from copy import deepcopy
from unittest.case import TestCase

import pytest

from datasets.arrow_dataset import Dataset
from datasets.features import Audio, ClassLabel, Features, Image, Sequence, Value
from datasets.info import DatasetInfo
from datasets.tasks import (
    AudioClassification,
    AutomaticSpeechRecognition,
    ImageClassification,
    LanguageModeling,
    QuestionAnsweringExtractive,
    Summarization,
    TextClassification,
    task_template_from_dict,
)
from datasets.utils.py_utils import asdict


snake_case__						      =			{
    "id": "5733be284776f41900661182",
    "title": "University_of_Notre_Dame",
    "context": "Architecturally, the school has a Catholic character. Atop the Main Building\'s gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend \"Venite Ad Me Omnes\". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive (and in a direct line that connects through 3 statues and the Gold Dome), is a simple, modern stone statue of Mary.",
    "question": "To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France?",
    "answers": {"text": ["Saint Bernadette Soubirous"], "answer_start": [515]},
}
@pytest.mark.parametrize(
    '''task_cls'''			,    [
        AudioClassification,
        AutomaticSpeechRecognition,
        ImageClassification,
        LanguageModeling,
        QuestionAnsweringExtractive,
        Summarization,
        TextClassification,
    ]			,    )
def    __magic_name__(	__UpperCAmelCase		)    ->     List[str]:



      '''simple docstring'''

      _lowerCamelCase           =					task_cls()
      _lowerCamelCase           =					asdict(UpperCAmelCase__		)
      _lowerCamelCase           =					task_template_from_dict(UpperCAmelCase__		)
      assert task == reloaded



class 			UpperCamelCase					:

     '''simple docstring'''

     def 		UpperCamelCase_    (       self    )			->					int:






           """simple docstring"""
           _lowerCamelCase           =					LanguageModeling(text_column='''input_text'''    )
           assert {"input_text": "text"} == task.column_mapping





     def 		UpperCamelCase_    (       self    )			->					Optional[int]:






           """simple docstring"""
           _lowerCamelCase           =					Features({'''text''': Value('''string'''    )}    )
           _lowerCamelCase           =					{'text_column': 'input_text'}
           _lowerCamelCase           =					LanguageModeling.from_dict(__UpperCAmelCase    )
           assert "language-modeling" == task.task
           assert input_schema == task.input_schema





class 			UpperCamelCase					(			_UpperCAmelCase			):

     '''simple docstring'''

     def 		UpperCamelCase_    (       self    )			->					Optional[int]:






           """simple docstring"""
           _lowerCamelCase           =					sorted(['''pos''', '''neg''']    )



     def 		UpperCamelCase_    (       self    )			->					List[Any]:






           """simple docstring"""
           _lowerCamelCase           =					TextClassification(text_column='''input_text'''					,					label_column='''input_label'''    )
           self.assertDictEqual({'''input_text''': '''text''', '''input_label''': '''labels'''}					,					task.column_mapping    )



     def 		UpperCamelCase_    (       self    )			->					Any:






           """simple docstring"""
           _lowerCamelCase           =					Features({'''text''': Value('''string'''    )}    )
           # Labels are cast to tuple during `TextClassification.__post_init__`, so we do the same here
           _lowerCamelCase           =					Features({'''labels''': ClassLabel}    )
           _lowerCamelCase           =					{'text_column': 'input_text', 'label_column': 'input_labels'}
           _lowerCamelCase           =					TextClassification.from_dict(__UpperCAmelCase    )
           self.assertEqual('''text-classification'''					,					task.task    )
           self.assertEqual(__UpperCAmelCase					,					task.input_schema    )
           self.assertEqual(__UpperCAmelCase					,					task.label_schema    )





     def 		UpperCamelCase_    (       self    )			->					int:






           """simple docstring"""
           _lowerCamelCase           =					TextClassification(text_column='''input_text'''					,					label_column='''input_label'''    )
           self.assertEqual(task.label_schema['''labels''']					,					__UpperCAmelCase    )
           _lowerCamelCase           =					task.align_with_features(Features({'''input_label''': ClassLabel(names=self.labels    )}    )    )
           self.assertEqual(task.label_schema['''labels''']					,					ClassLabel(names=self.labels    )    )





class 			UpperCamelCase					(			_UpperCAmelCase			):

     '''simple docstring'''

     def 		UpperCamelCase_    (       self    )			->					str:






