code: from dataclasses import dataclass
  from typing import List, Optional, Tuple, Union
  
  import numpy as np
  import torch
  
  from ..configuration_utils import ConfigMixin, register_to_config
  from ..utils import BaseOutput, logging, randn_tensor
  from .scheduling_utils import SchedulerMixin
  
  
  UpperCAmelCase					=				logging.get_logger(__name__)  # pylint: disable=invalid-name
  
  
  
  
  @dataclass
  class    __lowerCAmelCase  (       snake_case__						):
  
  
  
   '''simple docstring'''
   _lowerCamelCase						:	Union[str, Any]				    = 42
  
  
  
  
  
  
  
  class    __lowerCAmelCase  (       snake_case__,	snake_case__						):
  
  
  
   '''simple docstring'''
   _lowerCamelCase						:	Dict				    = 1
  
   @register_to_config
   def __init__(			self       :		List[Any]       ,			lowerCamelCase       :		Dict = 4_0       ,			lowerCamelCase       :		Dict = 0.0_0_2       ,			lowerCamelCase       :		Dict = 8_0.0       ,			lowerCamelCase       :		List[str] = 0.5       ,			lowerCamelCase       :		Optional[int] = 1.0       ,			lowerCamelCase       :		int = 7.0       ,			lowerCamelCase       :		Tuple = True       ,			) ->							int:
          # standard deviation of the initial noise distribution
          _UpperCamelCase     :       List[str]  						=  sigma_max
  
          _UpperCamelCase     :       List[Any]  						=  np.linspace(0       ,			1       ,			__snake_case  )
          _UpperCamelCase     :       Dict  						=  self._convert_to_karras(__snake_case  )
          _UpperCamelCase     :       List[str]  						=  self.sigma_to_t(__snake_case  )
  
          # setable values
          _UpperCamelCase     :       List[Any]  						=  None
          _UpperCamelCase     :       Optional[int]  						=  torch.from_numpy(__snake_case  )
          _UpperCamelCase     :       Tuple  						=  torch.from_numpy(__snake_case  )
          _UpperCamelCase     :       List[Any]  						=  False
          _UpperCamelCase     :       Optional[int]  						=  False
  
   def 					__snake_case					(			self       :		Any       ,			lowerCamelCase       :		List[str]       ,			lowerCamelCase       :		List[Any]=None  ) ->							str:
          if schedule_timesteps is None:
                 _UpperCamelCase     :       Optional[Any]  						=  self.timesteps
  
          _UpperCamelCase     :       List[str]  						=  (schedule_timesteps == timestep).nonzero()
          return indices.item()
  
   def 					__snake_case					(			self       :		Union[str, Any]       ,			lowerCamelCase       :		Any       ,			lowerCamelCase       :		str  ) ->							Any:
          # Get sigma corresponding to timestep
          if isinstance(__snake_case       ,			torch.Tensor  ):
                 _UpperCamelCase     :       Tuple  						=  timestep.to(self.timesteps.device  )
          _UpperCamelCase     :       Dict  						=  self.index_for_timestep(__snake_case  )
          _UpperCamelCase     :       List[str]  						=  self.sigmas[step_idx]
  
          _UpperCamelCase     :       List[Any]  						=  sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)
  
          _UpperCamelCase     :       Union[str, Any]  						=  True
          return sample
  
   def 					__snake_case					(			self       :		Tuple       ,			lowerCamelCase       :		str  ) ->							Union[str, Any]:
          if not isinstance(__snake_case       ,			np.ndarray  ):
                 _UpperCamelCase     :       Tuple  						=  np.array(__snake_case       ,			dtype=np.floataa  )
  
          _UpperCamelCase     :       List[Any]  						=  1_0_0_0 * 0.2_5 * np.log(sigmas + 1e-44  )
  
          return timesteps
  
   def 					__snake_case					(			self       :		List[Any]       ,			lowerCamelCase       :		List[Any] = None       ,			lowerCamelCase       :		List[str] = None       ,			lowerCamelCase       :		Optional[int] = None       ,			) ->							List[str]:
          if num_inference_steps is None and timesteps is None:
                 raise ValueError('Exactly one of `num_inference_steps` or `timesteps` must be supplied.'  )
  
          if num_inference_steps is not None and timesteps is not None:
                 raise ValueError('Can only pass one of `num_inference_steps` or `timesteps`.'  )
  
          # Follow DDPMScheduler custom timesteps logic
          if timesteps is not None:
                 for i in range(1       ,			len(__snake_case  )  ):
                        if timesteps[i] >= timesteps[i - 1]:
                               raise ValueError('`timesteps` must be in descending order.'  )
  
                 if timesteps[0] >= self.config.num_train_timesteps:
                        raise ValueError(
                            f"`timesteps` must start before `self.config.train_timesteps`:"
                            f" {self.config.num_train_timesteps}."  )
  
