
import unittest

from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


__lowerCAmelCase					   =       '▁'

__lowerCAmelCase					   =       get_tests_dir('fixtures/test_sentencepiece.model')





@require_sentencepiece
class     lowerCamelCase      (       __lowerCamelCase ,		unittest.TestCase					):
	UpperCamelCase_  :					List[Any]    		=	BertGenerationTokenizer
	UpperCamelCase_  :					Optional[int]    		=	False
	UpperCamelCase_  :					Tuple    		=	True







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







				"""simple docstring"""






				super().setUp()

				SCREAMING_SNAKE_CASE     						= BertGenerationTokenizer(lowercase   ,     keep_accents=lowercase			)
				tokenizer.save_pretrained(self.tmpdirname			)







	def  snake_case__     (     self      :Tuple			)		->      int:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= '''<s>'''
				SCREAMING_SNAKE_CASE     						= 1

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







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







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= list(self.get_tokenizer().get_vocab().keys()			)

				self.assertEqual(vocab_keys[0]   ,     '''<unk>'''			)
				self.assertEqual(vocab_keys[1]   ,     '''<s>'''			)
				self.assertEqual(vocab_keys[-1]   ,     '''<pad>'''			)
				self.assertEqual(len(lowercase			)   ,     1_0_0_2			)







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







				"""simple docstring"""






				self.assertEqual(self.get_tokenizer().vocab_size   ,     1_0_0_0			)







	def  snake_case__     (     self      :Optional[int]			)		->      Union[str, Any]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= BertGenerationTokenizer(lowercase   ,     keep_accents=lowercase			)

				SCREAMING_SNAKE_CASE     						= tokenizer.tokenize('''This is a test'''			)
				self.assertListEqual(lowercase   ,     ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']			)

				self.assertListEqual(
				    tokenizer.convert_tokens_to_ids(lowercase			)   ,     [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]   ,     )

				SCREAMING_SNAKE_CASE     						= tokenizer.tokenize('''I was born in 92000, and this is falsé.'''			)
				self.assertListEqual(
				    lowercase   ,     [
				        SPIECE_UNDERLINE + '''I''',
				        SPIECE_UNDERLINE + '''was''',
				        SPIECE_UNDERLINE + '''b''',
				        '''or''',
				        '''n''',
				        SPIECE_UNDERLINE + '''in''',
				        SPIECE_UNDERLINE + '''''',
				        '''9''',
				        '''2''',
				        '''0''',
				        '''0''',
				        '''0''',
				        ''',''',
				        SPIECE_UNDERLINE + '''and''',
				        SPIECE_UNDERLINE + '''this''',
				        SPIECE_UNDERLINE + '''is''',
				        SPIECE_UNDERLINE + '''f''',
				        '''al''',
				        '''s''',
				        '''é''',
				        '''.''',
				    ]   ,     )
				SCREAMING_SNAKE_CASE     						= tokenizer.convert_tokens_to_ids(lowercase			)
				self.assertListEqual(
				    lowercase   ,     [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 0, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 0, 4]   ,     )

				SCREAMING_SNAKE_CASE     						= tokenizer.convert_ids_to_tokens(lowercase			)
				self.assertListEqual(
				    lowercase   ,     [
				        SPIECE_UNDERLINE + '''I''',
				        SPIECE_UNDERLINE + '''was''',
				        SPIECE_UNDERLINE + '''b''',
				        '''or''',
				        '''n''',
				        SPIECE_UNDERLINE + '''in''',
				        SPIECE_UNDERLINE + '''''',
				        '''<unk>''',
				        '''2''',
				        '''0''',
				        '''0''',
				        '''0''',
				        ''',''',
				        SPIECE_UNDERLINE + '''and''',
				        SPIECE_UNDERLINE + '''this''',
				        SPIECE_UNDERLINE + '''is''',
				        SPIECE_UNDERLINE + '''f''',
				        '''al''',
				        '''s''',
				        '''<unk>''',
				        '''.''',
				    ]   ,     )







	@cached_property
	def  snake_case__     (     self      :Optional[Any]			)		->      List[str]:







				"""simple docstring"""






				return BertGenerationTokenizer.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder'''			)