           """simple docstring"""
           _lowerCamelCase           =					QuestionAnsweringExtractive(
               context_column='''input_context'''					,					question_column='''input_question'''					,					answers_column='''input_answers'''    )
           self.assertDictEqual(
               {'''input_context''': '''context''', '''input_question''': '''question''', '''input_answers''': '''answers'''}					,					task.column_mapping    )





     def 		UpperCamelCase_    (       self    )			->					Optional[int]:






           """simple docstring"""
           _lowerCamelCase           =					Features({'''question''': Value('''string'''    ), '''context''': Value('''string'''    )}    )
           _lowerCamelCase           =					Features(
               {
                   '''answers''': Sequence(
                       {
                           '''text''': Value('''string'''    ),
                           '''answer_start''': Value('''int32'''    ),
                       }    )
               }    )
           _lowerCamelCase           =					{
               'context_column': 'input_input_context',
               'question_column': 'input_question',
               'answers_column': 'input_answers',
           }
           _lowerCamelCase           =					QuestionAnsweringExtractive.from_dict(__UpperCAmelCase    )
           self.assertEqual('''question-answering-extractive'''					,					task.task    )
           self.assertEqual(__UpperCAmelCase					,					task.input_schema    )
           self.assertEqual(__UpperCAmelCase					,					task.label_schema    )





class 			UpperCamelCase					(			_UpperCAmelCase			):

     '''simple docstring'''

     def 		UpperCamelCase_    (       self    )			->					Union[str, Any]:






           """simple docstring"""
           _lowerCamelCase           =					Summarization(text_column='''input_text'''					,					summary_column='''input_summary'''    )
           self.assertDictEqual({'''input_text''': '''text''', '''input_summary''': '''summary'''}					,					task.column_mapping    )





     def 		UpperCamelCase_    (       self    )			->					List[Any]:






           """simple docstring"""
           _lowerCamelCase           =					Features({'''text''': Value('''string'''    )}    )
           _lowerCamelCase           =					Features({'''summary''': Value('''string'''    )}    )
           _lowerCamelCase           =					{'text_column': 'input_text', 'summary_column': 'input_summary'}
           _lowerCamelCase           =					Summarization.from_dict(__UpperCAmelCase    )
           self.assertEqual('''summarization'''					,					task.task    )
           self.assertEqual(__UpperCAmelCase					,					task.input_schema    )
           self.assertEqual(__UpperCAmelCase					,					task.label_schema    )





class 			UpperCamelCase					(			_UpperCAmelCase			):

     '''simple docstring'''

     def 		UpperCamelCase_    (       self    )			->					Optional[int]:






           """simple docstring"""
           _lowerCamelCase           =					AutomaticSpeechRecognition(audio_column='''input_audio'''					,					transcription_column='''input_transcription'''    )
           self.assertDictEqual({'''input_audio''': '''audio''', '''input_transcription''': '''transcription'''}					,					task.column_mapping    )





     def 		UpperCamelCase_    (       self    )			->					Union[str, Any]:






           """simple docstring"""
           _lowerCamelCase           =					Features({'''audio''': Audio()}    )
           _lowerCamelCase           =					Features({'''transcription''': Value('''string'''    )}    )
           _lowerCamelCase           =					{
               'audio_column': 'input_audio',
               'transcription_column': 'input_transcription',
           }
           _lowerCamelCase           =					AutomaticSpeechRecognition.from_dict(__UpperCAmelCase    )
           self.assertEqual('''automatic-speech-recognition'''					,					task.task    )
           self.assertEqual(__UpperCAmelCase					,					task.input_schema    )
           self.assertEqual(__UpperCAmelCase					,					task.label_schema    )





class 			UpperCamelCase					(			_UpperCAmelCase			):

     '''simple docstring'''

     def 		UpperCamelCase_    (       self    )			->					str:






           """simple docstring"""
           _lowerCamelCase           =					sorted(['''pos''', '''neg''']    )



     def 		UpperCamelCase_    (       self    )			->					Union[str, Any]:






           """simple docstring"""
           _lowerCamelCase           =					AudioClassification(audio_column='''input_audio'''					,					label_column='''input_label'''    )
           self.assertDictEqual({'''input_audio''': '''audio''', '''input_label''': '''labels'''}					,					task.column_mapping    )



     def 		UpperCamelCase_    (       self    )			->					List[Any]:






           """simple docstring"""
           _lowerCamelCase           =					Features({'''audio''': Audio()}    )
           _lowerCamelCase           =					Features({'''labels''': ClassLabel}    )
           _lowerCamelCase           =					{
               'audio_column': 'input_image',
               'label_column': 'input_label',
           }
           _lowerCamelCase           =					AudioClassification.from_dict(__UpperCAmelCase    )
           self.assertEqual('''audio-classification'''					,					task.task    )
           self.assertEqual(__UpperCAmelCase					,					task.input_schema    )
           self.assertEqual(__UpperCAmelCase					,					task.label_schema    )





     def 		UpperCamelCase_    (       self    )			->					Union[str, Any]:






           """simple docstring"""
           _lowerCamelCase           =					AudioClassification(audio_column='''input_audio'''					,					label_column='''input_label'''    )
           self.assertEqual(task.label_schema['''labels''']					,					__UpperCAmelCase    )
           _lowerCamelCase           =					task.align_with_features(Features({'''input_label''': ClassLabel(names=self.labels    )}    )    )
           self.assertEqual(task.label_schema['''labels''']					,					ClassLabel(names=self.labels    )    )





class 			UpperCamelCase					(			_UpperCAmelCase			):

     '''simple docstring'''

     def 		UpperCamelCase_    (       self    )			->					Tuple:






           """simple docstring"""
           _lowerCamelCase           =					sorted(['''pos''', '''neg''']    )



     def 		UpperCamelCase_    (       self    )			->					Optional[int]:






           """simple docstring"""
           _lowerCamelCase           =					ImageClassification(image_column='''input_image'''					,					label_column='''input_label'''    )
           self.assertDictEqual({'''input_image''': '''image''', '''input_label''': '''labels'''}					,					task.column_mapping    )



     def 		UpperCamelCase_    (       self    )			->					str:






           """simple docstring"""
           _lowerCamelCase           =					Features({'''image''': Image()}    )
           _lowerCamelCase           =					Features({'''labels''': ClassLabel}    )
           _lowerCamelCase           =					{
               'image_column': 'input_image',
               'label_column': 'input_label',
           }
           _lowerCamelCase           =					ImageClassification.from_dict(__UpperCAmelCase    )
           self.assertEqual('''image-classification'''					,					task.task    )
           self.assertEqual(__UpperCAmelCase					,					task.input_schema    )
           self.assertEqual(__UpperCAmelCase					,					task.label_schema    )





     def 		UpperCamelCase_    (       self    )			->					List[str]:






           """simple docstring"""
           _lowerCamelCase           =					ImageClassification(image_column='''input_image'''					,					label_column='''input_label'''    )
           self.assertEqual(task.label_schema['''labels''']					,					__UpperCAmelCase    )
           _lowerCamelCase           =					task.align_with_features(Features({'''input_label''': ClassLabel(names=self.labels    )}    )    )
           self.assertEqual(task.label_schema['''labels''']					,					ClassLabel(names=self.labels    )    )





class 			UpperCamelCase					(			_UpperCAmelCase			):

     '''simple docstring'''

     def 		UpperCamelCase_    (       self    )			->					Optional[Any]:






           """simple docstring"""
           _lowerCamelCase           =					DatasetInfo(task_templates=QuestionAnsweringExtractive()    )
           _lowerCamelCase           =					Dataset.from_dict({k: [v] for k, v in SAMPLE_QUESTION_ANSWERING_EXTRACTIVE.items()}					,					info=__UpperCAmelCase    )
           assert isinstance(dataset.info.task_templates					,					__UpperCAmelCase    )
           assert len(dataset.info.task_templates    ) == 1

           def keep_task(A_    ):
                 return x

           def dont_keep_task(A_    ):
                 _lowerCamelCase           =					deepcopy(__UpperCAmelCase    )
                 _lowerCamelCase           =					0
                 return out

           _lowerCamelCase           =					dataset.map(__UpperCAmelCase    )
           assert mapped_dataset.info.task_templates == dataset.info.task_templates
           # reload from cache
           _lowerCamelCase           =					dataset.map(__UpperCAmelCase    )
           assert mapped_dataset.info.task_templates == dataset.info.task_templates

           _lowerCamelCase           =					dataset.map(__UpperCAmelCase    )
           assert mapped_dataset.info.task_templates == []
           # reload from cache
           _lowerCamelCase           =					dataset.map(__UpperCAmelCase    )
           assert mapped_dataset.info.task_templates == []





     def 		UpperCamelCase_    (       self    )			->					str:






           """simple docstring"""
           _lowerCamelCase           =					Features({'''text''': Value('''string'''    ), '''label''': ClassLabel(names=('''pos''', '''neg''')    )}    )
           _lowerCamelCase           =					TextClassification(text_column='''text'''					,					label_column='''label'''    )
           _lowerCamelCase           =					DatasetInfo(features=__UpperCAmelCase					,					task_templates=__UpperCAmelCase    )
           _lowerCamelCase           =					Dataset.from_dict({'''text''': ['''A sentence.'''], '''label''': ['''pos''']}					,					info=__UpperCAmelCase    )

           def process(A_    ):
                 return example

           _lowerCamelCase           =					dataset.remove_columns('''label'''    )
           _lowerCamelCase           =					modified_dataset.map(__UpperCAmelCase    )
           assert mapped_dataset.info.task_templates == []



"""simple docstring"""






import os
import unittest

from transformers import RealmTokenizerFast
from transformers.models.bert.tokenization_bert import (
    VOCAB_FILES_NAMES,
    BasicTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.models.realm.tokenization_realm import RealmTokenizer
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english


@require_tokenizers
class 	A_       (     _UpperCAmelCase      ,  unittest.TestCase   ):





  """simple docstring"""

  lowercase		:	Any   =						RealmTokenizer
  lowercase		:	Any   =						RealmTokenizerFast
  lowercase		:	int   =						True
  lowercase		:	Union[str, Any]   =						True
  lowercase		:	int   =						filter_non_english
  def 							lowercase_     (						self						)				->					int:
     super().setUp()

     a   :	str						=    [
         '[UNK]',
         '[CLS]',
         '[SEP]',
         '[PAD]',
         '[MASK]',
         'want',
         '##want',
         '##ed',
         'wa',
         'un',
         'runn',
         '##ing',
         ',',
         'low',
         'lowest',
     ]
     a   :	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]						)						)
  def 							lowercase_     (						self  ,					__UpperCAmelCase						)				->					Optional[int]:
     a   :	List[str]						=    'UNwant\u00E9d,running'
     a   :	Dict						=    'unwanted, running'
     return input_text, output_text
  def 							lowercase_     (						self						)				->					Union[str, Any]:
     a   :	int						=    self.tokenizer_class(self.vocab_file						)

     a   :	List[Any]						=    tokenizer.tokenize('UNwant\u00E9d,running'						)
     self.assertListEqual(__UpperCAmelCase  ,					['un', '##want', '##ed', ',', 'runn', '##ing']						)
     self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase						)  ,					[9, 6, 7, 12, 10, 11]						)
  def 							lowercase_     (						self						)				->					Dict:
     if not self.test_rust_tokenizer:
        return

     a   :	int						=    self.get_tokenizer()
     a   :	List[str]						=    self.get_rust_tokenizer()

     a   :	str						=    'UNwant\u00E9d,running'

     a   :	Optional[int]						=    tokenizer.tokenize(__UpperCAmelCase						)
     a   :	Tuple						=    rust_tokenizer.tokenize(__UpperCAmelCase						)
     self.assertListEqual(__UpperCAmelCase  ,					__UpperCAmelCase						)

     a   :	Optional[int]						=    tokenizer.encode(__UpperCAmelCase  ,					add_special_tokens=__UpperCAmelCase						)
     a   :	Tuple						=    rust_tokenizer.encode(__UpperCAmelCase  ,					add_special_tokens=__UpperCAmelCase						)
     self.assertListEqual(__UpperCAmelCase  ,					__UpperCAmelCase						)

     a   :	Tuple						=    self.get_rust_tokenizer()
     a   :	Optional[int]						=    tokenizer.encode(__UpperCAmelCase						)
     a   :	Union[str, Any]						=    rust_tokenizer.encode(__UpperCAmelCase						)
     self.assertListEqual(__UpperCAmelCase  ,					__UpperCAmelCase						)

