





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

import numpy as np
import torch

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


UpperCAmelCase__    :      Optional[int]       					=logging.get_logger(__name__)  # pylint: disable=invalid-name

@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class    __A  (    _UpperCAmelCase     ):
				__A  =					42
				__A  =					None





def   _lowercase       (    _UpperCAmelCase      ,   _UpperCAmelCase=0.9_9_9      ,   _UpperCAmelCase="cosine"      ,   )    ->	Dict:





				if alpha_transform_type == "cosine":

								def alpha_bar_fn(_UpperCAmelCase					):
												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					):
												return math.exp(t * -1_2.0					)

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

				lowerCamelCase									=[]
				for i in range(snake_case__					):
								lowerCamelCase									=i / num_diffusion_timesteps
								lowerCamelCase									=(i + 1) / num_diffusion_timesteps
								betas.append(min(1 - alpha_bar_fn(snake_case__					) / alpha_bar_fn(snake_case__					)      ,   snake_case__					)					)
				return torch.tensor(snake_case__      ,   dtype=torch.floataa					)




class    __A  (    _UpperCAmelCase     ,       _UpperCAmelCase     ):
				__A  =					[e.name for e in KarrasDiffusionSchedulers]
				__A  =					1


				@register_to_config
				def __init__(		self ,			UpperCAmelCase_ = 1000 ,			UpperCAmelCase_ = 0.0_0_0_1 ,			UpperCAmelCase_ = 0.0_2 ,			UpperCAmelCase_ = "linear" ,			UpperCAmelCase_ = None ,			UpperCAmelCase_ = "epsilon" ,			UpperCAmelCase_ = "linear" ,			UpperCAmelCase_ = False ,			UpperCAmelCase_ = "linspace" ,			UpperCAmelCase_ = 0 ,			):




								if trained_betas is not None:
												lowerCamelCase									=torch.tensor(UpperCAmelCase_ ,			dtype=torch.floataa				)
								elif beta_schedule == "linear":
												lowerCamelCase									=torch.linspace(UpperCAmelCase_ ,			UpperCAmelCase_ ,			UpperCAmelCase_ ,			dtype=torch.floataa				)
								elif beta_schedule == "scaled_linear":
												# this schedule is very specific to the latent diffusion model.
												lowerCamelCase									=(
												    torch.linspace(beta_start**0.5 ,			beta_end**0.5 ,			UpperCAmelCase_ ,			dtype=torch.floataa				) ** 2
												)
								elif beta_schedule == "squaredcos_cap_v2":
												# Glide cosine schedule
												lowerCamelCase									=betas_for_alpha_bar(UpperCAmelCase_				)
								else:
												raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""				)

								lowerCamelCase									=1.0 - self.betas
								lowerCamelCase									=torch.cumprod(self.alphas ,			dim=0				)

								lowerCamelCase									=np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5				)
								lowerCamelCase									=np.concatenate([sigmas[::-1], [0.0]]				).astype(np.floataa				)
								lowerCamelCase									=torch.from_numpy(UpperCAmelCase_				)

								# setable values
								lowerCamelCase									=None
								lowerCamelCase									=np.linspace(0 ,			num_train_timesteps - 1 ,			UpperCAmelCase_ ,			dtype=UpperCAmelCase_				)[::-1].copy()
								lowerCamelCase									=torch.from_numpy(UpperCAmelCase_				)
								lowerCamelCase									=False
								lowerCamelCase									=use_karras_sigmas


				@property
				def _snake_case							(		self				):




								if self.config.timestep_spacing in ["linspace", "trailing"]:
												return self.sigmas.max()

								return (self.sigmas.max() ** 2 + 1) ** 0.5


				def _snake_case							(		self ,			UpperCAmelCase_ ,			UpperCAmelCase_				):




								if isinstance(UpperCAmelCase_ ,			torch.Tensor				):
												lowerCamelCase									=timestep.to(self.timesteps.device				)
								lowerCamelCase									=(self.timesteps == timestep).nonzero().item()
								lowerCamelCase									=self.sigmas[step_index]

								lowerCamelCase									=sample / ((sigma**2 + 1) ** 0.5)

								lowerCamelCase									=True
								return sample


				def _snake_case							(		self ,			UpperCAmelCase_ ,			UpperCAmelCase_ = None				):
								lowerCamelCase									=num_inference_steps

								# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
								if self.config.timestep_spacing == "linspace":
												lowerCamelCase									=np.linspace(0 ,			self.config.num_train_timesteps - 1 ,			UpperCAmelCase_ ,			dtype=UpperCAmelCase_				)[
												    ::-1
												].copy()
								elif self.config.timestep_spacing == "leading":
												lowerCamelCase									=self.config.num_train_timesteps // self.num_inference_steps
												# creates integer timesteps by multiplying by ratio
												# casting to int to avoid issues when num_inference_step is power of 3
												lowerCamelCase									=(np.arange(0 ,			UpperCAmelCase_				) * step_ratio).round()[::-1].copy().astype(UpperCAmelCase_				)
												timesteps += self.config.steps_offset
								elif self.config.timestep_spacing == "trailing":
												lowerCamelCase									=self.config.num_train_timesteps / self.num_inference_steps
												# creates integer timesteps by multiplying by ratio
												# casting to int to avoid issues when num_inference_step is power of 3
												lowerCamelCase									=(np.arange(self.config.num_train_timesteps ,			0 ,			-step_ratio				)).round().copy().astype(UpperCAmelCase_				)
												timesteps -= 1
								else:
												raise ValueError(
												    f"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'."""				)

								lowerCamelCase									=np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5				)
								lowerCamelCase									=np.log(UpperCAmelCase_				)

								if self.config.interpolation_type == "linear":
												lowerCamelCase									=np.interp(UpperCAmelCase_ ,			np.arange(0 ,			len(UpperCAmelCase_				)				) ,			UpperCAmelCase_				)
								elif self.config.interpolation_type == "log_linear":
												lowerCamelCase									=torch.linspace(np.log(sigmas[-1]				) ,			np.log(sigmas[0]				) ,			num_inference_steps + 1				).exp()
								else:
												raise ValueError(
												    f"""{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either"""
												    """ 'linear' or 'log_linear'"""				)

								if self.use_karras_sigmas:
												lowerCamelCase									=self._convert_to_karras(in_sigmas=UpperCAmelCase_ ,			num_inference_steps=self.num_inference_steps				)
												lowerCamelCase									=np.array([self._sigma_to_t(UpperCAmelCase_ ,			UpperCAmelCase_				) for sigma in sigmas]				)

								lowerCamelCase									=np.concatenate([sigmas, [0.0]]				).astype(np.floataa				)
								lowerCamelCase									=torch.from_numpy(UpperCAmelCase_				).to(device=UpperCAmelCase_				)
								if str(UpperCAmelCase_				).startswith("""mps"""				):
												# mps does not support float64
												lowerCamelCase									=torch.from_numpy(UpperCAmelCase_				).to(UpperCAmelCase_ ,			dtype=torch.floataa				)
								else:
												lowerCamelCase									=torch.from_numpy(UpperCAmelCase_				).to(device=UpperCAmelCase_				)


				def _snake_case							(		self ,			UpperCAmelCase_ ,			UpperCAmelCase_				):
								lowerCamelCase									=np.log(UpperCAmelCase_				)

								# get distribution
								lowerCamelCase									=log_sigma - log_sigmas[:, np.newaxis]

								# get sigmas range
								lowerCamelCase									=np.cumsum((dists >= 0) ,			axis=0				).argmax(axis=0				).clip(max=log_sigmas.shape[0] - 2				)
								lowerCamelCase									=low_idx + 1

								lowerCamelCase									=log_sigmas[low_idx]
								lowerCamelCase									=log_sigmas[high_idx]

								# interpolate sigmas
								lowerCamelCase									=(low - log_sigma) / (low - high)
								lowerCamelCase									=np.clip(UpperCAmelCase_ ,			0 ,			1				)

								# transform interpolation to time range
								lowerCamelCase									=(1 - w) * low_idx + w * high_idx
								lowerCamelCase									=t.reshape(sigma.shape				)
								return t


				def _snake_case							(		self ,			UpperCAmelCase_ ,			UpperCAmelCase_				):
								lowerCamelCase									=in_sigmas[-1].item()
								lowerCamelCase									=in_sigmas[0].item()

								lowerCamelCase									=7.0  # 7.0 is the value used in the paper
								lowerCamelCase									=np.linspace(0 ,			1 ,			UpperCAmelCase_				)
								lowerCamelCase									=sigma_min ** (1 / rho)
								lowerCamelCase									=sigma_max ** (1 / rho)
								lowerCamelCase									=(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
								return sigmas


				def _snake_case							(		self ,			UpperCAmelCase_ ,			UpperCAmelCase_ ,			UpperCAmelCase_ ,			UpperCAmelCase_ = 0.0 ,			UpperCAmelCase_ = 0.0 ,			UpperCAmelCase_ = float("""inf"""				) ,			UpperCAmelCase_ = 1.0 ,			UpperCAmelCase_ = None ,			UpperCAmelCase_ = True ,			):




								if (
								    isinstance(UpperCAmelCase_ ,			UpperCAmelCase_				)
								    or isinstance(UpperCAmelCase_ ,			torch.IntTensor				)
								    or isinstance(UpperCAmelCase_ ,			torch.LongTensor				)
								):
												raise ValueError(
												    (
												        """Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"""
												        """ `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"""
												        """ one of the `scheduler.timesteps` as a timestep."""
												    ) ,			)

