






'''simple docstring'''


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


SCREAMING_SNAKE_CASE_					=							logging.get_logger(__name__)  # pylint: disable=invalid-name




@dataclass
class 							lowerCAmelCase					(							snake_case__   ):







   """simple docstring"""







   _A						=   42




class 							lowerCAmelCase					(							snake_case__							,      snake_case__   ):







   """simple docstring"""







   _A						=   1



   @register_to_config
   def __init__( self   ,   _A = 40   ,   _A = 0.002   ,   _A = 80.0   ,   _A = 0.5   ,   _A = 1.0   ,   _A = 7.0   ,   _A = True   ,   )    ->   int:
    # standard deviation of the initial noise distribution
    __a    :	Dict									=						sigma_max

    __a    :	List[Any]									=						np.linspace(0   ,   1   ,   __snake_case			)
    __a    :	Optional[Any]									=						self._convert_to_karras(__snake_case			)
    __a    :	Optional[int]									=						self.sigma_to_t(__snake_case			)

    # setable values
    __a    :	List[str]									=						None
    __a    :	Tuple									=						torch.from_numpy(__snake_case			)
    __a    :	str									=						torch.from_numpy(__snake_case			)
    __a    :	Any									=						False
    __a    :	Tuple									=						False



   def    __magic_name__    ( self   ,   _A   ,   _A=None			)    ->   str:
    if schedule_timesteps is None:
     __a    :	Optional[int]									=						self.timesteps

    __a    :	int									=						(schedule_timesteps == timestep).nonzero()
    return indices.item()



   def    __magic_name__    ( self   ,   _A   ,   _A			)    ->   Optional[Any]:
    # Get sigma corresponding to timestep
    if isinstance(__snake_case   ,   torch.Tensor			):
     __a    :	Union[str, Any]									=						timestep.to(self.timesteps.device			)
    __a    :	Union[str, Any]									=						self.index_for_timestep(__snake_case			)
    __a    :	Optional[int]									=						self.sigmas[step_idx]

    __a    :	str									=						sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

    __a    :	int									=						True
    return sample



   def    __magic_name__    ( self   ,   _A			)    ->   Tuple:
    if not isinstance(__snake_case   ,   np.ndarray			):
     __a    :	List[str]									=						np.array(__snake_case   ,   dtype=np.floataa			)

    __a    :	Any									=						1000 * 0.25 * np.log(sigmas + 1E-4_4			)

    return timesteps



   def    __magic_name__    ( self   ,   _A = None   ,   _A = None   ,   _A = None   ,   )    ->   Tuple:
    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}.'''			)

     __a    :	Any									=						np.array(__snake_case   ,   dtype=np.intaa			)
     __a    :	str									=						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.'''			)

     __a    :	Any									=						num_inference_steps

     __a    :	int									=						self.config.num_train_timesteps // self.num_inference_steps
     __a    :	List[Any]									=						(np.arange(0   ,   __snake_case			) * step_ratio).round()[::-1].copy().astype(np.intaa			)
     __a    :	Optional[Any]									=						False

    # Map timesteps to Karras sigmas directly for multistep sampling
    # See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
    __a    :	str									=						self.config.num_train_timesteps
    __a    :	List[str]									=						timesteps[::-1].copy()
    __a    :	str									=						ramp / (num_train_timesteps - 1)
    __a    :	List[str]									=						self._convert_to_karras(__snake_case			)
    __a    :	Union[str, Any]									=						self.sigma_to_t(__snake_case			)

    __a    :	Dict									=						np.concatenate([sigmas, [self.sigma_min]]			).astype(np.floataa			)
    __a    :	List[Any]									=						torch.from_numpy(__snake_case			).to(device=__snake_case			)

    if str(__snake_case			).startswith('mps'			):
     # mps does not support float64
     __a    :	List[str]									=						torch.from_numpy(__snake_case			).to(__snake_case   ,   dtype=torch.floataa			)
    else:
     __a    :	Any									=						torch.from_numpy(__snake_case			).to(device=__snake_case			)



   def    __magic_name__    ( self   ,   _A			)    ->   List[str]:
    __a    :	int									=						self.config.sigma_min
    __a    :	Any									=						self.config.sigma_max

