code: 
  
  
  '''simple docstring'''
  
  from typing import Any
  
  import numpy as np
  
  
  
  
  
  
  
  class  UpperCAmelCase :
  
  	def __init__( self :	Optional[Any],					a_ :	np.ndarray,					a_ :	int					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				if np.any(tableau[:, -1],					where=tableau[:, -1] < 0					):
  							raise ValueError("RHS must be > 0"					)
  
  				UpperCamelCase__ 						=						tableau
  				UpperCamelCase__						,		UpperCamelCase__ 						=						tableau.shape
  
  				# Number of decision variables x1, x2, x3...
  				UpperCamelCase__ 						=						n_vars
  
  				# Number of artificial variables to be minimised
  				UpperCamelCase__ 						=						len(np.where(tableau[self.n_vars : -1] == -1					)[0]					)
  
  				# 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
  				UpperCamelCase__ 						=						(self.n_art_vars > 0) + 1
  
  				# Number of slack variables added to make inequalities into equalities
  				UpperCamelCase__ 						=						self.n_rows - self.n_stages
  
  				# Objectives for each stage
  				UpperCamelCase__ 						=						["max"]
  
  				# In two stage simplex, first minimise then maximise
  				if self.n_art_vars:
  							self.objectives.append("min"					)
  
  				UpperCamelCase__ 						=						[""]
  
  				# Index of current pivot row and column
  				UpperCamelCase__ 						=						None
  				UpperCamelCase__ 						=						None
  
  				# Does objective row only contain (non)-negative values?
  				UpperCamelCase__ 						=						False
  
  	@staticmethod
  	def 		lowercase_						( *a_ :	int					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				if len(a_					) != 3:
  							raise ValueError("Must provide n_vars, n_slack, and n_art_vars"					)
  
  				if not all(x >= 0 and isinstance(a_,					a_					) for x in args					):
  							raise ValueError("All arguments must be non-negative integers"					)
  
  				# decision | slack | artificial
  				UpperCamelCase__ 						=						["x", "s", "a"]
  				UpperCamelCase__ 						=						[]
  				for i in range(3					):
  							for j in range(args[i]					):
  										titles.append(string_starts[i] + str(j + 1					)					)
  				titles.append("RHS"					)
  				return titles
  
  	def 		lowercase_						( self :	int,					a_ :	np.ndarray					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				UpperCamelCase__ 						=						self.objectives[-1]
  
  				# Find entries of highest magnitude in objective rows
  				UpperCamelCase__ 						=						(objective == "min") - (objective == "max")
  				UpperCamelCase__ 						=						np.argmax(sign * tableau[0, : self.n_vars]					)
  
  				# Choice is only valid if below 0 for maximise, and above for minimise
  				if sign * self.tableau[0, col_idx] <= 0:
  							UpperCamelCase__ 						=						True
  							return 0, 0
  
  				# Pivot row is chosen as having the lowest quotient when elements of
  				# the pivot column divide the right-hand side
  
  				# Slice excluding the objective rows
  				UpperCamelCase__ 						=						slice(self.n_stages,					self.n_rows					)
  
  				# RHS
  				UpperCamelCase__ 						=						tableau[s, -1]
  
  				# Elements of pivot column within slice
  				UpperCamelCase__ 						=						tableau[s, col_idx]
  
  				# Array filled with nans
  				UpperCamelCase__ 						=						np.full(self.n_rows - self.n_stages,					np.nan					)
  
  				# If element in pivot column is greater than zeron_stages, return
  				# quotient or nan otherwise
  				UpperCamelCase__ 						=						np.divide(a_,					a_,					out=a_,					where=divisor > 0					)
  
  				# Arg of minimum quotient excluding the nan values. n_stages is added
  				# to compensate for earlier exclusion of objective columns
  				UpperCamelCase__ 						=						np.nanargmin(a_					) + self.n_stages
  				return row_idx, col_idx
  
  	def 		lowercase_						( self :	List[Any],					a_ :	np.ndarray,					a_ :	int,					a_ :	int					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				UpperCamelCase__ 						=						tableau[row_idx].copy()
  
  				UpperCamelCase__ 						=						piv_row[col_idx]
  
  				# Entry becomes 1
  				piv_row *= 1 / piv_val
  
  				# Variable in pivot column becomes basic, ie the only non-zero entry
  				for idx, coeff in enumerate(tableau[:, col_idx]					):
  							tableau[idx] += -coeff * piv_row
  				UpperCamelCase__ 						=						piv_row
  				return tableau
  
  	def 		lowercase_						( self :	Dict,					a_ :	np.ndarray					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				self.objectives.pop()
  
  				if not self.objectives:
  							return tableau
  
  				# Slice containing ids for artificial columns
  				UpperCamelCase__ 						=						slice(-self.n_art_vars - 1,					-1					)
  
  				# Delete the artificial variable columns
  				UpperCamelCase__ 						=						np.delete(a_,					a_,					axis=1					)
  
  				# Delete the objective row of the first stage
  				UpperCamelCase__ 						=						np.delete(a_,					0,					axis=0					)
  
  				UpperCamelCase__ 						=						1
  				self.n_rows -= 1
  				UpperCamelCase__ 						=						0
  				UpperCamelCase__ 						=						False
  				return tableau
  
  	def 		lowercase_						( self :	Optional[int]					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				for _ in range(100					):
  							# Completion of each stage removes an objective. If both stages
  							# are complete, then no objectives are left
  							if not self.objectives:
  										UpperCamelCase__ 						=						self.generate_col_titles(
  										    self.n_vars,					self.n_slack,					self.n_art_vars					)
  
  										# Find the values of each variable at optimal solution
  										return self.interpret_tableau(self.tableau,					self.col_titles					)
  
  							UpperCamelCase__						,		UpperCamelCase__ 						=						self.find_pivot(self.tableau					)
  
  							# If there are no more negative values in objective row
  							if self.stop_iter:
  										# Delete artificial variable columns and rows. Update attributes
  										UpperCamelCase__ 						=						self.change_stage(self.tableau					)
  							else:
  										UpperCamelCase__ 						=						self.pivot(self.tableau,					a_,					a_					)
  				return {}
  
  	def 		lowercase_						( self :	List[Any],					a_ :	np.ndarray,					a_ :	list[str]					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				UpperCamelCase__ 						=						{"P": abs(tableau[0, -1]					)}
  
  				for i in range(self.n_vars					):
  							# Gives ids of nonzero entries in the ith column
  							UpperCamelCase__ 						=						np.nonzero(tableau[:, i]					)
  							UpperCamelCase__ 						=						len(nonzero[0]					)
  
  							# First entry in the nonzero ids
  							UpperCamelCase__ 						=						nonzero[0][0]
  							UpperCamelCase__ 						=						tableau[nonzero_rowidx, i]
  
  							# If there is only one nonzero value in column, which is one
  							if n_nonzero == nonzero_val == 1:
  										UpperCamelCase__ 						=						tableau[nonzero_rowidx, -1]
  										UpperCamelCase__ 						=						rhs_val
  
  							 # Check for basic variables
  				for title in col_titles:
  							# Don't add RHS or slack variables to output dict
  							if title[0] not in "R-s-a":
  										output_dict.setdefault(a_,					0					)
  				return output_dict
  
  
  if __name__ == "__main__":
  							import doctest
  
  							doctest.testmod()
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."