




'''simple docstring'''



import unittest

from transformers import BigBirdTokenizer, BigBirdTokenizerFast
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


UpperCamelCase_    	=				"▁"

UpperCamelCase_    	=				get_tests_dir("fixtures/test_sentencepiece.model")






@require_sentencepiece
@require_tokenizers
class 							_a				(			SCREAMING_SNAKE_CASE ,      unittest.TestCase ):





       '''simple docstring'''






       A						:				Dict        =						BigBirdTokenizer
       A						:				Any        =						BigBirdTokenizerFast
       A						:				Optional[int]        =						True
       A						:				Any        =						True







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             super().setUp()

             SCREAMING_SNAKE_CASE   :      Tuple    					=						self.tokenizer_class(A,							keep_accents=A  )
             tokenizer.save_pretrained(self.tmpdirname  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						'<s>'
             SCREAMING_SNAKE_CASE   :      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        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						list(self.get_tokenizer().get_vocab().keys()  )

             self.assertEqual(vocab_keys[0],							'<unk>'  )
             self.assertEqual(vocab_keys[1],							'<s>'  )
             self.assertEqual(vocab_keys[-1],							'[MASK]'  )
             self.assertEqual(len(A  ),							1_004  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             self.assertEqual(self.get_tokenizer().vocab_size,							1_000  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             if not self.test_rust_tokenizer:
                   return

             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						self.get_tokenizer()
             SCREAMING_SNAKE_CASE   :      Any    					=						self.get_rust_tokenizer()

             SCREAMING_SNAKE_CASE   :      List[str]    					=						'I was born in 92000, and this is falsé.'

             SCREAMING_SNAKE_CASE   :      List[str]    					=						tokenizer.tokenize(A  )
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						rust_tokenizer.tokenize(A  )
             self.assertListEqual(A,							A  )

             SCREAMING_SNAKE_CASE   :      Dict    					=						tokenizer.encode(A,							add_special_tokens=A  )
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						rust_tokenizer.encode(A,							add_special_tokens=A  )
             self.assertListEqual(A,							A  )

             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						self.get_rust_tokenizer()
             SCREAMING_SNAKE_CASE   :      Tuple    					=						tokenizer.encode(A  )
             SCREAMING_SNAKE_CASE   :      Dict    					=						rust_tokenizer.encode(A  )
             self.assertListEqual(A,							A  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      int    					=						BigBirdTokenizer(A,							keep_accents=A  )

             SCREAMING_SNAKE_CASE   :      int    					=						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],							)

             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						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',
                     '.',
                 ],							)
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						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],							)

             SCREAMING_SNAKE_CASE   :      str    					=						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        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             return BigBirdTokenizer.from_pretrained('google/bigbird-roberta-base'  )







       @slow
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[Any]    					=						'Hello World!'
             SCREAMING_SNAKE_CASE   :      int    					=						[65, 18_536, 2_260, 101, 66]

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







       @slow
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Tuple    					=						(
                 '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'
             )
             # fmt: off
             SCREAMING_SNAKE_CASE   :      int    					=						[65, 871, 419, 358, 946, 991, 2_521, 452, 358, 1_357, 387, 7_751, 3_536, 112, 985, 456, 126, 865, 938, 5_400, 5_734, 458, 1_368, 467, 786, 2_462, 5_246, 1_159, 633, 865, 4_519, 457, 582, 852, 2_557, 427, 916, 508, 405, 34_324, 497, 391, 408, 11_342, 1_244, 385, 100, 938, 985, 456, 574, 362, 12_597, 3_200, 3_129, 1_172, 66]  # noqa: E231
             # fmt: on
             self.assertListEqual(A,							self.big_tokenizer.encode(A  )  )







       @require_torch
       @slow
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             import torch

             from transformers import BigBirdConfig, BigBirdModel

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

             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						BigBirdConfig(attention_type='original_full'  )
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						BigBirdModel(A  )

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

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







       @slow
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Tuple    					=						BigBirdTokenizer.from_pretrained('google/bigbird-roberta-base'  )
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						tokenizer.decode(tokenizer('Paris is the [MASK].'  ).input_ids  )

             self.assertTrue(decoded_text == '[CLS] Paris is the[MASK].[SEP]'  )