    __a    :	str									=						self.config.rho
    __a    :	List[str]									=						sigma_min ** (1 / rho)
    __a    :	int									=						sigma_max ** (1 / rho)
    __a    :	int									=						(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
    return sigmas



   def    __magic_name__    ( self   ,   _A			)    ->   Tuple:
    __a    :	Optional[int]									=						self.config.sigma_data

    __a    :	Any									=						sigma_data**2 / (sigma**2 + sigma_data**2)
    __a    :	str									=						sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
    return c_skip, c_out



   def    __magic_name__    ( self   ,   _A			)    ->   Optional[Any]:
    __a    :	Optional[Any]									=						self.config.sigma_min
    __a    :	List[Any]									=						self.config.sigma_data

    __a    :	Any									=						sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
    __a    :	Dict									=						(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
    return c_skip, c_out



   def    __magic_name__    ( self   ,   _A   ,   _A   ,   _A   ,   _A = None   ,   _A = True   ,   )    ->   List[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			):
     __a    :	int									=						timestep.to(self.timesteps.device			)

    __a    :	Any									=						self.config.sigma_min
    __a    :	Union[str, Any]									=						self.config.sigma_max

    __a    :	Optional[Any]									=						self.index_for_timestep(__snake_case			)

    # sigma_next corresponds to next_t in original implementation
    __a    :	Any									=						self.sigmas[step_index]
    if step_index + 1 < self.config.num_train_timesteps:
     __a    :	List[Any]									=						self.sigmas[step_index + 1]
    else:
     # Set sigma_next to sigma_min
     __a    :	Optional[int]									=						self.sigmas[-1]

    # Get scalings for boundary conditions
    __a			,    __a    :	Optional[Any]									=						self.get_scalings_for_boundary_condition(__snake_case			)

    # 1. Denoise model output using boundary conditions
    __a    :	Tuple									=						c_out * model_output + c_skip * sample
    if self.config.clip_denoised:
     __a    :	List[str]									=						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:
     __a    :	Dict									=						randn_tensor(
         model_output.shape   ,   dtype=model_output.dtype   ,   device=model_output.device   ,   generator=__snake_case			)
    else:
     __a    :	Union[str, Any]									=						torch.zeros_like(__snake_case			)
    __a    :	Union[str, Any]									=						noise * self.config.s_noise

    __a    :	Optional[int]									=						sigma_next.clamp(min=__snake_case   ,   max=__snake_case			)

    # 3. Return noisy sample
    # tau = sigma_hat, eps = sigma_min
    __a    :	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    __magic_name__    ( self   ,   _A   ,   _A   ,   _A   ,   )    ->   Any:
    # Make sure sigmas and timesteps have the same device and dtype as original_samples
    __a    :	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
     __a    :	Tuple									=						self.timesteps.to(original_samples.device   ,   dtype=torch.floataa			)
     __a    :	Dict									=						timesteps.to(original_samples.device   ,   dtype=torch.floataa			)
    else:
     __a    :	List[Any]									=						self.timesteps.to(original_samples.device			)
     __a    :	Optional[Any]									=						timesteps.to(original_samples.device			)

    __a    :	Optional[int]									=						[(schedule_timesteps == t).nonzero().item() for t in timesteps]

    __a    :	Optional[Any]									=						sigmas[step_indices].flatten()
    while len(sigma.shape			) < len(original_samples.shape			):
     __a    :	str									=						sigma.unsqueeze(-1			)

    __a    :	Optional[int]									=						original_samples + noise * sigma
    return noisy_samples



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












'''simple docstring'''


def   lowerCAmelCase__			(					SCREAMING_SNAKE_CASE__		):
 if len(SCREAMING_SNAKE_CASE__		) <= 1:
  return data
 else:
  return [
      *quick_sort([e for e in data[1:] if e <= data[0]]		),
      data[0],
      *quick_sort([e for e in data[1:] if e > data[0]]		),
  ]


if __name__ == "__main__":
  import doctest

  doctest.testmod()