     # With lower casing
     a   :	List[str]						=    self.get_tokenizer(do_lower_case=__UpperCAmelCase						)
     a   :	Any						=    self.get_rust_tokenizer(do_lower_case=__UpperCAmelCase						)

     a   :	Any						=    'UNwant\u00E9d,running'

     a   :	Tuple						=    tokenizer.tokenize(__UpperCAmelCase						)
     a   :	List[Any]						=    rust_tokenizer.tokenize(__UpperCAmelCase						)
     self.assertListEqual(__UpperCAmelCase  ,					__UpperCAmelCase						)

     a   :	Any						=    tokenizer.encode(__UpperCAmelCase  ,					add_special_tokens=__UpperCAmelCase						)
     a   :	Any						=    rust_tokenizer.encode(__UpperCAmelCase  ,					add_special_tokens=__UpperCAmelCase						)
     self.assertListEqual(__UpperCAmelCase  ,					__UpperCAmelCase						)

     a   :	Optional[int]						=    self.get_rust_tokenizer()
     a   :	int						=    tokenizer.encode(__UpperCAmelCase						)
     a   :	Union[str, Any]						=    rust_tokenizer.encode(__UpperCAmelCase						)
     self.assertListEqual(__UpperCAmelCase  ,					__UpperCAmelCase						)
  def 							lowercase_     (						self						)				->					Union[str, Any]:
     a   :	Any						=    BasicTokenizer()

     self.assertListEqual(tokenizer.tokenize('ah\u535A\u63A8zz'						)  ,					['ah', '\u535A', '\u63A8', 'zz']						)
  def 							lowercase_     (						self						)				->					Tuple:
     a   :	Tuple						=    BasicTokenizer(do_lower_case=__UpperCAmelCase						)

     self.assertListEqual(
         tokenizer.tokenize(' \tHeLLo!how  \n Are yoU?  '						)  ,					['hello', '!', 'how', 'are', 'you', '?']						)
     self.assertListEqual(tokenizer.tokenize('H\u00E9llo'						)  ,					['hello']						)
  def 							lowercase_     (						self						)				->					Dict:
     a   :	Optional[int]						=    BasicTokenizer(do_lower_case=__UpperCAmelCase  ,					strip_accents=__UpperCAmelCase						)

     self.assertListEqual(
         tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '						)  ,					['hällo', '!', 'how', 'are', 'you', '?']						)
     self.assertListEqual(tokenizer.tokenize('H\u00E9llo'						)  ,					['h\u00E9llo']						)
  def 							lowercase_     (						self						)				->					Optional[Any]:
     a   :	List[Any]						=    BasicTokenizer(do_lower_case=__UpperCAmelCase  ,					strip_accents=__UpperCAmelCase						)

     self.assertListEqual(
         tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '						)  ,					['hallo', '!', 'how', 'are', 'you', '?']						)
     self.assertListEqual(tokenizer.tokenize('H\u00E9llo'						)  ,					['hello']						)
  def 							lowercase_     (						self						)				->					Dict:
     a   :	Optional[Any]						=    BasicTokenizer(do_lower_case=__UpperCAmelCase						)

     self.assertListEqual(
         tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '						)  ,					['hallo', '!', 'how', 'are', 'you', '?']						)
     self.assertListEqual(tokenizer.tokenize('H\u00E9llo'						)  ,					['hello']						)
  def 							lowercase_     (						self						)				->					int:
     a   :	Any						=    BasicTokenizer(do_lower_case=__UpperCAmelCase						)

     self.assertListEqual(
         tokenizer.tokenize(' \tHeLLo!how  \n Are yoU?  '						)  ,					['HeLLo', '!', 'how', 'Are', 'yoU', '?']						)
  def 							lowercase_     (						self						)				->					Any:
     a   :	List[str]						=    BasicTokenizer(do_lower_case=__UpperCAmelCase  ,					strip_accents=__UpperCAmelCase						)

     self.assertListEqual(
         tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '						)  ,					['HäLLo', '!', 'how', 'Are', 'yoU', '?']						)
  def 							lowercase_     (						self						)				->					List[Any]:
     a   :	Optional[int]						=    BasicTokenizer(do_lower_case=__UpperCAmelCase  ,					strip_accents=__UpperCAmelCase						)

     self.assertListEqual(
         tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '						)  ,					['HaLLo', '!', 'how', 'Are', 'yoU', '?']						)
  def 							lowercase_     (						self						)				->					Optional[Any]:
     a   :	List[Any]						=    BasicTokenizer(do_lower_case=__UpperCAmelCase  ,					never_split=['[UNK]']						)

