'''simple docstring'''






from dataclasses import dataclass

_UpperCAmelCase						:    Any     					=					'''0.1'''
_UpperCAmelCase						:    Optional[int]     					=					'''Lucia Harcekova'''







@dataclass
class lowercase_					:







   """simple docstring"""

   __lowerCAmelCase    						=      42
   __lowerCAmelCase    						=      42
   __lowerCAmelCase    						=      42
   def __repr__(			self			: Union[str, Any]    )     -> str:
        return f'({self.offset}, {self.length}, {self.indicator})'







class lowercase_					:







   """simple docstring"""

   def __init__(			self			: str,   UpperCamelCase__			: int = 13,   UpperCamelCase__			: int = 6    )     -> None:
        _A								=     window_size
        _A								=     lookahead_buffer_size
        _A								=     self.window_size - self.lookahead_buffer_size
   def   __UpperCAmelCase      (			self			: Dict,   UpperCamelCase__			: str    )     -> list[Token]:
        _A								=     []
        _A								=     ''

        # 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)
             _A								=     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:
                  _A								=     search_buffer[-self.search_buffer_size :]

             # update the text
             _A								=     text[token.length + 1 :]

             # append the token to output
             output.append(lowerCAmelCase_    )

        return output
   def   __UpperCAmelCase      (			self			: Optional[int],   UpperCamelCase__			: list[Token]    )     -> str:
        _A								=     ''

        for token in tokens:
             for _ in range(token.length    ):
                  output += output[-token.offset]
             output += token.indicator

        return output
   def   __UpperCAmelCase      (			self			: Tuple,   UpperCamelCase__			: str,   UpperCamelCase__			: str    )     -> Token:






        if not text:
             raise ValueError('We need some text to work with.'    )

        # Initialise result parameters to default values
        _A		,					_A								=     0, 0

        if not search_buffer:
             return Token(lowerCAmelCase_,   lowerCAmelCase_,   text[length]    )

        for i, character in enumerate(lowerCAmelCase_    ):
             _A								=     len(lowerCAmelCase_    ) - i
             if character == text[0]:
                  _A								=     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:
                       _A		,					_A								=     found_offset, found_length

        return Token(lowerCAmelCase_,   lowerCAmelCase_,   text[length]    )

   def   __UpperCAmelCase      (			self			: Any,   UpperCamelCase__			: str,   UpperCamelCase__			: str,   UpperCamelCase__			: int,   UpperCamelCase__			: int    )     -> 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						:    int     					=					LZaaCompressor(window_size=13, lookahead_buffer_size=6)

   # Example
   _UpperCAmelCase						:    Optional[Any]     					=					'''cabracadabrarrarrad'''
   _UpperCAmelCase						:    Tuple     					=					lzaa_compressor.compress(TEXT)
   print(lzaa_compressor.compress('''ababcbababaa'''))
   _UpperCAmelCase						:    Optional[Any]     					=					lzaa_compressor.decompress(compressed_text)
   assert decompressed_text == TEXT, "The LZ77 algorithm returned the invalid result."




'''simple docstring'''






from sklearn.neural_network import MLPClassifier

_UpperCAmelCase						:    str     					=					[[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
_UpperCAmelCase						:    Any     					=					[0, 1, 0, 0]


_UpperCAmelCase						:    List[Any]     					=					MLPClassifier(
    solver='''lbfgs''', alpha=1E-5, hidden_layer_sizes=(5, 2), random_state=1
)

clf.fit(X, y)


_UpperCAmelCase						:    Optional[Any]     					=					[[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
_UpperCAmelCase						:    Tuple     					=					clf.predict(test)





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Optional[Any]							):
     return list(__snake_case							)


if __name__ == "__main__":
   import doctest

   doctest.testmod()



