code: 
  import os
  import warnings
  from pathlib import Path
  from typing import Optional, Union
  
  from .dynamic_module_utils import custom_object_save
  from .tokenization_utils_base import PreTrainedTokenizerBase
  from .utils import PushToHubMixin, copy_func, direct_transformers_import, logging
  
  
  UpperCamelCase_	:    Union[str, Any]  					=       logging.get_logger(__name__)
  
  # Dynamically import the Transformers module to grab the attribute classes of the processor form their names.
  UpperCamelCase_	:    Optional[Any]  					=       direct_transformers_import(Path(__file__).parent)
  
  
  UpperCamelCase_	:    Union[str, Any]  					=       {
      '''AutoTokenizer''': '''PreTrainedTokenizerBase''',
      '''AutoFeatureExtractor''': '''FeatureExtractionMixin''',
      '''AutoImageProcessor''': '''ImageProcessingMixin''',
  }
  
  
  
  
  class      __lowercase	(	lowercase__					):
   _A   			=      ["""feature_extractor""", """tokenizer"""]
   # Names need to be attr_class for attr in attributes
   _A   			=      None
   _A   			=      None
   _A   			=      None
  
  
  
  
  
  
   def __init__(self							:   Dict			,  *snake_case							:   int			,  **snake_case							:   Union[str, Any]						)   -> int:
         # Sanitize args and kwargs
         for key in kwargs:
               if key not in self.attributes:
                     raise TypeError(F"""Unexpected keyword argument {key}."""						)
         for arg, attribute_name in zip(_A			,  self.attributes						):
               if attribute_name in kwargs:
                     raise TypeError(F"""Got multiple values for argument {attribute_name}."""						)
               else:
                     _lowercase					:  Any    			=      arg
  
         if len(_A						) != len(self.attributes						):
               raise ValueError(
                   F"""This processor requires {len(self.attributes						)} arguments: {', '.join(self.attributes						)}. Got """
                   F"""{len(_A						)} arguments instead."""						)
  
         # Check each arg is of the proper class (this will also catch a user initializing in the wrong order)
         for attribute_name, arg in kwargs.items():
               _lowercase					:  List[Any]    			=      getattr(self			,  F"""{attribute_name}_class"""						)
               # Nothing is ever going to be an instance of "AutoXxx", in that case we check the base class.
               _lowercase					:  int    			=      AUTO_TO_BASE_CLASS_MAPPING.get(_A			,  _A						)
               if isinstance(_A			,  _A						):
                     _lowercase					:  List[Any]    			=      tuple(getattr(_A			,  _A						) for n in class_name if n is not None						)
               else:
                     _lowercase					:  Optional[int]    			=      getattr(_A			,  _A						)
  
               if not isinstance(_A			,  _A						):
                     raise ValueError(
                         F"""Received a {type(_A						).__name__} for argument {attribute_name}, but a {class_name} was expected."""						)
  
               setattr(self			,  _A			,  _A						)
  
  
  
  
  
  
   def __repr__(self							:   Union[str, Any]						)   -> Any:
         _lowercase					:  int    			=      [F"""- {name}: {repr(getattr(self			,  _A						)						)}""" for name in self.attributes]
         _lowercase					:  Union[str, Any]    			=      '\n'.join(_A						)
         return F"""{self.__class__.__name__}:\n{attributes_repr}"""
  
  
  
  
  
  
   def 			_a(self							:   List[Any]			,  snake_case							:   Any			,  snake_case							:   bool = False			,  **snake_case							:   List[str]						)   -> int:
         os.makedirs(_A			,  exist_ok=_A						)
  
         if push_to_hub:
               _lowercase					:  Dict    			=      kwargs.pop("commit_message"			,  _A						)
               _lowercase					:  Optional[Any]    			=      kwargs.pop("repo_id"			,  save_directory.split(os.path.sep						)[-1]						)
               _lowercase					:  List[str]    			=      self._create_repo(_A			,  **_A						)
               _lowercase					:  Optional[Any]    			=      self._get_files_timestamps(_A						)
         # If we have a custom config, we copy the file defining it in the folder and set the attributes so it can be
         # loaded from the Hub.
         if self._auto_class is not None:
               _lowercase					:  Optional[Any]    			=      [getattr(self			,  _A						) for attribute_name in self.attributes]
               _lowercase					:  Dict    			=      [(a.init_kwargs if isinstance(_A			,  _A						) else a) for a in attrs]
               custom_object_save(self			,  _A			,  config=_A						)
  
         for attribute_name in self.attributes:
               _lowercase					:  Union[str, Any]    			=      getattr(self			,  _A						)
               # Include the processor class in the attribute config so this processor can then be reloaded with the
               # `AutoProcessor` API.
               if hasattr(_A			,  "_set_processor_class"						):
                     attribute._set_processor_class(self.__class__.__name__						)
               attribute.save_pretrained(_A						)
  
         if self._auto_class is not None:
               # We added an attribute to the init_kwargs of the tokenizers, which needs to be cleaned up.
               for attribute_name in self.attributes:
                     _lowercase					:  Optional[int]    			=      getattr(self			,  _A						)
                     if isinstance(_A			,  _A						):
                           del attribute.init_kwargs["auto_map"]
  
         if push_to_hub:
               self._upload_modified_files(
                   _A			,  _A			,  _A			,  commit_message=_A			,  token=kwargs.get("use_auth_token"						)			,  )
  
  
  
  
  
