
"""simple docstring"""


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


_lowercase							      = '''▁'''

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







@require_sentencepiece
class  lowerCAmelCase_ (					snake_case_						,    unittest.TestCase			):



						'''simple docstring'''


						_lowerCamelCase:			Any					=			BertGenerationTokenizer
						_lowerCamelCase:			Dict					=			False
						_lowerCamelCase:			Any					=			True


						def   _SCREAMING_SNAKE_CASE					(       self   :					str					)   ->      Union[str, Any]:
							super().setUp()

							A        =      BertGenerationTokenizer(A_     ,keep_accents=A_					)
							tokenizer.save_pretrained(self.tmpdirname					)


						def   _SCREAMING_SNAKE_CASE					(       self   :					Union[str, Any]					)   ->      Optional[Any]:
							A        =      '<s>'
							A        =      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   :					List[str]					)   ->      Union[str, Any]:
							A        =      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(A_					)     ,1002					)


						def   _SCREAMING_SNAKE_CASE					(       self   :					List[Any]					)   ->      Any:
							self.assertEqual(self.get_tokenizer().vocab_size     ,1000					)


						def   _SCREAMING_SNAKE_CASE					(       self   :					Any					)   ->      Dict:
							A        =      BertGenerationTokenizer(A_     ,keep_accents=A_					)

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

							self.assertListEqual(
							    tokenizer.convert_tokens_to_ids(A_					)     ,[285, 46, 10, 170, 382]     ,)

							A        =      tokenizer.tokenize('I was born in 92000, and this is falsé.'					)
							self.assertListEqual(
							    A_     ,[
							        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',
							        '.',
							    ]     ,)
							A        =      tokenizer.convert_tokens_to_ids(A_					)
							self.assertListEqual(
							    A_     ,[8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]     ,)

							A        =      tokenizer.convert_ids_to_tokens(A_					)
							self.assertListEqual(
							    A_     ,[
							        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   _SCREAMING_SNAKE_CASE					(       self   :					Optional[int]					)   ->      List[str]:
							return BertGenerationTokenizer.from_pretrained('google/bert_for_seq_generation_L-24_bbc_encoder'					)


						@slow
						def   _SCREAMING_SNAKE_CASE					(       self   :					List[Any]					)   ->      Union[str, Any]:
							A        =      'Hello World!'
							A        =      [1_8536, 2260, 101]

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


						@slow
						def   _SCREAMING_SNAKE_CASE					(       self   :					int					)   ->      Any:
							A        =      (
							    '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'
							)
							A        =      [
							    871,
							    419,
							    358,
							    946,
							    991,
							    2521,
							    452,
							    358,
							    1357,
							    387,
							    7751,
							    3536,
							    112,
							    985,
							    456,
							    126,
							    865,
							    938,
							    5400,
							    5734,
							    458,
							    1368,
							    467,
							    786,
							    2462,
							    5246,
							    1159,
							    633,
							    865,
							    4519,
							    457,
							    582,
							    852,
							    2557,
							    427,
							    916,
							    508,
							    405,
							    3_4324,
							    497,
							    391,
							    408,
							    1_1342,
							    1244,
							    385,
							    100,
							    938,
							    985,
							    456,
							    574,
							    362,
							    1_2597,
							    3200,
							    3129,
							    1172,
							]

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


						@require_torch
						@slow
						def   _SCREAMING_SNAKE_CASE					(       self   :					Dict					)   ->      Union[str, Any]:
							import torch

							from transformers import BertGenerationConfig, BertGenerationEncoder

							# Build sequence
							A        =      list(self.big_tokenizer.get_vocab().keys()					)[:10]
							A        =      ' '.join(A_					)
							A        =      self.big_tokenizer.encode_plus(A_     ,return_tensors='pt'     ,return_token_type_ids=A_					)
							A        =      self.big_tokenizer.batch_encode_plus(
							    [sequence + ' ' + sequence]     ,return_tensors='pt'     ,return_token_type_ids=A_					)

