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    __A       ( _SCREAMING_SNAKE_CASE,					unittest.TestCase    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   __lowerCAmelCase				      =						BertweetTokenizer
   __lowerCAmelCase				      =						False
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Any:
       super().setUp()
  
       # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
       a					=['''I''', '''m''', '''V@@''', '''R@@''', '''r''', '''e@@''']
       a					=dict(zip(__A				,					range(len(__A		)		)		)		)
       a					=['''#version: 0.2''', '''a m</w>''']
       a					={'''unk_token''': '''<unk>'''}
  
       a					=os.path.join(self.tmpdirname				,					VOCAB_FILES_NAMES['''vocab_file''']		)
       a					=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(__A		)		)
  
  
   def    SCREAMING_SNAKE_CASE							(	self				,					**__A		)      ->  Optional[Any]:
       kwargs.update(self.special_tokens_map		)
       return BertweetTokenizer.from_pretrained(self.tmpdirname				,					**__A		)
  
  
   def    SCREAMING_SNAKE_CASE							(	self				,					__A		)      ->  Union[str, Any]:
       a					='''I am VinAI Research'''
       a					='''I <unk> m V<unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'''
       return input_text, output_text
  
  
  
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Any:
       a					=BertweetTokenizer(self.vocab_file				,					self.merges_file				,					**self.special_tokens_map		)
       a					='''I am VinAI Research'''
       a					='''I a@@ m V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'''.split()
       a					=tokenizer.tokenize(__A		)
       self.assertListEqual(__A				,					__A		)
  
       a					=tokens + [tokenizer.unk_token]
  
       a					=[4, 3, 5, 6, 3, 3, 3, 4, 7, 9, 3, 9, 3, 3, 3, 3, 3]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(__A		)				,					__A		)
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
  
  
  lowerCamelCase_     :     Any    		=						{"""configuration_dinat""": ["""DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DinatConfig"""]}
  
  
  try:
         if not is_torch_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         lowerCamelCase_     :     List[str]    		=						[
             """DINAT_PRETRAINED_MODEL_ARCHIVE_LIST""",
             """DinatForImageClassification""",
             """DinatModel""",
             """DinatPreTrainedModel""",
             """DinatBackbone""",
         ]
  
  if TYPE_CHECKING:
         from .configuration_dinat import DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP, DinatConfig
  
         try:
                if not is_torch_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .modeling_dinat import (
                    DINAT_PRETRAINED_MODEL_ARCHIVE_LIST,
                    DinatBackbone,
                    DinatForImageClassification,
                    DinatModel,
                    DinatPreTrainedModel,
                )
  
  else:
         import sys
  
         lowerCamelCase_     :     List[Any]    		=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)