



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





@dataclass
class 							UpperCAmelCase   (						snake_case__						):




	'''simple docstring'''





	snake_case_      					=  42





class 							UpperCAmelCase   (						snake_case__    ,    snake_case__						):




	'''simple docstring'''





	snake_case_      					=  1






	@register_to_config
	def __init__( self      :				Optional[Any]							,A      :				Optional[Any] = 40							,A      :				Tuple = 0.0_02							,A      :				Tuple = 80.0							,A      :				Union[str, Any] = 0.5							,A      :				Optional[Any] = 1.0							,A      :				Optional[int] = 7.0							,A      :				Any = True							,):
					# standard deviation of the initial noise distribution
					__A         =				sigma_max

					__A         =				np.linspace(0							,1							,__snake_case				)
					__A         =				self._convert_to_karras(__snake_case				)
					__A         =				self.sigma_to_t(__snake_case				)

					# setable values
					__A         =				None
					__A         =				torch.from_numpy(__snake_case				)
					__A         =				torch.from_numpy(__snake_case				)
					__A         =				False
					__A         =				False






	def        UpperCamelCase_						( self      :				int							,A      :				List[Any]							,A      :				List[str]=None				):
					if schedule_timesteps is None:
									__A         =				self.timesteps

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






	def        UpperCamelCase_						( self      :				str							,A      :				Optional[Any]							,A      :				int				):
					# Get sigma corresponding to timestep
					if isinstance(__snake_case							,torch.Tensor				):
									__A         =				timestep.to(self.timesteps.device				)
					__A         =				self.index_for_timestep(__snake_case				)
					__A         =				self.sigmas[step_idx]

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

					__A         =				True
					return sample






	def        UpperCamelCase_						( self      :				Any							,A      :				Optional[int]				):
					if not isinstance(__snake_case							,np.ndarray				):
									__A         =				np.array(__snake_case							,dtype=np.floataa				)

					__A         =				10_00 * 0.25 * np.log(sigmas + 1E-44				)

					return timesteps






	def        UpperCamelCase_						( self      :				Optional[Any]							,A      :				Dict = None							,A      :				int = None							,A      :				Any = None							,):
					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         =				np.array(__snake_case							,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							,__snake_case				) * 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(__snake_case				)
					__A         =				self.sigma_to_t(__snake_case				)

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

					if str(__snake_case				).startswith("mps"				):
									# mps does not support float64
									__A         =				torch.from_numpy(__snake_case				).to(__snake_case							,dtype=torch.floataa				)
					else:
									__A         =				torch.from_numpy(__snake_case				).to(device=__snake_case				)






	def        UpperCamelCase_						( self      :				Optional[Any]							,A      :				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        UpperCamelCase_						( self      :				str							,A      :				Tuple				):
					__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        UpperCamelCase_						( self      :				Dict							,A      :				Dict				):
					__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        UpperCamelCase_						( self      :				List[str]							,A      :				int							,A      :				Optional[int]							,A      :				Optional[int]							,A      :				Union[str, Any] = None							,A      :				Any = True							,):

					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         =				timestep.to(self.timesteps.device				)

					__A         =				self.config.sigma_min
					__A         =				self.config.sigma_max

					__A         =				self.index_for_timestep(__snake_case				)

					# 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(__snake_case				)

					# 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=__snake_case				)
					else:
									__A         =				torch.zeros_like(__snake_case				)
					__A         =				noise * self.config.s_noise

					__A         =				sigma_next.clamp(min=__snake_case							,max=__snake_case				)

					# 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=__snake_case				)






	def        UpperCamelCase_						( self      :				Union[str, Any]							,A      :				Union[str, Any]							,A      :				List[Any]							,A      :				Dict							,):
					# 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(__snake_case				):
									# 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				):
					return self.config.num_train_timesteps


def 		UpperCAmelCase    (			a_		)	->			int:







				"""simple docstring"""



				__A         =				str(a_		).strip()
				if not oct_string:
								raise ValueError("Empty string was passed to the function"		)
				__A         =				oct_string[0] == "-"
				if is_negative:
								__A         =				oct_string[1:]
				if not oct_string.isdigit() or not all(0 <= int(a_		) <= 7 for char in oct_string		):
								raise ValueError("Non-octal value was passed to the function"		)
				__A         =				0
				for char in oct_string:
								__A         =				8 * decimal_number + int(a_		)
				if is_negative:
								__A         =				-decimal_number
				return decimal_number


if __name__ == "__main__":
					from doctest import testmod

					testmod()
