code: 
  
  """simple docstring"""
  import os
  import unittest
  
  from transformers.models.bertweet.tokenization_bertweet import VOCAB_FILES_NAMES, BertweetTokenizer
  
  from ...test_tokenization_common import TokenizerTesterMixin
  
  class 			lowerCamelCase  (					lowerCAmelCase__				,				unittest.TestCase      ):
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    SCREAMING_SNAKE_CASE							=     BertweetTokenizer
    SCREAMING_SNAKE_CASE							=     False
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          super().setUp()
  
          # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
          UpperCAmelCase__ :       Optional[int]						=						["""I""", """m""", """V@@""", """R@@""", """r""", """e@@"""]
          UpperCAmelCase__ :       Any						=						dict(zip(_lowerCamelCase       ,					range(len(_lowerCamelCase  )  )  )  )
          UpperCAmelCase__ :       int						=						["""#version: 0.2""", """a m</w>"""]
          UpperCAmelCase__ :       Optional[Any]						=						{"""unk_token""": """<unk>"""}
  
          UpperCAmelCase__ :       Optional[Any]						=						os.path.join(self.tmpdirname       ,					VOCAB_FILES_NAMES["""vocab_file"""]  )
          UpperCAmelCase__ :       List[Any]						=						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"""
  
          UpperCAmelCase__ :       Tuple						=						"""I am VinAI Research"""
          UpperCAmelCase__ :       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"""
  
          UpperCAmelCase__ :       Any						=						BertweetTokenizer(self.vocab_file       ,					self.merges_file       ,					**self.special_tokens_map  )
          UpperCAmelCase__ :       Any						=						"""I am VinAI Research"""
          UpperCAmelCase__ :       Dict						=						"""I a@@ m V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h""".split()
          UpperCAmelCase__ :       Optional[Any]						=						tokenizer.tokenize(_lowerCamelCase  )
          self.assertListEqual(_lowerCamelCase       ,					_lowerCamelCase  )
  
          UpperCAmelCase__ :       Union[str, Any]						=						tokens + [tokenizer.unk_token]
  
          UpperCAmelCase__ :       List[str]						=						[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  )
  
  
style_context: 
  
  """simple docstring"""
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_keras_nlp_available,
      is_tensorflow_text_available,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  _A						=     {
      """configuration_gpt2""": ["""GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPT2Config""", """GPT2OnnxConfig"""],
      """tokenization_gpt2""": ["""GPT2Tokenizer"""],
  }
  
  try:
      if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      _A						=     ["""GPT2TokenizerFast"""]
  
  try:
      if not is_torch_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      _A						=     [
          """GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """GPT2DoubleHeadsModel""",
          """GPT2ForQuestionAnswering""",
          """GPT2ForSequenceClassification""",
          """GPT2ForTokenClassification""",
          """GPT2LMHeadModel""",
          """GPT2Model""",
          """GPT2PreTrainedModel""",
          """load_tf_weights_in_gpt2""",
      ]
  
  try:
      if not is_tf_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      _A						=     [
          """TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """TFGPT2DoubleHeadsModel""",
          """TFGPT2ForSequenceClassification""",
          """TFGPT2LMHeadModel""",
          """TFGPT2MainLayer""",
          """TFGPT2Model""",
          """TFGPT2PreTrainedModel""",
      ]
  
  try:
      if not is_keras_nlp_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      _A						=     ["""TFGPT2Tokenizer"""]
  
  try:
      if not is_flax_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      _A						=     ["""FlaxGPT2LMHeadModel""", """FlaxGPT2Model""", """FlaxGPT2PreTrainedModel"""]
  
  if TYPE_CHECKING:
      from .configuration_gpta import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTaConfig, GPTaOnnxConfig
      from .tokenization_gpta import GPTaTokenizer
  
      try:
          if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .tokenization_gpta_fast import GPTaTokenizerFast
  
      try:
          if not is_torch_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .modeling_gpta import (
              GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
              GPTaDoubleHeadsModel,
              GPTaForQuestionAnswering,
              GPTaForSequenceClassification,
              GPTaForTokenClassification,
              GPTaLMHeadModel,
              GPTaModel,
              GPTaPreTrainedModel,
              load_tf_weights_in_gpta,
          )
  
      try:
          if not is_tf_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .modeling_tf_gpta import (
              TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFGPTaDoubleHeadsModel,
              TFGPTaForSequenceClassification,
              TFGPTaLMHeadModel,
              TFGPTaMainLayer,
              TFGPTaModel,
              TFGPTaPreTrainedModel,
          )
  
      try:
          if not is_keras_nlp_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .tokenization_gpta_tf import TFGPTaTokenizer
  
      try:
          if not is_flax_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel, FlaxGPTaPreTrainedModel
  
  else:
      import sys
  
      _A						=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
  