





"""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
class  _UpperCAmelCase       (     lowercase_			):
					UpperCamelCase								=							4_2
					UpperCamelCase								=							4_2







def  A__    (      UpperCamelCase ,  UpperCamelCase=0.9_99 ,  UpperCamelCase="cosine" ,  ):
							if alpha_transform_type == "cosine":

														def alpha_bar_fn(UpperCamelCase	):
																					return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2	) ** 2

							elif alpha_transform_type == "exp":

														def alpha_bar_fn(UpperCamelCase	):
																					return math.exp(t * -12.0	)

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

							A		=  []
							for i in range(UpperCamelCase	):
														A		=  i / num_diffusion_timesteps
														A		=  (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  _UpperCAmelCase       (     lowercase_					,							lowercase_			):
					UpperCamelCase								=							1


					@register_to_config
					def __init__( self      :str					,	__UpperCamelCase      :int = 10_00					,	__UpperCamelCase      :float = 0.0_001					,	__UpperCamelCase      :float = 0.02					,	__UpperCamelCase      :str = "linear"					,	__UpperCamelCase      :float = 0.0					,	__UpperCamelCase      :Optional[np.ndarray] = None					,	__UpperCamelCase      :bool = True					,	):
												if trained_betas is not None:
																			A		=  torch.from_numpy(__UpperCamelCase		)
												elif beta_schedule == "linear":
																			A		=  torch.linspace(__UpperCamelCase					,	__UpperCamelCase					,	__UpperCamelCase					,	dtype=torch.floataa		)
												elif beta_schedule == "scaled_linear":
																			# this schedule is very specific to the latent diffusion model.
																			A		=  (
																			    torch.linspace(beta_start**0.5					,	beta_end**0.5					,	__UpperCamelCase					,	dtype=torch.floataa		) ** 2
																			)
												elif beta_schedule == "squaredcos_cap_v2":
																			# Glide cosine schedule
																			A		=  betas_for_alpha_bar(__UpperCamelCase		)
												elif beta_schedule == "sigmoid":
																			# GeoDiff sigmoid schedule
																			A		=  torch.linspace(-6					,	6					,	__UpperCamelCase		)
																			A		=  torch.sigmoid(__UpperCamelCase		) * (beta_end - beta_start) + beta_start
												else:
																			raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}"		)

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

												A		=  torch.tensor(1.0		)

												# standard deviation of the initial noise distribution
												A		=  1.0

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

												A		=  eta


					def      lowerCamelCase	( self      :Tuple					,	__UpperCamelCase      :torch.FloatTensor					,	__UpperCamelCase      :Optional[int] = None		):
												return sample


					def      lowerCamelCase	( self      :Any					,	__UpperCamelCase      :int					,	__UpperCamelCase      :int = 10					,	__UpperCamelCase      :int = 10					,	__UpperCamelCase      :Union[str, torch.device] = None					,	):
												A		=  min(self.config.num_train_timesteps					,	__UpperCamelCase		)
												A		=  num_inference_steps

												A		=  []

												A		=  {}
												for j in range(0					,	num_inference_steps - jump_length					,	__UpperCamelCase		):
																			A		=  jump_n_sample - 1

												A		=  num_inference_steps
												while t >= 1:
																			A		=  t - 1
																			timesteps.append(__UpperCamelCase		)

																			if jumps.get(__UpperCamelCase					,	0		) > 0:
																										A		=  jumps[t] - 1
																										for _ in range(__UpperCamelCase		):
																																	A		=  t + 1
																																	timesteps.append(__UpperCamelCase		)

												A		=  np.array(__UpperCamelCase		) * (self.config.num_train_timesteps // self.num_inference_steps)
												A		=  torch.from_numpy(__UpperCamelCase		).to(__UpperCamelCase		)


					def      lowerCamelCase	( self      :List[Any]					,	__UpperCamelCase      :Optional[Any]		):
												A		=  t - self.config.num_train_timesteps // self.num_inference_steps

												A		=  self.alphas_cumprod[t]
												A		=  self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
												A		=  1 - alpha_prod_t
												A		=  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]
												A		=  (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

												return variance


					def      lowerCamelCase	( self      :Union[str, Any]					,	__UpperCamelCase      :torch.FloatTensor					,	__UpperCamelCase      :int					,	__UpperCamelCase      :torch.FloatTensor					,	__UpperCamelCase      :torch.FloatTensor					,	__UpperCamelCase      :torch.FloatTensor					,	__UpperCamelCase      :Optional[torch.Generator] = None					,	__UpperCamelCase      :bool = True					,	):
												A		=  timestep
												A		=  timestep - self.config.num_train_timesteps // self.num_inference_steps

												# 1. compute alphas, betas
												A		=  self.alphas_cumprod[t]
												A		=  self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
												A		=  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
												A		=  (sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

												# 3. Clip "predicted x_0"
												if self.config.clip_sample:
																			A		=  torch.clamp(__UpperCamelCase					,	-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
												A		=  model_output.device
												A		=  randn_tensor(model_output.shape					,	generator=__UpperCamelCase					,	device=__UpperCamelCase					,	dtype=model_output.dtype		)
												A		=  self.eta * self._get_variance(__UpperCamelCase		) ** 0.5

