


'''simple docstring'''



from __future__ import annotations

import unittest

from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
      import tensorflow as tf

      from transformers.models.xglm.modeling_tf_xglm import (
          TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFXGLMForCausalLM,
          TFXGLMModel,
      )





@require_tf
class   UpperCAmelCase_    :





    """simple docstring"""






    lowerCamelCase    :   Dict					=     XGLMConfig
    lowerCamelCase    :   List[str]					=     {}
    lowerCamelCase    :   Union[str, Any]					=     'gelu'







    def __init__(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=14     ,	SCREAMING_SNAKE_CASE_=7     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=99     ,	SCREAMING_SNAKE_CASE_=32     ,	SCREAMING_SNAKE_CASE_=2     ,	SCREAMING_SNAKE_CASE_=4     ,	SCREAMING_SNAKE_CASE_=37     ,	SCREAMING_SNAKE_CASE_="gelu"     ,	SCREAMING_SNAKE_CASE_=0.1     ,	SCREAMING_SNAKE_CASE_=0.1     ,	SCREAMING_SNAKE_CASE_=5_12     ,	SCREAMING_SNAKE_CASE_=0.0_2     ,	)							->	Any:
         __lowerCamelCase  :      int														=						parent
         __lowerCamelCase  :      Optional[int]														=						batch_size
         __lowerCamelCase  :      Optional[Any]														=						seq_length
         __lowerCamelCase  :      Optional[int]														=						is_training
         __lowerCamelCase  :      str														=						use_input_mask
         __lowerCamelCase  :      Dict														=						use_labels
         __lowerCamelCase  :      Union[str, Any]														=						vocab_size
         __lowerCamelCase  :      List[Any]														=						d_model
         __lowerCamelCase  :      List[Any]														=						num_hidden_layers
         __lowerCamelCase  :      List[Any]														=						num_attention_heads
         __lowerCamelCase  :      Optional[Any]														=						ffn_dim
         __lowerCamelCase  :      List[Any]														=						activation_function
         __lowerCamelCase  :      List[Any]														=						activation_dropout
         __lowerCamelCase  :      List[Any]														=						attention_dropout
         __lowerCamelCase  :      Union[str, Any]														=						max_position_embeddings
         __lowerCamelCase  :      Tuple														=						initializer_range
         __lowerCamelCase  :      int														=						None
         __lowerCamelCase  :      int														=						0
         __lowerCamelCase  :      Tuple														=						2
         __lowerCamelCase  :      Tuple														=						1







    def  lowercase_							(				self      )							->	Any:
         return XGLMConfig.from_pretrained('facebook/xglm-564M'      )







    def  lowercase_							(				self      )							->	Tuple:
         __lowerCamelCase  :      Optional[Any]														=						tf.clip_by_value(
             ids_tensor([self.batch_size, self.seq_length]     ,	self.vocab_size      )     ,	clip_value_min=0     ,	clip_value_max=3      )

         __lowerCamelCase  :      Optional[int]														=						None
         if self.use_input_mask:
              __lowerCamelCase  :      Any														=						random_attention_mask([self.batch_size, self.seq_length]      )

         __lowerCamelCase  :      str														=						self.get_config()

         __lowerCamelCase  :      List[Any]														=						floats_tensor([self.num_hidden_layers, self.num_attention_heads]     ,	2      )

         return (
             config,
             input_ids,
             input_mask,
             head_mask,
         )







    def  lowercase_							(				self      )							->	Optional[int]:
         return XGLMConfig(
             vocab_size=self.vocab_size     ,	d_model=self.hidden_size     ,	num_layers=self.num_hidden_layers     ,	attention_heads=self.num_attention_heads     ,	ffn_dim=self.ffn_dim     ,	activation_function=self.activation_function     ,	activation_dropout=self.activation_dropout     ,	attention_dropout=self.attention_dropout     ,	max_position_embeddings=self.max_position_embeddings     ,	initializer_range=self.initializer_range     ,	use_cache=SCREAMING_SNAKE_CASE_     ,	bos_token_id=self.bos_token_id     ,	eos_token_id=self.eos_token_id     ,	pad_token_id=self.pad_token_id     ,	return_dict=SCREAMING_SNAKE_CASE_     ,	)







    def  lowercase_							(				self      )							->	str:
         __lowerCamelCase  :      List[Any]														=						self.prepare_config_and_inputs()

         (
             (
             __lowerCamelCase
         )							,			(
             __lowerCamelCase
         )							,			(
             __lowerCamelCase
         )							,			(
             __lowerCamelCase
         )							,			
         )  :      str														=						config_and_inputs

