




'''simple docstring'''







import numpy as np







def 	_A			(   UpperCAmelCase			,UpperCAmelCase			,UpperCAmelCase	):






   '''simple docstring'''







   A__         =    n_draws * class_counts / class_counts.sum()
   # floored means we don't overshoot n_samples, but probably undershoot
   A__         =    np.floor(UpperCAmelCase	)
   # we add samples according to how much "left over" probability
   # they had, until we arrive at n_samples
   A__         =    int(n_draws - floored.sum()	)
   if need_to_add > 0:
      A__         =    continuous - floored
      A__         =    np.sort(np.unique(UpperCAmelCase	)	)[::-1]
      # add according to remainder, but break ties
      # randomly to avoid biases
      for value in values:
         ((A__ )     ,  )         =    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
         A__         =    min(len(UpperCAmelCase	)			,UpperCAmelCase	)
         A__         =    rng.choice(UpperCAmelCase			,size=UpperCAmelCase			,replace=UpperCAmelCase	)
         floored[inds] += 1
         need_to_add -= add_now
         if need_to_add == 0:
            break
   return floored.astype(np.intaa	)







def 	_A			(   UpperCAmelCase			,UpperCAmelCase			,UpperCAmelCase			,UpperCAmelCase			,UpperCAmelCase=10	):






   '''simple docstring'''







   A__     , A__         =    np.unique(UpperCAmelCase			,return_inverse=UpperCAmelCase	)
   A__         =    classes.shape[0]
   A__         =    np.bincount(UpperCAmelCase	)
   if np.min(UpperCAmelCase	) < 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)	)
   A__         =    np.split(np.argsort(UpperCAmelCase			,kind='mergesort'	)			,np.cumsum(UpperCAmelCase	)[:-1]	)
   for _ in range(UpperCAmelCase	):
      A__         =    approximate_mode(UpperCAmelCase			,UpperCAmelCase			,UpperCAmelCase	)
      A__         =    class_counts - n_i
      A__         =    approximate_mode(UpperCAmelCase			,UpperCAmelCase			,UpperCAmelCase	)

      A__         =    []
      A__         =    []

      for i in range(UpperCAmelCase	):
         A__         =    rng.permutation(class_counts[i]	)
         A__         =    class_indices[i].take(UpperCAmelCase			,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]]	)
      A__         =    rng.permutation(UpperCAmelCase	)
      A__         =    rng.permutation(UpperCAmelCase	)

      yield train, test








'''simple docstring'''







def 	_A			(   UpperCAmelCase	):






   '''simple docstring'''







   if not isinstance(UpperCAmelCase			,UpperCAmelCase	) or not all(
       isinstance(UpperCAmelCase			,UpperCAmelCase	) and item >= 0 for item in citations	):
      raise ValueError('The citations should be a list of non negative integers.'	)

   citations.sort()
   A__         =    len(UpperCAmelCase	)

   for i in range(UpperCAmelCase	):
      if citations[len_citations - 1 - i] <= i:
         return i

   return len_citations


if __name__ == "__main__":
    import doctest

    doctest.testmod()


