code: 
  
  
  
  
  
  
  from dataclasses import dataclass
  
  lowerCamelCase__           =				'0.1'
  lowerCamelCase__           =				'Lucia Harcekova'
  
  
  
  
  @dataclass
  class 					UpperCamelCase	:
  					__UpperCamelCase						=   42
  					__UpperCamelCase						=   42
  					__UpperCamelCase						=   42
  
  
  
  
  					def __repr__( self :					List[Any]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							return F"""({self.offset}, {self.length}, {self.indicator})"""
  
  
  
  
  class 					UpperCamelCase	:
  
  
  
  
  					def __init__( self :					Tuple      ,_lowerCAmelCase :					Tuple = 13      ,_lowerCAmelCase :					Union[str, Any] = 6						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     window_size
  							__snake_case		       =     lookahead_buffer_size
  							__snake_case		       =     self.window_size - self.lookahead_buffer_size
  
  
  
  
  					def 							UpperCamelCase_						( self :					Union[str, Any]      ,_lowerCAmelCase :					Tuple						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     []
  							__snake_case		       =     """"""
  
  							# 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)
  									__snake_case		       =     self._find_encoding_token(_lowerCamelCase      ,_lowerCamelCase						)
  
  									# 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(_lowerCamelCase						) > self.search_buffer_size:
  											__snake_case		       =     search_buffer[-self.search_buffer_size :]
  
  									# update the text
  									__snake_case		       =     text[token.length + 1 :]
  
  									# append the token to output
  									output.append(_lowerCamelCase						)
  
  							return output
  
  
  
  
  					def 							UpperCamelCase_						( self :					Dict      ,_lowerCAmelCase :					int						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     """"""
  
  							for token in tokens:
  									for _ in range(token.length						):
  											output += output[-token.offset]
  									output += token.indicator
  
  							return output
  
  
  
  
  					def 							UpperCamelCase_						( self :					Optional[Any]      ,_lowerCAmelCase :					Any      ,_lowerCAmelCase :					Union[str, Any]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							if not text:
  									raise ValueError("We need some text to work with."						)
  
  							# Initialise result parameters to default values
  							__snake_case		       =     0, 0
  
  							if not search_buffer:
  									return Token(_lowerCamelCase      ,_lowerCamelCase      ,text[length]						)
  
  							for i, character in enumerate(_lowerCamelCase						):
  									__snake_case		       =     len(_lowerCamelCase						) - i
  									if character == text[0]:
  											__snake_case		       =     self._match_length_from_index(_lowerCamelCase      ,_lowerCamelCase      ,0      ,_lowerCamelCase						)
  											# 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:
  													__snake_case		       =     found_offset, found_length
  
  							return Token(_lowerCamelCase      ,_lowerCamelCase      ,text[length]						)
  
  
  
  
  
  
  
  					def 							UpperCamelCase_						( self :					int      ,_lowerCAmelCase :					Union[str, Any]      ,_lowerCAmelCase :					Optional[int]      ,_lowerCAmelCase :					Any      ,_lowerCAmelCase :					Optional[int]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							if not text or text[text_index] != window[window_index]:
  									return 0
  							return 1 + self._match_length_from_index(
  							    _lowerCamelCase      ,window + text[text_index]      ,text_index + 1      ,window_index + 1						)
  
  
  if __name__ == "__main__":
  		from doctest import testmod
  
  		testmod()
  		# Initialize compressor class
  		lowerCamelCase__           =				LZaaCompressor(window_size=13, lookahead_buffer_size=6)
  
  		# Example
  		lowerCamelCase__           =				'cabracadabrarrarrad'
  		lowerCamelCase__           =				lzaa_compressor.compress(TEXT)
  		print(lzaa_compressor.compress('ababcbababaa'))
  		lowerCamelCase__           =				lzaa_compressor.decompress(compressed_text)
  		assert decompressed_text == TEXT, "The LZ77 algorithm returned the invalid result."
  
  
  
  
  
  
style_context: import os
  from shutil import copyfile
  from typing import Optional, Tuple
  
  from ...tokenization_utils_fast import PreTrainedTokenizerFast
  from ...utils import is_sentencepiece_available, logging
  
  
  if is_sentencepiece_available():
  		from .tokenization_reformer import ReformerTokenizer
  else:
  		lowerCamelCase__           =				None
  
  
  lowerCamelCase__           =				logging.get_logger(__name__)
  
  
  lowerCamelCase__           =				'▁'
  
  lowerCamelCase__           =				{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}
  
  lowerCamelCase__           =				{
      'vocab_file': {
          'google/reformer-crime-and-punishment': (
              'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'
          )
      },
      'tokenizer_file': {
          'google/reformer-crime-and-punishment': (
              'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json'
          )
      },
  }
  
  lowerCamelCase__           =				{
      'google/reformer-crime-and-punishment': 52_42_88,
  }
  
  
  
  
  class 					UpperCamelCase	(     snake_case__      ):
  					__UpperCamelCase						=   VOCAB_FILES_NAMES
  					__UpperCamelCase						=   PRETRAINED_VOCAB_FILES_MAP
  					__UpperCamelCase						=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  					__UpperCamelCase						=   ["""input_ids""", """attention_mask"""]
  					__UpperCamelCase						=   ReformerTokenizer
  
  
  
  
  					def __init__( self :					Tuple      ,_lowerCAmelCase :					Dict=None      ,_lowerCAmelCase :					Union[str, Any]=None      ,_lowerCAmelCase :					int="</s>"      ,_lowerCAmelCase :					Dict="<unk>"      ,_lowerCAmelCase :					Dict=[]      ,**_lowerCAmelCase :					Optional[Any]      ,):
  
  
  							"""simple docstring"""
  
  
  
  
  							super().__init__(
  							    _lowerCAmelCase      ,tokenizer_file=_lowerCAmelCase      ,eos_token=_lowerCAmelCase      ,unk_token=_lowerCAmelCase      ,additional_special_tokens=_lowerCAmelCase      ,**_lowerCAmelCase      ,)
  
  							__snake_case		       =     vocab_file
  							__snake_case		       =     False if not self.vocab_file else True
  
  
  
  
  
  
  
  					def 							UpperCamelCase_						( self :					Dict      ,_lowerCAmelCase :					str      ,_lowerCAmelCase :					Optional[str] = None						):
  
  
  							"""simple docstring"""
  
  
  
  
  							if not self.can_save_slow_tokenizer:
  									raise ValueError(
  									    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
  									    "tokenizer."						)
  
  							if not os.path.isdir(_lowerCAmelCase						):
  									logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""						)
  									return
  							__snake_case		       =     os.path.join(
  							    _lowerCAmelCase      ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)
  
  							if os.path.abspath(self.vocab_file						) != os.path.abspath(_lowerCAmelCase						):
  									copyfile(self.vocab_file      ,_lowerCAmelCase						)
  
  							return (out_vocab_file,)
  
  
  
  
  
  