												A		=  0
												if t > 0 and self.eta > 0:
																			A		=  std_dev_t * noise

												# 6. compute "direction pointing to x_t" of formula (12)
												# from https://arxiv.org/pdf/2010.02502.pdf
												A		=  (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
												A		=  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
												A		=  (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
												A		=  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=__UpperCamelCase					,	pred_original_sample=__UpperCamelCase		)


					def      lowerCamelCase	( self      :str					,	__UpperCamelCase      :Optional[Any]					,	__UpperCamelCase      :List[str]					,	__UpperCamelCase      :List[Any]=None		):
												A		=  self.config.num_train_timesteps // self.num_inference_steps

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

																			# 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
																			A		=  (1 - beta) ** 0.5 * sample + beta**0.5 * noise

												return sample


					def      lowerCamelCase	( self      :List[Any]					,	__UpperCamelCase      :torch.FloatTensor					,	__UpperCamelCase      :torch.FloatTensor					,	__UpperCamelCase      :torch.IntTensor					,	):
												raise NotImplementedError("Use `DDPMScheduler.add_noise()` to train for sampling with RePaint."		)


					def __len__( self      :List[str]		):
												return self.config.num_train_timesteps









"""simple docstring"""
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 SchedulerMixin


_snake_case					:		str							=			logging.get_logger(__name__)  # pylint: disable=invalid-name






@dataclass
class  _UpperCAmelCase       (     lowercase_			):
					UpperCamelCase								=							42







class  _UpperCAmelCase       (     lowercase_					,							lowercase_			):
					UpperCamelCase								=							1


					@register_to_config
					def __init__( self      :Tuple					,	__UpperCamelCase      :int = 40					,	__UpperCamelCase      :float = 0.002					,	__UpperCamelCase      :float = 80.0					,	__UpperCamelCase      :float = 0.5					,	__UpperCamelCase      :float = 1.0					,	__UpperCamelCase      :float = 7.0					,	__UpperCamelCase      :bool = True					,	):
												# standard deviation of the initial noise distribution
												A		=  sigma_max

												A		=  np.linspace(0					,	1					,	__UpperCamelCase		)
												A		=  self._convert_to_karras(__UpperCamelCase		)
												A		=  self.sigma_to_t(__UpperCamelCase		)

												# setable values
												A		=  None
												A		=  torch.from_numpy(__UpperCamelCase		)
												A		=  torch.from_numpy(__UpperCamelCase		)
												A		=  False
												A		=  False


					def      lowerCamelCase	( self      :List[Any]					,	__UpperCamelCase      :Optional[Any]					,	__UpperCamelCase      :Dict=None		):
												if schedule_timesteps is None:
																			A		=  self.timesteps

												A		=  (schedule_timesteps == timestep).nonzero()
												return indices.item()


					def      lowerCamelCase	( self      :List[Any]					,	__UpperCamelCase      :torch.FloatTensor					,	__UpperCamelCase      :Union[float, torch.FloatTensor]		):
												# Get sigma corresponding to timestep
												if isinstance(__UpperCamelCase					,	torch.Tensor		):
																			A		=  timestep.to(self.timesteps.device		)
												A		=  self.index_for_timestep(__UpperCamelCase		)
												A		=  self.sigmas[step_idx]

												A		=  sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

												A		=  True
												return sample


					def      lowerCamelCase	( self      :Tuple					,	__UpperCamelCase      :Union[float, np.ndarray]		):
												if not isinstance(__UpperCamelCase					,	np.ndarray		):
																			A		=  np.array(__UpperCamelCase					,	dtype=np.floataa		)

												A		=  10_00 * 0.25 * np.log(sigmas + 1e-44		)

												return timesteps


					def      lowerCamelCase	( self      :str					,	__UpperCamelCase      :Optional[int] = None					,	__UpperCamelCase      :Union[str, torch.device] = None					,	__UpperCamelCase      :Optional[List[int]] = None					,	):
												if num_inference_steps is None and timesteps is None:
																			raise ValueError("Exactly one of `num_inference_steps` or `timesteps` must be supplied."		)

												if num_inference_steps is not None and timesteps is not None:
																			raise ValueError("Can only pass one of `num_inference_steps` or `timesteps`."		)

												# Follow DDPMScheduler custom timesteps logic
												if timesteps is not None:
																			for i in range(1					,	len(__UpperCamelCase		)		):
																										if timesteps[i] >= timesteps[i - 1]:
																																	raise ValueError("`timesteps` must be in descending order."		)

																			if timesteps[0] >= self.config.num_train_timesteps:
																										raise ValueError(
																										    f"`timesteps` must start before `self.config.train_timesteps`:"
																										    f" {self.config.num_train_timesteps}."		)

