





import numpy as np


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase       ):
       _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      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase=10       ):
       _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










def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   Union[str, Any]     ,	SCREAMING_SNAKE_CASE__     :   int							):
     if n < 0:
          return 0
     # table[i] represents the number of ways to get to amount i
     __UpperCamelCase       				=[0] * (n + 1)

     # There is exactly 1 way to get to zero(You pick no coins).
     __UpperCamelCase       				=1

     # Pick all coins one by one and update table[] values
     # after the index greater than or equal to the value of the
     # picked coin
     for coin_val in s:
          for j in range(SCREAMING_SNAKE_CASE__     ,	n + 1							):
               table[j] += table[j - coin_val]

     return table[n]


if __name__ == "__main__":
   import doctest

   doctest.testmod()





