




'''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      __UpperCAmelCase							(    lowercase__							):



     '''simple docstring'''





     _UpperCamelCase  	=		42





class      __UpperCAmelCase							(    lowercase__ ,lowercase__							):



     '''simple docstring'''





     _UpperCamelCase  	=		1



     @register_to_config
     def __init__(  self    :	int			,					_lowercase    :	int = 40			,					_lowercase    :	float = 0.0_02			,					_lowercase    :	float = 80.0			,					_lowercase    :	float = 0.5			,					_lowercase    :	float = 1.0			,					_lowercase    :	float = 7.0			,					_lowercase    :	bool = True			,					)						->       str:
        # standard deviation of the initial noise distribution
        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 						__snake_case	(  self    :	Optional[Any]			,					_lowercase    :	Any			,					_lowercase    :	Tuple=None)						->       List[Any]:
        if schedule_timesteps is None:
           A_       		=   self.timesteps

        A_       		=   (schedule_timesteps == timestep).nonzero()
        return indices.item()



     def 						__snake_case	(  self    :	Tuple			,					_lowercase    :	torch.FloatTensor			,					_lowercase    :	Union[float, torch.FloatTensor])						->       torch.FloatTensor:
        # Get sigma corresponding to timestep
        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 						__snake_case	(  self    :	Union[str, Any]			,					_lowercase    :	Union[float, np.ndarray])						->       List[Any]:
        if not isinstance(_A			,					np.ndarray):
           A_       		=   np.array(_A			,					dtype=np.floataa)

        A_       		=   1_000 * 0.25 * np.log(sigmas + 1E-44)

        return timesteps



     def 						__snake_case	(  self    :	Dict			,					_lowercase    :	Optional[int] = None			,					_lowercase    :	Union[str, torch.device] = None			,					_lowercase    :	Optional[List[int]] = None			,					)						->       List[Any]:
        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 						__snake_case	(  self    :	str			,					_lowercase    :	Optional[int])						->       Any:
        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 						__snake_case	(  self    :	Union[str, Any]			,					_lowercase    :	Dict)						->       str:
        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 						__snake_case	(  self    :	List[str]			,					_lowercase    :	str)						->       List[str]:
        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 						__snake_case	(  self    :	Optional[Any]			,					_lowercase    :	torch.FloatTensor			,					_lowercase    :	Union[float, torch.FloatTensor]			,					_lowercase    :	torch.FloatTensor			,					_lowercase    :	Optional[torch.Generator] = None			,					_lowercase    :	bool = True			,					)						->       Union[CMStochasticIterativeSchedulerOutput, Tuple]:

        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_       		=   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 						__snake_case	(  self    :	Dict			,					_lowercase    :	torch.FloatTensor			,					_lowercase    :	torch.FloatTensor			,					_lowercase    :	torch.FloatTensor			,					)						->       torch.FloatTensor:
        # Make sure sigmas and timesteps have the same device and dtype as original_samples
        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    :	str)						->       Optional[Any]:
        return self.config.num_train_timesteps










'''simple docstring'''
def  lowerCamelCase(       SCREAMING_SNAKE_CASE_    )		->		list:
			A_       		=   [True for i in range(n + 1    )]
			A_       		=   [i - 1 for i in range(n + 1    )]
			A_       		=   []
			for i in range(2					,n + 1    ):
						if is_prime[i]:
									primes.append(SCREAMING_SNAKE_CASE_    )
						for j in range(0					,len(SCREAMING_SNAKE_CASE_    )    ):
									if i * primes[j] >= n:
												break
									A_       		=   False

									if i % primes[j] == 0:
												A_       		=   totients[i] * primes[j]
												break

									A_       		=   totients[i] * (primes[j] - 1)

			return totients


if __name__ == "__main__":
							import doctest

							doctest.testmod()