       @slow
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[str]    					=						{'input_ids': [[65, 39_286, 458, 36_335, 2_001, 456, 13_073, 13_266, 455, 113, 7_746, 1_741, 11_157, 391, 13_073, 13_266, 455, 113, 3_967, 35_412, 113, 4_936, 109, 3_870, 2_377, 113, 30_084, 45_720, 458, 134, 17_496, 112, 503, 11_672, 113, 118, 112, 5_665, 13_347, 38_687, 112, 1_496, 31_389, 112, 3_268, 47_264, 134, 962, 112, 16_377, 8_035, 23_130, 430, 12_169, 15_518, 28_592, 458, 146, 41_697, 109, 391, 12_169, 15_518, 16_689, 458, 146, 41_358, 109, 452, 726, 4_034, 111, 763, 35_412, 5_082, 388, 1_903, 111, 9_051, 391, 2_870, 48_918, 1_900, 1_123, 550, 998, 112, 9_586, 15_985, 455, 391, 410, 22_955, 37_636, 114, 66], [65, 448, 17_496, 419, 3_663, 385, 763, 113, 27_533, 2_870, 3_283, 13_043, 1_639, 24_713, 523, 656, 24_013, 18_550, 2_521, 517, 27_014, 21_244, 420, 1_212, 1_465, 391, 927, 4_833, 388, 578, 11_786, 114, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [65, 484, 2_169, 7_687, 21_932, 18_146, 726, 363, 17_032, 3_391, 114, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 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, 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/bigbird-roberta-base',							revision='215c99f1600e06f83acce68422f2035b2b5c3510',							)








'''simple docstring'''



import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 							_a				(			SCREAMING_SNAKE_CASE ,      unittest.TestCase ):





       '''simple docstring'''






       A						:				str        =						LongformerTokenizer
       A						:				List[str]        =						True
       A						:				Optional[int]        =						LongformerTokenizerFast
       A						:				Tuple        =						True







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             super().setUp()

             # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
             SCREAMING_SNAKE_CASE   :      Any    					=						[
                 'l',
                 'o',
                 'w',
                 'e',
                 'r',
                 's',
                 't',
                 'i',
                 'd',
                 'n',
                 '\u0120',
                 '\u0120l',
                 '\u0120n',
                 '\u0120lo',
                 '\u0120low',
                 'er',
                 '\u0120lowest',
                 '\u0120newer',
                 '\u0120wider',
                 '<unk>',
             ]
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						dict(zip(A,							range(len(A  )  )  )  )
             SCREAMING_SNAKE_CASE   :      str    					=						['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
             SCREAMING_SNAKE_CASE   :      Tuple    					=						{'unk_token': '<unk>'}

             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						os.path.join(self.tmpdirname,							VOCAB_FILES_NAMES['vocab_file']  )
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						os.path.join(self.tmpdirname,							VOCAB_FILES_NAMES['merges_file']  )
             with open(self.vocab_file,							'w',							encoding='utf-8'  ) as fp:
                   fp.write(json.dumps(A  ) + '\n'  )
             with open(self.merges_file,							'w',							encoding='utf-8'  ) as fp:
                   fp.write('\n'.join(A  )  )







       def        UpperCamelCase_							(      self,							**A  ):







             '''simple docstring'''







             kwargs.update(self.special_tokens_map  )
             return self.tokenizer_class.from_pretrained(self.tmpdirname,							**A  )







       def        UpperCamelCase_							(      self,							**A  ):







             '''simple docstring'''







             kwargs.update(self.special_tokens_map  )
             return self.rust_tokenizer_class.from_pretrained(self.tmpdirname,							**A  )







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      int    					=						'lower newer'
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						'lower newer'
             return input_text, output_text







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[Any]    					=						self.tokenizer_class(self.vocab_file,							self.merges_file,							**self.special_tokens_map  )
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						'lower newer'
             SCREAMING_SNAKE_CASE   :      List[str]    					=						['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						tokenizer.tokenize(A  )  # , add_prefix_space=True)
             self.assertListEqual(A,							A  )

             SCREAMING_SNAKE_CASE   :      List[Any]    					=						tokens + [tokenizer.unk_token]
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
             self.assertListEqual(tokenizer.convert_tokens_to_ids(A  ),							A  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[Any]    					=						self.get_tokenizer()

             self.assertListEqual(tokenizer.encode('Hello world!',							add_special_tokens=A  ),							[0, 31_414, 232, 328, 2]  )
             self.assertListEqual(
                 tokenizer.encode('Hello world! cécé herlolip 418',							add_special_tokens=A  ),							[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2],							)







       @slow
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      str    					=						self.tokenizer_class.from_pretrained('allenai/longformer-base-4096'  )

             SCREAMING_SNAKE_CASE   :      Tuple    					=						tokenizer.encode('sequence builders',							add_special_tokens=A  )
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						tokenizer.encode('multi-sequence build',							add_special_tokens=A  )

             SCREAMING_SNAKE_CASE   :      int    					=						tokenizer.encode(
                 'sequence builders',							add_special_tokens=A,							add_prefix_space=A  )
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						tokenizer.encode(
                 'sequence builders',							'multi-sequence build',							add_special_tokens=A,							add_prefix_space=A  )