         __lowerCamelCase  :      Union[str, Any]														=						{
             'input_ids': input_ids,
             'head_mask': head_mask,
         }

         return config, inputs_dict







@require_tf
class   UpperCAmelCase_    (_UpperCAmelCase   ,						_UpperCAmelCase   ,						unittest.TestCase    ):





    """simple docstring"""






    lowerCamelCase    :   Optional[Any]					=     (TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
    lowerCamelCase    :   List[Any]					=     (TFXGLMForCausalLM,) if is_tf_available() else ()
    lowerCamelCase    :   Any					=     (
        {'feature-extraction': TFXGLMModel, 'text-generation': TFXGLMForCausalLM} if is_tf_available() else {}
    )
    lowerCamelCase    :   List[Any]					=     False
    lowerCamelCase    :   Dict					=     False
    lowerCamelCase    :   Union[str, Any]					=     False







    def  lowercase_							(				self      )							->	List[Any]:
         __lowerCamelCase  :      str														=						TFXGLMModelTester(self      )
         __lowerCamelCase  :      List[Any]														=						ConfigTester(self     ,	config_class=SCREAMING_SNAKE_CASE_     ,	n_embd=37      )







    def  lowercase_							(				self      )							->	Dict:
         self.config_tester.run_common_tests()







    @slow
    def  lowercase_							(				self      )							->	Optional[int]:
         for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              __lowerCamelCase  :      Optional[Any]														=						TFXGLMModel.from_pretrained(SCREAMING_SNAKE_CASE_      )
              self.assertIsNotNone(SCREAMING_SNAKE_CASE_      )







    @unittest.skip(reason='Currently, model embeddings are going to undergo a major refactor.'      )
    def  lowercase_							(				self      )							->	Any:
         super().test_resize_token_embeddings()







@require_tf
class   UpperCAmelCase_    (unittest.TestCase    ):





    """simple docstring"""






    @slow
    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_=True      )							->	List[str]:
         __lowerCamelCase  :      Optional[Any]														=						TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'      )
         __lowerCamelCase  :      int														=						tf.convert_to_tensor([[2, 2_68, 98_65]]     ,	dtype=tf.intaa      )  # The dog
         # </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
         # fmt: off
         __lowerCamelCase  :      Optional[int]														=						[2, 2_68, 98_65, 67, 11, 19_88, 5_72_52, 98_65, 5, 9_84, 67, 19_88, 21_38_38, 16_58, 53, 7_04_46, 33, 66_57, 2_78, 15_81]
         # fmt: on
         __lowerCamelCase  :      Any														=						model.generate(SCREAMING_SNAKE_CASE_     ,	do_sample=SCREAMING_SNAKE_CASE_     ,	num_beams=1      )
         if verify_outputs:
              self.assertListEqual(output_ids[0].numpy().tolist()     ,	SCREAMING_SNAKE_CASE_      )







    @slow
    def  lowercase_							(				self      )							->	int:
         __lowerCamelCase  :      Any														=						XGLMTokenizer.from_pretrained('facebook/xglm-564M'      )
         __lowerCamelCase  :      Tuple														=						TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'      )

         tf.random.set_seed(0      )
         __lowerCamelCase  :      List[Any]														=						tokenizer('Today is a nice day and'     ,	return_tensors='tf'      )
         __lowerCamelCase  :      int														=						tokenized.input_ids
         # forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
         with tf.device(':/CPU:0'      ):
              __lowerCamelCase  :      Tuple														=						model.generate(SCREAMING_SNAKE_CASE_     ,	do_sample=SCREAMING_SNAKE_CASE_     ,	seed=[7, 0]      )
         __lowerCamelCase  :      Optional[Any]														=						tokenizer.decode(output_ids[0]     ,	skip_special_tokens=SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      Union[str, Any]														=						(
             'Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due'
         )
         self.assertEqual(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )







    @slow
    def  lowercase_							(				self      )							->	int:
         __lowerCamelCase  :      Tuple														=						TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'      )
         __lowerCamelCase  :      Any														=						XGLMTokenizer.from_pretrained('facebook/xglm-564M'      )

         __lowerCamelCase  :      Any														=						'left'

         # use different length sentences to test batching
         __lowerCamelCase  :      Any														=						[
             'This is an extremelly long sentence that only exists to test the ability of the model to cope with '
             'left-padding, such as in batched generation. The output for the sequence below should be the same '
             'regardless of whether left padding is applied or not. When',
             'Hello, my dog is a little',
         ]

