






import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class 							A_    (						__lowercase ,   unittest.TestCase			):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:			Optional[Any] =					BarthezTokenizer
 _SCREAMING_SNAKE_CASE							:			int =					BarthezTokenizerFast
 _SCREAMING_SNAKE_CASE							:			Dict =					True
 _SCREAMING_SNAKE_CASE							:			Optional[Any] =					True


 def  snake_case__				(	self) ->		Any:

     """simple docstring"""



     super().setUp()

     _UpperCAmelCase				:      Any			  =				BarthezTokenizerFast.from_pretrained('''moussaKam/mbarthez''')
     tokenizer.save_pretrained(self.tmpdirname)
     tokenizer.save_pretrained(self.tmpdirname							,  legacy_format=_A)
     _UpperCAmelCase				:      int			  =				tokenizer


 def  snake_case__				(	self) ->		Dict:

     """simple docstring"""



     _UpperCAmelCase				:      Tuple			  =				'''<pad>'''
     _UpperCAmelCase				:      Optional[int]			  =				1

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


 def  snake_case__				(	self) ->		Any:

     """simple docstring"""



     _UpperCAmelCase				:      List[str]			  =				list(self.get_tokenizer().get_vocab().keys())

     self.assertEqual(vocab_keys[0]							,  '''<s>''')
     self.assertEqual(vocab_keys[1]							,  '''<pad>''')
     self.assertEqual(vocab_keys[-1]							,  '''<mask>''')
     self.assertEqual(len(_A)							,  101122)


 def  snake_case__				(	self) ->		str:

     """simple docstring"""



     self.assertEqual(self.get_tokenizer().vocab_size							,  101122)


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

     """simple docstring"""



     _UpperCAmelCase				:      Union[str, Any]			  =				['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
     _UpperCAmelCase				:      Optional[Any]			  =				[0, 57, 3018, 70307, 91, 2]

     _UpperCAmelCase				:      List[str]			  =				self.tokenizer(
         _A							,  max_length=len(_A)							,  padding=_A							,  truncation=_A							,  return_tensors='''pt''')
     self.assertIsInstance(_A							,  _A)

     self.assertEqual((2, 6)							,  batch.input_ids.shape)
     self.assertEqual((2, 6)							,  batch.attention_mask.shape)
     _UpperCAmelCase				:      Optional[Any]			  =				batch.input_ids.tolist()[0]
     self.assertListEqual(_A							,  _A)


 def  snake_case__				(	self) ->		Any:

     """simple docstring"""



     if not self.test_rust_tokenizer:
         return

     _UpperCAmelCase				:      List[Any]			  =				self.get_tokenizer()
     _UpperCAmelCase				:      Dict			  =				self.get_rust_tokenizer()

     _UpperCAmelCase				:      str			  =				'''I was born in 92000, and this is falsé.'''

     _UpperCAmelCase				:      List[str]			  =				tokenizer.tokenize(_A)
     _UpperCAmelCase				:      Any			  =				rust_tokenizer.tokenize(_A)
     self.assertListEqual(_A							,  _A)

     _UpperCAmelCase				:      Any			  =				tokenizer.encode(_A							,  add_special_tokens=_A)
     _UpperCAmelCase				:      Tuple			  =				rust_tokenizer.encode(_A							,  add_special_tokens=_A)
     self.assertListEqual(_A							,  _A)

     _UpperCAmelCase				:      List[Any]			  =				self.get_rust_tokenizer()
     _UpperCAmelCase				:      List[Any]			  =				tokenizer.encode(_A)
     _UpperCAmelCase				:      List[Any]			  =				rust_tokenizer.encode(_A)
     self.assertListEqual(_A							,  _A)


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

     """simple docstring"""



     _UpperCAmelCase				:      Dict			  =				{'''input_ids''': [[0, 490, 14328, 4507, 354, 47, 43669, 95, 25, 78117, 20215, 19779, 190, 22, 400, 4, 35343, 80310, 603, 86, 24937, 105, 33438, 94762, 196, 39642, 7, 15, 15933, 173, 2, 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, 10534, 87, 25, 66, 3358, 196, 55289, 8, 82961, 81, 2204, 75203, 7, 15, 763, 12956, 216, 178, 14328, 9595, 1377, 69693, 7, 448, 71021, 196, 18106, 1437, 13974, 108, 9083, 4, 49315, 7, 39, 86, 1326, 2793, 46333, 4, 448, 196, 74588, 7, 49315, 7, 39, 21, 822, 38470, 74, 21, 66723, 62480, 8, 22050, 5, 2]], '''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, 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, 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]]}  # noqa: E501
     # fmt: on

     # moussaKam/mbarthez is a french model. So we also use french texts.
     _UpperCAmelCase				:      List[str]			  =				[
         '''Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '''
         '''utilisé principalement dans le domaine du traitement automatique des langues (TAL).''',
         '''À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '''
         '''pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '''
         '''telles que la traduction et la synthèse de texte.''',
     ]

     self.tokenizer_integration_test_util(
         expected_encoding=_A							,  model_name='''moussaKam/mbarthez'''							,  revision='''c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6'''							,  sequences=_A							,  )












import heapq

def      _lowerCamelCase	(						__A    :							dict )      ->					set[int]:
    _UpperCAmelCase				:      list[list]			  =				[]

    # for each node and his adjacency list add them and the rank of the node to queue
    # using heapq module the queue will be filled like a Priority Queue
    # heapq works with a min priority queue, so I used -1*len(v) to build it
    for key, value in graph.items():
        # O(log(n))
        heapq.heappush(__A		,		[-1 * len(__A ), (key, value)] )

    # chosen_vertices = set of chosen vertices
    _UpperCAmelCase				:      Union[str, Any]			  =				set()

    # while queue isn't empty and there are still edges
    #   (queue[0][0] is the rank of the node with max rank)
    while queue and queue[0][0] != 0:
        # extract vertex with max rank from queue and add it to chosen_vertices
        _UpperCAmelCase				:      Tuple			  =				heapq.heappop(__A )[1][0]
        chosen_vertices.add(__A )

        # Remove all arcs adjacent to argmax
        for elem in queue:
            # if v haven't adjacent node, skip
            if elem[0] == 0:
                continue
            # if argmax is reachable from elem
            # remove argmax from elem's adjacent list and update his rank
            if argmax in elem[1][1]:
                _UpperCAmelCase				:      Dict			  =				elem[1][1].index(__A )
                del elem[1][1][index]
                elem[0] += 1
        # re-order the queue
        heapq.heapify(__A )
    return chosen_vertices


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  SCREAMING_SNAKE_CASE 							=						{0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
  print(F'Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}')




