'''simple docstring'''




import math
import sys

def 	UpperCamelCase_							(					_UpperCAmelCase  :      str    )    ->    str:


       """simple docstring"""







       _UpperCAmelCase     :  Any							      =    ""
       try:
              with open(_UpperCAmelCase			,	"rb"    ) as binary_file:
                     _UpperCAmelCase     :  Union[str, Any]							      =    binary_file.read()
              for dat in data:
                     _UpperCAmelCase     :  int							      =    F"""{dat:08b}"""
                     result += curr_byte
              return result
       except OSError:
              print("File not accessible"    )
              sys.exit()

def 	UpperCamelCase_							(					_UpperCAmelCase  :      str    )    ->    str:


       """simple docstring"""







       _UpperCAmelCase     :  Optional[Any]							      =    {"0": "0", "1": "1"}
       _UpperCAmelCase      ,  _UpperCAmelCase     :  str							      =    "", ""
       _UpperCAmelCase     :  Any							      =    len(_UpperCAmelCase    )

       for i in range(len(_UpperCAmelCase    )    ):
              curr_string += data_bits[i]
              if curr_string not in lexicon:
                     continue

              _UpperCAmelCase     :  List[Any]							      =    lexicon[curr_string]
              result += last_match_id
              _UpperCAmelCase     :  Tuple							      =    last_match_id + "0"

              if math.loga(_UpperCAmelCase    ).is_integer():
                     _UpperCAmelCase     :  List[Any]							      =    {}
                     for curr_key in list(_UpperCAmelCase    ):
                            _UpperCAmelCase     :  Any							      =    lexicon.pop(_UpperCAmelCase    )
                     _UpperCAmelCase     :  int							      =    new_lex

              _UpperCAmelCase     :  Dict							      =    last_match_id + "1"
              index += 1
              _UpperCAmelCase     :  Tuple							      =    ""
       return result

def 	UpperCamelCase_							(					_UpperCAmelCase  :      str			,	_UpperCAmelCase  :      str    )    ->    None:


       """simple docstring"""







       _UpperCAmelCase     :  Tuple							      =    8
       try:
              with open(_UpperCAmelCase			,	"wb"    ) as opened_file:
                     _UpperCAmelCase     :  str							      =    [
                         to_write[i : i + byte_length]
                         for i in range(0			,	len(_UpperCAmelCase    )			,	_UpperCAmelCase    )
                     ]

                     if len(result_byte_array[-1]    ) % byte_length == 0:
                            result_byte_array.append("10000000"    )
                     else:
                            result_byte_array[-1] += "1" + "0" * (
                                byte_length - len(result_byte_array[-1]    ) - 1
                            )

                     for elem in result_byte_array[:-1]:
                            opened_file.write(int(_UpperCAmelCase			,	2    ).to_bytes(1			,	byteorder="big"    )    )
       except OSError:
              print("File not accessible"    )
              sys.exit()

def 	UpperCamelCase_							(					_UpperCAmelCase  :      str    )    ->    str:


       """simple docstring"""







       _UpperCAmelCase     :  Tuple							      =    0
       for letter in data_bits:
              if letter == "1":
                     break
              counter += 1

       _UpperCAmelCase     :  List[Any]							      =    data_bits[counter:]
       _UpperCAmelCase     :  Union[str, Any]							      =    data_bits[counter + 1 :]
       return data_bits

def 	UpperCamelCase_							(					_UpperCAmelCase  :      str			,	_UpperCAmelCase  :      str    )    ->    None:


       """simple docstring"""







       _UpperCAmelCase     :  Optional[int]							      =    read_file_binary(_UpperCAmelCase    )
       _UpperCAmelCase     :  List[Any]							      =    remove_prefix(_UpperCAmelCase    )
       _UpperCAmelCase     :  Tuple							      =    decompress_data(_UpperCAmelCase    )
       write_file_binary(_UpperCAmelCase			,	_UpperCAmelCase    )


if __name__ == "__main__":
  compress(sys.argv[1], sys.argv[2])


"""simple docstring"""






import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize



def   a_						(					lowerCamelCase       ):
				return np.dot(lowerCamelCase					,			lowerCamelCase       )
class 				snake_case       :




