code: 
  '''simple docstring'''
  
  from dataclasses import dataclass
  
  _UpperCAmelCase   :      List[str] 						=  '''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       : str    =      window_size
  				_UpperCamelCase       : Tuple    =      lookahead_buffer_size
  				_UpperCamelCase       : Tuple    =      self.window_size - self.lookahead_buffer_size
  		def _lowercase  (    self  ,					_snake_case     )				->    list[Token]:
  				_UpperCamelCase       : Any    =      []
  				_UpperCamelCase       : List[str]    =      ''''''
  
  				# 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       : Any    =      self._find_encoding_token(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE     )
  
  						# 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(__SCREAMING_SNAKE_CASE     ) > self.search_buffer_size:
  								_UpperCamelCase       : Optional[int]    =      search_buffer[-self.search_buffer_size :]
  
  						# update the text
  						_UpperCamelCase       : Any    =      text[token.length + 1 :]
  
  						# append the token to output
  						output.append(__SCREAMING_SNAKE_CASE     )
  
  				return output
  		def _lowercase  (    self  ,					_snake_case     )				->    str:
  				_UpperCamelCase       : Union[str, Any]    =      ''''''
  
  				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       : Any    =      0, 0
  
  				if not search_buffer:
  						return Token(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					text[length]     )
  
  				for i, character in enumerate(__SCREAMING_SNAKE_CASE     ):
  						_UpperCamelCase       : Dict    =      len(__SCREAMING_SNAKE_CASE     ) - i
  						if character == text[0]:
  								_UpperCamelCase       : List[Any]    =      self._match_length_from_index(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					0  ,					__SCREAMING_SNAKE_CASE     )
  								# 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       : str    =      found_offset, found_length
  
  				return Token(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE  ,					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(
  				    __SCREAMING_SNAKE_CASE  ,					window + text[text_index]  ,					text_index + 1  ,					window_index + 1     )
  
  
  if __name__ == "__main__":
  	from doctest import testmod
  
  	testmod()
  	# Initialize compressor class
  	_UpperCAmelCase   :      Optional[int] 						=  LZaaCompressor(window_size=13, lookahead_buffer_size=6)
  
  	# Example
  	_UpperCAmelCase   :      List[str] 						=  '''cabracadabrarrarrad'''
  	_UpperCAmelCase   :      List[str] 						=  lzaa_compressor.compress(TEXT)
  	print(lzaa_compressor.compress("""ababcbababaa"""))
  	_UpperCAmelCase   :      List[Any] 						=  lzaa_compressor.decompress(compressed_text)
  	assert decompressed_text == TEXT, "The LZ77 algorithm returned the invalid result."
  
  
style_context: 
  '''simple docstring'''
  
  from collections import OrderedDict
  from typing import Mapping
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig
  from ...utils import logging
  
  
  _UpperCAmelCase   :      List[Any] 						=  logging.get_logger(__name__)
  
  _UpperCAmelCase   :      List[Any] 						=  {
      """nghuyong/ernie-1.0-base-zh""": """https://huggingface.co/nghuyong/ernie-1.0-base-zh/resolve/main/config.json""",
      """nghuyong/ernie-2.0-base-en""": """https://huggingface.co/nghuyong/ernie-2.0-base-en/resolve/main/config.json""",
      """nghuyong/ernie-2.0-large-en""": """https://huggingface.co/nghuyong/ernie-2.0-large-en/resolve/main/config.json""",
      """nghuyong/ernie-3.0-base-zh""": """https://huggingface.co/nghuyong/ernie-3.0-base-zh/resolve/main/config.json""",
      """nghuyong/ernie-3.0-medium-zh""": """https://huggingface.co/nghuyong/ernie-3.0-medium-zh/resolve/main/config.json""",
      """nghuyong/ernie-3.0-mini-zh""": """https://huggingface.co/nghuyong/ernie-3.0-mini-zh/resolve/main/config.json""",
      """nghuyong/ernie-3.0-micro-zh""": """https://huggingface.co/nghuyong/ernie-3.0-micro-zh/resolve/main/config.json""",
      """nghuyong/ernie-3.0-nano-zh""": """https://huggingface.co/nghuyong/ernie-3.0-nano-zh/resolve/main/config.json""",
      """nghuyong/ernie-gram-zh""": """https://huggingface.co/nghuyong/ernie-gram-zh/resolve/main/config.json""",
      """nghuyong/ernie-health-zh""": """https://huggingface.co/nghuyong/ernie-health-zh/resolve/main/config.json""",
  }
  
  class 			UpperCAmelCase				(					a_ ):
  
  
  
  
  
  		"""simple docstring"""
  
  
  
  
  
  		A__  :   str          =    'ernie'
  		def __init__(    self  ,					_snake_case=30522  ,					_snake_case=768  ,					_snake_case=12  ,					_snake_case=12  ,					_snake_case=3072  ,					_snake_case="gelu"  ,					_snake_case=0.1  ,					_snake_case=0.1  ,					_snake_case=512  ,					_snake_case=2  ,					_snake_case=3  ,					_snake_case=False  ,					_snake_case=0.02  ,					_snake_case=1E-12  ,					_snake_case=0  ,					_snake_case="absolute"  ,					_snake_case=True  ,					_snake_case=None  ,					**_snake_case  ,					)				->    List[Any]:
  				super().__init__(pad_token_id=_snake_case  ,					**_snake_case     )
  
  				_UpperCamelCase       : Dict    =      vocab_size
  				_UpperCamelCase       : int    =      hidden_size
  				_UpperCamelCase       : List[Any]    =      num_hidden_layers
  				_UpperCamelCase       : Tuple    =      num_attention_heads
  				_UpperCamelCase       : List[str]    =      hidden_act
  				_UpperCamelCase       : Optional[int]    =      intermediate_size
  				_UpperCamelCase       : Tuple    =      hidden_dropout_prob
  				_UpperCamelCase       : Optional[int]    =      attention_probs_dropout_prob
  				_UpperCamelCase       : List[str]    =      max_position_embeddings
  				_UpperCamelCase       : Tuple    =      type_vocab_size
  				_UpperCamelCase       : Union[str, Any]    =      task_type_vocab_size
  				_UpperCamelCase       : Union[str, Any]    =      use_task_id
  				_UpperCamelCase       : Any    =      initializer_range
  				_UpperCamelCase       : Optional[int]    =      layer_norm_eps
  				_UpperCamelCase       : str    =      position_embedding_type
  				_UpperCamelCase       : Any    =      use_cache
  				_UpperCamelCase       : str    =      classifier_dropout
  
  
  
  
  
  
  
  class 			UpperCAmelCase				(					a_ ):
  
  
  
  
  
  		"""simple docstring"""
  
  
  
  
  
  		@property
  		def _lowercase  (    self     )				->    Mapping[str, Mapping[int, str]]:
  				if self.task == "multiple-choice":
  						_UpperCamelCase       : str    =      {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
  				else:
  						_UpperCamelCase       : Dict    =      {0: '''batch''', 1: '''sequence'''}
  				return OrderedDict(
  				    [
  				        ('''input_ids''', dynamic_axis),
  				        ('''attention_mask''', dynamic_axis),
  				        ('''token_type_ids''', dynamic_axis),
  				        ('''task_type_ids''', dynamic_axis),
  				    ]     )
  
  
  
  
  
  