





def  A					(				UpperCAmelCase					):
					if num < 0:
										return -1
					return 1 if num else 0







def  A					(				):
					assert signum(5					) == 1
					assert signum(-5					) == -1
					assert signum(0					) == 0


if __name__ == "__main__":
							print(signum(12))
							print(signum(-12))
							print(signum(0))






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
class 							_a(     __A     ):
		lowerCamelCase__  :torch.FloatTensor
		lowerCamelCase__  :torch.FloatTensor







def  A					(				UpperCAmelCase	,	UpperCAmelCase=0.9_9_9	,	UpperCAmelCase="cosine"	,	):
					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}"""					)

					_snake_case							:       Optional[int]												= []
					for i in range(UpperCAmelCase					):
										_snake_case							:       Optional[int]												= i / num_diffusion_timesteps
										_snake_case							:       int												= (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 							_a(     __A						, __A     ):
		lowerCamelCase__  :Any 					=		1







		@register_to_config
		def __init__(     self			,    __snake_case = 1_0_0_0			,    __snake_case = 0.00_01			,    __snake_case = 0.02			,    __snake_case = "linear"			,    __snake_case = 0.0			,    __snake_case = None			,    __snake_case = True			,    )   ->   Tuple:


							'''simple docstring'''

							if trained_betas is not None:
												_snake_case							:       Any												= torch.from_numpy(__snake_case					)
							elif beta_schedule == "linear":
												_snake_case							:       List[Any]												= torch.linspace(__snake_case			,    __snake_case			,    __snake_case			,    dtype=torch.floataa					)
							elif beta_schedule == "scaled_linear":
												# this schedule is very specific to the latent diffusion model.
												_snake_case							:       Tuple												= (
												    torch.linspace(beta_start**0.5			,    beta_end**0.5			,    __snake_case			,    dtype=torch.floataa					) ** 2
												)
							elif beta_schedule == "squaredcos_cap_v2":
												# Glide cosine schedule
												_snake_case							:       Optional[int]												= betas_for_alpha_bar(__snake_case					)
							elif beta_schedule == "sigmoid":
												# GeoDiff sigmoid schedule
												_snake_case							:       Union[str, Any]												= torch.linspace(-6			,    6			,    __snake_case					)
												_snake_case							:       Optional[Any]												= torch.sigmoid(__snake_case					) * (beta_end - beta_start) + beta_start
							else:
												raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""					)

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

							_snake_case							:       Any												= torch.tensor(1.0					)

							# standard deviation of the initial noise distribution
							_snake_case							:       int												= 1.0

							# setable values
							_snake_case							:       Optional[Any]												= None
							_snake_case							:       str												= torch.from_numpy(np.arange(0			,    __snake_case					)[::-1].copy()					)

							_snake_case							:       Any												= eta







		def 							lowercase							(     self			,    __snake_case			,    __snake_case = None					)   ->   torch.FloatTensor:


							'''simple docstring'''

							return sample







		def 							lowercase							(     self			,    __snake_case			,    __snake_case = 1_0			,    __snake_case = 1_0			,    __snake_case = None			,    )   ->   List[str]:


							'''simple docstring'''

							_snake_case							:       Optional[int]												= min(self.config.num_train_timesteps			,    __snake_case					)
							_snake_case							:       Tuple												= num_inference_steps

							_snake_case							:       List[str]												= []

							_snake_case							:       str												= {}
							for j in range(0			,    num_inference_steps - jump_length			,    __snake_case					):
												_snake_case							:       Tuple												= jump_n_sample - 1

							_snake_case							:       int												= num_inference_steps
							while t >= 1:
												_snake_case							:       Union[str, Any]												= t - 1
												timesteps.append(__snake_case					)

												if jumps.get(__snake_case			,    0					) > 0:
																	_snake_case							:       Any												= jumps[t] - 1
																	for _ in range(__snake_case					):
																						_snake_case							:       List[str]												= t + 1
																						timesteps.append(__snake_case					)

							_snake_case							:       Optional[Any]												= np.array(__snake_case					) * (self.config.num_train_timesteps // self.num_inference_steps)
							_snake_case							:       int												= torch.from_numpy(__snake_case					).to(__snake_case					)







		def 							lowercase							(     self			,    __snake_case					)   ->   int:


