





'''simple docstring'''






import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

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


@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class   _lowercase						(							a				):
   _UpperCamelCase       		=   42
   _UpperCamelCase       		=   None







def 							_lowerCamelCase(  UpperCamelCase__    : List[str]    ,       UpperCamelCase__    : List[str]=0.9_9_9    ,       UpperCamelCase__    : str="cosine"    ,       )		->  int:
    if alpha_transform_type == "cosine":

        def alpha_bar_fn(UpperCamelCase__    : Tuple		):
            return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2		) ** 2

    elif alpha_transform_type == "exp":

        def alpha_bar_fn(UpperCamelCase__    : Optional[Any]		):
            return math.exp(t * -1_2.0		)

    else:
        raise ValueError(F'''Unsupported alpha_tranform_type: {alpha_transform_type}'''		)

    A       :       Union[str, Any]							=						[]
    for i in range(UpperCamelCase__		):
        A       :       str							=						i / num_diffusion_timesteps
        A       :       Union[str, Any]							=						(i + 1) / num_diffusion_timesteps
        betas.append(min(1 - alpha_bar_fn(UpperCamelCase__		) / alpha_bar_fn(UpperCamelCase__		)    ,       UpperCamelCase__		)		)
    return torch.tensor(UpperCamelCase__    ,       dtype=torch.floataa		)





class   _lowercase						(							a		,					a				):


   @register_to_config
   def __init__( self    ,   _UpperCAmelCase = 1_000    ,   _UpperCAmelCase = "fixed_small_log"    ,   _UpperCAmelCase = True    ,   _UpperCAmelCase = 1.0    ,   _UpperCAmelCase = "epsilon"    ,   _UpperCAmelCase = "squaredcos_cap_v2"    ,   ):
       if beta_schedule != "squaredcos_cap_v2":
           raise ValueError('''UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\''''					)

       A       :       str							=						betas_for_alpha_bar(_UpperCAmelCase					)

       A       :       str							=						1.0 - self.betas
       A       :       Optional[int]							=						torch.cumprod(self.alphas    ,   dim=0					)
       A       :       Tuple							=						torch.tensor(1.0					)

       # standard deviation of the initial noise distribution
       A       :       List[str]							=						1.0

       # setable values
       A       :       Tuple							=						None
       A       :       str							=						torch.from_numpy(np.arange(0    ,   _UpperCAmelCase					)[::-1].copy()					)

       A       :       Any							=						variance_type


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


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase = None					):
       A       :       List[Any]							=						num_inference_steps
       A       :       Dict							=						(self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
       A       :       Dict							=						(np.arange(0    ,   _UpperCAmelCase					) * step_ratio).round()[::-1].copy().astype(np.intaa					)
       A       :       Optional[int]							=						torch.from_numpy(_UpperCAmelCase					).to(_UpperCAmelCase					)


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase=None    ,   _UpperCAmelCase=None    ,   _UpperCAmelCase=None					):
       if prev_timestep is None:
           A       :       int							=						t - 1

       A       :       Optional[int]							=						self.alphas_cumprod[t]
       A       :       Optional[int]							=						self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
       A       :       Optional[Any]							=						1 - alpha_prod_t
       A       :       str							=						1 - alpha_prod_t_prev

       if prev_timestep == t - 1:
           A       :       Union[str, Any]							=						self.betas[t]
       else:
           A       :       int							=						1 - alpha_prod_t / alpha_prod_t_prev

       # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
       # and sample from it to get previous sample
       # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
       A       :       List[Any]							=						beta_prod_t_prev / beta_prod_t * beta

       if variance_type is None:
           A       :       Tuple							=						self.config.variance_type

       # hacks - were probably added for training stability
       if variance_type == "fixed_small_log":
           A       :       str							=						torch.log(torch.clamp(_UpperCAmelCase    ,   min=1E-2_0					)					)
           A       :       List[str]							=						torch.exp(0.5 * variance					)
       elif variance_type == "learned_range":
           # NOTE difference with DDPM scheduler
           A       :       Any							=						variance.log()
           A       :       Optional[Any]							=						beta.log()

