
"""simple docstring"""


import numpy as np



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

		_lowerCAmelCase			=[]
		_lowerCAmelCase			=[]

		for i in range(__UpperCamelCase ):
			_lowerCAmelCase			=rng.permutation(class_counts[i] )
			_lowerCAmelCase			=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]] )
		_lowerCAmelCase			=rng.permutation(__UpperCamelCase )
		_lowerCAmelCase			=rng.permutation(__UpperCamelCase )

		yield train, test





"""simple docstring"""


class 	lowerCamelCase__      :





			'''simple docstring'''



			def __init__(							self  ,  __UpperCAmelCase=None						)				->  List[Any]:
				_lowerCAmelCase			=data
				_lowerCAmelCase			=None



			def __repr__(							self						)				->  int:
				_lowerCAmelCase			=""""""
				_lowerCAmelCase			=self
				while temp:
					string_rep += f'''<{temp.data}> ---> '''
					_lowerCAmelCase			=temp.next
				string_rep += "<END>"
				return string_rep







def 					_lowerCamelCase(__UpperCamelCase )       ->				str:

	# if elements_list is empty
	if not elements_list:
		raise Exception("""The Elements List is empty""" )

	# Set first element as Head
	_lowerCAmelCase			=Node(elements_list[0] )
	_lowerCAmelCase			=head
	# Loop through elements from position 1
	for data in elements_list[1:]:
		_lowerCAmelCase			=Node(__UpperCamelCase )
		_lowerCAmelCase			=current.next
	return head


__A 		= [1, 3, 5, 32, 44, 12, 43]
print(F"""List: {list_data}""")
print('Creating Linked List from List.')
__A 		= make_linked_list(list_data)
print('Linked List:')

print(linked_list)