					"""simple docstring"""

					def __init__(				self   :	int    ,*,
					    lowerCamelCase__   :	float = np.inf    ,lowerCamelCase__   :	str = "linear"    ,lowerCamelCase__   :	float = 0.0    ,):
									UpperCAmelCase__             =							regularization
									UpperCAmelCase__             =							gamma
									if kernel == "linear":
													UpperCAmelCase__             =							self.__linear
									elif kernel == "rbf":
													if self.gamma == 0:
																	raise ValueError('rbf kernel requires gamma'			)
													if not isinstance(self.gamma    ,(float, int)			):
																	raise ValueError('gamma must be float or int'			)
													if not self.gamma > 0:
																	raise ValueError('gamma must be > 0'			)
													UpperCAmelCase__             =							self.__rbf
													# in the future, there could be a default value like in sklearn
													# sklear: def_gamma = 1/(n_features * X.var()) (wiki)
													# previously it was 1/(n_features)
									else:
													UpperCAmelCase__             =							f'''Unknown kernel: {kernel}'''
													raise ValueError(lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	List[Any]    ,lowerCamelCase__   :	ndarray    ,lowerCamelCase__   :	ndarray			):
									return np.dot(lowerCamelCase__    ,lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	Any    ,lowerCamelCase__   :	ndarray    ,lowerCamelCase__   :	ndarray			):
									return np.exp(-(self.gamma * norm_squared(vectora - vectora			))			)







					def 		__lowerCAmelCase						(				self   :	Tuple    ,lowerCamelCase__   :	list[ndarray]    ,lowerCamelCase__   :	ndarray			):
									UpperCAmelCase__             =							observations
									UpperCAmelCase__             =							classes

									# using Wolfe's Dual to calculate w.
									# Primal problem: minimize 1/2*norm_squared(w)
									#   constraint: yn(w . xn + b) >= 1
									#
									# With l a vector
									# Dual problem: maximize sum_n(ln) -
									#       1/2 * sum_n(sum_m(ln*lm*yn*ym*xn . xm))
									#   constraint: self.C >= ln >= 0
									#           and sum_n(ln*yn) = 0
									# Then we get w using w = sum_n(ln*yn*xn)
									# At the end we can get b ~= mean(yn - w . xn)
									#
									# Since we use kernels, we only need l_star to calculate b
									# and to classify observations

									((UpperCAmelCase__)      ,	)             =							np.shape(lowerCamelCase__			)

									def to_minimize(lowerCamelCase__   :	ndarray			) -> float:
													UpperCAmelCase__             =							0
													((UpperCAmelCase__)      ,	)             =							np.shape(lowerCamelCase__			)
													for i in range(lowerCamelCase__			):
																	for j in range(lowerCamelCase__			):
																					s += (
																					    candidate[i]
																					    * candidate[j]
																					    * classes[i]
																					    * classes[j]
																					    * self.kernel(observations[i]    ,observations[j]			)
																					)
													return 1 / 2 * s - sum(lowerCamelCase__			)

									UpperCAmelCase__             =							LinearConstraint(lowerCamelCase__    ,0    ,0			)
									UpperCAmelCase__             =							Bounds(0    ,self.regularization			)

									UpperCAmelCase__             =							minimize(
									    lowerCamelCase__    ,np.ones(lowerCamelCase__			)    ,bounds=lowerCamelCase__    ,constraints=[ly_contraint]			).x
									UpperCAmelCase__             =							l_star

									# calculating mean offset of separation plane to points
									UpperCAmelCase__             =							0
									for i in range(lowerCamelCase__			):
													for j in range(lowerCamelCase__			):
																	s += classes[i] - classes[i] * self.optimum[i] * self.kernel(
																	    observations[i]    ,observations[j]			)
									UpperCAmelCase__             =							s / n







					def 		__lowerCAmelCase						(				self   :	int    ,lowerCamelCase__   :	ndarray			):
									UpperCAmelCase__             =							sum(
									    self.optimum[n]
									    * self.classes[n]
									    * self.kernel(self.observations[n]    ,lowerCamelCase__			)
									    for n in range(len(self.classes			)			)			)
									return 1 if s + self.offset >= 0 else -1


if __name__ == "__main__":
						import doctest

						doctest.testmod()



