code: 
  '''simple docstring'''
  
  from dataclasses import dataclass
  
  _UpperCAmelCase   :      Tuple 						=  """0.1"""
  _UpperCAmelCase   :      Dict 						=  """Lucia Harcekova"""
  
  @dataclass
  class 			UpperCAmelCase				:
  
  
  
  
  
  		"""simple docstring"""
  
  
  
  
  
  		A__  :   int
  		A__  :   int
  		A__  :   str
  		def __repr__(    self     )				->    str:
  				return F'''({self.offset}, {self.length}, {self.indicator})'''
  
  
  
  
  
  
  
  class 			UpperCAmelCase				:
  
  
  
  
  
  		"""simple docstring"""
  
  
  
  
  
  		def __init__(    self  ,					_snake_case = 13  ,					_snake_case = 6     )				->    None:
  				_UpperCamelCase       : List[str]    =      window_size
  				_UpperCamelCase       : str    =      lookahead_buffer_size
  				_UpperCamelCase       : Any    =      self.window_size - self.lookahead_buffer_size
  		def _lowercase  (    self  ,					_snake_case     )				->    list[Token]:
  				_UpperCamelCase       : Dict    =      []
  				_UpperCamelCase       : 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       : Optional[int]    =      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:
  								_UpperCamelCase       : Optional[int]    =      search_buffer[-self.search_buffer_size :]
  
  						# update the text
  						_UpperCamelCase       : Optional[Any]    =      text[token.length + 1 :]
  
  						# append the token to output
  						output.append(lowerCAmelCase_     )
  
  				return output
  		def _lowercase  (    self  ,					_snake_case     )				->    str:
  				_UpperCamelCase       : Optional[int]    =      ''''''
  
  				for token in tokens:
  						for _ in range(token.length     ):
  								output += output[-token.offset]
  						output += token.indicator
  
  				return output
  		def _lowercase  (    self  ,					_snake_case  ,					_snake_case     )				->    Token:
  
  
  
  
  
  
  				if not text:
  						raise ValueError('''We need some text to work with.'''     )
  
  				# Initialise result parameters to default values
  				_UpperCamelCase,							_UpperCamelCase       : List[str]    =      0, 0
  
  				if not search_buffer:
  						return Token(lowerCAmelCase_  ,					lowerCAmelCase_  ,					text[length]     )
  
  				for i, character in enumerate(lowerCAmelCase_     ):
  						_UpperCamelCase       : List[str]    =      len(lowerCAmelCase_     ) - i
  						if character == text[0]:
  								_UpperCamelCase       : str    =      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:
  										_UpperCamelCase,							_UpperCamelCase       : Any    =      found_offset, found_length
  
  				return Token(lowerCAmelCase_  ,					lowerCAmelCase_  ,					text[length]     )
  
  
  
  
  		def _lowercase  (    self  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case     )				->    int:
  
  
  
  
  
  
  				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
  	_UpperCAmelCase   :      List[str] 						=  LZaaCompressor(window_size=13, lookahead_buffer_size=6)
  
  	# Example
  	_UpperCAmelCase   :      Any 						=  """cabracadabrarrarrad"""
  	_UpperCAmelCase   :      Optional[int] 						=  lzaa_compressor.compress(TEXT)
  	print(lzaa_compressor.compress("""ababcbababaa"""))
  	_UpperCAmelCase   :      Tuple 						=  lzaa_compressor.decompress(compressed_text)
  	assert decompressed_text == TEXT, "The LZ77 algorithm returned the invalid result."
  
  
  
  
style_context: 
  '''simple docstring'''
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_tf_available,
      is_torch_available,
  )
  
  
  _UpperCAmelCase   :      int 						=  {
      """configuration_gptsan_japanese""": ["""GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTSanJapaneseConfig"""],
      """tokenization_gptsan_japanese""": ["""GPTSanJapaneseTokenizer"""],
  }
  
  try:
  	if not is_torch_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	_UpperCAmelCase   :      int 						=  [
  	    """GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST""",
  	    """GPTSanJapaneseForConditionalGeneration""",
  	    """GPTSanJapaneseModel""",
  	    """GPTSanJapanesePreTrainedModel""",
  	]
  	_UpperCAmelCase   :      str 						=  [
  	    """GPTSanJapaneseTokenizer""",
  	]
  
  
  if TYPE_CHECKING:
  	from .configuration_gptsan_japanese import GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTSanJapaneseConfig
  	from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer
  
  	try:
  		if not is_torch_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .modeling_gptsan_japanese import (
  		    GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
  		    GPTSanJapaneseForConditionalGeneration,
  		    GPTSanJapaneseModel,
  		    GPTSanJapanesePreTrainedModel,
  		)
  		from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer
  
  
  else:
  	import sys
  
  	_UpperCAmelCase   :      List[str] 						=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
  
  
  
  
  