


'''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 _lowerCamelCase	(						lowerCamelCase_							):



		'''simple docstring'''







		lowerCAmelCase     :      torch.FloatTensor
		lowerCAmelCase     :      Optional[torch.FloatTensor]				   =    None







def lowerCamelCase      (      lowerCAmelCase    :     Optional[int]		,		lowerCAmelCase    :     Tuple=0.999		,		lowerCAmelCase    :     List[str]="cosine"		,		):





	"""simple docstring"""







	if alpha_transform_type == "cosine":

		def alpha_bar_fn(lowerCAmelCase    :     str		):
			return math.cos((t + 0.008) / 1.008 * math.pi / 2		) ** 2

	elif alpha_transform_type == "exp":

		def alpha_bar_fn(lowerCAmelCase    :     Optional[int]		):
			return math.exp(t * -12.0		)

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

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





class _lowerCamelCase	(						lowerCamelCase_		,					lowerCamelCase_							):



		'''simple docstring'''







		@register_to_config
		def __init__( self		:						List[str]  ,					_A		:						int = 1000  ,					_A		:						str = "fixed_small_log"  ,					_A		:						bool = True  ,					_A		:						Optional[float] = 1.0  ,					_A		:						str = "epsilon"  ,					_A		:						str = "squaredcos_cap_v2"  ,					)  ->			Optional[int]:






			if beta_schedule != "squaredcos_cap_v2":
				raise ValueError('UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\''      )

			__magic_name__	:   Tuple					     =					betas_for_alpha_bar(_UpperCAmelCase      )

			__magic_name__	:   Dict					     =					1.0 - self.betas
			__magic_name__	:   Tuple					     =					torch.cumprod(self.alphas  ,					dim=0      )
			__magic_name__	:   Tuple					     =					torch.tensor(1.0      )

			# standard deviation of the initial noise distribution
			__magic_name__	:   Dict					     =					1.0

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

			__magic_name__	:   Tuple					     =					variance_type







		def        __lowerCAmelCase		( self		:						Union[str, Any]  ,					_A		:						torch.FloatTensor  ,					_A		:						Optional[int] = None      )  ->			Tuple:
			return sample







		def        __lowerCAmelCase		( self		:						List[str]  ,					_A		:						int  ,					_A		:						Union[str, torch.device] = None      )  ->			Tuple:
			__magic_name__	:   Optional[Any]					     =					num_inference_steps
			__magic_name__	:   Dict					     =					(self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
			__magic_name__	:   int					     =					(np.arange(0  ,					_UpperCAmelCase      ) * step_ratio).round()[::-1].copy().astype(np.intaa      )
			__magic_name__	:   Optional[Any]					     =					torch.from_numpy(_UpperCAmelCase      ).to(_UpperCAmelCase      )







		def        __lowerCAmelCase		( self		:						Optional[Any]  ,					_A		:						int  ,					_A		:						Dict=None  ,					_A		:						int=None  ,					_A		:						int=None      )  ->			Any:






			if prev_timestep is None:
				__magic_name__	:   Dict					     =					t - 1

			__magic_name__	:   List[str]					     =					self.alphas_cumprod[t]
			__magic_name__	:   Any					     =					self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
			__magic_name__	:   Dict					     =					1 - alpha_prod_t
			__magic_name__	:   List[str]					     =					1 - alpha_prod_t_prev

			if prev_timestep == t - 1:
				__magic_name__	:   Tuple					     =					self.betas[t]
			else:
				__magic_name__	:   str					     =					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
			__magic_name__	:   str					     =					beta_prod_t_prev / beta_prod_t * beta

			if variance_type is None:
				__magic_name__	:   int					     =					self.config.variance_type

			# hacks - were probably added for training stability
			if variance_type == "fixed_small_log":
				__magic_name__	:   List[str]					     =					torch.log(torch.clamp(_UpperCAmelCase  ,					min=1E-20      )      )
				__magic_name__	:   List[Any]					     =					torch.exp(0.5 * variance      )
			elif variance_type == "learned_range":
				# NOTE difference with DDPM scheduler
				__magic_name__	:   List[Any]					     =					variance.log()
				__magic_name__	:   Dict					     =					beta.log()

