



'''simple docstring'''






import numpy as np


def 			snake_case_					( _lowerCAmelCase  :						List[Any]       ,				_lowerCAmelCase  :						Optional[Any]       ,				_lowerCAmelCase  :						List[str]  )				->       List[str]:
							UpperCAmelCase			:	Optional[int]										=		n_draws * class_counts / class_counts.sum()
							# floored means we don't overshoot n_samples, but probably undershoot
							UpperCAmelCase			:	Any										=		np.floor(_lowerCAmelCase  )
							# we add samples according to how much "left over" probability
							# they had, until we arrive at n_samples
							UpperCAmelCase			:	Any										=		int(n_draws - floored.sum()  )
							if need_to_add > 0:
														UpperCAmelCase			:	Union[str, Any]										=		continuous - floored
														UpperCAmelCase			:	Tuple										=		np.sort(np.unique(_lowerCAmelCase  )  )[::-1]
														# add according to remainder, but break ties
														# randomly to avoid biases
														for value in values:
																					((UpperCAmelCase)							,		)			:	Optional[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			:	int										=		min(len(_lowerCAmelCase  )       ,				_lowerCAmelCase  )
																					UpperCAmelCase			:	Dict										=		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 			snake_case_					( _lowerCAmelCase  :						Union[str, Any]       ,				_lowerCAmelCase  :						Tuple       ,				_lowerCAmelCase  :						List[str]       ,				_lowerCAmelCase  :						List[Any]       ,				_lowerCAmelCase  :						int=10  )				->       int:
							UpperCAmelCase							,		UpperCAmelCase			:	List[str]										=		np.unique(_lowerCAmelCase       ,				return_inverse=_lowerCAmelCase  )
							UpperCAmelCase			:	Optional[Any]										=		classes.shape[0]
							UpperCAmelCase			:	Tuple										=		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			:	Tuple										=		np.split(np.argsort(_lowerCAmelCase       ,				kind='''mergesort'''  )       ,				np.cumsum(_lowerCAmelCase  )[:-1]  )
							for _ in range(_lowerCAmelCase  ):
														UpperCAmelCase			:	Any										=		approximate_mode(_lowerCAmelCase       ,				_lowerCAmelCase       ,				_lowerCAmelCase  )
														UpperCAmelCase			:	List[str]										=		class_counts - n_i
														UpperCAmelCase			:	Any										=		approximate_mode(_lowerCAmelCase       ,				_lowerCAmelCase       ,				_lowerCAmelCase  )

														UpperCAmelCase			:	List[str]										=		[]
														UpperCAmelCase			:	Union[str, Any]										=		[]

														for i in range(_lowerCAmelCase  ):
																					UpperCAmelCase			:	Any										=		rng.permutation(class_counts[i]  )
																					UpperCAmelCase			:	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			:	Any										=		rng.permutation(_lowerCAmelCase  )
														UpperCAmelCase			:	Optional[Any]										=		rng.permutation(_lowerCAmelCase  )

														yield train, test










'''simple docstring'''






from __future__ import annotations


def 			snake_case_					( _lowerCAmelCase  :						list[int]       ,				_lowerCAmelCase  :						int  )				->       list[int]:
							UpperCAmelCase			:	dict[int, int]										=		{}
							for index, val in enumerate(_lowerCAmelCase  ):
														UpperCAmelCase			:	Tuple										=		target - val
														if compl in chk_map:
																					return [chk_map[compl], index]
														UpperCAmelCase			:	Any										=		index
							return []


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						print(F"{two_sum([2, 7, 11, 15], 9) = }")





