




'''simple docstring'''




import numpy as np

def    _UpperCamelCase      (				SCREAMING_SNAKE_CASE__			:      Any       ,						SCREAMING_SNAKE_CASE__			:      Dict       ,						SCREAMING_SNAKE_CASE__			:      Tuple       ):
 '''simple docstring'''





 UpperCAmelCase__										=    n_draws * class_counts / class_counts.sum()
 # floored means we don't overshoot n_samples, but probably undershoot
 UpperCAmelCase__										=    np.floor(SCREAMING_SNAKE_CASE__       )
 # we add samples according to how much "left over" probability
 # they had, until we arrive at n_samples
 UpperCAmelCase__										=    int(n_draws - floored.sum()       )
 if need_to_add > 0:
  UpperCAmelCase__										=    continuous - floored
  UpperCAmelCase__										=    np.sort(np.unique(SCREAMING_SNAKE_CASE__       )       )[::-1]
  # add according to remainder, but break ties
  # randomly to avoid biases
  for value in values:
   ((UpperCAmelCase__			)							,						)										=    np.where(remainder == value       )
   # if we need_to_add less than what's in inds
   # we draw randomly from them.
   # if we need to add more, we add them all and
   # go to the next value
   UpperCAmelCase__										=    min(len(SCREAMING_SNAKE_CASE__       )       ,						SCREAMING_SNAKE_CASE__       )
   UpperCAmelCase__										=    rng.choice(SCREAMING_SNAKE_CASE__       ,						size=SCREAMING_SNAKE_CASE__       ,						replace=SCREAMING_SNAKE_CASE__       )
   floored[inds] += 1
   need_to_add -= add_now
   if need_to_add == 0:
    break
 return floored.astype(np.intaa       )







def    _UpperCamelCase      (				SCREAMING_SNAKE_CASE__			:      List[Any]       ,						SCREAMING_SNAKE_CASE__			:      Any       ,						SCREAMING_SNAKE_CASE__			:      List[Any]       ,						SCREAMING_SNAKE_CASE__			:      int       ,						SCREAMING_SNAKE_CASE__			:      Tuple=10       ):
 '''simple docstring'''





 UpperCAmelCase__							,			UpperCAmelCase__										=    np.unique(SCREAMING_SNAKE_CASE__       ,						return_inverse=SCREAMING_SNAKE_CASE__       )
 UpperCAmelCase__										=    classes.shape[0]
 UpperCAmelCase__										=    np.bincount(SCREAMING_SNAKE_CASE__       )
 if np.min(SCREAMING_SNAKE_CASE__       ) < 2:
  raise ValueError("""Minimum class count error"""       )
 if n_train < n_classes:
  raise ValueError(
      """The train_size = %d should be greater or """ """equal to the number of classes = %d""" % (n_train, n_classes)       )
 if n_test < n_classes:
  raise ValueError(
      """The test_size = %d should be greater or """ """equal to the number of classes = %d""" % (n_test, n_classes)       )
 UpperCAmelCase__										=    np.split(np.argsort(SCREAMING_SNAKE_CASE__       ,						kind="""mergesort"""       )       ,						np.cumsum(SCREAMING_SNAKE_CASE__       )[:-1]       )
 for _ in range(SCREAMING_SNAKE_CASE__       ):
  UpperCAmelCase__										=    approximate_mode(SCREAMING_SNAKE_CASE__       ,						SCREAMING_SNAKE_CASE__       ,						SCREAMING_SNAKE_CASE__       )
  UpperCAmelCase__										=    class_counts - n_i
  UpperCAmelCase__										=    approximate_mode(SCREAMING_SNAKE_CASE__       ,						SCREAMING_SNAKE_CASE__       ,						SCREAMING_SNAKE_CASE__       )