				__magic_name__	:   List[str]					     =					(predicted_variance + 1) / 2
				__magic_name__	:   List[str]					     =					frac * max_log + (1 - frac) * min_log

			return variance







		def        __lowerCAmelCase		( self		:						Tuple  ,					_A		:						torch.FloatTensor  ,					_A		:						int  ,					_A		:						torch.FloatTensor  ,					_A		:						Optional[int] = None  ,					_A		:						List[str]=None  ,					_A		:						bool = True  ,					)  ->			Dict:
			__magic_name__	:   int					     =					timestep

			if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
				__magic_name__     ,    __magic_name__	:   str					     =					torch.split(_UpperCAmelCase  ,					sample.shape[1]  ,					dim=1      )
			else:
				__magic_name__	:   Union[str, Any]					     =					None

			# 1. compute alphas, betas
			if prev_timestep is None:
				__magic_name__	:   Tuple					     =					t - 1

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

			if prev_timestep == t - 1:
				__magic_name__	:   str					     =					self.betas[t]
				__magic_name__	:   Any					     =					self.alphas[t]
			else:
				__magic_name__	:   Any					     =					1 - alpha_prod_t / alpha_prod_t_prev
				__magic_name__	:   List[Any]					     =					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":
				__magic_name__	:   Union[str, Any]					     =					(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
			elif self.config.prediction_type == "sample":
				__magic_name__	:   Optional[int]					     =					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:
				__magic_name__	:   List[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
			__magic_name__	:   Any					     =					(alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
			__magic_name__	:   List[str]					     =					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
			__magic_name__	:   Dict					     =					pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

			# 6. Add noise
			__magic_name__	:   Tuple					     =					0
			if t > 0:
				__magic_name__	:   Optional[Any]					     =					randn_tensor(
				    model_output.shape  ,					dtype=model_output.dtype  ,					generator=_UpperCAmelCase  ,					device=model_output.device      )

				__magic_name__	:   Dict					     =					self._get_variance(
				    _UpperCAmelCase  ,					predicted_variance=_UpperCAmelCase  ,					prev_timestep=_UpperCAmelCase  ,					)

				if self.variance_type == "fixed_small_log":
					__magic_name__	:   int					     =					variance
				elif self.variance_type == "learned_range":
					__magic_name__	:   List[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.'      )

				__magic_name__	:   Dict					     =					variance * variance_noise

			__magic_name__	:   str					     =					pred_prev_sample + variance

			if not return_dict:
				return (pred_prev_sample,)

			return UnCLIPSchedulerOutput(prev_sample=_UpperCAmelCase  ,					pred_original_sample=_UpperCAmelCase      )







		def        __lowerCAmelCase		( self		:						List[str]  ,					_A		:						torch.FloatTensor  ,					_A		:						torch.FloatTensor  ,					_A		:						torch.IntTensor  ,					)  ->			Optional[Any]:
			__magic_name__	:   str					     =					self.alphas_cumprod.to(device=original_samples.device  ,					dtype=original_samples.dtype      )
			__magic_name__	:   str					     =					timesteps.to(original_samples.device      )

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

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

			__magic_name__	:   int					     =					sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
			return noisy_samples



'''simple docstring'''

def lowerCamelCase      (      lowerCAmelCase    :     int		):





	"""simple docstring"""







	if not isinstance(lowerCAmelCase		,		lowerCAmelCase		):
		__magic_name__	:   int					     =					f'Input value of [number={number}] must be an integer'
		raise TypeError(lowerCAmelCase		)
	if number < 0:
		return False
	__magic_name__	:   Tuple					     =					number * number
	while number > 0:
		if number % 10 != number_square % 10:
			return False
		number //= 10
		number_square //= 10
	return True


if __name__ == "__main__":
							import doctest

							doctest.testmod()