

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							(      __snake_case      ):
      lowerCamelCase_      : torch.FloatTensor
      lowerCamelCase_      : torch.FloatTensor



def   UpperCamelCase(   lowercase_     ,   lowercase_=0.9_99     ,   lowercase_="cosine"     ,   )      ->							Any:





  '''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}'''				)

  snake_case_					= []
  for i in range(lowercase_				):
    snake_case_					= i / num_diffusion_timesteps
    snake_case_					= (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 					__lowerCamelCase							(      __snake_case    ,				__snake_case      ):
      lowerCamelCase_      : Union[str, Any]  			=   1







      @register_to_config
      def __init__(	self				,    lowerCamelCase = 1000				,    lowerCamelCase = 0.0001				,    lowerCamelCase = 0.02				,    lowerCamelCase = "linear"				,    lowerCamelCase = 0.0				,    lowerCamelCase = None				,    lowerCamelCase = True				,    )		->			List[Any]:
        if trained_betas is not None:
          snake_case_					= torch.from_numpy(lowerCamelCase  )
        elif beta_schedule == "linear":
          snake_case_					= torch.linspace(lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    dtype=torch.floataa  )
        elif beta_schedule == "scaled_linear":
          # this schedule is very specific to the latent diffusion model.
          snake_case_					= (
              torch.linspace(beta_start**0.5				,    beta_end**0.5				,    lowerCamelCase				,    dtype=torch.floataa  ) ** 2
          )
        elif beta_schedule == "squaredcos_cap_v2":
          # Glide cosine schedule
          snake_case_					= betas_for_alpha_bar(lowerCamelCase  )
        elif beta_schedule == "sigmoid":
          # GeoDiff sigmoid schedule
          snake_case_					= torch.linspace(-6				,    6				,    lowerCamelCase  )
          snake_case_					= torch.sigmoid(lowerCamelCase  ) * (beta_end - beta_start) + beta_start
        else:
          raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''  )

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

        snake_case_					= torch.tensor(1.0  )

        # standard deviation of the initial noise distribution
        snake_case_					= 1.0

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

        snake_case_					= eta







      def       lowerCAmelCase_       (	self				,    lowerCamelCase				,    lowerCamelCase = None  )		->			torch.FloatTensor:
        return sample







      def       lowerCAmelCase_       (	self				,    lowerCamelCase				,    lowerCamelCase = 10				,    lowerCamelCase = 10				,    lowerCamelCase = None				,    )		->			int:
        snake_case_					= min(self.config.num_train_timesteps				,    lowerCamelCase  )
        snake_case_					= num_inference_steps

        snake_case_					= []

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

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

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

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







      def       lowerCAmelCase_       (	self				,    lowerCamelCase  )		->			Any:
        snake_case_					= t - self.config.num_train_timesteps // self.num_inference_steps

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

        return variance







      def       lowerCAmelCase_       (	self				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase = None				,    lowerCamelCase = True				,    )		->			Union[RePaintSchedulerOutput, Tuple]:
        snake_case_					= timestep
        snake_case_					= timestep - self.config.num_train_timesteps // self.num_inference_steps

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

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

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

        # 6. compute "direction pointing to x_t" of formula (12)
        # from https://arxiv.org/pdf/2010.02502.pdf
        snake_case_					= (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
        snake_case_					= 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
        snake_case_					= (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
        snake_case_					= 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       lowerCAmelCase_       (	self				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase=None  )		->			Union[str, Any]:
        snake_case_					= self.config.num_train_timesteps // self.num_inference_steps

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

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

        return sample







      def       lowerCAmelCase_       (	self				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    )		->			torch.FloatTensor:
        raise NotImplementedError("""Use `DDPMScheduler.add_noise()` to train for sampling with RePaint."""  )







      def __len__(	self  )		->			Optional[Any]:
        return self.config.num_train_timesteps


from math import pi



def   UpperCamelCase(   lowercase_				)      ->							float:





  '''simple docstring'''





  return degree / (180 / pi)


if __name__ == "__main__":
    from doctest import testmod

    testmod()