  UpperCAmelCase__										=    []
  UpperCAmelCase__										=    []

  for i in range(SCREAMING_SNAKE_CASE__       ):
   UpperCAmelCase__										=    rng.permutation(class_counts[i]       )
   UpperCAmelCase__										=    class_indices[i].take(SCREAMING_SNAKE_CASE__       ,						mode="""clip"""       )
   train.extend(perm_indices_class_i[: n_i[i]]       )
   test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]       )
  UpperCAmelCase__										=    rng.permutation(SCREAMING_SNAKE_CASE__       )
  UpperCAmelCase__										=    rng.permutation(SCREAMING_SNAKE_CASE__       )

  yield train, test






'''simple docstring'''




from __future__ import annotations

import random
import string

class 						lowerCAmelCase_   :







  '''simple docstring'''







  def __init__(						self    :	int						,		_UpperCAmelCase    :	str | None = None ):






   """simple docstring"""






   UpperCAmelCase__										=    passcode or self.__passcode_creator()
   UpperCAmelCase__										=    self.__make_key_list()
   UpperCAmelCase__										=    self.__make_shift_key()


  def __str__(						self    :	int ):






   """simple docstring"""






   return "".join(self.__passcode )


  def 							SCREAMING_SNAKE_CASE__			(						self    :	List[str]						,		_UpperCAmelCase    :	list[int] ):






   """simple docstring"""






   for i in range(1						,		len(_UpperCAmelCase )						,		2 ):
    iterlist[i] *= -1
   return iterlist


  def 							SCREAMING_SNAKE_CASE__			(						self    :	int ):






   """simple docstring"""






   UpperCAmelCase__										=    string.ascii_letters + string.digits
   UpperCAmelCase__										=    [random.choice(_UpperCAmelCase ) for _ in range(random.randint(10						,		20 ) )]
   return password


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Tuple ):






   """simple docstring"""






   UpperCAmelCase__										=    (
       string.ascii_letters + string.digits + string.punctuation + """ \t\n"""
   )

   UpperCAmelCase__										=    []

   # creates points known as breakpoints to break the key_list_options at those
   # points and pivot each substring
   UpperCAmelCase__										=    sorted(set(self.__passcode ) )
   UpperCAmelCase__										=    []

   # algorithm for creating a new shuffled list, keys_l, out of key_list_options
   for i in key_list_options:
    temp_list.extend(_UpperCAmelCase )

    # checking breakpoints at which to pivot temporary sublist and add it into
    # keys_l
    if i in breakpoints or i == key_list_options[-1]:
     keys_l.extend(temp_list[::-1] )
     temp_list.clear()

        # returning a shuffled keys_l to prevent brute force guessing of shift key
   return keys_l


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Tuple ):






   """simple docstring"""






   UpperCAmelCase__										=    sum(self.__neg_pos([ord(_UpperCAmelCase ) for x in self.__passcode] ) )
   return num if num > 0 else len(self.__passcode )


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[Any]						,		_UpperCAmelCase    :	str ):






   """simple docstring"""






   UpperCAmelCase__										=    """"""

   # decoding shift like Caesar cipher algorithm implementing negative shift or
   # reverse shift or left shift
   for i in encoded_message:
    UpperCAmelCase__										=    self.__key_list.index(_UpperCAmelCase )
    decoded_message += self.__key_list[
        (position - self.__shift_key) % -len(self.__key_list )
    ]

   return decoded_message



  def 							SCREAMING_SNAKE_CASE__			(						self    :	List[str]						,		_UpperCAmelCase    :	str ):






   """simple docstring"""






   UpperCAmelCase__										=    """"""

   # encoding shift like Caesar cipher algorithm implementing positive shift or
   # forward shift or right shift
   for i in plaintext:
    UpperCAmelCase__										=    self.__key_list.index(_UpperCAmelCase )
    encoded_message += self.__key_list[
        (position + self.__shift_key) % len(self.__key_list )
    ]

   return encoded_message

def    _UpperCamelCase      (				SCREAMING_SNAKE_CASE__			:      str = "Hello, this is a modified Caesar cipher"       ):
 '''simple docstring'''





 UpperCAmelCase__										=    ShuffledShiftCipher()
 return cipa.decrypt(cipa.encrypt(SCREAMING_SNAKE_CASE__       )       )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