  
   @classmethod
   def 			_a(cls							:   str			,  snake_case							:   Union[str, os.PathLike]			,  snake_case							:   Optional[Union[str, os.PathLike]] = None			,  snake_case							:   bool = False			,  snake_case							:   bool = False			,  snake_case							:   Optional[Union[str, bool]] = None			,  snake_case							:   str = "main"			,  **snake_case							:   Any			,  )   -> Optional[int]:
         _lowercase					:  str    			=      cache_dir
         _lowercase					:  Any    			=      force_download
         _lowercase					:  List[str]    			=      local_files_only
         _lowercase					:  Optional[int]    			=      revision
  
         _lowercase					:  Optional[Any]    			=      kwargs.pop("use_auth_token"			,  _A						)
         if use_auth_token is not None:
               warnings.warn(
                   "The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers."			,  _A						)
               if token is not None:
                     raise ValueError(
                         "`token` and `use_auth_token` are both specified. Please set only the argument `token`."						)
               _lowercase					:  str    			=      use_auth_token
  
         if token is not None:
               # change to `token` in a follow-up PR
               _lowercase					:  Any    			=      token
  
         _lowercase					:  Dict    			=      cls._get_arguments_from_pretrained(_A			,  **_A						)
         return cls(*_A						)
  
  
  
  
  
  
   @classmethod
   def 			_a(cls							:   str			,  snake_case							:   Optional[Any]="AutoProcessor"						)   -> Tuple:
         if not isinstance(_A			,  _A						):
               _lowercase					:  Dict    			=      auto_class.__name__
  
         import transformers.models.auto as auto_module
  
         if not hasattr(_A			,  _A						):
               raise ValueError(F"""{auto_class} is not a valid auto class."""						)
  
         _lowercase					:  Tuple    			=      auto_class
  
  
  
  
  
  
   @classmethod
   def 			_a(cls							:   Dict			,  snake_case							:   int			,  **snake_case							:   List[Any]						)   -> str:
         _lowercase					:  List[str]    			=      []
         for attribute_name in cls.attributes:
               _lowercase					:  List[str]    			=      getattr(cls			,  F"""{attribute_name}_class"""						)
               if isinstance(_A			,  _A						):
                     _lowercase					:  int    			=      tuple(getattr(_A			,  _A						) if n is not None else None for n in class_name						)
                     _lowercase					:  Tuple    			=      kwargs.get("use_fast"			,  _A						)
                     if use_fast and classes[1] is not None:
                           _lowercase					:  Tuple    			=      classes[1]
                     else:
                           _lowercase					:  Optional[Any]    			=      classes[0]
               else:
                     _lowercase					:  List[str]    			=      getattr(_A			,  _A						)
  
               args.append(attribute_class.from_pretrained(_A			,  **_A						)						)
         return args
  
  
  
  
  
  
   @property
   def 			_a(self							:   Tuple						)   -> Dict:
         _lowercase					:  Optional[Any]    			=      getattr(self			,  self.attributes[0]						)
         return getattr(_A			,  "model_input_names"			,  _A						)
  
  
  UpperCamelCase_	:    str  					=       copy_func(ProcessorMixin.push_to_hub)
  if ProcessorMixin.push_to_hub.__doc__ is not None:
         UpperCamelCase_	:    str  					=       ProcessorMixin.push_to_hub.__doc__.format(
             object="""processor""", object_class="""AutoProcessor""", object_files="""processor files"""
         )
  
  
  
  
  
style_context: 
  import os
  import unittest
  
  from transformers.models.bertweet.tokenization_bertweet import VOCAB_FILES_NAMES, BertweetTokenizer
  
  from ...test_tokenization_common import TokenizerTesterMixin
  
  
  
  
  class      __lowercase	(	__snake_case      ,   unittest.TestCase					):
  	_A   			=      BertweetTokenizer
  	_A   			=      False
  
  
  
  
  
  
  	def 			_a(self							:   Tuple						)   -> Any:
  							super().setUp()
  
  							# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
  							_lowercase					:  Tuple    			=      ["I", "m", "V@@", "R@@", "r", "e@@"]
  							_lowercase					:  Tuple    			=      dict(zip(snake_case			,  range(len(snake_case						)						)						)						)
  							_lowercase					:  Tuple    			=      ["#version: 0.2", "a m</w>"]
  							_lowercase					:  Optional[Any]    			=      {"unk_token": "<unk>"}
  
  							_lowercase					:  int    			=      os.path.join(self.tmpdirname			,  VOCAB_FILES_NAMES["vocab_file"]						)
  							_lowercase					:  Optional[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(snake_case						)						)
  
  
  
  
  
  
  	def 			_a(self							:   List[Any]			,  **snake_case							:   Tuple						)   -> Union[str, Any]:
  							kwargs.update(self.special_tokens_map						)
  							return BertweetTokenizer.from_pretrained(self.tmpdirname			,  **snake_case						)
  
  
  
  
  
  
  	def 			_a(self							:   Any			,  snake_case							:   Any						)   -> List[Any]:
  							_lowercase					:  Tuple    			=      "I am VinAI Research"
  							_lowercase					:  Optional[int]    			=      "I <unk> m V<unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"
  							return input_text, output_text
  
  
  
  
  
  
  	def 			_a(self							:   str						)   -> Optional[int]:
  							_lowercase					:  str    			=      BertweetTokenizer(self.vocab_file			,  self.merges_file			,  **self.special_tokens_map						)
  							_lowercase					:  Optional[Any]    			=      "I am VinAI Research"
  							_lowercase					:  str    			=      "I a@@ m V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h".split()
  							_lowercase					:  Tuple    			=      tokenizer.tokenize(snake_case						)
  							self.assertListEqual(snake_case			,  snake_case						)
  
  							_lowercase					:  str    			=      tokens + [tokenizer.unk_token]
  
  							_lowercase					:  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(snake_case						)			,  snake_case						)
  
  
  
  
  