
from collections.abc import Generator
from math import sin






def 						__magic_name__ (    __a			:       Any						):



     '''simple docstring'''




     if len(A__						) != 32:
          raise ValueError("""Input must be of length 32"""						)

     UpperCamelCase__				=				B''
     for i in [3, 2, 1, 0]:
          little_endian += string_aa[8 * i : 8 * i + 8]
     return little_endian






def 						__magic_name__ (    __a			:       Any						):



     '''simple docstring'''




     if i < 0:
          raise ValueError("""Input must be non-negative"""						)

     UpperCamelCase__				=				format(A__       ,		"""08x"""						)[-8:]
     UpperCamelCase__				=				B''
     for i in [3, 2, 1, 0]:
          little_endian_hex += hex_rep[2 * i : 2 * i + 2].encode("""utf-8"""						)
     return little_endian_hex






def 						__magic_name__ (    __a			:       str						):



     '''simple docstring'''




     UpperCamelCase__				=				B''
     for char in message:
          bit_string += format(A__       ,		"""08b"""						).encode("""utf-8"""						)
     UpperCamelCase__				=				format(len(A__						)       ,		"""064b"""						).encode("""utf-8"""						)

     # Pad bit_string to a multiple of 512 chars
     bit_string += b"1"
     while len(A__						) % 512 != 448:
          bit_string += b"0"
     bit_string += to_little_endian(start_len[32:]						) + to_little_endian(start_len[:32]						)

     return bit_string






def 						__magic_name__ (    __a			:       Union[str, Any]						):



     '''simple docstring'''




     if len(A__						) % 512 != 0:
          raise ValueError("""Input must have length that\'s a multiple of 512"""						)

     for pos in range(0       ,		len(A__						)       ,		512						):
          UpperCamelCase__				=				bit_string[pos : pos + 512]
          UpperCamelCase__				=				[]
          for i in range(0       ,		512       ,		32						):
               block_words.append(int(to_little_endian(block[i : i + 32]						)       ,		2						)						)
          yield block_words






def 						__magic_name__ (    __a			:       Union[str, Any]						):



     '''simple docstring'''




     if i < 0:
          raise ValueError("""Input must be non-negative"""						)

     UpperCamelCase__				=				format(A__       ,		"""032b"""						)
     UpperCamelCase__				=				''
     for c in i_str:
          new_str += "1" if c == "0" else "0"
     return int(A__       ,		2						)






def 						__magic_name__ (    __a			:       List[Any]       ,		__a			:       Tuple						):



     '''simple docstring'''




     return (a + b) % 2**32






def 						__magic_name__ (    __a			:       int       ,		__a			:       int						):



     '''simple docstring'''




     if i < 0:
          raise ValueError("""Input must be non-negative"""						)
     if shift < 0:
          raise ValueError("""Shift must be non-negative"""						)
     return ((i << shift) ^ (i >> (32 - shift))) % 2**32






def 						__magic_name__ (    __a			:       Union[str, Any]						):



     '''simple docstring'''




     UpperCamelCase__				=				preprocess(A__						)

     UpperCamelCase__				=				[int(2**32 * abs(sin(i + 1						)						)						) for i in range(64						)]

     # Starting states
     UpperCamelCase__				=				0x67_452_301
     UpperCamelCase__				=				0xEF_CDA_B89
     UpperCamelCase__				=				0x98_BAD_CFE
     UpperCamelCase__				=				0x10_325_476

     UpperCamelCase__				=				[
         7,
         12,
         17,
         22,
         7,
         12,
         17,
         22,
         7,
         12,
         17,
         22,
         7,
         12,
         17,
         22,
         5,
         9,
         14,
         20,
         5,
         9,
         14,
         20,
         5,
         9,
         14,
         20,
         5,
         9,
         14,
         20,
         4,
         11,
         16,
         23,
         4,
         11,
         16,
         23,
         4,
         11,
         16,
         23,
         4,
         11,
         16,
         23,
         6,
         10,
         15,
         21,
         6,
         10,
         15,
         21,
         6,
         10,
         15,
         21,
         6,
         10,
         15,
         21,
     ]