                 _UpperCamelCase     :       Union[str, Any]  						=  np.array(__snake_case       ,			dtype=np.intaa  )
                 _UpperCamelCase     :       Tuple  						=  True
          else:
                 if num_inference_steps > self.config.num_train_timesteps:
                        raise ValueError(
                            f"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"
                            f" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"
                            f" maximal {self.config.num_train_timesteps} timesteps."  )
  
                 _UpperCamelCase     :       str  						=  num_inference_steps
  
                 _UpperCamelCase     :       Union[str, Any]  						=  self.config.num_train_timesteps // self.num_inference_steps
                 _UpperCamelCase     :       Union[str, Any]  						=  (np.arange(0       ,			__snake_case  ) * step_ratio).round()[::-1].copy().astype(np.intaa  )
                 _UpperCamelCase     :       str  						=  False
  
          # Map timesteps to Karras sigmas directly for multistep sampling
          # See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
          _UpperCamelCase     :       Optional[Any]  						=  self.config.num_train_timesteps
          _UpperCamelCase     :       Dict  						=  timesteps[::-1].copy()
          _UpperCamelCase     :       str  						=  ramp / (num_train_timesteps - 1)
          _UpperCamelCase     :       Optional[Any]  						=  self._convert_to_karras(__snake_case  )
          _UpperCamelCase     :       List[str]  						=  self.sigma_to_t(__snake_case  )
  
          _UpperCamelCase     :       List[Any]  						=  np.concatenate([sigmas, [self.sigma_min]]  ).astype(np.floataa  )
          _UpperCamelCase     :       Any  						=  torch.from_numpy(__snake_case  ).to(device=__snake_case  )
  
          if str(__snake_case  ).startswith('mps'  ):
                 # mps does not support float64
                 _UpperCamelCase     :       Tuple  						=  torch.from_numpy(__snake_case  ).to(__snake_case       ,			dtype=torch.floataa  )
          else:
                 _UpperCamelCase     :       int  						=  torch.from_numpy(__snake_case  ).to(device=__snake_case  )
  
   def 					__snake_case					(			self       :		str       ,			lowerCamelCase       :		Union[str, Any]  ) ->							List[Any]:
          _UpperCamelCase     :       Optional[int]  						=  self.config.sigma_min
          _UpperCamelCase     :       str  						=  self.config.sigma_max
  
          _UpperCamelCase     :       Dict  						=  self.config.rho
          _UpperCamelCase     :       Optional[int]  						=  sigma_min ** (1 / rho)
          _UpperCamelCase     :       str  						=  sigma_max ** (1 / rho)
          _UpperCamelCase     :       Any  						=  (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
          return sigmas
  
   def 					__snake_case					(			self       :		str       ,			lowerCamelCase       :		Optional[Any]  ) ->							int:
          _UpperCamelCase     :       Optional[int]  						=  self.config.sigma_data
  
          _UpperCamelCase     :       Optional[int]  						=  sigma_data**2 / (sigma**2 + sigma_data**2)
          _UpperCamelCase     :       Optional[int]  						=  sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
          return c_skip, c_out
  
   def 					__snake_case					(			self       :		Union[str, Any]       ,			lowerCamelCase       :		Optional[int]  ) ->							Any:
          _UpperCamelCase     :       int  						=  self.config.sigma_min
          _UpperCamelCase     :       List[Any]  						=  self.config.sigma_data
  
          _UpperCamelCase     :       int  						=  sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
          _UpperCamelCase     :       str  						=  (sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
          return c_skip, c_out
  
   def 					__snake_case					(			self       :		Optional[int]       ,			lowerCamelCase       :		List[Any]       ,			lowerCamelCase       :		List[Any]       ,			lowerCamelCase       :		str       ,			lowerCamelCase       :		Dict = None       ,			lowerCamelCase       :		int = True       ,			) ->							Any:
  
          if (
              isinstance(__snake_case       ,			__snake_case  )
              or isinstance(__snake_case       ,			torch.IntTensor  )
              or isinstance(__snake_case       ,			torch.LongTensor  )
          ):
                 raise ValueError(
                     (
                         'Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'
                         f" `{self.__class__}.step()` is not supported. Make sure to pass"
                         ' one of the `scheduler.timesteps` as a timestep.'
                     )       ,			)
  
          if not self.is_scale_input_called:
                 logger.warning(
                     'The `scale_model_input` function should be called before `step` to ensure correct denoising. '
                     'See `StableDiffusionPipeline` for a usage example.'  )
  
          if isinstance(__snake_case       ,			torch.Tensor  ):
                 _UpperCamelCase     :       List[Any]  						=  timestep.to(self.timesteps.device  )
  
