code: 
  
  
  
  
  
  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
  
  
style_context: 
  
  
  
  
  
  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
  
  