code: 
  
  """simple docstring"""
  from dataclasses import dataclass
  
  _A						=     """0.1"""
  _A						=     """Lucia Harcekova"""
  
  @dataclass
  class 			lowerCamelCase  :
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    SCREAMING_SNAKE_CASE							=     42
    SCREAMING_SNAKE_CASE							=     42
    SCREAMING_SNAKE_CASE							=     42
  
  
  
    def __repr__(self  ):
  
  
          """simple docstring"""
  
          return F"""({self.offset}, {self.length}, {self.indicator})"""
  
  
  
  
  class 			lowerCamelCase  :
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    def __init__(self       ,					_lowerCamelCase = 13       ,					_lowerCamelCase = 6  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Optional[int]						=						window_size
          UpperCAmelCase__ :       Optional[Any]						=						lookahead_buffer_size
          UpperCAmelCase__ :       Tuple						=						self.window_size - self.lookahead_buffer_size
  
  
  
    def    _a			(self       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       int						=						[]
          UpperCAmelCase__ :       Union[str, 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__ :       Union[str, Any]						=						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__ :       Union[str, Any]						=						search_buffer[-self.search_buffer_size :]
  
                # update the text
                UpperCAmelCase__ :       Union[str, Any]						=						text[token.length + 1 :]
  
                # append the token to output
                output.append(_lowerCamelCase  )
  
          return output
  
  
  
    def    _a			(self       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Tuple						=						""""""
  
          for token in tokens:
                for _ in range(token.length  ):
                      output += output[-token.offset]
                output += token.indicator
  
          return output
  
  
  
    def    _a			(self       ,					_lowerCamelCase       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          if not text:
                raise ValueError("""We need some text to work with."""  )
  
          # Initialise result parameters to default values
          UpperCAmelCase__				,		UpperCAmelCase__ :       str						=						0, 0
  
          if not search_buffer:
                return Token(_lowerCamelCase       ,					_lowerCamelCase       ,					text[length]  )
  
          for i, character in enumerate(_lowerCamelCase  ):
                UpperCAmelCase__ :       List[Any]						=						len(_lowerCamelCase  ) - i
                if character == text[0]:
                      UpperCAmelCase__ :       Tuple						=						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__ :       Union[str, Any]						=						found_offset, found_length
  
          return Token(_lowerCamelCase       ,					_lowerCamelCase       ,					text[length]  )
  
  
  
  
    def    _a			(self       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase       ,					_lowerCamelCase  ):
  
  
          """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
      _A						=     LZaaCompressor(window_size=13, lookahead_buffer_size=6)
  
      # Example
      _A						=     """cabracadabrarrarrad"""
      _A						=     lzaa_compressor.compress(TEXT)
      print(lzaa_compressor.compress("""ababcbababaa"""))
      _A						=     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_keras_nlp_available,
      is_tensorflow_text_available,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  _A						=     {
      """configuration_gpt2""": ["""GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPT2Config""", """GPT2OnnxConfig"""],
      """tokenization_gpt2""": ["""GPT2Tokenizer"""],
  }
  
  try:
      if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      _A						=     ["""GPT2TokenizerFast"""]
  
  try:
      if not is_torch_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      _A						=     [
          """GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """GPT2DoubleHeadsModel""",
          """GPT2ForQuestionAnswering""",
          """GPT2ForSequenceClassification""",
          """GPT2ForTokenClassification""",
          """GPT2LMHeadModel""",
          """GPT2Model""",
          """GPT2PreTrainedModel""",
          """load_tf_weights_in_gpt2""",
      ]
  
  try:
      if not is_tf_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      _A						=     [
          """TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """TFGPT2DoubleHeadsModel""",
          """TFGPT2ForSequenceClassification""",
          """TFGPT2LMHeadModel""",
          """TFGPT2MainLayer""",
          """TFGPT2Model""",
          """TFGPT2PreTrainedModel""",
      ]
  
  try:
      if not is_keras_nlp_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      _A						=     ["""TFGPT2Tokenizer"""]
  
  try:
      if not is_flax_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      _A						=     ["""FlaxGPT2LMHeadModel""", """FlaxGPT2Model""", """FlaxGPT2PreTrainedModel"""]
  
  if TYPE_CHECKING:
      from .configuration_gpta import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTaConfig, GPTaOnnxConfig
      from .tokenization_gpta import GPTaTokenizer
  
      try:
          if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .tokenization_gpta_fast import GPTaTokenizerFast
  
      try:
          if not is_torch_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .modeling_gpta import (
              GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
              GPTaDoubleHeadsModel,
              GPTaForQuestionAnswering,
              GPTaForSequenceClassification,
              GPTaForTokenClassification,
              GPTaLMHeadModel,
              GPTaModel,
              GPTaPreTrainedModel,
              load_tf_weights_in_gpta,
          )
  
      try:
          if not is_tf_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .modeling_tf_gpta import (
              TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFGPTaDoubleHeadsModel,
              TFGPTaForSequenceClassification,
              TFGPTaLMHeadModel,
              TFGPTaMainLayer,
              TFGPTaModel,
              TFGPTaPreTrainedModel,
          )
  
      try:
          if not is_keras_nlp_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .tokenization_gpta_tf import TFGPTaTokenizer
  
      try:
          if not is_flax_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel, FlaxGPTaPreTrainedModel
  
  else:
      import sys
  
      _A						=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
  