								if not self.is_scale_input_called:
												logger.warning(
												    """The `scale_model_input` function should be called before `step` to ensure correct denoising. """
												    """See `StableDiffusionPipeline` for a usage example."""				)

								if isinstance(UpperCAmelCase_ ,			torch.Tensor				):
												lowerCamelCase									=timestep.to(self.timesteps.device				)

								lowerCamelCase									=(self.timesteps == timestep).nonzero().item()
								lowerCamelCase									=self.sigmas[step_index]

								lowerCamelCase									=min(s_churn / (len(self.sigmas				) - 1) ,			2**0.5 - 1				) if s_tmin <= sigma <= s_tmax else 0.0

								lowerCamelCase									=randn_tensor(
								    model_output.shape ,			dtype=model_output.dtype ,			device=model_output.device ,			generator=UpperCAmelCase_				)

								lowerCamelCase									=noise * s_noise
								lowerCamelCase									=sigma * (gamma + 1)

								if gamma > 0:
												lowerCamelCase									=sample + eps * (sigma_hat**2 - sigma**2) ** 0.5

								# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
								# NOTE: "original_sample" should not be an expected prediction_type but is left in for
								# backwards compatibility
								if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
												lowerCamelCase									=model_output
								elif self.config.prediction_type == "epsilon":
												lowerCamelCase									=sample - sigma_hat * model_output
								elif self.config.prediction_type == "v_prediction":
												# * c_out + input * c_skip
												lowerCamelCase									=model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
								else:
												raise ValueError(
												    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""				)

								# 2. Convert to an ODE derivative
								lowerCamelCase									=(sample - pred_original_sample) / sigma_hat

								lowerCamelCase									=self.sigmas[step_index + 1] - sigma_hat

								lowerCamelCase									=sample + derivative * dt

								if not return_dict:
												return (prev_sample,)

								return EulerDiscreteSchedulerOutput(prev_sample=UpperCAmelCase_ ,			pred_original_sample=UpperCAmelCase_				)


				def _snake_case							(		self ,			UpperCAmelCase_ ,			UpperCAmelCase_ ,			UpperCAmelCase_ ,			):
								lowerCamelCase									=self.sigmas.to(device=original_samples.device ,			dtype=original_samples.dtype				)
								if original_samples.device.type == "mps" and torch.is_floating_point(UpperCAmelCase_				):
												# mps does not support float64
												lowerCamelCase									=self.timesteps.to(original_samples.device ,			dtype=torch.floataa				)
												lowerCamelCase									=timesteps.to(original_samples.device ,			dtype=torch.floataa				)
								else:
												lowerCamelCase									=self.timesteps.to(original_samples.device				)
												lowerCamelCase									=timesteps.to(original_samples.device				)

								lowerCamelCase									=[(schedule_timesteps == t).nonzero().item() for t in timesteps]

								lowerCamelCase									=sigmas[step_indices].flatten()
								while len(sigma.shape				) < len(original_samples.shape				):
												lowerCamelCase									=sigma.unsqueeze(-1				)

								lowerCamelCase									=original_samples + noise * sigma
								return noisy_samples


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













def 			__lowerCamelCase     (						snake_case__			,snake_case__						)				->    Dict:






						"""simple docstring"""





						if not root or root not in binary_tree_mirror_dictionary:
												return
						_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE       =    binary_tree_mirror_dictionary[root][:2]
						_SCREAMING_SNAKE_CASE       =    [right_child, left_child]
						binary_tree_mirror_dict(snake_case__			,snake_case__						)
						binary_tree_mirror_dict(snake_case__			,snake_case__						)

def 			__lowerCamelCase     (						snake_case__			,snake_case__ = 1						)				->    dict:






						"""simple docstring"""





						if not binary_tree:
												raise ValueError("""binary tree cannot be empty"""						)
						if root not in binary_tree:
												_SCREAMING_SNAKE_CASE       =    F'root {root} is not present in the binary_tree'
												raise ValueError(snake_case__						)
						_SCREAMING_SNAKE_CASE       =    dict(snake_case__						)
						binary_tree_mirror_dict(snake_case__			,snake_case__						)
						return binary_tree_mirror_dictionary


if __name__ == "__main__":
		UpperCamelCase												=      {1: [2, 3], 2: [4, 5], 3: [6, 7], 7: [8, 9]}
		print(f"Binary tree: {binary_tree}")
		UpperCamelCase												=      binary_tree_mirror(binary_tree, 5)
		print(f"Binary tree mirror: {binary_tree_mirror_dictionary}")

