



"""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       __lowerCamelCase	(   __lowercase    ):
   __UpperCamelCase									=   42
   __UpperCamelCase									=   42






def 	__UpperCAmelCase					(       snake_case_  :  Any   ,	snake_case_  :  Optional[int]=0.9_9_9   ,	snake_case_  :  Optional[Any]="cosine"   ,	)							->	Tuple:






   """simple docstring"""


   if alpha_transform_type == "cosine":

      def alpha_bar_fn(snake_case_  :  List[str]   ):
         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(snake_case_  :  Dict   ):
         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       __lowerCamelCase	(   __lowercase   ,							__lowercase    ):
   __UpperCamelCase									=   1







   @register_to_config
   def __init__(self       ,	lowerCamelCase = 1_000       ,	lowerCamelCase = 0.0001       ,	lowerCamelCase = 0.02       ,	lowerCamelCase = "linear"       ,	lowerCamelCase = 0.0       ,	lowerCamelCase = None       ,	lowerCamelCase = True       ,	):




      '''simple docstring'''




      if trained_betas is not None:
         _lowerCAmelCase					     =		torch.from_numpy(lowerCamelCase				)
      elif beta_schedule == "linear":
         _lowerCAmelCase					     =		torch.linspace(lowerCamelCase       ,	lowerCamelCase       ,	lowerCamelCase       ,	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       ,	lowerCamelCase       ,	dtype=torch.floataa				) ** 2
         )
      elif beta_schedule == "squaredcos_cap_v2":
         # Glide cosine schedule
         _lowerCAmelCase					     =		betas_for_alpha_bar(lowerCamelCase				)
      elif beta_schedule == "sigmoid":
         # GeoDiff sigmoid schedule
         _lowerCAmelCase					     =		torch.linspace(-6       ,	6       ,	lowerCamelCase				)
         _lowerCAmelCase					     =		torch.sigmoid(lowerCamelCase				) * (beta_end - beta_start) + beta_start
      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					     =		torch.tensor(1.0				)

      _lowerCAmelCase					     =		torch.tensor(1.0				)

      # standard deviation of the initial noise distribution
      _lowerCAmelCase					     =		1.0

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

      _lowerCAmelCase					     =		eta







   def 							A__						(self       ,	lowerCamelCase       ,	lowerCamelCase = None				):




      '''simple docstring'''




      return sample







   def 							A__						(self       ,	lowerCamelCase       ,	lowerCamelCase = 10       ,	lowerCamelCase = 10       ,	lowerCamelCase = None       ,	):




      '''simple docstring'''




      _lowerCAmelCase					     =		min(self.config.num_train_timesteps       ,	lowerCamelCase				)
      _lowerCAmelCase					     =		num_inference_steps

      _lowerCAmelCase					     =		[]

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

      _lowerCAmelCase					     =		num_inference_steps
      while t >= 1:
         _lowerCAmelCase					     =		t - 1
         timesteps.append(lowerCamelCase				)

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

      _lowerCAmelCase					     =		np.array(lowerCamelCase				) * (self.config.num_train_timesteps // self.num_inference_steps)
      _lowerCAmelCase					     =		torch.from_numpy(lowerCamelCase				).to(lowerCamelCase				)







   def 							A__						(self       ,	lowerCamelCase				):




      '''simple docstring'''




      _lowerCAmelCase					     =		t - self.config.num_train_timesteps // self.num_inference_steps

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

      return variance







   def 							A__						(self       ,	lowerCamelCase       ,	lowerCamelCase       ,	lowerCamelCase       ,	lowerCamelCase       ,	lowerCamelCase       ,	lowerCamelCase = None       ,	lowerCamelCase = True       ,	):




      '''simple docstring'''




      _lowerCAmelCase					     =		timestep
      _lowerCAmelCase					     =		timestep - self.config.num_train_timesteps // self.num_inference_steps

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

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

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

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







   def 							A__						(self       ,	lowerCamelCase       ,	lowerCamelCase       ,	lowerCamelCase=None				):




      '''simple docstring'''




      _lowerCAmelCase					     =		self.config.num_train_timesteps // self.num_inference_steps

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

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

      return sample







   def 							A__						(self       ,	lowerCamelCase       ,	lowerCamelCase       ,	lowerCamelCase       ,	):




      '''simple docstring'''




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







   def __len__(self				):




      '''simple docstring'''




      return self.config.num_train_timesteps




"""simple docstring"""





def 	__UpperCAmelCase					(       snake_case_  :  float   )							->	int:






   """simple docstring"""


   return int(snake_case_   ) if x - int(snake_case_   ) <= 0 else int(snake_case_   ) + 1


if __name__ == "__main__":
  import doctest

  doctest.testmod()