





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"""






      UpperCAmelCase_						:      torch.FloatTensor
      UpperCAmelCase_						:      torch.FloatTensor







def       lowerCAmelCase__(	lowercase       :    Optional[int]   ,     lowercase       :    List[str]=0.9_9_9   ,     lowercase       :    int="cosine"   ,     ) ->    List[Any]:
  if alpha_transform_type == "cosine":

    def alpha_bar_fn(lowercase       :    Dict				):
      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(lowercase       :    Union[str, Any]				):
      return math.exp(t * -1_2.0				)

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

  __snake_case   :				Dict        =       []
  for i in range(lowercase				):
    __snake_case   :				Union[str, Any]        =       i / num_diffusion_timesteps
    __snake_case   :				Optional[Any]        =       (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 (    a ,   a    ):



      """simple docstring"""






      UpperCAmelCase_						:      Tuple       =1







      @register_to_config
      def __init__( self				,						UpperCAmelCase = 1000				,						UpperCAmelCase = 0.0_001				,						UpperCAmelCase = 0.02				,						UpperCAmelCase = "linear"				,						UpperCAmelCase = 0.0				,						UpperCAmelCase = None				,						UpperCAmelCase = True				,						)       ->   Optional[Any]:






        '''simple docstring'''






        if trained_betas is not None:
          __snake_case   :				List[Any]        =       torch.from_numpy(UpperCAmelCase   )
        elif beta_schedule == "linear":
          __snake_case   :				Tuple        =       torch.linspace(UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						dtype=torch.floataa   )
        elif beta_schedule == "scaled_linear":
          # this schedule is very specific to the latent diffusion model.
          __snake_case   :				Any        =       (
              torch.linspace(beta_start**0.5				,						beta_end**0.5				,						UpperCAmelCase				,						dtype=torch.floataa   ) ** 2
          )
        elif beta_schedule == "squaredcos_cap_v2":
          # Glide cosine schedule
          __snake_case   :				Dict        =       betas_for_alpha_bar(UpperCAmelCase   )
        elif beta_schedule == "sigmoid":
          # GeoDiff sigmoid schedule
          __snake_case   :				Union[str, Any]        =       torch.linspace(-6				,						6				,						UpperCAmelCase   )
          __snake_case   :				Any        =       torch.sigmoid(UpperCAmelCase   ) * (beta_end - beta_start) + beta_start
        else:
          raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}"""   )

        __snake_case   :				Optional[int]        =       1.0 - self.betas
        __snake_case   :				Union[str, Any]        =       torch.cumprod(self.alphas				,						dim=0   )
        __snake_case   :				Any        =       torch.tensor(1.0   )

        __snake_case   :				str        =       torch.tensor(1.0   )

        # standard deviation of the initial noise distribution
        __snake_case   :				int        =       1.0

        # setable values
        __snake_case   :				int        =       None
        __snake_case   :				Optional[Any]        =       torch.from_numpy(np.arange(0				,						UpperCAmelCase   )[::-1].copy()   )

        __snake_case   :				Optional[Any]        =       eta







      def 						UpperCAmelCase		( self				,						UpperCAmelCase				,						UpperCAmelCase = None   )       ->   torch.FloatTensor:






        '''simple docstring'''






        return sample







      def 						UpperCAmelCase		( self				,						UpperCAmelCase				,						UpperCAmelCase = 10				,						UpperCAmelCase = 10				,						UpperCAmelCase = None				,						)       ->   Optional[int]:






        '''simple docstring'''






        __snake_case   :				int        =       min(self.config.num_train_timesteps				,						UpperCAmelCase   )
        __snake_case   :				Any        =       num_inference_steps

        __snake_case   :				str        =       []

        __snake_case   :				Optional[int]        =       {}
        for j in range(0				,						num_inference_steps - jump_length				,						UpperCAmelCase   ):
          __snake_case   :				int        =       jump_n_sample - 1

        __snake_case   :				List[str]        =       num_inference_steps
        while t >= 1:
          __snake_case   :				Optional[Any]        =       t - 1
          timesteps.append(UpperCAmelCase   )

          if jumps.get(UpperCAmelCase				,						0   ) > 0:
            __snake_case   :				int        =       jumps[t] - 1
            for _ in range(UpperCAmelCase   ):
              __snake_case   :				str        =       t + 1
              timesteps.append(UpperCAmelCase   )

        __snake_case   :				Optional[int]        =       np.array(UpperCAmelCase   ) * (self.config.num_train_timesteps // self.num_inference_steps)
        __snake_case   :				Optional[int]        =       torch.from_numpy(UpperCAmelCase   ).to(UpperCAmelCase   )







      def 						UpperCAmelCase		( self				,						UpperCAmelCase   )       ->   Union[str, Any]:






        '''simple docstring'''