         __lowerCamelCase  :      Any														=						tokenizer(SCREAMING_SNAKE_CASE_     ,	return_tensors='tf'     ,	padding=SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      Optional[int]														=						inputs['input_ids']

         __lowerCamelCase  :      str														=						model.generate(input_ids=SCREAMING_SNAKE_CASE_     ,	attention_mask=inputs['attention_mask']     ,	max_new_tokens=12      )

         __lowerCamelCase  :      Optional[int]														=						tokenizer(sentences[0]     ,	return_tensors='tf'      ).input_ids
         __lowerCamelCase  :      int														=						model.generate(input_ids=SCREAMING_SNAKE_CASE_     ,	max_new_tokens=12      )

         __lowerCamelCase  :      Optional[Any]														=						tokenizer(sentences[1]     ,	return_tensors='tf'      ).input_ids
         __lowerCamelCase  :      Optional[Any]														=						model.generate(input_ids=SCREAMING_SNAKE_CASE_     ,	max_new_tokens=12      )

         __lowerCamelCase  :      Union[str, Any]														=						tokenizer.batch_decode(SCREAMING_SNAKE_CASE_     ,	skip_special_tokens=SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      Union[str, Any]														=						tokenizer.decode(output_non_padded[0]     ,	skip_special_tokens=SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      Union[str, Any]														=						tokenizer.decode(output_padded[0]     ,	skip_special_tokens=SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      Optional[int]														=						[
             'This is an extremelly long sentence that only exists to test the ability of the model to cope with '
             'left-padding, such as in batched generation. The output for the sequence below should be the same '
             'regardless of whether left padding is applied or not. When left padding is applied, the sequence will be '
             'a single',
             'Hello, my dog is a little bit of a shy one, but he is very friendly',
         ]
         self.assertListEqual(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )
         self.assertListEqual(SCREAMING_SNAKE_CASE_     ,	[non_padded_sentence, padded_sentence]      )












"""simple docstring"""






import os
import unittest

from transformers import MobileBertTokenizer, MobileBertTokenizerFast
from transformers.models.bert.tokenization_bert import (
    VOCAB_FILES_NAMES,
    BasicTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english






@require_tokenizers
class lowerCAmelCase__      (	lowercase,      unittest.TestCase		):






			'''simple docstring'''



			lowerCamelCase__ =     MobileBertTokenizer
			lowerCamelCase__ =     MobileBertTokenizerFast
			lowerCamelCase__ =     True
			lowerCamelCase__ =     True
			lowerCamelCase__ =     filter_non_english
			lowerCamelCase__ =     """google/mobilebert-uncased"""
			def    A_       (    self    ):
							super().setUp()

							_lowerCamelCase	:     Optional[int]       =       [
							    '[UNK]',
							    '[CLS]',
							    '[SEP]',
							    '[PAD]',
							    '[MASK]',
							    'want',
							    '##want',
							    '##ed',
							    'wa',
							    'un',
							    'runn',
							    '##ing',
							    ',',
							    'low',
							    'lowest',
							]
							_lowerCamelCase	:     Union[str, Any]       =       os.path.join(self.tmpdirname    , VOCAB_FILES_NAMES['vocab_file']    )
							with open(self.vocab_file    , 'w'    , encoding='utf-8'    ) as vocab_writer:
											vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]    )    )

							_lowerCamelCase	:     Any       =       [
							    (tokenizer_def[0], self.pre_trained_model_path, tokenizer_def[2])  # else the 'google/' prefix is stripped
							    for tokenizer_def in self.tokenizers_list
							]
			def    A_       (    self    , lowercase    ):
							_lowerCamelCase	:     Union[str, Any]       =       'UNwant\u00E9d,running'
							_lowerCamelCase	:     List[Any]       =       'unwanted, running'
							return input_text, output_text
			def    A_       (    self    ):
							_lowerCamelCase	:     Dict       =       self.tokenizer_class(self.vocab_file    )

							_lowerCamelCase	:     Union[str, Any]       =       tokenizer.tokenize('UNwant\u00E9d,running'    )
							self.assertListEqual(lowercase    , ['un', '##want', '##ed', ',', 'runn', '##ing']    )
							self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase    )    , [9, 6, 7, 12, 10, 11]    )
			def    A_       (    self    ):
							if not self.test_rust_tokenizer:
											return