          _UpperCamelCase     :       Any  						=  self.config.sigma_min
          _UpperCamelCase     :       int  						=  self.config.sigma_max
  
          _UpperCamelCase     :       Optional[int]  						=  self.index_for_timestep(__snake_case  )
  
          # sigma_next corresponds to next_t in original implementation
          _UpperCamelCase     :       Any  						=  self.sigmas[step_index]
          if step_index + 1 < self.config.num_train_timesteps:
                 _UpperCamelCase     :       str  						=  self.sigmas[step_index + 1]
          else:
                 # Set sigma_next to sigma_min
                 _UpperCamelCase     :       List[Any]  						=  self.sigmas[-1]
  
          # Get scalings for boundary conditions
          _UpperCamelCase						,      _UpperCamelCase     :       int  						=  self.get_scalings_for_boundary_condition(__snake_case  )
  
          # 1. Denoise model output using boundary conditions
          _UpperCamelCase     :       Optional[Any]  						=  c_out * model_output + c_skip * sample
          if self.config.clip_denoised:
                 _UpperCamelCase     :       Any  						=  denoised.clamp(-1       ,			1  )
  
          # 2. Sample z ~ N(0, s_noise^2 * I)
          # Noise is not used for onestep sampling.
          if len(self.timesteps  ) > 1:
                 _UpperCamelCase     :       List[Any]  						=  randn_tensor(
                     model_output.shape       ,			dtype=model_output.dtype       ,			device=model_output.device       ,			generator=__snake_case  )
          else:
                 _UpperCamelCase     :       Tuple  						=  torch.zeros_like(__snake_case  )
          _UpperCamelCase     :       List[Any]  						=  noise * self.config.s_noise
  
          _UpperCamelCase     :       List[Any]  						=  sigma_next.clamp(min=__snake_case       ,			max=__snake_case  )
  
          # 3. Return noisy sample
          # tau = sigma_hat, eps = sigma_min
          _UpperCamelCase     :       Union[str, Any]  						=  denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5
  
          if not return_dict:
                 return (prev_sample,)
  
          return CMStochasticIterativeSchedulerOutput(prev_sample=__snake_case  )
  
   def 					__snake_case					(			self       :		Union[str, Any]       ,			lowerCamelCase       :		str       ,			lowerCamelCase       :		Optional[Any]       ,			lowerCamelCase       :		Any       ,			) ->							Union[str, Any]:
          # Make sure sigmas and timesteps have the same device and dtype as original_samples
          _UpperCamelCase     :       Tuple  						=  self.sigmas.to(device=original_samples.device       ,			dtype=original_samples.dtype  )
          if original_samples.device.type == "mps" and torch.is_floating_point(__snake_case  ):
                 # mps does not support float64
                 _UpperCamelCase     :       int  						=  self.timesteps.to(original_samples.device       ,			dtype=torch.floataa  )
                 _UpperCamelCase     :       Any  						=  timesteps.to(original_samples.device       ,			dtype=torch.floataa  )
          else:
                 _UpperCamelCase     :       str  						=  self.timesteps.to(original_samples.device  )
                 _UpperCamelCase     :       Dict  						=  timesteps.to(original_samples.device  )
  
          _UpperCamelCase     :       int  						=  [(schedule_timesteps == t).nonzero().item() for t in timesteps]
  
          _UpperCamelCase     :       Optional[Any]  						=  sigmas[step_indices].flatten()
          while len(sigma.shape  ) < len(original_samples.shape  ):
                 _UpperCamelCase     :       Optional[int]  						=  sigma.unsqueeze(-1  )
  
          _UpperCamelCase     :       Optional[Any]  						=  original_samples + noise * sigma
          return noisy_samples
  
   def __len__(			self       :		Tuple  ) ->							List[Any]:
          return self.config.num_train_timesteps
  
  
  
  
style_context: from ..utils import requires_backends
  
  
  UpperCAmelCase					=				None
  
  
  
  
  class    __lowerCAmelCase  :
  
  
  
   '''simple docstring'''
  
   def __init__(			self       :		Optional[int]       ,			*lowerCamelCase       :		Dict       ,			**lowerCamelCase       :		int  ) ->							Tuple:
          requires_backends(self       ,			['detectron2']  )
  
   @classmethod
   def 					__snake_case					(			cls       :		Optional[Any]       ,			*lowerCamelCase       :		Optional[int]       ,			**lowerCamelCase       :		Tuple  ) ->							List[str]:
          requires_backends(cls       ,			['detectron2']  )
  
  
  
  