

import os
import unittest

from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir

from ...test_tokenization_common import TokenizerTesterMixin


_lowerCamelCase      :					Dict					    =      get_tests_dir('''fixtures/test_sentencepiece_bpe.model''')





class        lowerCAmelCase__  (					__magic_name__				,							unittest.TestCase			):



       '''simple docstring'''







       lowercase_						=    BartphoTokenizer
       lowercase_						=    False
       lowercase_						=    True

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             super().setUp()

             __A    =['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']
             __A    =dict(zip(lowercase__ ,   range(len(lowercase__     )     )     )     )
             __A    ={'''unk_token''': '''<unk>'''}

             __A    =os.path.join(self.tmpdirname ,   VOCAB_FILES_NAMES['''monolingual_vocab_file''']     )
             with open(self.monolingual_vocab_file ,   '''w''' ,   encoding='''utf-8'''     ) as fp:
                   for token in vocab_tokens:
                         fp.write(f'''{token} {vocab_tokens[token]}\n'''     )

             __A    =BartphoTokenizer(lowercase__ ,   self.monolingual_vocab_file ,   **self.special_tokens_map     )
             tokenizer.save_pretrained(self.tmpdirname     )

       def     __UpperCamelCase     (							self ,   **lowercase__     ):







             '''simple docstring'''



             kwargs.update(self.special_tokens_map     )
             return BartphoTokenizer.from_pretrained(self.tmpdirname ,   **lowercase__     )

       def     __UpperCamelCase     (							self ,   lowercase__     ):







             '''simple docstring'''



             __A    ='''This is a là test'''
             __A    ='''This is a<unk><unk> test'''
             return input_text, output_text


       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =BartphoTokenizer(lowercase__ ,   self.monolingual_vocab_file ,   **self.special_tokens_map     )
             __A    ='''This is a là test'''
             __A    ='''▁This ▁is ▁a ▁l à ▁t est'''.split()
             __A    =tokenizer.tokenize(lowercase__     )
             self.assertListEqual(lowercase__ ,   lowercase__     )

             __A    =tokens + [tokenizer.unk_token]
             __A    =[4, 5, 6, 3, 3, 7, 8, 3]
             self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase__     ) ,   lowercase__     )





from typing import TYPE_CHECKING

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


_lowerCamelCase      :					Tuple					    =      {
    '''configuration_squeezebert''': [
        '''SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SqueezeBertConfig''',
        '''SqueezeBertOnnxConfig''',
    ],
    '''tokenization_squeezebert''': ['''SqueezeBertTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowerCamelCase      :					List[str]					    =      ['''SqueezeBertTokenizerFast''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowerCamelCase      :					Tuple					    =      [
       '''SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''SqueezeBertForMaskedLM''',
       '''SqueezeBertForMultipleChoice''',
       '''SqueezeBertForQuestionAnswering''',
       '''SqueezeBertForSequenceClassification''',
       '''SqueezeBertForTokenClassification''',
       '''SqueezeBertModel''',
       '''SqueezeBertModule''',
       '''SqueezeBertPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_squeezebert import (
       SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
       SqueezeBertConfig,
       SqueezeBertOnnxConfig,
   )
   from .tokenization_squeezebert import SqueezeBertTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_squeezebert import (
          SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          SqueezeBertForMaskedLM,
          SqueezeBertForMultipleChoice,
          SqueezeBertForQuestionAnswering,
          SqueezeBertForSequenceClassification,
          SqueezeBertForTokenClassification,
          SqueezeBertModel,
          SqueezeBertModule,
          SqueezeBertPreTrainedModel,
      )

else:
   import sys

   _lowerCamelCase      :					List[str]					    =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


