__A	,			offset=0	,			mask_token_sent=__A	,			mask_token="[MASK]")
   tokenizer.save_pretrained(self.tmpdirname)

 @cached_property
 def 							SCREAMING_SNAKE_CASE_ (self    :       List[str])	->str:



   '''simple docstring'''



   return PegasusTokenizer.from_pretrained("google/bigbird-pegasus-large-arxiv")

 def 							SCREAMING_SNAKE_CASE_ (self    :       Union[str, Any]	,			**UpperCAmelCase_    :       int)	->PegasusTokenizer:



   '''simple docstring'''



   return PegasusTokenizer.from_pretrained(self.tmpdirname	,			**__A)

 def 							SCREAMING_SNAKE_CASE_ (self    :       List[Any]	,			UpperCAmelCase_    :       Union[str, Any])	->List[str]:



   '''simple docstring'''



   return ("This is a test", "This is a test")

 def 							SCREAMING_SNAKE_CASE_ (self    :       List[Any])	->List[str]:



   '''simple docstring'''



   lowerCamelCase__:				Tuple        =self.rust_tokenizer_class.from_pretrained(self.tmpdirname)
   lowerCamelCase__:				int        =self.tokenizer_class.from_pretrained(self.tmpdirname)
   lowerCamelCase__:				List[Any]        =(
       "Let\'s see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>"
       " <pad> <pad> <pad>"
   )
   lowerCamelCase__:				Dict        =rust_tokenizer([raw_input_str]	,			return_tensors=__A	,			add_special_tokens=__A).input_ids[0]
   lowerCamelCase__:				Tuple        =py_tokenizer([raw_input_str]	,			return_tensors=__A	,			add_special_tokens=__A).input_ids[0]
   self.assertListEqual(__A	,			__A)

 @require_torch
 def 							SCREAMING_SNAKE_CASE_ (self    :       List[Any])	->Dict:



   '''simple docstring'''



   lowerCamelCase__:				Tuple        =["This is going to be way too long." * 1_000, "short example"]
   lowerCamelCase__:				Dict        =["not super long but more than 5 tokens", "tiny"]
   lowerCamelCase__:				Union[str, Any]        =self._large_tokenizer(__A	,			padding=__A	,			truncation=__A	,			return_tensors="pt")
   lowerCamelCase__:				List[str]        =self._large_tokenizer(
       text_target=__A	,			max_length=5	,			padding=__A	,			truncation=__A	,			return_tensors="pt")

   assert batch.input_ids.shape == (2, 4_096)
   assert batch.attention_mask.shape == (2, 4_096)
   assert targets["input_ids"].shape == (2, 5)
   assert len(__A) == 2  # input_ids, attention_mask.







 def 							SCREAMING_SNAKE_CASE_ (self    :       Any)	->str:



   '''simple docstring'''



   lowerCamelCase__:				List[str]        =(
       "This is an example string that is used to test the original TF implementation against the HF"
       " implementation"
   )

   lowerCamelCase__:				str        =self._large_tokenizer(__A).input_ids

   self.assertListEqual(
       __A	,			[182, 117, 142, 587, 4_211, 120, 117, 263, 112, 804, 109, 856, 25_016, 3_137, 464, 109, 26_955, 3_137, 1]	,			)









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)

