






import numpy as np






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







def     lowerCamelCase__				(   __lowerCamelCase      :					Dict  ,		__lowerCamelCase      :					str  ,		__lowerCamelCase      :					Tuple  ,		__lowerCamelCase      :					Any  ,		__lowerCamelCase      :					Tuple=10		):
		__UpperCAmelCase							:     List[str]									=  np.unique(__lowerCamelCase  ,		return_inverse=__lowerCamelCase		)
		__UpperCAmelCase							:     int									=  classes.shape[0]
		__UpperCAmelCase							:     Any									=  np.bincount(__lowerCamelCase		)
		if np.min(__lowerCamelCase		) < 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							:     int									=  np.split(np.argsort(__lowerCamelCase  ,		kind="""mergesort"""		)  ,		np.cumsum(__lowerCamelCase		)[:-1]		)
		for _ in range(__lowerCamelCase		):
				__UpperCAmelCase							:     int									=  approximate_mode(__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase		)
				__UpperCAmelCase							:     List[str]									=  class_counts - n_i
				__UpperCAmelCase							:     Dict									=  approximate_mode(__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase		)

				__UpperCAmelCase							:     str									=  []
				__UpperCAmelCase							:     str									=  []

				for i in range(__lowerCamelCase		):
						__UpperCAmelCase							:     Tuple									=  rng.permutation(class_counts[i]		)
						__UpperCAmelCase							:     Optional[int]									=  class_indices[i].take(__lowerCamelCase  ,		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							:     List[Any]									=  rng.permutation(__lowerCamelCase		)
				__UpperCAmelCase							:     List[str]									=  rng.permutation(__lowerCamelCase		)

				yield train, test










def     lowerCamelCase__				(   __lowerCamelCase      :					list		):
  if len(__lowerCamelCase		) <= 1:
    return data
  else:
    return [
        *quick_sort([e for e in data[1:] if e <= data[0]]		),
        data[0],
        *quick_sort([e for e in data[1:] if e > data[0]]		),
    ]


if __name__ == "__main__":
       import doctest

       doctest.testmod()


