




import numpy as np






def    snake_case   (				A		,  A		,  A     )				->				List[str]:




	'''simple docstring'''


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






def    snake_case   (				A		,  A		,  A		,  A		,  A=10     )				->				Optional[Any]:




	'''simple docstring'''


	lowerCamelCase_  ,     lowerCamelCase_				:						Any				  =				np.unique(A		,  return_inverse=A     )
	lowerCamelCase_				:						List[str]				  =				classes.shape[0]
	lowerCamelCase_				:						Union[str, Any]				  =				np.bincount(A     )
	if np.min(A     ) < 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)     )
	lowerCamelCase_				:						Tuple				  =				np.split(np.argsort(A		,  kind='mergesort'     )		,  np.cumsum(A     )[:-1]     )
	for _ in range(A     ):
		lowerCamelCase_				:						List[Any]				  =				approximate_mode(A		,  A		,  A     )
		lowerCamelCase_				:						List[str]				  =				class_counts - n_i
		lowerCamelCase_				:						Optional[Any]				  =				approximate_mode(A		,  A		,  A     )

		lowerCamelCase_				:						List[Any]				  =				[]
		lowerCamelCase_				:						Optional[Any]				  =				[]

		for i in range(A     ):
			lowerCamelCase_				:						List[Any]				  =				rng.permutation(class_counts[i]     )
			lowerCamelCase_				:						Any				  =				class_indices[i].take(A		,  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]]     )
		lowerCamelCase_				:						List[Any]				  =				rng.permutation(A     )
		lowerCamelCase_				:						Any				  =				rng.permutation(A     )

		yield train, test



def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		list							):





    """simple docstring"""


    if not isinstance(_SCREAMING_SNAKE_CASE			, _SCREAMING_SNAKE_CASE							):
        raise ValueError('Input series is not valid, valid series - [2, 4, 8]'							)
    if len(_SCREAMING_SNAKE_CASE							) == 0:
        raise ValueError('Input list must be a non empty list'							)
    if len(_SCREAMING_SNAKE_CASE							) == 1:
        return True
    try:
        SCREAMING_SNAKE_CASE_						=    series[1] / series[0]
        for index in range(len(_SCREAMING_SNAKE_CASE							) - 1							):
            if series[index + 1] / series[index] != common_ratio:
                return False
    except ZeroDivisionError:
        return False
    return True


def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		list							):





    """simple docstring"""


    if not isinstance(_SCREAMING_SNAKE_CASE			, _SCREAMING_SNAKE_CASE							):
        raise ValueError('Input series is not valid, valid series - [2, 4, 8]'							)
    if len(_SCREAMING_SNAKE_CASE							) == 0:
        raise ValueError('Input list must be a non empty list'							)
    SCREAMING_SNAKE_CASE_						=    1
    for value in series:
        answer *= value
    return pow(_SCREAMING_SNAKE_CASE			, 1 / len(_SCREAMING_SNAKE_CASE							)							)


if __name__ == "__main__":
       import doctest

       doctest.testmod()


