code: 
  
  
  
  """simple docstring"""
  
  
  
  
  from dataclasses import dataclass
  
  _lowerCAmelCase       :      Union[str, Any]							=							"0.1"
  _lowerCAmelCase       :      Union[str, Any]							=							"Lucia Harcekova"
  
  
  
  
  
  
  @dataclass
  class 			UpperCAmelCase_						:
        __SCREAMING_SNAKE_CASE		:     int
        __SCREAMING_SNAKE_CASE		:     int
        __SCREAMING_SNAKE_CASE		:     str
  
  
  
        def __repr__(  self  :			Tuple		):
             return f'({self.offset}, {self.length}, {self.indicator})'
  
  
  
  
  
  
  
  class 			UpperCAmelCase_						:
  
  
  
        def __init__(  self  :			Optional[int]  ,			A  :			int = 1_3  ,			A  :			int = 6		):
             _UpperCAmelCase    :			List[str]              =					window_size
             _UpperCAmelCase    :			List[str]              =					lookahead_buffer_size
             _UpperCAmelCase    :			Optional[int]              =					self.window_size - self.lookahead_buffer_size
  
  
  
        def 		snake_case_      (  self  :			Optional[int]  ,			A  :			str		):
             _UpperCAmelCase    :			Dict              =					[]
             _UpperCAmelCase    :			List[Any]              =					""
  
             # while there are still characters in text to compress
             while text:
                  # find the next encoding phrase
                  # - triplet with offset, length, indicator (the next encoding character)
                  _UpperCAmelCase    :			int              =					self._find_encoding_token(A  ,			A		)
  
                  # update the search buffer:
                  # - add new characters from text into it
                  # - check if size exceed the max search buffer size, if so, drop the
                  #   oldest elements
                  search_buffer += text[: token.length + 1]
                  if len(A		) > self.search_buffer_size:
                       _UpperCAmelCase    :			Optional[int]              =					search_buffer[-self.search_buffer_size :]
  
                  # update the text
                  _UpperCAmelCase    :			int              =					text[token.length + 1 :]
  
                  # append the token to output
                  output.append(A		)
  
             return output
  
  
  
        def 		snake_case_      (  self  :			List[str]  ,			A  :			list[Token]		):
             _UpperCAmelCase    :			Optional[Any]              =					""
  
             for token in tokens:
                  for _ in range(token.length		):
                       output += output[-token.offset]
                  output += token.indicator
  
             return output
  
  
  
        def 		snake_case_      (  self  :			Tuple  ,			A  :			str  ,			A  :			str		):
  
             if not text:
                  raise ValueError("We need some text to work with."		)
  
             # Initialise result parameters to default values
             _UpperCAmelCase       , _UpperCAmelCase    :			List[Any]              =					0, 0
  
             if not search_buffer:
                  return Token(A  ,			A  ,			text[length]		)
  
             for i, character in enumerate(A		):
                  _UpperCAmelCase    :			Union[str, Any]              =					len(A		) - i
                  if character == text[0]:
                       _UpperCAmelCase    :			List[str]              =					self._match_length_from_index(A  ,			A  ,			0  ,			A		)
                       # if the found length is bigger than the current or if it's equal,
                       # which means it's offset is smaller: update offset and length
                       if found_length >= length:
                            _UpperCAmelCase       , _UpperCAmelCase    :			Optional[int]              =					found_offset, found_length
  
             return Token(A  ,			A  ,			text[length]		)
  
  
  
        def 		snake_case_      (  self  :			List[str]  ,			A  :			str  ,			A  :			str  ,			A  :			int  ,			A  :			int		):
             if not text or text[text_index] != window[window_index]:
                  return 0
             return 1 + self._match_length_from_index(
                 A  ,			window + text[text_index]  ,			text_index + 1  ,			window_index + 1		)
  
  
  if __name__ == "__main__":
   from doctest import testmod
  
   testmod()
   # Initialize compressor class
   _lowerCAmelCase       :      Any							=							LZaaCompressor(window_size=13, lookahead_buffer_size=6)
  
   # Example
   _lowerCAmelCase       :      Tuple							=							"cabracadabrarrarrad"
   _lowerCAmelCase       :      List[str]							=							lzaa_compressor.compress(TEXT)
   print(lzaa_compressor.compress("ababcbababaa"))
   _lowerCAmelCase       :      List[Any]							=							lzaa_compressor.decompress(compressed_text)
   assert decompressed_text == TEXT, "The LZ77 algorithm returned the invalid result."
  
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)
  