							_lowerCamelCase	:     Any       =       self.get_tokenizer()
							_lowerCamelCase	:     Any       =       self.get_rust_tokenizer()

							_lowerCamelCase	:     int       =       'UNwant\u00E9d,running'

							_lowerCamelCase	:     Union[str, Any]       =       tokenizer.tokenize(lowercase    )
							_lowerCamelCase	:     List[Any]       =       rust_tokenizer.tokenize(lowercase    )
							self.assertListEqual(lowercase    , lowercase    )

							_lowerCamelCase	:     Dict       =       tokenizer.encode(lowercase    , add_special_tokens=lowercase    )
							_lowerCamelCase	:     Dict       =       rust_tokenizer.encode(lowercase    , add_special_tokens=lowercase    )
							self.assertListEqual(lowercase    , lowercase    )

							_lowerCamelCase	:     Optional[Any]       =       self.get_rust_tokenizer()
							_lowerCamelCase	:     Optional[int]       =       tokenizer.encode(lowercase    )
							_lowerCamelCase	:     List[str]       =       rust_tokenizer.encode(lowercase    )
							self.assertListEqual(lowercase    , lowercase    )

							# With lower casing
							_lowerCamelCase	:     List[Any]       =       self.get_tokenizer(do_lower_case=lowercase    )
							_lowerCamelCase	:     int       =       self.get_rust_tokenizer(do_lower_case=lowercase    )

							_lowerCamelCase	:     Optional[Any]       =       'UNwant\u00E9d,running'

							_lowerCamelCase	:     Dict       =       tokenizer.tokenize(lowercase    )
							_lowerCamelCase	:     int       =       rust_tokenizer.tokenize(lowercase    )
							self.assertListEqual(lowercase    , lowercase    )

							_lowerCamelCase	:     Dict       =       tokenizer.encode(lowercase    , add_special_tokens=lowercase    )
							_lowerCamelCase	:     Dict       =       rust_tokenizer.encode(lowercase    , add_special_tokens=lowercase    )
							self.assertListEqual(lowercase    , lowercase    )

							_lowerCamelCase	:     Any       =       self.get_rust_tokenizer()
							_lowerCamelCase	:     Union[str, Any]       =       tokenizer.encode(lowercase    )
							_lowerCamelCase	:     Dict       =       rust_tokenizer.encode(lowercase    )
							self.assertListEqual(lowercase    , lowercase    )
			def    A_       (    self    ):
							_lowerCamelCase	:     Dict       =       BasicTokenizer()

							self.assertListEqual(tokenizer.tokenize('ah\u535A\u63A8zz'    )    , ['ah', '\u535A', '\u63A8', 'zz']    )
			def    A_       (    self    ):
							_lowerCamelCase	:     Optional[Any]       =       BasicTokenizer(do_lower_case=lowercase    )

							self.assertListEqual(
							    tokenizer.tokenize(' \tHeLLo!how  \n Are yoU?  '    )    , ['hello', '!', 'how', 'are', 'you', '?']    )
							self.assertListEqual(tokenizer.tokenize('H\u00E9llo'    )    , ['hello']    )
			def    A_       (    self    ):
							_lowerCamelCase	:     List[str]       =       BasicTokenizer(do_lower_case=lowercase    , strip_accents=lowercase    )

							self.assertListEqual(
							    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '    )    , ['hällo', '!', 'how', 'are', 'you', '?']    )
							self.assertListEqual(tokenizer.tokenize('H\u00E9llo'    )    , ['h\u00E9llo']    )
			def    A_       (    self    ):
							_lowerCamelCase	:     List[Any]       =       BasicTokenizer(do_lower_case=lowercase    , strip_accents=lowercase    )

							self.assertListEqual(
							    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '    )    , ['hallo', '!', 'how', 'are', 'you', '?']    )
							self.assertListEqual(tokenizer.tokenize('H\u00E9llo'    )    , ['hello']    )
			def    A_       (    self    ):
							_lowerCamelCase	:     int       =       BasicTokenizer(do_lower_case=lowercase    )

							self.assertListEqual(
							    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '    )    , ['hallo', '!', 'how', 'are', 'you', '?']    )
							self.assertListEqual(tokenizer.tokenize('H\u00E9llo'    )    , ['hello']    )
			def    A_       (    self    ):
							_lowerCamelCase	:     List[str]       =       BasicTokenizer(do_lower_case=lowercase    )

