





"""simple docstring"""



class 		__snake_case :





 """simple docstring"""


 def __init__(   self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase  ):



     '''simple docstring'''




     __A   :						List[str]									=					None
     __A   :						str									=					None
     __A   :						Tuple									=					graph

     self._normalize_graph(__lowerCamelCase ,						__lowerCamelCase  )
     __A   :						Dict									=					len(__lowerCamelCase  )
     __A   :						Dict									=					None

 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase  ):



     '''simple docstring'''




     if sources is int:
         __A   :						int									=					[sources]
     if sinks is int:
         __A   :						Any									=					[sinks]

     if len(__lowerCamelCase  ) == 0 or len(__lowerCamelCase  ) == 0:
         return

     __A   :						Dict									=					sources[0]
     __A   :						Optional[Any]									=					sinks[0]

     # make fake vertex if there are more
     # than one source or sink
     if len(__lowerCamelCase  ) > 1 or len(__lowerCamelCase  ) > 1:
         __A   :						Optional[int]									=					0
         for i in sources:
             max_input_flow += sum(self.graph[i]  )

         __A   :						Optional[int]									=					len(self.graph  ) + 1
         for room in self.graph:
             room.insert(0 ,						0  )
         self.graph.insert(0 ,						[0] * size  )
         for i in sources:
             __A   :						str									=					max_input_flow
         __A   :						Any									=					0

         __A   :						int									=					len(self.graph  ) + 1
         for room in self.graph:
             room.append(0  )
         self.graph.append([0] * size  )
         for i in sinks:
             __A   :						Tuple									=					max_input_flow
         __A   :						Any									=					size - 1

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     if self.maximum_flow_algorithm is None:
         raise Exception('''You need to set maximum flow algorithm before.'''  )
     if self.source_index is None or self.sink_index is None:
         return 0

     self.maximum_flow_algorithm.execute()
     return self.maximum_flow_algorithm.getMaximumFlow()







 def  UpperCamelCase__(   self ,						__lowerCamelCase  ):



     '''simple docstring'''




     __A   :						str									=					algorithm(self  )






class 		__snake_case :





 """simple docstring"""


 def __init__(   self ,						__lowerCamelCase  ):



     '''simple docstring'''




     __A   :						Optional[int]									=					flow_network
     __A   :						Union[str, Any]									=					flow_network.verticesCount
     __A   :						List[str]									=					flow_network.sourceIndex
     __A   :						Dict									=					flow_network.sinkIndex
     # it's just a reference, so you shouldn't change
     # it in your algorithms, use deep copy before doing that
     __A   :						int									=					flow_network.graph
     __A   :						Optional[Any]									=					False

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     if not self.executed:
         self._algorithm()
         __A   :						Dict									=					True







 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     pass






class 		__snake_case (		SCREAMING_SNAKE_CASE__					):





 """simple docstring"""


 def __init__(   self ,						__lowerCamelCase  ):



     '''simple docstring'''




     super().__init__(__lowerCamelCase  )
     # use this to save your result
     __A   :						str									=					-1







 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     if not self.executed:
         raise Exception('''You should execute algorithm before using its result!'''  )

     return self.maximum_flow






class 		__snake_case (		SCREAMING_SNAKE_CASE__					):





 """simple docstring"""


 def __init__(   self ,						__lowerCamelCase  ):



     '''simple docstring'''




     super().__init__(__lowerCamelCase  )

     __A   :						int									=					[[0] * self.verticies_count for i in range(self.verticies_count  )]

     __A   :						str									=					[0] * self.verticies_count
     __A   :						Dict									=					[0] * self.verticies_count

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						List[str]									=					self.verticies_count

     # push some substance to graph
     for nextvertex_index, bandwidth in enumerate(self.graph[self.source_index]  ):
         self.preflow[self.source_index][nextvertex_index] += bandwidth
         self.preflow[nextvertex_index][self.source_index] -= bandwidth
         self.excesses[nextvertex_index] += bandwidth

     # Relabel-to-front selection rule
     __A   :						int									=					[
         i
         for i in range(self.verticies_count  )
         if i != self.source_index and i != self.sink_index
     ]

     # move through list
     __A   :						Optional[Any]									=					0
     while i < len(__lowerCamelCase  ):
         __A   :						Optional[int]									=					vertices_list[i]
         __A   :						int									=					self.heights[vertex_index]
         self.process_vertex(__lowerCamelCase  )
         if self.heights[vertex_index] > previous_height:
             # if it was relabeled, swap elements
             # and start from 0 index
             vertices_list.insert(0 ,						vertices_list.pop(__lowerCamelCase  )  )
             __A   :						Tuple									=					0
         else:
             i += 1

     __A   :						Any									=					sum(self.preflow[self.source_index]  )

 def  UpperCamelCase__(   self ,						__lowerCamelCase  ):



     '''simple docstring'''




     while self.excesses[vertex_index] > 0:
         for neighbour_index in range(self.verticies_count  ):
             # if it's neighbour and current vertex is higher
             if (
                 self.graph[vertex_index][neighbour_index]
                 - self.preflow[vertex_index][neighbour_index]
                 > 0
                 and self.heights[vertex_index] > self.heights[neighbour_index]
             ):
                 self.push(__lowerCamelCase ,						__lowerCamelCase  )

         self.relabel(__lowerCamelCase  )

 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase  ):



     '''simple docstring'''




