
'''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__  (    SCREAMING_SNAKE_CASE):
 UpperCAmelCase__    :    torch.FloatTensor
 UpperCAmelCase__    :    torch.FloatTensor





def        snake_case							(		UpperCAmelCase      ,  UpperCAmelCase=0.999      ,  UpperCAmelCase="cosine"      ,  )->			Union[str, Any]:







      """simple docstring"""
      if alpha_transform_type == "cosine":

            def alpha_bar_fn(UpperCAmelCase	):
                  return math.cos((t + 0.008) / 1.008 * 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__  (    SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE):
 UpperCAmelCase__    :    List[str]										=					1






 @register_to_config
 def __init__(			self  :int	,				_A  :int = 1_000	,				_A  :float = 0.0_001	,				_A  :float = 0.02	,				_A  :str = "linear"	,				_A  :float = 0.0	,				_A  :Optional[np.ndarray] = None	,				_A  :bool = True	,				)				->			Any:







       '''simple docstring'''



       if trained_betas is not None:
             __A       	=			torch.from_numpy(_A			)
       elif beta_schedule == "linear":
             __A       	=			torch.linspace(_A	,				_A	,				_A	,				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	,				_A	,				dtype=torch.floataa			) ** 2
             )
       elif beta_schedule == "squaredcos_cap_v2":
             # Glide cosine schedule
             __A       	=			betas_for_alpha_bar(_A			)
       elif beta_schedule == "sigmoid":
             # GeoDiff sigmoid schedule
             __A       	=			torch.linspace(-6	,				6	,				_A			)
             __A       	=			torch.sigmoid(_A			) * (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	,				_A			)[::-1].copy()			)

       __A       	=			eta






 def 	lowercase_						(			self  :Optional[Any]	,				_A  :torch.FloatTensor	,				_A  :Optional[int] = None			)				->			torch.FloatTensor:







       '''simple docstring'''



       return sample






 def 	lowercase_						(			self  :int	,				_A  :int	,				_A  :int = 10	,				_A  :int = 10	,				_A  :Union[str, torch.device] = None	,				)				->			int:







       '''simple docstring'''



       __A       	=			min(self.config.num_train_timesteps	,				_A			)
       __A       	=			num_inference_steps

       __A       	=			[]

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

       __A       	=			num_inference_steps
       while t >= 1:
             __A       	=			t - 1
             timesteps.append(_A			)

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

       __A       	=			np.array(_A			) * (self.config.num_train_timesteps // self.num_inference_steps)
       __A       	=			torch.from_numpy(_A			).to(_A			)






 def 	lowercase_						(			self  :Optional[Any]	,				_A  :Tuple			)				->			Optional[Any]:







       '''simple docstring'''



       __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 	lowercase_						(			self  :List[str]	,				_A  :torch.FloatTensor	,				_A  :int	,				_A  :torch.FloatTensor	,				_A  :torch.FloatTensor	,				_A  :torch.FloatTensor	,				_A  :Optional[torch.Generator] = None	,				_A  :bool = True	,				)				->			Union[RePaintSchedulerOutput, Tuple]:







       '''simple docstring'''



       __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(_A	,				-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=_A	,				device=_A	,				dtype=model_output.dtype			)
       __A       	=			self.eta * self._get_variance(_A			) ** 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=_A	,				pred_original_sample=_A			)






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







       '''simple docstring'''



       __A       	=			self.config.num_train_timesteps // self.num_inference_steps

       for i in range(_A			):
             __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=_A			)
                   __A       	=			noise.to(sample.device			)
             else:
                   __A       	=			randn_tensor(sample.shape	,				generator=_A	,				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 	lowercase_						(			self  :List[Any]	,				_A  :torch.FloatTensor	,				_A  :torch.FloatTensor	,				_A  :torch.IntTensor	,				)				->			torch.FloatTensor:







       '''simple docstring'''



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






 def __len__(			self  :Optional[int]			)				->			Tuple:







       '''simple docstring'''



       return self.config.num_train_timesteps








'''simple docstring'''





def        snake_case							(		UpperCAmelCase	)->			int:







      """simple docstring"""
      return int(UpperCAmelCase	) if x - int(UpperCAmelCase	) <= 0 else int(UpperCAmelCase	) + 1


if __name__ == "__main__":
 import doctest

 doctest.testmod()