							self.assertListEqual(
							    tokenizer.tokenize(' \tHeLLo!how  \n Are yoU?  '    )    , ['HeLLo', '!', 'how', 'Are', 'yoU', '?']    )
			def    A_       (    self    ):
							_lowerCamelCase	:     Union[str, Any]       =       BasicTokenizer(do_lower_case=lowercase    , strip_accents=lowercase    )

							self.assertListEqual(
							    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '    )    , ['HäLLo', '!', 'how', 'Are', 'yoU', '?']    )
			def    A_       (    self    ):
							_lowerCamelCase	:     Union[str, Any]       =       BasicTokenizer(do_lower_case=lowercase    , strip_accents=lowercase    )

							self.assertListEqual(
							    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '    )    , ['HaLLo', '!', 'how', 'Are', 'yoU', '?']    )
			def    A_       (    self    ):
							_lowerCamelCase	:     int       =       BasicTokenizer(do_lower_case=lowercase    , never_split=['[UNK]']    )

							self.assertListEqual(
							    tokenizer.tokenize(' \tHeLLo!how  \n Are yoU? [UNK]'    )    , ['HeLLo', '!', 'how', 'Are', 'yoU', '?', '[UNK]']    )
			def    A_       (    self    ):
							_lowerCamelCase	:     Union[str, Any]       =       ['[UNK]', '[CLS]', '[SEP]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing']

							_lowerCamelCase	:     Tuple       =       {}
							for i, token in enumerate(lowercase    ):
											_lowerCamelCase	:     Union[str, Any]       =       i
							_lowerCamelCase	:     str       =       WordpieceTokenizer(vocab=lowercase    , unk_token='[UNK]'    )

							self.assertListEqual(tokenizer.tokenize(''    )    , []    )

							self.assertListEqual(tokenizer.tokenize('unwanted running'    )    , ['un', '##want', '##ed', 'runn', '##ing']    )

							self.assertListEqual(tokenizer.tokenize('unwantedX running'    )    , ['[UNK]', 'runn', '##ing']    )
			def    A_       (    self    ):
							self.assertTrue(_is_whitespace(' '    )    )
							self.assertTrue(_is_whitespace('\t'    )    )
							self.assertTrue(_is_whitespace('\r'    )    )
							self.assertTrue(_is_whitespace('\n'    )    )
							self.assertTrue(_is_whitespace('\u00A0'    )    )

							self.assertFalse(_is_whitespace('A'    )    )
							self.assertFalse(_is_whitespace('-'    )    )
			def    A_       (    self    ):
							self.assertTrue(_is_control('\u0005'    )    )

							self.assertFalse(_is_control('A'    )    )
							self.assertFalse(_is_control(' '    )    )
							self.assertFalse(_is_control('\t'    )    )
							self.assertFalse(_is_control('\r'    )    )
			def    A_       (    self    ):
							self.assertTrue(_is_punctuation('-'    )    )
							self.assertTrue(_is_punctuation('$'    )    )
							self.assertTrue(_is_punctuation('`'    )    )
							self.assertTrue(_is_punctuation('.'    )    )

							self.assertFalse(_is_punctuation('A'    )    )
							self.assertFalse(_is_punctuation(' '    )    )
			def    A_       (    self    ):
							_lowerCamelCase	:     List[Any]       =       self.get_tokenizer()
							_lowerCamelCase	:     Union[str, Any]       =       self.get_rust_tokenizer()

							# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
							self.assertListEqual([tokenizer.tokenize(lowercase    ) for t in ['Test', '\xad', 'test']]    , [['[UNK]'], [], ['[UNK]']]    )

							self.assertListEqual(
							    [rust_tokenizer.tokenize(lowercase    ) for t in ['Test', '\xad', 'test']]    , [['[UNK]'], [], ['[UNK]']]    )
			@slow
			def    A_       (    self    ):
							_lowerCamelCase	:     Optional[Any]       =       self.tokenizer_class.from_pretrained('google/mobilebert-uncased'    )

							_lowerCamelCase	:     List[str]       =       tokenizer.encode('sequence builders'    , add_special_tokens=lowercase    )
							_lowerCamelCase	:     Dict       =       tokenizer.encode('multi-sequence build'    , add_special_tokens=lowercase    )

							_lowerCamelCase	:     Optional[int]       =       tokenizer.build_inputs_with_special_tokens(lowercase    )
							_lowerCamelCase	:     List[Any]       =       tokenizer.build_inputs_with_special_tokens(lowercase    , lowercase    )

							assert encoded_sentence == [101] + text + [102]
							assert encoded_pair == [101] + text + [102] + text_a + [102]
			def    A_       (    self    ):
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''    ):
															_lowerCamelCase	:     int       =       self.rust_tokenizer_class.from_pretrained(lowercase    , **lowercase    )