     # Process bit string in chunks, each with 16 32-char words
     for block_words in get_block_words(A__						):
          UpperCamelCase__				=				aa
          UpperCamelCase__				=				ba
          UpperCamelCase__				=				ca
          UpperCamelCase__				=				da

          # Hash current chunk
          for i in range(64						):
               if i <= 15:
                    # f = (b & c) | (not_32(b) & d)     # Alternate definition for f
                    UpperCamelCase__				=				d ^ (b & (c ^ d))
                    UpperCamelCase__				=				i
               elif i <= 31:
                    # f = (d & b) | (not_32(d) & c)     # Alternate definition for f
                    UpperCamelCase__				=				c ^ (d & (b ^ c))
                    UpperCamelCase__				=				(5 * i + 1) % 16
               elif i <= 47:
                    UpperCamelCase__				=				b ^ c ^ d
                    UpperCamelCase__				=				(3 * i + 5) % 16
               else:
                    UpperCamelCase__				=				c ^ (b | not_aa(A__						))
                    UpperCamelCase__				=				(7 * i) % 16
               UpperCamelCase__				=				(f + a + added_consts[i] + block_words[g]) % 2**32
               UpperCamelCase__				=				d
               UpperCamelCase__				=				c
               UpperCamelCase__				=				b
               UpperCamelCase__				=				sum_aa(A__       ,		left_rotate_aa(A__       ,		shift_amounts[i]						)						)

          # Add hashed chunk to running total
          UpperCamelCase__				=				sum_aa(A__       ,		A__						)
          UpperCamelCase__				=				sum_aa(A__       ,		A__						)
          UpperCamelCase__				=				sum_aa(A__       ,		A__						)
          UpperCamelCase__				=				sum_aa(A__       ,		A__						)

     UpperCamelCase__				=				reformat_hex(A__						) + reformat_hex(A__						) + reformat_hex(A__						) + reformat_hex(A__						)
     return digest


if __name__ == "__main__":
    import doctest

    doctest.testmod()












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



def A_	(		A__				)  ->		float:
       return np.dot(A__		,      A__				)





class  A__    :




   """simple docstring"""



   def __init__(					self					,  *,
       lowercase = np.inf					,  lowercase = "linear"					,  lowercase = 0.0					,  )			->						None:







          '''simple docstring'''

          a__						:	Tuple =				regularization
          a__						:	Optional[Any] =				gamma
          if kernel == "linear":
                 a__						:	Optional[Any] =				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')
                 a__						:	str =				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:
                 a__						:	Optional[int] =				F'Unknown kernel: {kernel}'
                 raise ValueError(lowercase)

   def 							__lowercase		(					self					,  lowercase					,  lowercase)			->						float:







          '''simple docstring'''

          return np.dot(lowercase					,  lowercase)

   def 							__lowercase		(					self					,  lowercase					,  lowercase)			->						float:







          '''simple docstring'''

          return np.exp(-(self.gamma * norm_squared(vectora - vectora)))

   def 							__lowercase		(					self					,  lowercase					,  lowercase)			->						None:







          '''simple docstring'''

          a__						:	List[str] =				observations
          a__						:	Dict =				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

          ((a__) ,							)						:	Optional[int] =				np.shape(lowercase)

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

          a__						:	Optional[Any] =				LinearConstraint(lowercase					,  0					,  0)
          a__						:	str =				Bounds(0					,  self.regularization)

          a__						:	List[str] =				minimize(
              lowercase					,  np.ones(lowercase)					,  bounds=lowercase					,  constraints=[ly_contraint]).x
          a__						:	Dict =				l_star

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

   def 							__lowercase		(					self					,  lowercase)			->						int:







          '''simple docstring'''

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


if __name__ == "__main__":
    import doctest

    doctest.testmod()


