





'''simple docstring'''






import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin


class   _lowercase						(							_UpperCAmelCase		,					_UpperCAmelCase				):
   _UpperCamelCase       		=   1


   @register_to_config
   def __init__( self    ,   _UpperCAmelCase=2_000    ,   _UpperCAmelCase=0.1    ,   _UpperCAmelCase=20    ,   _UpperCAmelCase=1E-3					):
       A       :       Tuple							=						None
       A       :       int							=						None
       A       :       List[str]							=						None


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase = None					):
       A       :       Optional[int]							=						torch.linspace(1    ,   self.config.sampling_eps    ,   lowerCamelCase_    ,   device=lowerCamelCase_					)


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase=None					):




       if self.timesteps is None:
           raise ValueError(
               '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''					)

       # TODO(Patrick) better comments + non-PyTorch
       # postprocess model score
       A       :       Optional[int]							=						(
           -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
       )
       A       :       Dict							=						torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff					)					)
       A       :       Union[str, Any]							=						std.flatten()
       while len(std.shape					) < len(score.shape					):
           A       :       Optional[Any]							=						std.unsqueeze(-1					)
       A       :       Optional[Any]							=						-score / std

       # compute
       A       :       Union[str, Any]							=						-1.0 / len(self.timesteps					)

       A       :       Union[str, Any]							=						self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
       A       :       List[str]							=						beta_t.flatten()
       while len(beta_t.shape					) < len(x.shape					):
           A       :       Optional[int]							=						beta_t.unsqueeze(-1					)
       A       :       int							=						-0.5 * beta_t * x

       A       :       Optional[Any]							=						torch.sqrt(lowerCamelCase_					)
       A       :       Union[str, Any]							=						drift - diffusion**2 * score
       A       :       Optional[Any]							=						x + drift * dt

       # add noise
       A       :       str							=						randn_tensor(x.shape    ,   layout=x.layout    ,   generator=lowerCamelCase_    ,   device=x.device    ,   dtype=x.dtype					)
       A       :       Dict							=						x_mean + diffusion * math.sqrt(-dt					) * noise

       return x, x_mean


   def __len__( self					):
       return self.config.num_train_timesteps







'''simple docstring'''






from __future__ import annotations


class   _lowercase						:


   def __init__( self    ,   _UpperCAmelCase					):
       A       :       str							=						data
       A       :       Node | None							=						None
       A       :       Node | None							=						None







def 							_lowerCamelCase(  UpperCamelCase__    : Node | None		)		->  None:  # In Order traversal of the tree
    if tree:
        display(tree.left		)
        print(tree.data		)
        display(tree.right		)
def 							_lowerCamelCase(  UpperCamelCase__    : Node | None		)		->  int:
    return 1 + max(depth_of_tree(tree.left		)    ,       depth_of_tree(tree.right		)		) if tree else 0
def 							_lowerCamelCase(  UpperCamelCase__    : Node		)		->  bool:
    if not tree:
        return True
    if tree.left and tree.right:
        return is_full_binary_tree(tree.left		) and is_full_binary_tree(tree.right		)
    else:
        return not tree.left and not tree.right
def 							_lowerCamelCase(  )		->  None:  # Main function for testing.
    A       :       Optional[int]							=						Node(1		)
    A       :       Tuple							=						Node(2		)
    A       :       Dict							=						Node(3		)
    A       :       List[str]							=						Node(4		)
    A       :       Union[str, Any]							=						Node(5		)
    A       :       str							=						Node(6		)
    A       :       Any							=						Node(7		)
    A       :       str							=						Node(8		)
    A       :       Optional[int]							=						Node(9		)

    print(is_full_binary_tree(UpperCamelCase__		)		)
    print(depth_of_tree(UpperCamelCase__		)		)
    print('''Tree is: '''		)
    display(UpperCamelCase__		)


if __name__ == "__main__":
     main()
