




'''simple docstring'''





import os
import unittest

from transformers.models.bertweet.tokenization_bertweet import VOCAB_FILES_NAMES, BertweetTokenizer

from ...test_tokenization_common import TokenizerTesterMixin




class     UpperCamelCase					(  snake_case__			,		unittest.TestCase					):
  a    :Dict      					=				BertweetTokenizer
  a    :Optional[int]      					=				False



  def 						a_       ( self   ):







      '''simple docstring'''


      super().setUp()

      # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
      _a							:       List[str]       					=			["""I""", """m""", """V@@""", """R@@""", """r""", """e@@"""]
      _a							:       List[str]       					=			dict(zip(__lowerCAmelCase		,    range(len(__lowerCAmelCase   )   )   )   )
      _a							:       int       					=			["""#version: 0.2""", """a m</w>"""]
      _a							:       str       					=			{"""unk_token""": """<unk>"""}

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



  def 						a_       ( self		,    **__lowerCAmelCase   ):







      '''simple docstring'''


      kwargs.update(self.special_tokens_map   )
      return BertweetTokenizer.from_pretrained(self.tmpdirname		,    **__lowerCAmelCase   )



  def 						a_       ( self		,    __lowerCAmelCase   ):







      '''simple docstring'''


      _a							:       Any       					=			"""I am VinAI Research"""
      _a							:       Union[str, Any]       					=			"""I <unk> m V<unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"""
      return input_text, output_text



  def 						a_       ( self   ):







      '''simple docstring'''


      _a							:       List[Any]       					=			BertweetTokenizer(self.vocab_file		,    self.merges_file		,    **self.special_tokens_map   )
      _a							:       Tuple       					=			"""I am VinAI Research"""
      _a							:       Tuple       					=			"""I a@@ m V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h""".split()
      _a							:       Optional[int]       					=			tokenizer.tokenize(__lowerCAmelCase   )
      self.assertListEqual(__lowerCAmelCase		,    __lowerCAmelCase   )

      _a							:       List[str]       					=			tokens + [tokenizer.unk_token]

      _a							:       Optional[int]       					=			[4, 3, 5, 6, 3, 3, 3, 4, 7, 9, 3, 9, 3, 3, 3, 3, 3]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCAmelCase   )		,    __lowerCAmelCase   )








'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


a		:  Dict   			=			{
    '''configuration_mobilevitv2''': [
        '''MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''MobileViTV2Config''',
        '''MobileViTV2OnnxConfig''',
    ],
}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a		:  List[str]   			=			[
      '''MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''MobileViTV2ForImageClassification''',
      '''MobileViTV2ForSemanticSegmentation''',
      '''MobileViTV2Model''',
      '''MobileViTV2PreTrainedModel''',
  ]

if TYPE_CHECKING:
  from .configuration_mobilevitva import (
      MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
      MobileViTVaConfig,
      MobileViTVaOnnxConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_mobilevitva import (
        MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
        MobileViTVaForImageClassification,
        MobileViTVaForSemanticSegmentation,
        MobileViTVaModel,
        MobileViTVaPreTrainedModel,
    )

else:
  import sys

  a		:  Dict   			=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


