code: 
  
  
  '''simple docstring'''
  
  from collections import OrderedDict
  from typing import Mapping
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfigWithPast
  from ...utils import logging
  
  
  __lowercase:		List[str]							=     logging.get_logger(__name__)
  
  __lowercase:		Any							=     {
      """uclanlp/plbart-base""": """https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json""",
      # See all PLBART models at https://huggingface.co/models?filter=plbart
  }
  
  
  
  
  
  
  
  class  UpperCAmelCase (       lowerCAmelCase_):
  	_lowerCamelCase :					Tuple    					=					"""plbart"""
  	_lowerCamelCase :					Tuple    					=					["""past_key_values"""]
  	_lowerCamelCase :					Optional[int]    					=					{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
  
  	def __init__( self :	Tuple,					a_ :	Any=5_0005,					a_ :	List[Any]=1024,					a_ :	List[str]=6,					a_ :	int=3072,					a_ :	str=12,					a_ :	Tuple=6,					a_ :	Tuple=3072,					a_ :	int=12,					a_ :	int=0.0,					a_ :	List[Any]=0.0,					a_ :	int=True,					a_ :	str=True,					a_ :	str="gelu",					a_ :	List[Any]=768,					a_ :	Optional[Any]=0.1,					a_ :	Dict=0.1,					a_ :	Any=0.0,					a_ :	Tuple=0.02,					a_ :	Tuple=0.0,					a_ :	Any=True,					a_ :	Union[str, Any]=1,					a_ :	int=0,					a_ :	int=2,					a_ :	Tuple=2,					**a_ :	Tuple,					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				UpperCamelCase__ 						=						vocab_size
  				UpperCamelCase__ 						=						max_position_embeddings
  				UpperCamelCase__ 						=						d_model
  				UpperCamelCase__ 						=						encoder_ffn_dim
  				UpperCamelCase__ 						=						encoder_layers
  				UpperCamelCase__ 						=						encoder_attention_heads
  				UpperCamelCase__ 						=						decoder_ffn_dim
  				UpperCamelCase__ 						=						decoder_layers
  				UpperCamelCase__ 						=						decoder_attention_heads
  				UpperCamelCase__ 						=						dropout
  				UpperCamelCase__ 						=						attention_dropout
  				UpperCamelCase__ 						=						activation_dropout
  				UpperCamelCase__ 						=						activation_function
  				UpperCamelCase__ 						=						init_std
  				UpperCamelCase__ 						=						encoder_layerdrop
  				UpperCamelCase__ 						=						decoder_layerdrop
  				UpperCamelCase__ 						=						classifier_dropout
  				UpperCamelCase__ 						=						use_cache
  				UpperCamelCase__ 						=						encoder_layers
  				UpperCamelCase__ 						=						scale_embedding  # scale factor will be sqrt(d_model) if True
  				super().__init__(
  				    pad_token_id=__SCREAMING_SNAKE_CASE,					bos_token_id=__SCREAMING_SNAKE_CASE,					eos_token_id=__SCREAMING_SNAKE_CASE,					is_encoder_decoder=__SCREAMING_SNAKE_CASE,					forced_eos_token_id=__SCREAMING_SNAKE_CASE,					**__SCREAMING_SNAKE_CASE,					)
  
  
  
  
  
  
  
  class  UpperCAmelCase (       lowerCAmelCase_):
  
  	@property
  	def 		lowercase_						( self :	Any					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				return OrderedDict(
  				    [
  				        ("input_ids", {0: "batch", 1: "sequence"}),
  				        ("attention_mask", {0: "batch", 1: "sequence"}),
  				    ]					)
  
  	@property
  	def 		lowercase_						( self :	Any					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				if self.use_past:
  							return OrderedDict(
  							    [
  							        ("last_hidden_state", {0: "batch", 1: "sequence"}),
  							        ("past_keys", {0: "batch", 2: "sequence"}),
  							        ("encoder_last_hidden_state", {0: "batch", 1: "sequence"}),
  							    ]					)
  				else:
  							return OrderedDict(
  							    [
  							        ("last_hidden_state", {0: "batch", 1: "sequence"}),
  							        ("encoder_last_hidden_state", {0: "batch", 1: "sequence"}),
  							    ]					)
style_context: 
  
  
  '''simple docstring'''
  
  from dataclasses import dataclass
  
  __lowercase:		List[str]							=     "0.1"
  __lowercase:		Union[str, Any]							=     "Lucia Harcekova"
  
  
  
  
  
  
  
  @dataclass
  class  UpperCAmelCase :
   _lowerCamelCase :					int
   _lowerCamelCase :					int
   _lowerCamelCase :					str
  
   def __repr__( self :	List[str]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      return f'({self.offset}, {self.length}, {self.indicator})'
  
  
  
  
  
  
  
  class  UpperCAmelCase :
  
   def __init__( self :	Optional[int],					a_ :	int = 13,					a_ :	int = 6					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						window_size
      UpperCamelCase__ 						=						lookahead_buffer_size
      UpperCamelCase__ 						=						self.window_size - self.lookahead_buffer_size
  
   def 		lowercase_						( self :	Optional[int],					a_ :	str					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						[]
      UpperCamelCase__ 						=						""
  
      # 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__ 						=						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__ 						=						search_buffer[-self.search_buffer_size :]
  
         # update the text
         UpperCamelCase__ 						=						text[token.length + 1 :]
  
         # append the token to output
         output.append(a_					)
  
      return output
  
   def 		lowercase_						( self :	List[Any],					a_ :	list[Token]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						""
  
      for token in tokens:
         for _ in range(token.length					):
            output += output[-token.offset]
         output += token.indicator
  
      return output
  
   def 		lowercase_						( self :	Any,					a_ :	str,					a_ :	str					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      if not text:
         raise ValueError("We need some text to work with."					)
  
      # Initialise result parameters to default values
      UpperCamelCase__						,		UpperCamelCase__ 						=						0, 0
  
      if not search_buffer:
         return Token(a_,					a_,					text[length]					)
  
      for i, character in enumerate(a_					):
         UpperCamelCase__ 						=						len(a_					) - i
         if character == text[0]:
            UpperCamelCase__ 						=						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__ 						=						found_offset, found_length
  
      return Token(a_,					a_,					text[length]					)
  
   def 		lowercase_						( self :	Optional[int],					a_ :	str,					a_ :	str,					a_ :	int,					a_ :	int					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      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
         __lowercase:		Optional[Any]							=     LZaaCompressor(window_size=13, lookahead_buffer_size=6)
  
         # Example
         __lowercase:		Union[str, Any]							=     "cabracadabrarrarrad"
         __lowercase:		Optional[int]							=     lzaa_compressor.compress(TEXT)
         print(lzaa_compressor.compress("ababcbababaa"))
         __lowercase:		Optional[int]							=     lzaa_compressor.decompress(compressed_text)
         assert decompressed_text == TEXT, "The LZ77 algorithm returned the invalid result."