





import os
import unittest

from transformers import MPNetTokenizerFast
from transformers.models.mpnet.tokenization_mpnet import VOCAB_FILES_NAMES, MPNetTokenizer
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class UpperCAmelCase_							(   _SCREAMING_SNAKE_CASE    ,		unittest.TestCase   ):
  __lowerCAmelCase      :	List[Any]  	=      MPNetTokenizer
  __lowerCAmelCase      :	Tuple  	=      MPNetTokenizerFast
  __lowerCAmelCase      :	Dict  	=      True
  __lowerCAmelCase      :	int  	=      True


  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       super().setUp()

       A_					      =					[
           '''[UNK]''',
           '''[CLS]''',
           '''[SEP]''',
           '''[PAD]''',
           '''[MASK]''',
           '''want''',
           '''##want''',
           '''##ed''',
           '''wa''',
           '''un''',
           '''runn''',
           '''##ing''',
           ''',''',
           '''low''',
           '''lowest''',
       ]
       A_					      =					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 	__UpperCAmelCase    (       self   ,__snake_case   ):



       """simple docstring"""





       A_					      =					'''UNwant\u00E9d,running'''
       A_					      =					'''unwanted, running'''
       return input_text, output_text


  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       A_					      =					self.tokenizer_class(self.vocab_file   )

       A_					      =					tokenizer.tokenize('''UNwant\u00E9d,running'''   )
       self.assertListEqual(__snake_case   ,['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing''']   )
       self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case   )   ,[9, 6, 7, 1_2, 1_0, 1_1]   )




  @slow
  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       A_					      =					self.tokenizer_class.from_pretrained('''microsoft/mpnet-base'''   )

       A_					      =					tokenizer.encode('''sequence builders'''   ,add_special_tokens=__snake_case   )
       A_					      =					tokenizer.encode('''multi-sequence build'''   ,add_special_tokens=__snake_case   )

       A_					      =					tokenizer.build_inputs_with_special_tokens(__snake_case   )
       A_					      =					tokenizer.build_inputs_with_special_tokens(__snake_case   ,__snake_case   )

       assert encoded_sentence == [0] + text + [2]
       assert encoded_pair == [0] + text + [2] + [2] + text_a + [2]






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a__			:    Tuple			      =		{'configuration_yoso': ['YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP', 'YosoConfig']}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      a__			:    Optional[int]			      =		[
          'YOSO_PRETRAINED_MODEL_ARCHIVE_LIST',
          'YosoForMaskedLM',
          'YosoForMultipleChoice',
          'YosoForQuestionAnswering',
          'YosoForSequenceClassification',
          'YosoForTokenClassification',
          'YosoLayer',
          'YosoModel',
          'YosoPreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_yoso import YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP, YosoConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_yoso import (
                YOSO_PRETRAINED_MODEL_ARCHIVE_LIST,
                YosoForMaskedLM,
                YosoForMultipleChoice,
                YosoForQuestionAnswering,
                YosoForSequenceClassification,
                YosoForTokenClassification,
                YosoLayer,
                YosoModel,
                YosoPreTrainedModel,
            )


else:
      import sys

      a__			:    Union[str, Any]			      =		_LazyModule(__name__, globals()['__file__'], _import_structure)



