
'''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


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






@dataclass
class 				lowerCAmelCase__     (						lowerCamelCase_  ):
					lowerCAmelCase_       =  4_2







class 				lowerCAmelCase__     (						lowerCamelCase_					,		lowerCamelCase_  ):
					lowerCAmelCase_       =  1






					@register_to_config
					def __init__(     self       ,							__SCREAMING_SNAKE_CASE = 40       ,							__SCREAMING_SNAKE_CASE = 0.002       ,							__SCREAMING_SNAKE_CASE = 80.0       ,							__SCREAMING_SNAKE_CASE = 0.5       ,							__SCREAMING_SNAKE_CASE = 1.0       ,							__SCREAMING_SNAKE_CASE = 7.0       ,							__SCREAMING_SNAKE_CASE = True       ,							):


											"""simple docstring"""





											lowercase_			:  str									= sigma_max

											lowercase_			:  Dict									= np.linspace(0       ,							1       ,							__SCREAMING_SNAKE_CASE	)
											lowercase_			:  Optional[Any]									= self._convert_to_karras(__SCREAMING_SNAKE_CASE	)
											lowercase_			:  int									= self.sigma_to_t(__SCREAMING_SNAKE_CASE	)

											# setable values
											lowercase_			:  Optional[Any]									= None
											lowercase_			:  str									= torch.from_numpy(__SCREAMING_SNAKE_CASE	)
											lowercase_			:  str									= torch.from_numpy(__SCREAMING_SNAKE_CASE	)
											lowercase_			:  Optional[int]									= False
											lowercase_			:  List[Any]									= False






					def 			_snake_case					(     self       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE=None	):


											"""simple docstring"""





											if schedule_timesteps is None:
																	lowercase_			:  Any									= self.timesteps

											lowercase_			:  Any									= (schedule_timesteps == timestep).nonzero()
											return indices.item()






					def 			_snake_case					(     self       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											if isinstance(__SCREAMING_SNAKE_CASE       ,							torch.Tensor	):
																	lowercase_			:  Any									= timestep.to(self.timesteps.device	)
											lowercase_			:  Tuple									= self.index_for_timestep(__SCREAMING_SNAKE_CASE	)
											lowercase_			:  int									= self.sigmas[step_idx]

											lowercase_			:  Any									= sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

											lowercase_			:  Tuple									= True
											return sample






					def 			_snake_case					(     self       ,							__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											if not isinstance(__SCREAMING_SNAKE_CASE       ,							np.ndarray	):
																	lowercase_			:  Optional[int]									= np.array(__SCREAMING_SNAKE_CASE       ,							dtype=np.floataa	)

											lowercase_			:  str									= 10_00 * 0.25 * np.log(sigmas + 1E-4_4	)

											return timesteps






					def 			_snake_case					(     self       ,							__SCREAMING_SNAKE_CASE = None       ,							__SCREAMING_SNAKE_CASE = None       ,							__SCREAMING_SNAKE_CASE = None       ,							):


											"""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(__SCREAMING_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}.'''	)

																	lowercase_			:  Optional[int]									= np.array(__SCREAMING_SNAKE_CASE       ,							dtype=np.intaa	)
																	lowercase_			:  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.'''	)

																	lowercase_			:  List[str]									= num_inference_steps

																	lowercase_			:  List[str]									= self.config.num_train_timesteps // self.num_inference_steps
																	lowercase_			:  Optional[int]									= (np.arange(0       ,							__SCREAMING_SNAKE_CASE	) * step_ratio).round()[::-1].copy().astype(np.intaa	)
																	lowercase_			:  List[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
											lowercase_			:  List[Any]									= self.config.num_train_timesteps
											lowercase_			:  Optional[Any]									= timesteps[::-1].copy()
											lowercase_			:  int									= ramp / (num_train_timesteps - 1)
											lowercase_			:  List[Any]									= self._convert_to_karras(__SCREAMING_SNAKE_CASE	)
											lowercase_			:  List[Any]									= self.sigma_to_t(__SCREAMING_SNAKE_CASE	)

											lowercase_			:  int									= np.concatenate([sigmas, [self.sigma_min]]	).astype(np.floataa	)
											lowercase_			:  Union[str, Any]									= torch.from_numpy(__SCREAMING_SNAKE_CASE	).to(device=__SCREAMING_SNAKE_CASE	)

