code: 
  
  """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				(     _A		):
  						SCREAMING_SNAKE_CASE              =					42
  						SCREAMING_SNAKE_CASE              =					42
  
  def      __SCREAMING_SNAKE_CASE		(		lowercase__    ,							lowercase__=0.9_99    ,							lowercase__="cosine"    ,							):
  					"""simple docstring"""
  					if alpha_transform_type == "cosine":
  
  										def alpha_bar_fn(lowercase__   ):
  															return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2   ) ** 2
  
  					elif alpha_transform_type == "exp":
  
  										def alpha_bar_fn(lowercase__   ):
  															return math.exp(t * -12.0   )
  
  					else:
  										raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}"""   )
  
  					A    =      []
  					for i in range(lowercase__   ):
  										A    =      i / num_diffusion_timesteps
  										A    =      (i + 1) / num_diffusion_timesteps
  										betas.append(min(1 - alpha_bar_fn(lowercase__   ) / alpha_bar_fn(lowercase__   )    ,							lowercase__   )   )
  					return torch.tensor(lowercase__    ,							dtype=torch.floataa   )
  
  
  
  
  
  class     __UpperCamelCase				(     _A  ,	_A		):
  						SCREAMING_SNAKE_CASE              =					1
  
  
  						@register_to_config
  						def __init__(self				:	Tuple  ,  __SCREAMING_SNAKE_CASE				:	int = 1_0_0_0  ,  __SCREAMING_SNAKE_CASE				:	float = 0.0_0_0_1  ,  __SCREAMING_SNAKE_CASE				:	float = 0.0_2  ,  __SCREAMING_SNAKE_CASE				:	str = "linear"  ,  __SCREAMING_SNAKE_CASE				:	float = 0.0  ,  __SCREAMING_SNAKE_CASE				:	Optional[np.ndarray] = None  ,  __SCREAMING_SNAKE_CASE				:	bool = True  ,  ):
  											if trained_betas is not None:
  																A    =      torch.from_numpy(__SCREAMING_SNAKE_CASE)
  											elif beta_schedule == "linear":
  																A    =      torch.linspace(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE  ,  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  ,  __SCREAMING_SNAKE_CASE  ,  dtype=torch.floataa) ** 2
  																)
  											elif beta_schedule == "squaredcos_cap_v2":
  																# Glide cosine schedule
  																A    =      betas_for_alpha_bar(__SCREAMING_SNAKE_CASE)
  											elif beta_schedule == "sigmoid":
  																# GeoDiff sigmoid schedule
  																A    =      torch.linspace(-6  ,  6  ,  __SCREAMING_SNAKE_CASE)
  																A    =      torch.sigmoid(__SCREAMING_SNAKE_CASE) * (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  ,  __SCREAMING_SNAKE_CASE)[::-1].copy())
  
  											A    =      eta
  
  
  						def       SCREAMING_SNAKE_CASE__     (self				:	str  ,  __SCREAMING_SNAKE_CASE				:	torch.FloatTensor  ,  __SCREAMING_SNAKE_CASE				:	Optional[int] = None):
  											return sample
  
  
  						def       SCREAMING_SNAKE_CASE__     (self				:	List[str]  ,  __SCREAMING_SNAKE_CASE				:	int  ,  __SCREAMING_SNAKE_CASE				:	int = 1_0  ,  __SCREAMING_SNAKE_CASE				:	int = 1_0  ,  __SCREAMING_SNAKE_CASE				:	Union[str, torch.device] = None  ,  ):
  											A    =      min(self.config.num_train_timesteps  ,  __SCREAMING_SNAKE_CASE)
  											A    =      num_inference_steps
  
  											A    =      []
  
  											A    =      {}
  											for j in range(0  ,  num_inference_steps - jump_length  ,  __SCREAMING_SNAKE_CASE):
  																A    =      jump_n_sample - 1
  
  											A    =      num_inference_steps
  											while t >= 1:
  																A    =      t - 1
  																timesteps.append(__SCREAMING_SNAKE_CASE)
  
  																if jumps.get(__SCREAMING_SNAKE_CASE  ,  0) > 0:
  																					A    =      jumps[t] - 1
  																					for _ in range(__SCREAMING_SNAKE_CASE):
  																										A    =      t + 1
  																										timesteps.append(__SCREAMING_SNAKE_CASE)
  
  											A    =      np.array(__SCREAMING_SNAKE_CASE) * (self.config.num_train_timesteps // self.num_inference_steps)
  											A    =      torch.from_numpy(__SCREAMING_SNAKE_CASE).to(__SCREAMING_SNAKE_CASE)
  
  
  						def       SCREAMING_SNAKE_CASE__     (self				:	Optional[Any]  ,  __SCREAMING_SNAKE_CASE				:	Tuple):
  											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       SCREAMING_SNAKE_CASE__     (self				:	Any  ,  __SCREAMING_SNAKE_CASE				:	torch.FloatTensor  ,  __SCREAMING_SNAKE_CASE				:	int  ,  __SCREAMING_SNAKE_CASE				:	torch.FloatTensor  ,  __SCREAMING_SNAKE_CASE				:	torch.FloatTensor  ,  __SCREAMING_SNAKE_CASE				:	torch.FloatTensor  ,  __SCREAMING_SNAKE_CASE				:	Optional[torch.Generator] = None  ,  __SCREAMING_SNAKE_CASE				:	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(__SCREAMING_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
  											A    =      model_output.device
  											A    =      randn_tensor(model_output.shape  ,  generator=__SCREAMING_SNAKE_CASE  ,  device=__SCREAMING_SNAKE_CASE  ,  dtype=model_output.dtype)
  											A    =      self.eta * self._get_variance(__SCREAMING_SNAKE_CASE) ** 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=__SCREAMING_SNAKE_CASE  ,  pred_original_sample=__SCREAMING_SNAKE_CASE)
  
  
  						def       SCREAMING_SNAKE_CASE__     (self				:	int  ,  __SCREAMING_SNAKE_CASE				:	List[str]  ,  __SCREAMING_SNAKE_CASE				:	Tuple  ,  __SCREAMING_SNAKE_CASE				:	List[Any]=None):
  											A    =      self.config.num_train_timesteps // self.num_inference_steps
  
  											for i in range(__SCREAMING_SNAKE_CASE):
  																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=__SCREAMING_SNAKE_CASE)
  																					A    =      noise.to(sample.device)
  																else:
  																					A    =      randn_tensor(sample.shape  ,  generator=__SCREAMING_SNAKE_CASE  ,  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       SCREAMING_SNAKE_CASE__     (self				:	List[str]  ,  __SCREAMING_SNAKE_CASE				:	torch.FloatTensor  ,  __SCREAMING_SNAKE_CASE				:	torch.FloatTensor  ,  __SCREAMING_SNAKE_CASE				:	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
  
  
  
  
  
  
style_context: 
  
  """simple docstring"""
  
  
  
  
  def      __SCREAMING_SNAKE_CASE		(		lowercase__    ,							lowercase__   ):
  					"""simple docstring"""
  					A    =      1
  					while b > 0:
  										if b & 1:
  															res *= a
  
  										a *= a
  										b >>= 1
  
  					return res
  def      __SCREAMING_SNAKE_CASE		(		lowercase__    ,							lowercase__    ,							lowercase__   ):
  					"""simple docstring"""
  					A    =      1
  					while b > 0:
  										if b & 1:
  															A    =      ((res % c) * (a % c)) % c
  
  										a *= a
  										b >>= 1
  
  					return res
  
  
  
  
  
  