																			A		=  np.array(__UpperCamelCase					,	dtype=np.intaa		)
																			A		=  True
												else:
																			if num_inference_steps > self.config.num_train_timesteps:
																										raise ValueError(
																										    f"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"
																										    f" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"
																										    f" maximal {self.config.num_train_timesteps} timesteps."		)

																			A		=  num_inference_steps

																			A		=  self.config.num_train_timesteps // self.num_inference_steps
																			A		=  (np.arange(0					,	__UpperCamelCase		) * step_ratio).round()[::-1].copy().astype(np.intaa		)
																			A		=  False

												# Map timesteps to Karras sigmas directly for multistep sampling
												# See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
												A		=  self.config.num_train_timesteps
												A		=  timesteps[::-1].copy()
												A		=  ramp / (num_train_timesteps - 1)
												A		=  self._convert_to_karras(__UpperCamelCase		)
												A		=  self.sigma_to_t(__UpperCamelCase		)

												A		=  np.concatenate([sigmas, [self.sigma_min]]		).astype(np.floataa		)
												A		=  torch.from_numpy(__UpperCamelCase		).to(device=__UpperCamelCase		)

												if str(__UpperCamelCase		).startswith("mps"		):
																			# mps does not support float64
																			A		=  torch.from_numpy(__UpperCamelCase		).to(__UpperCamelCase					,	dtype=torch.floataa		)
												else:
																			A		=  torch.from_numpy(__UpperCamelCase		).to(device=__UpperCamelCase		)


					def      lowerCamelCase	( self      :Optional[Any]					,	__UpperCamelCase      :Union[str, Any]		):
												A		=  self.config.sigma_min
												A		=  self.config.sigma_max

												A		=  self.config.rho
												A		=  sigma_min ** (1 / rho)
												A		=  sigma_max ** (1 / rho)
												A		=  (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
												return sigmas


					def      lowerCamelCase	( self      :Union[str, Any]					,	__UpperCamelCase      :Dict		):
												A		=  self.config.sigma_data

												A		=  sigma_data**2 / (sigma**2 + sigma_data**2)
												A		=  sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
												return c_skip, c_out


					def      lowerCamelCase	( self      :Any					,	__UpperCamelCase      :Tuple		):
												A		=  self.config.sigma_min
												A		=  self.config.sigma_data

												A		=  sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
												A		=  (sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
												return c_skip, c_out


					def      lowerCamelCase	( self      :Optional[int]					,	__UpperCamelCase      :torch.FloatTensor					,	__UpperCamelCase      :Union[float, torch.FloatTensor]					,	__UpperCamelCase      :torch.FloatTensor					,	__UpperCamelCase      :Optional[torch.Generator] = None					,	__UpperCamelCase      :bool = 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"
																			        f" `{self.__class__}.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		):
																			A		=  timestep.to(self.timesteps.device		)

												A		=  self.config.sigma_min
												A		=  self.config.sigma_max

												A		=  self.index_for_timestep(__UpperCamelCase		)

												# sigma_next corresponds to next_t in original implementation
												A		=  self.sigmas[step_index]
												if step_index + 1 < self.config.num_train_timesteps:
																			A		=  self.sigmas[step_index + 1]
												else:
																			# Set sigma_next to sigma_min
																			A		=  self.sigmas[-1]

												# Get scalings for boundary conditions
												A,     A		=  self.get_scalings_for_boundary_condition(__UpperCamelCase		)

												# 1. Denoise model output using boundary conditions
												A		=  c_out * model_output + c_skip * sample
												if self.config.clip_denoised:
																			A		=  denoised.clamp(-1					,	1		)

												# 2. Sample z ~ N(0, s_noise^2 * I)
												# Noise is not used for onestep sampling.
												if len(self.timesteps		) > 1:
																			A		=  randn_tensor(
																			    model_output.shape					,	dtype=model_output.dtype					,	device=model_output.device					,	generator=__UpperCamelCase		)
												else:
																			A		=  torch.zeros_like(__UpperCamelCase		)
												A		=  noise * self.config.s_noise

												A		=  sigma_next.clamp(min=__UpperCamelCase					,	max=__UpperCamelCase		)

												# 3. Return noisy sample
												# tau = sigma_hat, eps = sigma_min
												A		=  denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

												if not return_dict:
																			return (prev_sample,)

												return CMStochasticIterativeSchedulerOutput(prev_sample=__UpperCamelCase		)


					def      lowerCamelCase	( self      :Union[str, Any]					,	__UpperCamelCase      :torch.FloatTensor					,	__UpperCamelCase      :torch.FloatTensor					,	__UpperCamelCase      :torch.FloatTensor					,	):
												# Make sure sigmas and timesteps have the same device and dtype as original_samples
												A		=  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
																			A		=  self.timesteps.to(original_samples.device					,	dtype=torch.floataa		)
																			A		=  timesteps.to(original_samples.device					,	dtype=torch.floataa		)
												else:
																			A		=  self.timesteps.to(original_samples.device		)
																			A		=  timesteps.to(original_samples.device		)

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

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

												A		=  original_samples + noise * sigma
												return noisy_samples


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