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






					def 			_snake_case					(     self       ,							__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											lowercase_			:  float									= self.config.sigma_min
											lowercase_			:  float									= self.config.sigma_max

											lowercase_			:  Tuple									= self.config.rho
											lowercase_			:  List[Any]									= sigma_min ** (1 / rho)
											lowercase_			:  Optional[Any]									= sigma_max ** (1 / rho)
											lowercase_			:  Union[str, Any]									= (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
											return sigmas






					def 			_snake_case					(     self       ,							__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											lowercase_			:  List[Any]									= self.config.sigma_data

											lowercase_			:  Optional[Any]									= sigma_data**2 / (sigma**2 + sigma_data**2)
											lowercase_			:  Any									= sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
											return c_skip, c_out






					def 			_snake_case					(     self       ,							__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											lowercase_			:  Any									= self.config.sigma_min
											lowercase_			:  Optional[Any]									= self.config.sigma_data

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






					def 			_snake_case					(     self       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE = None       ,							__SCREAMING_SNAKE_CASE = True       ,							):


											"""simple docstring"""





											if (
											    isinstance(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	)
											    or isinstance(__SCREAMING_SNAKE_CASE       ,							torch.IntTensor	)
											    or isinstance(__SCREAMING_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(__SCREAMING_SNAKE_CASE       ,							torch.Tensor	):
																	lowercase_			:  Optional[int]									= timestep.to(self.timesteps.device	)

											lowercase_			:  Dict									= self.config.sigma_min
											lowercase_			:  List[Any]									= self.config.sigma_max

											lowercase_			:  List[str]									= self.index_for_timestep(__SCREAMING_SNAKE_CASE	)

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

											# Get scalings for boundary conditions
											lowercase_			:  List[str]									= self.get_scalings_for_boundary_condition(__SCREAMING_SNAKE_CASE	)

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

											lowercase_			:  int									= sigma_next.clamp(min=__SCREAMING_SNAKE_CASE       ,							max=__SCREAMING_SNAKE_CASE	)

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

											if not return_dict:
																	return (prev_sample,)

											return CMStochasticIterativeSchedulerOutput(prev_sample=__SCREAMING_SNAKE_CASE	)






					def 			_snake_case					(     self       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE       ,							):


											"""simple docstring"""





											lowercase_			:  Dict									= self.sigmas.to(device=original_samples.device       ,							dtype=original_samples.dtype	)
											if original_samples.device.type == "mps" and torch.is_floating_point(__SCREAMING_SNAKE_CASE	):
																	# mps does not support float64
																	lowercase_			:  Dict									= self.timesteps.to(original_samples.device       ,							dtype=torch.floataa	)
																	lowercase_			:  List[Any]									= timesteps.to(original_samples.device       ,							dtype=torch.floataa	)
											else:
																	lowercase_			:  List[str]									= self.timesteps.to(original_samples.device	)
																	lowercase_			:  Union[str, Any]									= timesteps.to(original_samples.device	)

											lowercase_			:  Tuple									= [(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

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






					def __len__(     self	):


											"""simple docstring"""





											return self.config.num_train_timesteps








'''simple docstring'''







def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				int = 1000							):



						"""simple docstring"""





						lowercase_			:  List[Any]									= 0
						for i in range(__SCREAMING_SNAKE_CASE							):
												if i % 3 == 0 or i % 5 == 0:
																		result += i
						return result


if __name__ == "__main__":
							print(f"""{solution() = }""")