	@slow
	def  snake_case__     (     self      :int			)		->      Tuple:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= '''Hello World!'''
				SCREAMING_SNAKE_CASE     						= [1_8_5_3_6, 2_2_6_0, 1_0_1]

				self.assertListEqual(lowercase   ,     self.big_tokenizer.encode(lowercase			)			)







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







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= (
				    '''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
				    ''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
				)
				SCREAMING_SNAKE_CASE     						= [
				    8_7_1,
				    4_1_9,
				    3_5_8,
				    9_4_6,
				    9_9_1,
				    2_5_2_1,
				    4_5_2,
				    3_5_8,
				    1_3_5_7,
				    3_8_7,
				    7_7_5_1,
				    3_5_3_6,
				    1_1_2,
				    9_8_5,
				    4_5_6,
				    1_2_6,
				    8_6_5,
				    9_3_8,
				    5_4_0_0,
				    5_7_3_4,
				    4_5_8,
				    1_3_6_8,
				    4_6_7,
				    7_8_6,
				    2_4_6_2,
				    5_2_4_6,
				    1_1_5_9,
				    6_3_3,
				    8_6_5,
				    4_5_1_9,
				    4_5_7,
				    5_8_2,
				    8_5_2,
				    2_5_5_7,
				    4_2_7,
				    9_1_6,
				    5_0_8,
				    4_0_5,
				    3_4_3_2_4,
				    4_9_7,
				    3_9_1,
				    4_0_8,
				    1_1_3_4_2,
				    1_2_4_4,
				    3_8_5,
				    1_0_0,
				    9_3_8,
				    9_8_5,
				    4_5_6,
				    5_7_4,
				    3_6_2,
				    1_2_5_9_7,
				    3_2_0_0,
				    3_1_2_9,
				    1_1_7_2,
				]

				self.assertListEqual(lowercase   ,     self.big_tokenizer.encode(lowercase			)			)







	@require_torch
	@slow
	def  snake_case__     (     self      :Dict			)		->      Optional[int]:







				"""simple docstring"""






				import torch

				from transformers import BertGenerationConfig, BertGenerationEncoder

				# Build sequence
				SCREAMING_SNAKE_CASE     						= list(self.big_tokenizer.get_vocab().keys()			)[:1_0]
				SCREAMING_SNAKE_CASE     						= ''' '''.join(lowercase			)
				SCREAMING_SNAKE_CASE     						= self.big_tokenizer.encode_plus(lowercase   ,     return_tensors='''pt'''   ,     return_token_type_ids=lowercase			)
				SCREAMING_SNAKE_CASE     						= self.big_tokenizer.batch_encode_plus(
				    [sequence + ''' ''' + sequence]   ,     return_tensors='''pt'''   ,     return_token_type_ids=lowercase			)

				SCREAMING_SNAKE_CASE     						= BertGenerationConfig()
				SCREAMING_SNAKE_CASE     						= BertGenerationEncoder(lowercase			)

				assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

				with torch.no_grad():
							model(**lowercase			)
							model(**lowercase			)