							'''simple docstring'''

							_snake_case							:       Optional[int]												= t - self.config.num_train_timesteps // self.num_inference_steps

							_snake_case							:       Optional[int]												= self.alphas_cumprod[t]
							_snake_case							:       Union[str, Any]												= self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
							_snake_case							:       Optional[Any]												= 1 - alpha_prod_t
							_snake_case							:       str												= 1 - 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
							# Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
							# without eta.
							# variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
							_snake_case							:       Dict												= (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

							return variance







		def 							lowercase							(     self			,    __snake_case			,    __snake_case			,    __snake_case			,    __snake_case			,    __snake_case			,    __snake_case = None			,    __snake_case = True			,    )   ->   Union[RePaintSchedulerOutput, Tuple]:


							'''simple docstring'''

							_snake_case							:       List[Any]												= timestep
							_snake_case							:       Optional[int]												= timestep - self.config.num_train_timesteps // self.num_inference_steps

							# 1. compute alphas, betas
							_snake_case							:       Optional[Any]												= self.alphas_cumprod[t]
							_snake_case							:       Any												= self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
							_snake_case							:       Dict												= 1 - alpha_prod_t

							# 2. compute predicted original sample from predicted noise also called
							# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
							_snake_case							:       List[str]												= (sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

							# 3. Clip "predicted x_0"
							if self.config.clip_sample:
												_snake_case							:       str												= torch.clamp(__snake_case			,    -1			,    1					)

							# We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
							# substitute formula (7) in the algorithm coming from DDPM paper
							# (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
							# DDIM schedule gives the same results as DDPM with eta = 1.0
							# Noise is being reused in 7. and 8., but no impact on quality has
							# been observed.

							# 5. Add noise
							_snake_case							:       Tuple												= model_output.device
							_snake_case							:       Any												= randn_tensor(model_output.shape			,    generator=__snake_case			,    device=__snake_case			,    dtype=model_output.dtype					)
							_snake_case							:       Tuple												= self.eta * self._get_variance(__snake_case					) ** 0.5

							_snake_case							:       str												= 0
							if t > 0 and self.eta > 0:
												_snake_case							:       List[str]												= std_dev_t * noise

							# 6. compute "direction pointing to x_t" of formula (12)
							# from https://arxiv.org/pdf/2010.02502.pdf
							_snake_case							:       List[str]												= (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

							# 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
							_snake_case							:       int												= alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance

							# 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
							_snake_case							:       Tuple												= (alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise

							# 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
							_snake_case							:       int												= mask * prev_known_part + (1.0 - mask) * prev_unknown_part

							if not return_dict:
												return (
												    pred_prev_sample,
												    pred_original_sample,
												)

							return RePaintSchedulerOutput(prev_sample=__snake_case			,    pred_original_sample=__snake_case					)







		def 							lowercase							(     self			,    __snake_case			,    __snake_case			,    __snake_case=None					)   ->   Optional[Any]:


							'''simple docstring'''

							_snake_case							:       Optional[Any]												= self.config.num_train_timesteps // self.num_inference_steps

							for i in range(__snake_case					):
												_snake_case							:       List[Any]												= self.betas[timestep + i]
												if sample.device.type == "mps":
																	# randn does not work reproducibly on mps
																	_snake_case							:       List[Any]												= randn_tensor(sample.shape			,    dtype=sample.dtype			,    generator=__snake_case					)
																	_snake_case							:       int												= noise.to(sample.device					)
												else:
																	_snake_case							:       Any												= randn_tensor(sample.shape			,    generator=__snake_case			,    device=sample.device			,    dtype=sample.dtype					)

												# 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
												_snake_case							:       List[Any]												= (1 - beta) ** 0.5 * sample + beta**0.5 * noise

							return sample







		def 							lowercase							(     self			,    __snake_case			,    __snake_case			,    __snake_case			,    )   ->   torch.FloatTensor:


							'''simple docstring'''

							raise NotImplementedError("Use `DDPMScheduler.add_noise()` to train for sampling with RePaint."					)







		def __len__(     self					)   ->   Optional[Any]:


							'''simple docstring'''

							return self.config.num_train_timesteps