





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       (			__SCREAMING_SNAKE_CASE       ,      unittest.TestCase			):



      """simple docstring"""






      UpperCAmelCase_  :					List[Any]      			=BartphoTokenizer
      UpperCAmelCase_  :					str      			=False
      UpperCAmelCase_  :					str      			=True







      def   UpperCAmelCase						(       self      )				->      Tuple:






        '''simple docstring'''






        super().setUp()

        __snake_case      : int						=				["▁This", "▁is", "▁a", "▁t", "est"]
        __snake_case      : Tuple						=				dict(zip(UpperCAmelCase_ ,  range(len(UpperCAmelCase_      )      )      )      )
        __snake_case      : List[Any]						=				{"unk_token": "<unk>"}

        __snake_case      : Dict						=				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"""      )

        __snake_case      : Dict						=				BartphoTokenizer(UpperCAmelCase_ ,  self.monolingual_vocab_file ,  **self.special_tokens_map      )
        tokenizer.save_pretrained(self.tmpdirname      )







      def   UpperCAmelCase						(       self ,  **UpperCAmelCase      )				->      str:






        '''simple docstring'''






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







      def   UpperCAmelCase						(       self ,  UpperCAmelCase      )				->      List[Any]:






        '''simple docstring'''






        __snake_case      : Optional[int]						=				"This is a là test"
        __snake_case      : Optional[Any]						=				"This is a<unk><unk> test"
        return input_text, output_text







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






        '''simple docstring'''






        __snake_case      : str						=				BartphoTokenizer(UpperCAmelCase_ ,  self.monolingual_vocab_file ,  **self.special_tokens_map      )
        __snake_case      : List[Any]						=				"This is a là test"
        __snake_case      : Optional[int]						=				"▁This ▁is ▁a ▁l à ▁t est".split()
        __snake_case      : Optional[int]						=				tokenizer.tokenize(UpperCAmelCase_      )
        self.assertListEqual(UpperCAmelCase_ ,  UpperCAmelCase_      )

        __snake_case      : Tuple						=				tokens + [tokenizer.unk_token]
        __snake_case      : List[Any]						=				[4, 5, 6, 3, 3, 7, 8, 3]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase_      ) ,  UpperCAmelCase_      )








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__A									=				{
    "configuration_distilbert": [
        "DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "DistilBertConfig",
        "DistilBertOnnxConfig",
    ],
    "tokenization_distilbert": ["DistilBertTokenizer"],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __A									=				["DistilBertTokenizerFast"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __A									=				[
         "DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "DistilBertForMaskedLM",
         "DistilBertForMultipleChoice",
         "DistilBertForQuestionAnswering",
         "DistilBertForSequenceClassification",
         "DistilBertForTokenClassification",
         "DistilBertModel",
         "DistilBertPreTrainedModel",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __A									=				[
         "TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "TFDistilBertForMaskedLM",
         "TFDistilBertForMultipleChoice",
         "TFDistilBertForQuestionAnswering",
         "TFDistilBertForSequenceClassification",
         "TFDistilBertForTokenClassification",
         "TFDistilBertMainLayer",
         "TFDistilBertModel",
         "TFDistilBertPreTrainedModel",
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __A									=				[
         "FlaxDistilBertForMaskedLM",
         "FlaxDistilBertForMultipleChoice",
         "FlaxDistilBertForQuestionAnswering",
         "FlaxDistilBertForSequenceClassification",
         "FlaxDistilBertForTokenClassification",
         "FlaxDistilBertModel",
         "FlaxDistilBertPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_distilbert import (
         DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
         DistilBertConfig,
         DistilBertOnnxConfig,
     )
     from .tokenization_distilbert import DistilBertTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_distilbert_fast import DistilBertTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_distilbert import (
              DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              DistilBertForMaskedLM,
              DistilBertForMultipleChoice,
              DistilBertForQuestionAnswering,
              DistilBertForSequenceClassification,
              DistilBertForTokenClassification,
              DistilBertModel,
              DistilBertPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_distilbert import (
              TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFDistilBertForMaskedLM,
              TFDistilBertForMultipleChoice,
              TFDistilBertForQuestionAnswering,
              TFDistilBertForSequenceClassification,
              TFDistilBertForTokenClassification,
              TFDistilBertMainLayer,
              TFDistilBertModel,
              TFDistilBertPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_distilbert import (
              FlaxDistilBertForMaskedLM,
              FlaxDistilBertForMultipleChoice,
              FlaxDistilBertForQuestionAnswering,
              FlaxDistilBertForSequenceClassification,
              FlaxDistilBertForTokenClassification,
              FlaxDistilBertModel,
              FlaxDistilBertPreTrainedModel,
          )

else:
     import sys

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

