



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


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



@dataclass
class 						_snake_case (     lowerCamelCase			):





 """simple docstring"""



 lowerCamelCase_       =     42







class 						_snake_case (     lowerCamelCase   ,lowerCamelCase			):





 """simple docstring"""



 lowerCamelCase_       =     1
 @register_to_config
 def __init__(  self		,      a = 4_0		,      a = 0.002		,      a = 80.0		,      a = 0.5		,      a = 1.0		,      a = 7.0		,      a = True		,      )		->							Dict:




   """simple docstring"""







   _A          =      sigma_max

   _A          =      np.linspace(0		,      1		,      a   )
   _A          =      self._convert_to_karras(a   )
   _A          =      self.sigma_to_t(a   )

   # setable values
   _A          =      None
   _A          =      torch.from_numpy(a   )
   _A          =      torch.from_numpy(a   )
   _A          =      False
   _A          =      False
 def lowercase_						(  self		,      a		,      a=None   )		->							List[Any]:




   """simple docstring"""







   if schedule_timesteps is None:
     _A          =      self.timesteps

   _A          =      (schedule_timesteps == timestep).nonzero()
   return indices.item()
 def lowercase_						(  self		,      a		,      a   )		->							torch.FloatTensor:




   """simple docstring"""







   if isinstance(a		,      torch.Tensor   ):
     _A          =      timestep.to(self.timesteps.device   )
   _A          =      self.index_for_timestep(a   )
   _A          =      self.sigmas[step_idx]

   _A          =      sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

   _A          =      True
   return sample
 def lowercase_						(  self		,      a   )		->							Optional[int]:




   """simple docstring"""







   if not isinstance(a		,      np.ndarray   ):
     _A          =      np.array(a		,      dtype=np.floataa   )

   _A          =      1_0_0_0 * 0.25 * np.log(sigmas + 1e-44   )

   return timesteps
 def lowercase_						(  self		,      a = None		,      a = None		,      a = None		,      )		->							List[str]:




   """simple docstring"""







   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(a   )   ):
       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          =      np.array(a		,      dtype=np.intaa   )
     _A          =      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          =      num_inference_steps

     _A          =      self.config.num_train_timesteps // self.num_inference_steps
     _A          =      (np.arange(0		,      a   ) * step_ratio).round()[::-1].copy().astype(np.intaa   )
     _A          =      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          =      self.config.num_train_timesteps
   _A          =      timesteps[::-1].copy()
   _A          =      ramp / (num_train_timesteps - 1)
   _A          =      self._convert_to_karras(a   )
   _A          =      self.sigma_to_t(a   )

   _A          =      np.concatenate([sigmas, [self.sigma_min]]   ).astype(np.floataa   )
   _A          =      torch.from_numpy(a   ).to(device=a   )

   if str(a   ).startswith('''mps'''   ):
     # mps does not support float64
     _A          =      torch.from_numpy(a   ).to(a		,      dtype=torch.floataa   )
   else:
     _A          =      torch.from_numpy(a   ).to(device=a   )
 def lowercase_						(  self		,      a   )		->							Tuple:




   """simple docstring"""







   _A          =      self.config.sigma_min
   _A          =      self.config.sigma_max

   _A          =      self.config.rho
   _A          =      sigma_min ** (1 / rho)
   _A          =      sigma_max ** (1 / rho)
   _A          =      (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
   return sigmas
 def lowercase_						(  self		,      a   )		->							Dict:




   """simple docstring"""







   _A          =      self.config.sigma_data

   _A          =      sigma_data**2 / (sigma**2 + sigma_data**2)
   _A          =      sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
   return c_skip, c_out
 def lowercase_						(  self		,      a   )		->							Optional[Any]:




   """simple docstring"""







   _A          =      self.config.sigma_min
   _A          =      self.config.sigma_data

   _A          =      sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
   _A          =      (sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
   return c_skip, c_out
 def lowercase_						(  self		,      a		,      a		,      a		,      a = None		,      a = True		,      )		->							Union[CMStochasticIterativeSchedulerOutput, Tuple]:




   """simple docstring"""







   if (
       isinstance(a		,      a   )
       or isinstance(a		,      torch.IntTensor   )
       or isinstance(a		,      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(a		,      torch.Tensor   ):
     _A          =      timestep.to(self.timesteps.device   )

   _A          =      self.config.sigma_min
   _A          =      self.config.sigma_max

   _A          =      self.index_for_timestep(a   )

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

   # Get scalings for boundary conditions
   _A      ,				_A          =      self.get_scalings_for_boundary_condition(a   )

   # 1. Denoise model output using boundary conditions
   _A          =      c_out * model_output + c_skip * sample
   if self.config.clip_denoised:
     _A          =      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          =      randn_tensor(
         model_output.shape		,      dtype=model_output.dtype		,      device=model_output.device		,      generator=a   )
   else:
     _A          =      torch.zeros_like(a   )
   _A          =      noise * self.config.s_noise

   _A          =      sigma_next.clamp(min=a		,      max=a   )

   # 3. Return noisy sample
   # tau = sigma_hat, eps = sigma_min
   _A          =      denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

   if not return_dict:
     return (prev_sample,)

   return CMStochasticIterativeSchedulerOutput(prev_sample=a   )
 def lowercase_						(  self		,      a		,      a		,      a		,      )		->							torch.FloatTensor:




   """simple docstring"""







   _A          =      self.sigmas.to(device=original_samples.device		,      dtype=original_samples.dtype   )
   if original_samples.device.type == "mps" and torch.is_floating_point(a   ):
     # mps does not support float64
     _A          =      self.timesteps.to(original_samples.device		,      dtype=torch.floataa   )
     _A          =      timesteps.to(original_samples.device		,      dtype=torch.floataa   )
   else:
     _A          =      self.timesteps.to(original_samples.device   )
     _A          =      timesteps.to(original_samples.device   )

   _A          =      [(schedule_timesteps == t).nonzero().item() for t in timesteps]

   _A          =      sigmas[step_indices].flatten()
   while len(sigma.shape   ) < len(original_samples.shape   ):
     _A          =      sigma.unsqueeze(-1   )

   _A          =      original_samples + noise * sigma
   return noisy_samples



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




   """simple docstring"""







   return self.config.num_train_timesteps




import math
from timeit import timeit




def       UpperCAmelCase__	(							__snake_case							)				->	int:
  _A          =      0
  _A          =      abs(__snake_case							)
  while True:
    _A          =      n // 10
    digits += 1
    if n == 0:
      break
  return digits




def       UpperCAmelCase__	(							__snake_case							)				->	int:
  return 1 if n == 0 else math.floor(math.log(abs(__snake_case							)  ,			10							) + 1							)




def       UpperCAmelCase__	(							__snake_case							)				->	int:
  return len(str(abs(__snake_case							)							)							)




def       UpperCAmelCase__	(							)				->	None:
  from collections.abc import Callable

  def benchmark_a_function(__snake_case  ,			__snake_case							) -> None:
    _A          =      F'''{func.__name__}({value})'''
    _A          =      timeit(F'''__main__.{call}'''  ,			setup='''import __main__'''							)
    print(F'''{call}: {func(__snake_case							)} -- {timing} seconds'''							)

  for value in (262_144, 1_125_899_906_842_624, 1_267_650_600_228_229_401_496_703_205_376):
    for func in (num_digits, num_digits_fast, num_digits_faster):
      benchmark_a_function(__snake_case  ,			__snake_case							)
    print()


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    benchmark()