     self.assertListEqual(
         tokenizer.tokenize(' \tHeLLo!how  \n Are yoU? [UNK]'						)  ,					['HeLLo', '!', 'how', 'Are', 'yoU', '?', '[UNK]']						)
  def 							lowercase_     (						self						)				->					int:
     a   :	Optional[int]						=    ['[UNK]', '[CLS]', '[SEP]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing']

     a   :	str						=    {}
     for i, token in enumerate(__UpperCAmelCase						):
        a   :	str						=    i
     a   :	List[Any]						=    WordpieceTokenizer(vocab=__UpperCAmelCase  ,					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 							lowercase_     (						self						)				->					Any:
     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 							lowercase_     (						self						)				->					Optional[int]:
     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 							lowercase_     (						self						)				->					Union[str, Any]:
     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 							lowercase_     (						self						)				->					Union[str, Any]:
     a   :	List[str]						=    self.get_tokenizer()

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

     if self.test_rust_tokenizer:
        a   :	Tuple						=    self.get_rust_tokenizer()
        self.assertListEqual(
            [rust_tokenizer.tokenize(__UpperCAmelCase						) for t in ['Test', '\xad', 'test']]  ,					[['[UNK]'], [], ['[UNK]']]						)
  @slow
  def 							lowercase_     (						self						)				->					Dict:
     a   :	Optional[int]						=    self.tokenizer_class.from_pretrained('bert-base-uncased'						)

     a   :	Optional[Any]						=    tokenizer.encode('sequence builders'  ,					add_special_tokens=__UpperCAmelCase						)
     a   :	Optional[int]						=    tokenizer.encode('multi-sequence build'  ,					add_special_tokens=__UpperCAmelCase						)

     a   :	str						=    tokenizer.build_inputs_with_special_tokens(__UpperCAmelCase						)
     a   :	Union[str, Any]						=    tokenizer.build_inputs_with_special_tokens(__UpperCAmelCase  ,					__UpperCAmelCase						)

     assert encoded_sentence == [1_01] + text + [1_02]
     assert encoded_pair == [1_01] + text + [1_02] + text_a + [1_02]
  def 							lowercase_     (						self						)				->					List[str]:
     for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
        with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'						):
           a   :	str						=    self.rust_tokenizer_class.from_pretrained(__UpperCAmelCase  ,					**__UpperCAmelCase						)

           a   :	Tuple						=    f'A, naïve {tokenizer_r.mask_token} AllenNLP sentence.'
           a   :	Union[str, Any]						=    tokenizer_r.encode_plus(
               __UpperCAmelCase  ,					return_attention_mask=__UpperCAmelCase  ,					return_token_type_ids=__UpperCAmelCase  ,					return_offsets_mapping=__UpperCAmelCase  ,					add_special_tokens=__UpperCAmelCase  ,					)

           a   :	List[Any]						=    tokenizer_r.do_lower_case if hasattr(__UpperCAmelCase  ,					'do_lower_case'						) else False
           a   :	str						=    (
               [
                   ((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']						)






  @slow
  def 							lowercase_     (						self						)				->					Dict:
     for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
        with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'						):
           a   :	str						=    self.rust_tokenizer_class.from_pretrained(__UpperCAmelCase  ,					**__UpperCAmelCase						)
           a   :	str						=    self.tokenizer_class.from_pretrained(__UpperCAmelCase  ,					**__UpperCAmelCase						)
           a   :	Dict						=    [['Hello world!', 'Nice to meet you!'], ['The cute cat.', 'The adorable dog.']]

           a   :	Optional[int]						=    tokenizer_r.batch_encode_candidates(__UpperCAmelCase  ,					max_length=10  ,					return_tensors='np'						)
           a   :	str						=    tokenizer_p.batch_encode_candidates(__UpperCAmelCase  ,					max_length=10  ,					return_tensors='np'						)

           a   :	Dict						=    (2, 2, 10)

           self.assertEqual(encoded_sentence_r['input_ids'].shape  ,					__UpperCAmelCase						)
           self.assertEqual(encoded_sentence_r['attention_mask'].shape  ,					__UpperCAmelCase						)
           self.assertEqual(encoded_sentence_r['token_type_ids'].shape  ,					__UpperCAmelCase						)

           self.assertEqual(encoded_sentence_p['input_ids'].shape  ,					__UpperCAmelCase						)
           self.assertEqual(encoded_sentence_p['attention_mask'].shape  ,					__UpperCAmelCase						)
           self.assertEqual(encoded_sentence_p['token_type_ids'].shape  ,					__UpperCAmelCase						)





