code: 
  
  
  
  """simple docstring"""
  
  
  
  
  from typing import Optional, Union
  
  from ...file_utils import TensorType
  from ...processing_utils import ProcessorMixin
  from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, TruncationStrategy
  
  
  
  
  
  
  class 			UpperCAmelCase_						(       _UpperCamelCase		):
        __SCREAMING_SNAKE_CASE		:     Optional[Any]				  =				'MarkupLMFeatureExtractor'
        __SCREAMING_SNAKE_CASE		:     Optional[int]				  =				('MarkupLMTokenizer', 'MarkupLMTokenizerFast')
        __SCREAMING_SNAKE_CASE		:     List[str]				  =				True
  
  
  
        def __call__(  self  :			Any  ,			A  :			Any=None  ,			A  :			Tuple=None  ,			A  :			Optional[Any]=None  ,			A  :			str=None  ,			A  :			str=None  ,			A  :			bool = True  ,			A  :			Union[bool, str, PaddingStrategy] = False  ,			A  :			Union[bool, str, TruncationStrategy] = None  ,			A  :			Optional[int] = None  ,			A  :			int = 0  ,			A  :			Optional[int] = None  ,			A  :			Optional[bool] = None  ,			A  :			Optional[bool] = None  ,			A  :			bool = False  ,			A  :			bool = False  ,			A  :			bool = False  ,			A  :			bool = False  ,			A  :			bool = True  ,			A  :			Optional[Union[str, TensorType]] = None  ,			**A  :			Dict  ,			):
             # first, create nodes and xpaths
             if self.parse_html:
                  if html_strings is None:
                       raise ValueError("Make sure to pass HTML strings in case `parse_html` is set to `True`"		)
  
                  if nodes is not None or xpaths is not None or node_labels is not None:
                       raise ValueError(
                           "Please don't pass nodes, xpaths nor node labels in case `parse_html` is set to `True`"		)
  
                  _UpperCAmelCase    :			Union[str, Any]              =					self.feature_extractor(A		)
                  _UpperCAmelCase    :			Tuple              =					features["nodes"]
                  _UpperCAmelCase    :			Optional[int]              =					features["xpaths"]
             else:
                  if html_strings is not None:
                       raise ValueError("You have passed HTML strings but `parse_html` is set to `False`."		)
                  if nodes is None or xpaths is None:
                       raise ValueError("Make sure to pass nodes and xpaths in case `parse_html` is set to `False`"		)
  
          # # second, apply the tokenizer
             if questions is not None and self.parse_html:
                  if isinstance(A  ,			A		):
                       _UpperCAmelCase    :			Union[str, Any]              =					[questions]  # add batch dimension (as the feature extractor always adds a batch dimension)
  
             _UpperCAmelCase    :			List[Any]              =					self.tokenizer(
                 text=questions if questions is not None else nodes  ,			text_pair=nodes if questions is not None else None  ,			xpaths=A  ,			node_labels=A  ,			add_special_tokens=A  ,			padding=A  ,			truncation=A  ,			max_length=A  ,			stride=A  ,			pad_to_multiple_of=A  ,			return_token_type_ids=A  ,			return_attention_mask=A  ,			return_overflowing_tokens=A  ,			return_special_tokens_mask=A  ,			return_offsets_mapping=A  ,			return_length=A  ,			verbose=A  ,			return_tensors=A  ,			**A  ,			)
  
             return encoded_inputs
  
  
  
        def 		snake_case_      (  self  :			Dict  ,			*A  :			List[str]  ,			**A  :			List[str]		):
             return self.tokenizer.batch_decode(*A  ,			**A		)
  
  
  
        def 		snake_case_      (  self  :			Dict  ,			*A  :			Union[str, Any]  ,			**A  :			Union[str, Any]		):
             return self.tokenizer.decode(*A  ,			**A		)
  
  
  
        @property
        def 		snake_case_      (  self  :			Union[str, Any]		):
             _UpperCAmelCase    :			Any              =					self.tokenizer.model_input_names
             return tokenizer_input_names
  
style_context: 
  
  
  
  """simple docstring"""
  
  
  
  
  from arguments import TokenizerTrainingArguments
  from datasets import load_dataset
  from tqdm import tqdm
  
  from transformers import AutoTokenizer, HfArgumentParser
  from transformers.models.gpta.tokenization_gpta import bytes_to_unicode
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		str=10       )						->     List[Any]:
  
  
  
  
  
       '''simple docstring'''
  
       for _ in tqdm(range(0		,						args.n_examples		,						SCREAMING_SNAKE_CASE__       )       ):
            yield [next(SCREAMING_SNAKE_CASE__       )[args.text_column] for _ in range(SCREAMING_SNAKE_CASE__       )]
  
  
  # Configuration
  _lowerCAmelCase       :      Optional[Any]							=							HfArgumentParser(TokenizerTrainingArguments)
  _lowerCAmelCase       :      Dict							=							parser.parse_args()
  
  # Base tokenizer
  _lowerCAmelCase       :      int							=							AutoTokenizer.from_pretrained(args.base_tokenizer)
  _lowerCAmelCase       :      List[str]							=							list(bytes_to_unicode().values())
  
  # Load dataset
  _lowerCAmelCase       :      Dict							=							load_dataset(args.dataset_name, split="train", streaming=True)
  _lowerCAmelCase       :      List[Any]							=							iter(dataset)
  
  
  # Training and saving
  _lowerCAmelCase       :      List[str]							=							tokenizer.train_new_from_iterator(
      batch_iterator(), vocab_size=args.vocab_size, initial_alphabet=base_vocab
  )
  new_tokenizer.save_pretrained(args.tokenizer_name, push_to_hub=args.push_to_hub)
  