             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						tokenizer.build_inputs_with_special_tokens(A  )
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						tokenizer.build_inputs_with_special_tokens(A,							A  )

             assert encoded_sentence == encoded_text_from_decode
             assert encoded_pair == encoded_pair_from_decode







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						self.get_tokenizer()

             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						'Encode this sequence.'
             SCREAMING_SNAKE_CASE   :      List[str]    					=						tokenizer.byte_encoder[' '.encode('utf-8'  )[0]]

             # Testing encoder arguments
             SCREAMING_SNAKE_CASE   :      List[str]    					=						tokenizer.encode(A,							add_special_tokens=A,							add_prefix_space=A  )
             SCREAMING_SNAKE_CASE   :      Dict    					=						tokenizer.convert_ids_to_tokens(encoded[0]  )[0]
             self.assertNotEqual(A,							A  )

             SCREAMING_SNAKE_CASE   :      str    					=						tokenizer.encode(A,							add_special_tokens=A,							add_prefix_space=A  )
             SCREAMING_SNAKE_CASE   :      str    					=						tokenizer.convert_ids_to_tokens(encoded[0]  )[0]
             self.assertEqual(A,							A  )

             tokenizer.add_special_tokens({'bos_token': '<s>'}  )
             SCREAMING_SNAKE_CASE   :      List[str]    					=						tokenizer.encode(A,							add_special_tokens=A  )
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						tokenizer.convert_ids_to_tokens(encoded[1]  )[0]
             self.assertNotEqual(A,							A  )

             # Testing spaces after special tokens
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						'<mask>'
             tokenizer.add_special_tokens(
                 {'mask_token': AddedToken(A,							lstrip=A,							rstrip=A  )}  )  # mask token has a left space
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						tokenizer.convert_tokens_to_ids(A  )

             SCREAMING_SNAKE_CASE   :      List[str]    					=						'Encode <mask> sequence'
             SCREAMING_SNAKE_CASE   :      List[str]    					=						'Encode <mask>sequence'

             SCREAMING_SNAKE_CASE   :      List[Any]    					=						tokenizer.encode(A  )
             SCREAMING_SNAKE_CASE   :      Tuple    					=						encoded.index(A  )
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]  )[0]
             self.assertEqual(A,							A  )

             SCREAMING_SNAKE_CASE   :      Tuple    					=						tokenizer.encode(A  )
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						encoded.index(A  )
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]  )[0]
             self.assertNotEqual(A,							A  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             pass







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                   with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"  ):
                         SCREAMING_SNAKE_CASE   :      Optional[int]    					=						self.rust_tokenizer_class.from_pretrained(A,							**A  )
                         SCREAMING_SNAKE_CASE   :      Tuple    					=						self.tokenizer_class.from_pretrained(A,							**A  )
                         SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						'A, <mask> AllenNLP sentence.'
                         SCREAMING_SNAKE_CASE   :      Any    					=						tokenizer_r.encode_plus(A,							add_special_tokens=A,							return_token_type_ids=A  )
                         SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						tokenizer_p.encode_plus(A,							add_special_tokens=A,							return_token_type_ids=A  )

                         # token_type_ids should put 0 everywhere
                         self.assertEqual(sum(tokens_r['token_type_ids']  ),							sum(tokens_p['token_type_ids']  )  )

                         # attention_mask should put 1 everywhere, so sum over length should be 1
                         self.assertEqual(
                             sum(tokens_r['attention_mask']  ) / len(tokens_r['attention_mask']  ),							sum(tokens_p['attention_mask']  ) / len(tokens_p['attention_mask']  ),							)

                         SCREAMING_SNAKE_CASE   :      Dict    					=						tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']  )
                         SCREAMING_SNAKE_CASE   :      List[str]    					=						tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']  )

                         # Rust correctly handles the space before the mask while python doesnt
                         self.assertSequenceEqual(tokens_p['input_ids'],							[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]  )
                         self.assertSequenceEqual(tokens_r['input_ids'],							[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]  )

                         self.assertSequenceEqual(
                             A,							['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']  )
                         self.assertSequenceEqual(
                             A,							['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             for trim_offsets, add_prefix_space in itertools.product([True, False],							repeat=2  ):
                   SCREAMING_SNAKE_CASE   :      List[Any]    					=						self.rust_tokenizer_class.from_pretrained(
                       self.tmpdirname,							use_fast=A,							add_prefix_space=A,							trim_offsets=A  )

