


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


UpperCamelCase								=	get_tests_dir("""fixtures/test_sentencepiece_bpe.model""")






class   _lowerCamelCase (					UpperCamelCase					,							unittest.TestCase			):




       """simple docstring"""
       snake_case      =    BartphoTokenizer
       snake_case      =    False
       snake_case      =    True



       def        _snake_case       (      self		)->Dict:




         '''simple docstring'''





         super().setUp()

         A_      :				Optional[Any]		     =					['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']
         A_      :				Optional[int]		     =					dict(zip(_SCREAMING_SNAKE_CASE				,       range(len(_SCREAMING_SNAKE_CASE		)		)		)		)
         A_      :				str		     =					{'''unk_token''': '''<unk>'''}

         A_      :				Any		     =					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_      :				Optional[int]		     =					BartphoTokenizer(_SCREAMING_SNAKE_CASE				,       self.monolingual_vocab_file				,       **self.special_tokens_map		)
         tokenizer.save_pretrained(self.tmpdirname		)



       def        _snake_case       (      self				,       **_SCREAMING_SNAKE_CASE		)->str:




         '''simple docstring'''





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



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE		)->Optional[int]:




         '''simple docstring'''





         A_      :				Optional[Any]		     =					'''This is a là test'''
         A_      :				str		     =					'''This is a<unk><unk> test'''
         return input_text, output_text







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




         '''simple docstring'''





         A_      :				Optional[Any]		     =					BartphoTokenizer(_SCREAMING_SNAKE_CASE				,       self.monolingual_vocab_file				,       **self.special_tokens_map		)
         A_      :				List[str]		     =					'''This is a là test'''
         A_      :				str		     =					'''▁This ▁is ▁a ▁l à ▁t est'''.split()
         A_      :				Tuple		     =					tokenizer.tokenize(_SCREAMING_SNAKE_CASE		)
         self.assertListEqual(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)

         A_      :				Optional[Any]		     =					tokens + [tokenizer.unk_token]
         A_      :				Dict		     =					[4, 5, 6, 3, 3, 7, 8, 3]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE		)				,       _SCREAMING_SNAKE_CASE		)








from typing import TYPE_CHECKING

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


UpperCamelCase								=	{
    """configuration_nezha""": ["""NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NezhaConfig"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase								=	[
         """NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """NezhaForNextSentencePrediction""",
         """NezhaForMaskedLM""",
         """NezhaForPreTraining""",
         """NezhaForMultipleChoice""",
         """NezhaForQuestionAnswering""",
         """NezhaForSequenceClassification""",
         """NezhaForTokenClassification""",
         """NezhaModel""",
         """NezhaPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_nezha import (
              NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
              NezhaForMaskedLM,
              NezhaForMultipleChoice,
              NezhaForNextSentencePrediction,
              NezhaForPreTraining,
              NezhaForQuestionAnswering,
              NezhaForSequenceClassification,
              NezhaForTokenClassification,
              NezhaModel,
              NezhaPreTrainedModel,
          )


else:
     import sys

     UpperCamelCase								=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




