





class 		__a							:



       def __init__(     self	:   Any					,			UpperCAmelCase	:   int			):
           lowerCAmelCase_					:	Any						=   data
           lowerCAmelCase_					:	BinaryTreeNode | None						=   None
           lowerCAmelCase_					:	BinaryTreeNode | None						=   None






def 			__UpperCamelCase				(			lowercase__	:       BinaryTreeNode | None		,    lowercase__	:       int		)	->      BinaryTreeNode | None:
    '''simple docstring'''





    if node is None:
        lowerCAmelCase_					:	int						=   BinaryTreeNode(lowercase__		)
        return node

    # binary search tree is not empty,
    # so we will insert it into the tree
    # if new_value is less than value of data in node,
    #  add it to left subtree and proceed recursively
    if new_value < node.data:
        lowerCAmelCase_					:	Union[str, Any]						=   insert(node.left_child		,    lowercase__		)
    else:
        # if new_value is greater than value of data in node,
        #  add it to right subtree and proceed recursively
        lowerCAmelCase_					:	Tuple						=   insert(node.right_child		,    lowercase__		)
    return node
def 			__UpperCamelCase				(			lowercase__	:       None | BinaryTreeNode		)	->      list[int]:  # if node is None,return
    '''simple docstring'''





    if node:
        lowerCAmelCase_					:	Any						=   inorder(node.left_child		)
        lowerCAmelCase_					:	Dict						=   [*inorder_array, node.data]
        lowerCAmelCase_					:	Dict						=   inorder_array + inorder(node.right_child		)
    else:
        lowerCAmelCase_					:	List[Any]						=   []
    return inorder_array
def 			__UpperCamelCase				(			)	->      BinaryTreeNode | None:
    '''simple docstring'''





    lowerCAmelCase_					:	List[Any]						=   insert(lowercase__		,    15		)
    insert(lowercase__		,    10		)
    insert(lowercase__		,    25		)
    insert(lowercase__		,    6		)
    insert(lowercase__		,    14		)
    insert(lowercase__		,    20		)
    insert(lowercase__		,    60		)
    return root
def 			__UpperCamelCase				(			)	->      None:
    '''simple docstring'''





    lowerCAmelCase_					:	List[Any]						=   make_tree()
    print("""Printing values of binary search tree in Inorder Traversal."""		)
    inorder(lowercase__		)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()









import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest

class 		__a							(	__UpperCamelCase      ):
       __snake_case						:   Optional[Any]      =		(VQDiffusionScheduler,)



       def  A							(     self	:   Optional[int]					,			**UpperCAmelCase	:   List[Any]			):
           lowerCAmelCase_					:	Any						=   {
               """num_vec_classes""": 40_97,
               """num_train_timesteps""": 1_00,
           }

           config.update(**UpperCAmelCase			)
           return config



       def  A							(     self	:   str					,			UpperCAmelCase	:   List[Any]			):
           lowerCAmelCase_					:	Union[str, Any]						=   4
           lowerCAmelCase_					:	str						=   8
           lowerCAmelCase_					:	Optional[Any]						=   8

           lowerCAmelCase_					:	str						=   torch.randint(0					,			UpperCAmelCase					,			(batch_size, height * width)			)

           return sample



       @property
       def  A							(     self	:   Optional[int]			):
           assert False



       def  A							(     self	:   int					,			UpperCAmelCase	:   Dict			):
           def model(UpperCAmelCase	:   Union[str, Any]					,			UpperCAmelCase	:   List[str]					,			*UpperCAmelCase	:   List[Any]			):
               lowerCAmelCase_		,		lowerCAmelCase_					:	Tuple						=   sample.shape
               lowerCAmelCase_					:	int						=   torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)			)
               lowerCAmelCase_					:	str						=   F.log_softmax(logits.double()					,			dim=1			).float()
               return return_value

           return model



       def  A							(     self	:   Union[str, Any]			):
           for timesteps in [2, 5, 1_00, 10_00]:
               self.check_over_configs(num_train_timesteps=UpperCAmelCase			)



       def  A							(     self	:   str			):
           for num_vec_classes in [5, 1_00, 10_00, 40_00]:
               self.check_over_configs(num_vec_classes=UpperCAmelCase			)



       def  A							(     self	:   Optional[Any]			):
           for t in [0, 50, 99]:
               self.check_over_forward(time_step=UpperCAmelCase			)



       def  A							(     self	:   int			):
           pass