     __A   :						str									=					min(
         self.excesses[from_index] ,						self.graph[from_index][to_index] - self.preflow[from_index][to_index] ,						)
     self.preflow[from_index][to_index] += preflow_delta
     self.preflow[to_index][from_index] -= preflow_delta
     self.excesses[from_index] -= preflow_delta
     self.excesses[to_index] += preflow_delta







 def  UpperCamelCase__(   self ,						__lowerCamelCase  ):



     '''simple docstring'''




     __A   :						str									=					None
     for to_index in range(self.verticies_count  ):
         if (
             self.graph[vertex_index][to_index]
             - self.preflow[vertex_index][to_index]
             > 0
         ) and (min_height is None or self.heights[to_index] < min_height):
             __A   :						str									=					self.heights[to_index]

     if min_height is not None:
         __A   :						List[Any]									=					min_height + 1


if __name__ == "__main__":
       a_			=      [0]
       a_			=      [3]
       # graph = [
       #     [0, 0, 4, 6, 0, 0],
       #     [0, 0, 5, 2, 0, 0],
       #     [0, 0, 0, 0, 4, 4],
       #     [0, 0, 0, 0, 6, 6],
       #     [0, 0, 0, 0, 0, 0],
       #     [0, 0, 0, 0, 0, 0],
       # ]
       a_			=      [[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]

       # prepare our network
       a_			=      FlowNetwork(graph, entrances, exits)
       # set algorithm
       flow_network.set_maximum_flow_algorithm(PushRelabelExecutor)
       # and calculate
       a_			=      flow_network.find_maximum_flow()

       print(f'''maximum flow is {maximum_flow}''')











"""simple docstring"""



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 		__snake_case (		SCREAMING_SNAKE_CASE__							,							unittest.TestCase					):





 """simple docstring"""


 _lowerCamelCase				=					BarthezTokenizer
 _lowerCamelCase				=					BarthezTokenizerFast
 _lowerCamelCase				=					True
 _lowerCamelCase				=					True

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     super().setUp()

     __A   :						List[Any]									=					BarthezTokenizerFast.from_pretrained('''moussaKam/mbarthez'''  )
     tokenizer.save_pretrained(self.tmpdirname  )
     tokenizer.save_pretrained(self.tmpdirname ,						legacy_format=__lowerCamelCase  )
     __A   :						Any									=					tokenizer

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Any									=					'''<pad>'''
     __A   :						Union[str, Any]									=					1

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

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Optional[Any]									=					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(__lowerCamelCase  ) ,						10_1122  )

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     self.assertEqual(self.get_tokenizer().vocab_size ,						10_1122  )

 @require_torch
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						List[Any]									=					['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
     __A   :						Any									=					[0, 57, 3018, 7_0307, 91, 2]

     __A   :						List[Any]									=					self.tokenizer(
         __lowerCamelCase ,						max_length=len(__lowerCamelCase  ) ,						padding=__lowerCamelCase ,						truncation=__lowerCamelCase ,						return_tensors='''pt'''  )
     self.assertIsInstance(__lowerCamelCase ,						__lowerCamelCase  )

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

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     if not self.test_rust_tokenizer:
         return

     __A   :						Tuple									=					self.get_tokenizer()
     __A   :						Dict									=					self.get_rust_tokenizer()

     __A   :						Union[str, Any]									=					'''I was born in 92000, and this is falsé.'''

     __A   :						List[Any]									=					tokenizer.tokenize(__lowerCamelCase  )
     __A   :						Optional[int]									=					rust_tokenizer.tokenize(__lowerCamelCase  )
     self.assertListEqual(__lowerCamelCase ,						__lowerCamelCase  )

     __A   :						str									=					tokenizer.encode(__lowerCamelCase ,						add_special_tokens=__lowerCamelCase  )
     __A   :						Union[str, Any]									=					rust_tokenizer.encode(__lowerCamelCase ,						add_special_tokens=__lowerCamelCase  )
     self.assertListEqual(__lowerCamelCase ,						__lowerCamelCase  )

     __A   :						str									=					self.get_rust_tokenizer()
     __A   :						Optional[int]									=					tokenizer.encode(__lowerCamelCase  )
     __A   :						Any									=					rust_tokenizer.encode(__lowerCamelCase  )
     self.assertListEqual(__lowerCamelCase ,						__lowerCamelCase  )







 @slow
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						List[str]									=					{'''input_ids''': [[0, 490, 1_4328, 4507, 354, 47, 4_3669, 95, 25, 7_8117, 2_0215, 1_9779, 190, 22, 400, 4, 3_5343, 8_0310, 603, 86, 2_4937, 105, 3_3438, 9_4762, 196, 3_9642, 7, 15, 1_5933, 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, 1_0534, 87, 25, 66, 3358, 196, 5_5289, 8, 8_2961, 81, 2204, 7_5203, 7, 15, 763, 1_2956, 216, 178, 1_4328, 9595, 1377, 6_9693, 7, 448, 7_1021, 196, 1_8106, 1437, 1_3974, 108, 9083, 4, 4_9315, 7, 39, 86, 1326, 2793, 4_6333, 4, 448, 196, 7_4588, 7, 4_9315, 7, 39, 21, 822, 3_8470, 74, 21, 6_6723, 6_2480, 8, 2_2050, 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.
     __A   :						Union[str, Any]									=					[
         '''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=__lowerCamelCase ,						model_name='''moussaKam/mbarthez''' ,						revision='''c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6''' ,						sequences=__lowerCamelCase ,						)




