


"""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	(       a__					):

     '''simple docstring'''
     A_							:						torch.FloatTensor
     A_							:						torch.FloatTensor







def    A_ ( _lowerCAmelCase  :       Optional[int],  _lowerCAmelCase  :       Optional[Any]=0.9_9_9,  _lowerCAmelCase  :       Optional[Any]="cosine",  ):






     """simple docstring"""

     if alpha_transform_type == "cosine":

          def alpha_bar_fn(_lowerCAmelCase  :       int							):
               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(_lowerCAmelCase  :       Dict							):
               return math.exp(t * -1_2.0							)

     else:
          raise ValueError(f'Unsupported alpha_tranform_type: {alpha_transform_type}'							)

     _a     						=					[]
     for i in range(_lowerCAmelCase							):
          _a     						=					i / num_diffusion_timesteps
          _a     						=					(i + 1) / num_diffusion_timesteps
          betas.append(min(1 - alpha_bar_fn(_lowerCAmelCase							) / alpha_bar_fn(_lowerCAmelCase							),  _lowerCAmelCase							)							)
     return torch.tensor(_lowerCAmelCase,  dtype=torch.floataa							)


class      __lowerCamelCase	(       a__	,       a__					):

     '''simple docstring'''
     A_							:						List[str]     =		1


     @register_to_config
     def __init__(   self     ,					__UpperCAmelCase = 1000     ,					__UpperCAmelCase = 0.0001     ,					__UpperCAmelCase = 0.02     ,					__UpperCAmelCase = "linear"     ,					__UpperCAmelCase = 0.0     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = True     ,					)							->				List[Any]:
          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 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase = None				)							->				torch.FloatTensor:
          return sample


     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase = 10     ,					__UpperCAmelCase = 10     ,					__UpperCAmelCase = None     ,					)							->				Union[str, Any]:
          _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 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase				)							->				int:
          _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 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase = None     ,					__UpperCAmelCase = True     ,					)							->				Union[RePaintSchedulerOutput, Tuple]:
          _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 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase=None				)							->				List[str]:
          _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 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					)							->				torch.FloatTensor:
          raise NotImplementedError('''Use `DDPMScheduler.add_noise()` to train for sampling with RePaint.'''				)





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



"""simple docstring"""






import math
import os

import cva
import numpy as np

__snake_case												=							255.0
def    A_ ( _lowerCAmelCase  :       float,  _lowerCAmelCase  :       float							):






     """simple docstring"""

     _a     						=					np.mean((original - contrast) ** 2							)
     if mse == 0:
          return 1_00

     return 20 * math.logaa(PIXEL_MAX / math.sqrt(_lowerCAmelCase							)							)
def    A_ ( ):






     """simple docstring"""

     _a     						=					os.path.dirname(os.path.realpath(_lowerCAmelCase							)							)
     # Loading images (original image and compressed image)
     _a     						=					cva.imread(os.path.join(_lowerCAmelCase,  '''image_data/original_image.png'''							)							)
     _a     						=					cva.imread(os.path.join(_lowerCAmelCase,  '''image_data/compressed_image.png'''							),  1							)

     _a     						=					cva.imread(os.path.join(_lowerCAmelCase,  '''image_data/PSNR-example-base.png'''							)							)
     _a     						=					cva.imread(
         os.path.join(_lowerCAmelCase,  '''image_data/PSNR-example-comp-10.jpg'''							),  1							)

     # Value expected: 29.73dB
     print('''-- First Test --'''							)
     print(f'PSNR value is {peak_signal_to_noise_ratio(_lowerCAmelCase,  _lowerCAmelCase							)} dB'							)

     # # Value expected: 31.53dB (Wikipedia Example)
     print('''\n-- Second Test --'''							)
     print(f'PSNR value is {peak_signal_to_noise_ratio(_lowerCAmelCase,  _lowerCAmelCase							)} dB'							)


if __name__ == "__main__":
   main()