


import numpy as np


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

												__SCREAMING_SNAKE_CASE						:					Tuple		      =		[]
												__SCREAMING_SNAKE_CASE						:					int		      =		[]

												for i in range(SCREAMING_SNAKE_CASE_				):
																		__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		rng.permutation(class_counts[i]				)
																		__SCREAMING_SNAKE_CASE						:					List[Any]		      =		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]]				)
												__SCREAMING_SNAKE_CASE						:					Tuple		      =		rng.permutation(SCREAMING_SNAKE_CASE_				)
												__SCREAMING_SNAKE_CASE						:					Dict		      =		rng.permutation(SCREAMING_SNAKE_CASE_				)

												yield train, test




def 	_UpperCamelCase							(				lowercase__   ,	lowercase__				):
						__SCREAMING_SNAKE_CASE						:					int		      =		len(lowercase__				)
						__SCREAMING_SNAKE_CASE						:					int		      =		len(lowercase__				)
						__SCREAMING_SNAKE_CASE						:					int		      =		(
						    first_input_list_length
						    if first_input_list_length > second_input_list_length
						    else second_input_list_length
						)
						__SCREAMING_SNAKE_CASE						:					list		      =		[]
						for char_count in range(lowercase__				):
												if char_count < first_input_list_length:
																		output_result_list.append(first_input_list[char_count]				)
												if char_count < second_input_list_length:
																		output_result_list.append(second_input_list[char_count]				)

						return output_result_list


if __name__ == "__main__":
						print(alternative_list_arrange(['A', 'B', 'C'], [1, 2, 3, 4, 5]), end=' ')