                   SCREAMING_SNAKE_CASE   :      Tuple    					=						json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()  )
                   SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()  )

                   self.assertEqual(pre_tokenizer_state['add_prefix_space'],							A  )

                   self.assertEqual(post_processor_state['add_prefix_space'],							A  )
                   self.assertEqual(post_processor_state['trim_offsets'],							A  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                   with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"  ):
                         SCREAMING_SNAKE_CASE   :      str    					=						'hello'  # `hello` is a token in the vocabulary of `pretrained_name`
                         SCREAMING_SNAKE_CASE   :      Tuple    					=						F"{text_of_1_token} {text_of_1_token}"

                         SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						self.rust_tokenizer_class.from_pretrained(
                             A,							use_fast=A,							add_prefix_space=A,							trim_offsets=A  )
                         SCREAMING_SNAKE_CASE   :      Tuple    					=						tokenizer_r(A,							return_offsets_mapping=A,							add_special_tokens=A  )
                         self.assertEqual(encoding.offset_mapping[0],							(0, len(A  ))  )
                         self.assertEqual(
                             encoding.offset_mapping[1],							(len(A  ) + 1, len(A  ) + 1 + len(A  )),							)

                         SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						self.rust_tokenizer_class.from_pretrained(
                             A,							use_fast=A,							add_prefix_space=A,							trim_offsets=A  )
                         SCREAMING_SNAKE_CASE   :      List[Any]    					=						tokenizer_r(A,							return_offsets_mapping=A,							add_special_tokens=A  )
                         self.assertEqual(encoding.offset_mapping[0],							(0, len(A  ))  )
                         self.assertEqual(
                             encoding.offset_mapping[1],							(len(A  ) + 1, len(A  ) + 1 + len(A  )),							)

                         SCREAMING_SNAKE_CASE   :      List[str]    					=						self.rust_tokenizer_class.from_pretrained(
                             A,							use_fast=A,							add_prefix_space=A,							trim_offsets=A  )
                         SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						tokenizer_r(A,							return_offsets_mapping=A,							add_special_tokens=A  )
                         self.assertEqual(encoding.offset_mapping[0],							(0, len(A  ))  )
                         self.assertEqual(
                             encoding.offset_mapping[1],							(len(A  ), len(A  ) + 1 + len(A  )),							)

                         SCREAMING_SNAKE_CASE   :      Any    					=						self.rust_tokenizer_class.from_pretrained(
                             A,							use_fast=A,							add_prefix_space=A,							trim_offsets=A  )
                         SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						tokenizer_r(A,							return_offsets_mapping=A,							add_special_tokens=A  )
                         self.assertEqual(encoding.offset_mapping[0],							(0, len(A  ))  )
                         self.assertEqual(
                             encoding.offset_mapping[1],							(len(A  ), len(A  ) + 1 + len(A  )),							)

                         SCREAMING_SNAKE_CASE   :      Any    					=						F" {text}"

                         # tokenizer_r = self.rust_tokenizer_class.from_pretrained(
                         #     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
                         # )
                         # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
                         # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
                         # self.assertEqual(
                         #     encoding.offset_mapping[1],
                         #     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
                         # )

                         SCREAMING_SNAKE_CASE   :      str    					=						self.rust_tokenizer_class.from_pretrained(
                             A,							use_fast=A,							add_prefix_space=A,							trim_offsets=A  )
                         SCREAMING_SNAKE_CASE   :      List[str]    					=						tokenizer_r(A,							return_offsets_mapping=A,							add_special_tokens=A  )
                         self.assertEqual(encoding.offset_mapping[0],							(1, 1 + len(A  ))  )
                         self.assertEqual(
                             encoding.offset_mapping[1],							(1 + len(A  ) + 1, 1 + len(A  ) + 1 + len(A  )),							)

                         SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						self.rust_tokenizer_class.from_pretrained(
                             A,							use_fast=A,							add_prefix_space=A,							trim_offsets=A  )
                         SCREAMING_SNAKE_CASE   :      str    					=						tokenizer_r(A,							return_offsets_mapping=A,							add_special_tokens=A  )
                         self.assertEqual(encoding.offset_mapping[0],							(0, 1 + len(A  ))  )
                         self.assertEqual(
                             encoding.offset_mapping[1],							(1 + len(A  ), 1 + len(A  ) + 1 + len(A  )),							)

                         SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						self.rust_tokenizer_class.from_pretrained(
                             A,							use_fast=A,							add_prefix_space=A,							trim_offsets=A  )
                         SCREAMING_SNAKE_CASE   :      List[Any]    					=						tokenizer_r(A,							return_offsets_mapping=A,							add_special_tokens=A  )
                         self.assertEqual(encoding.offset_mapping[0],							(0, 1 + len(A  ))  )
                         self.assertEqual(
                             encoding.offset_mapping[1],							(1 + len(A  ), 1 + len(A  ) + 1 + len(A  )),							)