           A       :       str							=						(predicted_variance + 1) / 2
           A       :       Union[str, Any]							=						frac * max_log + (1 - frac) * min_log

       return variance


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase = None    ,   _UpperCAmelCase=None    ,   _UpperCAmelCase = True    ,   ):
       A       :       Optional[Any]							=						timestep

       if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
           A,	A       :       Dict							=						torch.split(_UpperCAmelCase    ,   sample.shape[1]    ,   dim=1					)
       else:
           A       :       Tuple							=						None

       # 1. compute alphas, betas
       if prev_timestep is None:
           A       :       List[str]							=						t - 1

       A       :       Optional[int]							=						self.alphas_cumprod[t]
       A       :       List[Any]							=						self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
       A       :       str							=						1 - alpha_prod_t
       A       :       Tuple							=						1 - alpha_prod_t_prev

       if prev_timestep == t - 1:
           A       :       Union[str, Any]							=						self.betas[t]
           A       :       Optional[Any]							=						self.alphas[t]
       else:
           A       :       List[str]							=						1 - alpha_prod_t / alpha_prod_t_prev
           A       :       Optional[int]							=						1 - beta

       # 2. compute predicted original sample from predicted noise also called
       # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
       if self.config.prediction_type == "epsilon":
           A       :       Any							=						(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
       elif self.config.prediction_type == "sample":
           A       :       Union[str, Any]							=						model_output
       else:
           raise ValueError(
               f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`'''
               ''' for the UnCLIPScheduler.'''					)

       # 3. Clip "predicted x_0"
       if self.config.clip_sample:
           A       :       str							=						torch.clamp(
               _UpperCAmelCase    ,   -self.config.clip_sample_range    ,   self.config.clip_sample_range					)

       # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
       # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
       A       :       List[Any]							=						(alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
       A       :       int							=						alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

       # 5. Compute predicted previous sample µ_t
       # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
       A       :       List[Any]							=						pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

       # 6. Add noise
       A       :       str							=						0
       if t > 0:
           A       :       Dict							=						randn_tensor(
               model_output.shape    ,   dtype=model_output.dtype    ,   generator=_UpperCAmelCase    ,   device=model_output.device					)

           A       :       Optional[Any]							=						self._get_variance(
               _UpperCAmelCase    ,   predicted_variance=_UpperCAmelCase    ,   prev_timestep=_UpperCAmelCase    ,   )

           if self.variance_type == "fixed_small_log":
               A       :       int							=						variance
           elif self.variance_type == "learned_range":
               A       :       str							=						(0.5 * variance).exp()
           else:
               raise ValueError(
                   f'''variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`'''
                   ''' for the UnCLIPScheduler.'''					)

           A       :       Union[str, Any]							=						variance * variance_noise

       A       :       Union[str, Any]							=						pred_prev_sample + variance

       if not return_dict:
           return (pred_prev_sample,)

       return UnCLIPSchedulerOutput(prev_sample=_UpperCAmelCase    ,   pred_original_sample=_UpperCAmelCase					)


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   ):
       # Make sure alphas_cumprod and timestep have same device and dtype as original_samples
       A       :       Dict							=						self.alphas_cumprod.to(device=original_samples.device    ,   dtype=original_samples.dtype					)
       A       :       Optional[Any]							=						timesteps.to(original_samples.device					)

       A       :       Tuple							=						alphas_cumprod[timesteps] ** 0.5
       A       :       Tuple							=						sqrt_alpha_prod.flatten()
       while len(sqrt_alpha_prod.shape					) < len(original_samples.shape					):
           A       :       Any							=						sqrt_alpha_prod.unsqueeze(-1					)

       A       :       Any							=						(1 - alphas_cumprod[timesteps]) ** 0.5
       A       :       Tuple							=						sqrt_one_minus_alpha_prod.flatten()
       while len(sqrt_one_minus_alpha_prod.shape					) < len(original_samples.shape					):
           A       :       List[str]							=						sqrt_one_minus_alpha_prod.unsqueeze(-1					)

       A       :       Union[str, Any]							=						sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
       return noisy_samples







'''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()
