






"""simple docstring"""


from dataclasses import dataclass

_UpperCamelCase   :						Dict     						= """0.1"""
_UpperCamelCase   :						Tuple     						= """Lucia Harcekova"""






@dataclass
class __a   :
  __lowercase    :    int
  __lowercase    :    int
  __lowercase    :    str



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






class __a   :



  def __init__(self  :		Tuple				,			lowercase_  :		int = 13				,			lowercase_  :		int = 6   )		->							None:
   __A	:       List[Any]       =	window_size
   __A	:       int       =	lookahead_buffer_size
   __A	:       List[Any]       =	self.window_size - self.lookahead_buffer_size



  def 						A_				(self  :		int				,			lowercase_  :		str   )		->							list[Token]:
   __A	:       Dict       =	[]
   __A	:       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)
    __A	:       Tuple       =	self._find_encoding_token(lowercase_				,			lowercase_   )

    # 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(lowercase_   ) > self.search_buffer_size:
     __A	:       int       =	search_buffer[-self.search_buffer_size :]

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

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

   return output



  def 						A_				(self  :		Optional[Any]				,			lowercase_  :		list[Token]   )		->							str:
   __A	:       Tuple       =	''

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

   return output



  def 						A_				(self  :		List[Any]				,			lowercase_  :		str				,			lowercase_  :		str   )		->							Token:

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

   # Initialise result parameters to default values
   __A	:       Tuple       =	0, 0

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

   for i, character in enumerate(lowercase_   ):
    __A	:       int       =	len(lowercase_   ) - i
    if character == text[0]:
     __A	:       Tuple       =	self._match_length_from_index(lowercase_				,			lowercase_				,			0				,			lowercase_   )
     # 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	:       str       =	found_offset, found_length

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



  def 						A_				(self  :		Optional[Any]				,			lowercase_  :		str				,			lowercase_  :		str				,			lowercase_  :		int				,			lowercase_  :		int   )		->							int:
   if not text or text[text_index] != window[window_index]:
    return 0
   return 1 + self._match_length_from_index(
       lowercase_				,			window + text[text_index]				,			text_index + 1				,			window_index + 1   )


if __name__ == "__main__":
   from doctest import testmod

   testmod()
   # Initialize compressor class
   _UpperCamelCase   :						Dict     						= LZaaCompressor(window_size=13, lookahead_buffer_size=6)

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













"""simple docstring"""


from string import ascii_uppercase




def 				a (     UpperCamelCase_							:				str     ,	UpperCamelCase_							:				str     ,	UpperCamelCase_							:				bool = False     ,	UpperCamelCase_							:				str = ascii_uppercase		)  ->		str:
 __A	:       List[str]       =	keyword.upper()
 __A	:       str       =	plaintext.upper()
 __A	:       int       =	set(UpperCamelCase_		)

 # create a list of unique characters in the keyword - their order matters
 # it determines how we will map plaintext characters to the ciphertext
 __A	:       int       =	[]
 for char in keyword:
  if char in alphabet_set and char not in unique_chars:
   unique_chars.append(UpperCamelCase_		)
    # the number of those unique characters will determine the number of rows
 __A	:       Any       =	len(UpperCamelCase_		)

 # create a shifted version of the alphabet
 __A	:       str       =	unique_chars + [
     char for char in alphabet if char not in unique_chars
 ]

 # create a modified alphabet by splitting the shifted alphabet into rows
 __A	:       int       =	[
     shifted_alphabet[k : k + num_unique_chars_in_keyword]
     for k in range(0     ,	2_6     ,	UpperCamelCase_		)
 ]

 # map the alphabet characters to the modified alphabet characters
 # going 'vertically' through the modified alphabet - consider columns first
 __A	:       Any       =	{}
 __A	:       Union[str, Any]       =	0
 for column in range(UpperCamelCase_		):
  for row in modified_alphabet:
   # if current row (the last one) is too short, break out of loop
   if len(UpperCamelCase_		) <= column:
    break

   # map current letter to letter in modified alphabet
   __A	:       str       =	row[column]
   letter_index += 1

 if verbose:
  print(UpperCamelCase_		)
 # create the encrypted text by mapping the plaintext to the modified alphabet
 return "".join(mapping[char] if char in mapping else char for char in plaintext		)


if __name__ == "__main__":
   # example use
   print(mixed_keyword("""college""", """UNIVERSITY"""))