	@slow
	def  snake_case__     (     self      :int			)		->      int:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= {'''input_ids''': [[3_9_2_8_6, 4_5_8, 3_6_3_3_5, 2_0_0_1, 4_5_6, 1_3_0_7_3, 1_3_2_6_6, 4_5_5, 1_1_3, 7_7_4_6, 1_7_4_1, 1_1_1_5_7, 3_9_1, 1_3_0_7_3, 1_3_2_6_6, 4_5_5, 1_1_3, 3_9_6_7, 3_5_4_1_2, 1_1_3, 4_9_3_6, 1_0_9, 3_8_7_0, 2_3_7_7, 1_1_3, 3_0_0_8_4, 4_5_7_2_0, 4_5_8, 1_3_4, 1_7_4_9_6, 1_1_2, 5_0_3, 1_1_6_7_2, 1_1_3, 1_1_8, 1_1_2, 5_6_6_5, 1_3_3_4_7, 3_8_6_8_7, 1_1_2, 1_4_9_6, 3_1_3_8_9, 1_1_2, 3_2_6_8, 4_7_2_6_4, 1_3_4, 9_6_2, 1_1_2, 1_6_3_7_7, 8_0_3_5, 2_3_1_3_0, 4_3_0, 1_2_1_6_9, 1_5_5_1_8, 2_8_5_9_2, 4_5_8, 1_4_6, 4_1_6_9_7, 1_0_9, 3_9_1, 1_2_1_6_9, 1_5_5_1_8, 1_6_6_8_9, 4_5_8, 1_4_6, 4_1_3_5_8, 1_0_9, 4_5_2, 7_2_6, 4_0_3_4, 1_1_1, 7_6_3, 3_5_4_1_2, 5_0_8_2, 3_8_8, 1_9_0_3, 1_1_1, 9_0_5_1, 3_9_1, 2_8_7_0, 4_8_9_1_8, 1_9_0_0, 1_1_2_3, 5_5_0, 9_9_8, 1_1_2, 9_5_8_6, 1_5_9_8_5, 4_5_5, 3_9_1, 4_1_0, 2_2_9_5_5, 3_7_6_3_6, 1_1_4], [4_4_8, 1_7_4_9_6, 4_1_9, 3_6_6_3, 3_8_5, 7_6_3, 1_1_3, 2_7_5_3_3, 2_8_7_0, 3_2_8_3, 1_3_0_4_3, 1_6_3_9, 2_4_7_1_3, 5_2_3, 6_5_6, 2_4_0_1_3, 1_8_5_5_0, 2_5_2_1, 5_1_7, 2_7_0_1_4, 2_1_2_4_4, 4_2_0, 1_2_1_2, 1_4_6_5, 3_9_1, 9_2_7, 4_8_3_3, 3_8_8, 5_7_8, 1_1_7_8_6, 1_1_4, 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], [4_8_4, 2_1_6_9, 7_6_8_7, 2_1_9_3_2, 1_8_1_4_6, 7_2_6, 3_6_3, 1_7_0_3_2, 3_3_9_1, 1_1_4, 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, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0], [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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
				# fmt: on

				self.tokenizer_integration_test_util(
				    expected_encoding=lowercase   ,     model_name='''google/bert_for_seq_generation_L-24_bbc_encoder'''   ,     revision='''c817d1fd1be2ffa69431227a1fe320544943d4db'''   ,     )

from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass





@dataclass
class     lowerCamelCase      :
	UpperCamelCase_  :					int
	UpperCamelCase_  :					int





class     lowerCamelCase      :







	def __init__(     self      :Dict   ,     lowercase      :int			)		->      Union[str, Any]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= [[] for _ in range(lowercase			)]
				SCREAMING_SNAKE_CASE     						= size







	def __getitem__(     self      :Union[str, Any]   ,     lowercase      :int			)		->      Iterator[Edge]:







				"""simple docstring"""






				return iter(self._graph[vertex]			)







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







				"""simple docstring"""






				return self._size







	def  snake_case__     (     self      :Optional[Any]   ,     lowercase      :int   ,     lowercase      :int   ,     lowercase      :int			)		->      List[Any]:







				"""simple docstring"""






				if weight not in (0, 1):
							raise ValueError('''Edge weight must be either 0 or 1.'''			)

				if to_vertex < 0 or to_vertex >= self.size:
							raise ValueError('''Vertex indexes must be in [0; size).'''			)

				self._graph[from_vertex].append(Edge(lowercase   ,     lowercase			)			)







	def  snake_case__     (     self      :List[str]   ,     lowercase      :int   ,     lowercase      :int			)		->      int | None:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= deque([start_vertex]			)
				SCREAMING_SNAKE_CASE     						= [None] * self.size
				SCREAMING_SNAKE_CASE     						= 0

				while queue:
							SCREAMING_SNAKE_CASE     						= queue.popleft()
							SCREAMING_SNAKE_CASE     						= distances[current_vertex]
							if current_distance is None:
										continue

							for edge in self[current_vertex]:
										SCREAMING_SNAKE_CASE     						= current_distance + edge.weight
										SCREAMING_SNAKE_CASE     						= distances[edge.destination_vertex]
										if (
										    isinstance(lowercase   ,     lowercase			)
										    and new_distance >= dest_vertex_distance
										):
													continue
										SCREAMING_SNAKE_CASE     						= new_distance
										if edge.weight == 0:
													queue.appendleft(edge.destination_vertex			)
										else:
													queue.append(edge.destination_vertex			)

				if distances[finish_vertex] is None:
							raise ValueError('''No path from start_vertex to finish_vertex.'''			)

				return distances[finish_vertex]


if __name__ == "__main__":
							import doctest

							doctest.testmod()