        __snake_case   :				List[str]        =       t - self.config.num_train_timesteps // self.num_inference_steps

        __snake_case   :				Optional[Any]        =       self.alphas_cumprod[t]
        __snake_case   :				Tuple        =       self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
        __snake_case   :				Tuple        =       1 - alpha_prod_t
        __snake_case   :				Optional[Any]        =       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   :				List[Any]        =       (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]:






        '''simple docstring'''






        __snake_case   :				List[str]        =       timestep
        __snake_case   :				str        =       timestep - self.config.num_train_timesteps // self.num_inference_steps

        # 1. compute alphas, betas
        __snake_case   :				str        =       self.alphas_cumprod[t]
        __snake_case   :				List[str]        =       self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
        __snake_case   :				Any        =       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   :				Tuple        =       (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   :				Union[str, Any]        =       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
        __snake_case   :				Dict        =       model_output.device
        __snake_case   :				List[str]        =       randn_tensor(model_output.shape				,						generator=UpperCAmelCase				,						device=UpperCAmelCase				,						dtype=model_output.dtype   )
        __snake_case   :				List[str]        =       self.eta * self._get_variance(UpperCAmelCase   ) ** 0.5

        __snake_case   :				Optional[Any]        =       0
        if t > 0 and self.eta > 0:
          __snake_case   :				List[Any]        =       std_dev_t * noise

        # 6. compute "direction pointing to x_t" of formula (12)
        # from https://arxiv.org/pdf/2010.02502.pdf
        __snake_case   :				Optional[Any]        =       (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   :				Optional[Any]        =       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   :				Optional[Any]        =       (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   :				str        =       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   )       ->   str:






        '''simple docstring'''






        __snake_case   :				Optional[int]        =       self.config.num_train_timesteps // self.num_inference_steps

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

          # 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
          __snake_case   :				Optional[int]        =       (1 - beta) ** 0.5 * sample + beta**0.5 * noise

        return sample







      def 						UpperCAmelCase		( self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						)       ->   torch.FloatTensor:






        '''simple docstring'''






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







      def __len__( self   )       ->   Optional[Any]:






        '''simple docstring'''






        return self.config.num_train_timesteps








import torch
from torch import autograd




class 				_lowerCamelCase (    autograd.Function    ):



      """simple docstring"""






      @staticmethod
      def 						UpperCAmelCase		( UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase   )       ->   str:






        '''simple docstring'''






        __snake_case   :				Dict        =       inputs.numel()
        __snake_case   :				Optional[int]        =       int(0.005 * nb_elems   ) + 1
        if sigmoid:
          __snake_case   :				Any        =       (torch.sigmoid(UpperCAmelCase   ) > threshold).type(inputs.type()   )
        else:
          __snake_case   :				Optional[int]        =       (inputs > threshold).type(inputs.type()   )
        if mask.sum() < nb_min:
          # We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
          __snake_case   :				List[str]        =       inputs.flatten().kthvalue(max(nb_elems - nb_min				,						1   )   ).values
          __snake_case   :				Union[str, Any]        =       (inputs > k_threshold).type(inputs.type()   )
        return mask







      @staticmethod
      def 						UpperCAmelCase		( UpperCAmelCase				,						UpperCAmelCase   )       ->   Optional[int]:






        '''simple docstring'''






        return gradOutput, None, None




class 				_lowerCamelCase (    autograd.Function    ):



      """simple docstring"""






      @staticmethod
      def 						UpperCAmelCase		( UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase   )       ->   Optional[int]:






        '''simple docstring'''






        __snake_case   :				Optional[int]        =       inputs.clone()
        __snake_case    ,   __snake_case   :				int        =       inputs.flatten().sort(descending=UpperCAmelCase   )
        __snake_case   :				Tuple        =       int(threshold * inputs.numel()   )

        # flat_out and mask access the same memory.
        __snake_case   :				Optional[int]        =       mask.flatten()
        __snake_case   :				Union[str, Any]        =       0
        __snake_case   :				Any        =       1
        return mask







      @staticmethod
      def 						UpperCAmelCase		( UpperCAmelCase				,						UpperCAmelCase   )       ->   List[str]:






        '''simple docstring'''






        return gradOutput, None




class 				_lowerCamelCase (    a    ):



      """simple docstring"""






      @staticmethod
      def 						UpperCAmelCase		( UpperCAmelCase				,						UpperCAmelCase   )       ->   Any:






        '''simple docstring'''






        __snake_case   :				Union[str, Any]        =       inputs.clone()
        __snake_case    ,   __snake_case   :				Optional[Any]        =       inputs.abs().flatten().sort(descending=UpperCAmelCase   )
        __snake_case   :				List[Any]        =       int(threshold * inputs.numel()   )

        # flat_out and mask access the same memory.
        __snake_case   :				Optional[Any]        =       mask.flatten()
        __snake_case   :				Tuple        =       0
        __snake_case   :				Any        =       1
        return mask