															_lowerCamelCase	:     int       =       F'''A, naïve {tokenizer_r.mask_token} AllenNLP sentence.'''
															_lowerCamelCase	:     int       =       tokenizer_r.encode_plus(
															    lowercase    , return_attention_mask=lowercase    , return_token_type_ids=lowercase    , return_offsets_mapping=lowercase    , add_special_tokens=lowercase    , )

															_lowerCamelCase	:     Tuple       =       tokenizer_r.do_lower_case if hasattr(lowercase    , 'do_lower_case'    ) else False
															_lowerCamelCase	:     Dict       =       (
															    [
															        ((0, 0), tokenizer_r.cls_token),
															        ((0, 1), 'A'),
															        ((1, 2), ','),
															        ((3, 5), 'na'),
															        ((5, 6), '##ï'),
															        ((6, 8), '##ve'),
															        ((9, 15), tokenizer_r.mask_token),
															        ((16, 21), 'Allen'),
															        ((21, 23), '##NL'),
															        ((23, 24), '##P'),
															        ((25, 33), 'sentence'),
															        ((33, 34), '.'),
															        ((0, 0), tokenizer_r.sep_token),
															    ]
															    if not do_lower_case
															    else [
															        ((0, 0), tokenizer_r.cls_token),
															        ((0, 1), 'a'),
															        ((1, 2), ','),
															        ((3, 8), 'naive'),
															        ((9, 15), tokenizer_r.mask_token),
															        ((16, 21), 'allen'),
															        ((21, 23), '##nl'),
															        ((23, 24), '##p'),
															        ((25, 33), 'sentence'),
															        ((33, 34), '.'),
															        ((0, 0), tokenizer_r.sep_token),
															    ]
															)

															self.assertEqual(
															    [e[1] for e in expected_results]    , tokenizer_r.convert_ids_to_tokens(tokens['input_ids']    )    )
															self.assertEqual([e[0] for e in expected_results]    , tokens['offset_mapping']    )






			def    A_       (    self    ):
							_lowerCamelCase	:     Tuple       =       ['的', '人', '有']
							_lowerCamelCase	:     Optional[Any]       =       ''.join(lowercase    )
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''    ):
															_lowerCamelCase	:     int       =       True
															_lowerCamelCase	:     Any       =       self.tokenizer_class.from_pretrained(lowercase    , **lowercase    )
															_lowerCamelCase	:     Dict       =       self.rust_tokenizer_class.from_pretrained(lowercase    , **lowercase    )

															_lowerCamelCase	:     List[str]       =       tokenizer_p.encode(lowercase    , add_special_tokens=lowercase    )
															_lowerCamelCase	:     List[Any]       =       tokenizer_r.encode(lowercase    , add_special_tokens=lowercase    )

															_lowerCamelCase	:     Any       =       tokenizer_r.convert_ids_to_tokens(lowercase    )
															_lowerCamelCase	:     Dict       =       tokenizer_p.convert_ids_to_tokens(lowercase    )

															# it is expected that each Chinese character is not preceded by "##"
															self.assertListEqual(lowercase    , lowercase    )
															self.assertListEqual(lowercase    , lowercase    )

															_lowerCamelCase	:     Optional[Any]       =       False
															_lowerCamelCase	:     Dict       =       self.rust_tokenizer_class.from_pretrained(lowercase    , **lowercase    )
															_lowerCamelCase	:     Optional[int]       =       self.tokenizer_class.from_pretrained(lowercase    , **lowercase    )

															_lowerCamelCase	:     Optional[Any]       =       tokenizer_r.encode(lowercase    , add_special_tokens=lowercase    )
															_lowerCamelCase	:     str       =       tokenizer_p.encode(lowercase    , add_special_tokens=lowercase    )

															_lowerCamelCase	:     List[Any]       =       tokenizer_r.convert_ids_to_tokens(lowercase    )
															_lowerCamelCase	:     Dict       =       tokenizer_p.convert_ids_to_tokens(lowercase    )

															# it is expected that only the first Chinese character is not preceded by "##".
															_lowerCamelCase	:     List[str]       =       [
															    F'''##{token}''' if idx != 0 else token for idx, token in enumerate(lowercase    )
															]
															self.assertListEqual(lowercase    , lowercase    )
															self.assertListEqual(lowercase    , lowercase    )