							A        =      BertGenerationConfig()
							A        =      BertGenerationEncoder(A_					)

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

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




						@slow
						def   _SCREAMING_SNAKE_CASE					(       self   :					Dict					)   ->      Optional[Any]:
							# fmt: off
							A        =      {'input_ids': [[3_9286, 458, 3_6335, 2001, 456, 1_3073, 1_3266, 455, 113, 7746, 1741, 1_1157, 391, 1_3073, 1_3266, 455, 113, 3967, 3_5412, 113, 4936, 109, 3870, 2377, 113, 3_0084, 4_5720, 458, 134, 1_7496, 112, 503, 1_1672, 113, 118, 112, 5665, 1_3347, 3_8687, 112, 1496, 3_1389, 112, 3268, 4_7264, 134, 962, 112, 1_6377, 8035, 2_3130, 430, 1_2169, 1_5518, 2_8592, 458, 146, 4_1697, 109, 391, 1_2169, 1_5518, 1_6689, 458, 146, 4_1358, 109, 452, 726, 4034, 111, 763, 3_5412, 5082, 388, 1903, 111, 9051, 391, 2870, 4_8918, 1900, 1123, 550, 998, 112, 9586, 1_5985, 455, 391, 410, 2_2955, 3_7636, 114], [448, 1_7496, 419, 3663, 385, 763, 113, 2_7533, 2870, 3283, 1_3043, 1639, 2_4713, 523, 656, 2_4013, 1_8550, 2521, 517, 2_7014, 2_1244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 1_1786, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [484, 2169, 7687, 2_1932, 1_8146, 726, 363, 1_7032, 3391, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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=A_     ,model_name='google/bert_for_seq_generation_L-24_bbc_encoder'     ,revision='c817d1fd1be2ffa69431227a1fe320544943d4db'     ,)

from collections import deque





def 			lowerCamelCase_ (				lowerCamelCase__    ):
				lowerCamelCase_						=			len(lowerCamelCase__    )
				lowerCamelCase_						=			deque()
				lowerCamelCase_						=			[False for _ in range(lowerCamelCase__    )]
				lowerCamelCase_						=			[-1 for _ in range(lowerCamelCase__    )]
				lowerCamelCase_						=			index_of[:]

				def strong_connect(lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    ):
								lowerCamelCase_						=			index  # the number when this node is seen
								lowerCamelCase_						=			index  # lowest rank node reachable from here
								index += 1
								stack.append(lowerCamelCase__    )
								lowerCamelCase_						=			True

								for w in g[v]:
												if index_of[w] == -1:
																lowerCamelCase_						=			strong_connect(lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    )
																lowerCamelCase_						=			(
																    lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
																)
												elif on_stack[w]:
																lowerCamelCase_						=			(
																    lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
																)

								if lowlink_of[v] == index_of[v]:
												lowerCamelCase_						=			[]
												lowerCamelCase_						=			stack.pop()
												lowerCamelCase_						=			False
												component.append(lowerCamelCase__    )
												while w != v:
																lowerCamelCase_						=			stack.pop()
																lowerCamelCase_						=			False
																component.append(lowerCamelCase__    )
												components.append(lowerCamelCase__    )
								return index

				lowerCamelCase_						=			[]
				for v in range(lowerCamelCase__    ):
								if index_of[v] == -1:
												strong_connect(lowerCamelCase__	, 0	, lowerCamelCase__    )

				return components





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__    ):
				lowerCamelCase_						=			[[] for _ in range(lowerCamelCase__    )]
				for u, v in edges:
								g[u].append(lowerCamelCase__    )
				return g


if __name__ == "__main__":
			# Test
			__A				=7
			__A				=[0, 0, 1, 2, 3, 3, 4, 4, 6]
			__A				=[1, 3, 2, 0, 1, 4, 5, 6, 5]
			__A				=[(u, v) for u, v in zip(source, target)]
			__A				=create_graph(n_vertices, edges)

			assert [[5], [6], [4], [3, 2, 1, 0]] == tarjan(g)


