




"""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


__A							=     logging.get_logger(__name__)  # pylint: disable=invalid-name
@dataclass
class      lowerCamelCase__      (					lowercase_     ):
			a__	: int        =       42







class      lowerCamelCase__      (					lowercase_			,					lowercase_     ):
			a__	: Any        =       1


			@register_to_config
			def __init__(       self			,	SCREAMING_SNAKE_CASE = 40			,	SCREAMING_SNAKE_CASE = 0.0_02			,	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"""







					snake_case						:  List[Any]        =			sigma_max

					snake_case						:  Any        =			np.linspace(0			,	1			,	UpperCamelCase_							)
					snake_case						:  Tuple        =			self._convert_to_karras(UpperCamelCase_							)
					snake_case						:  Union[str, Any]        =			self.sigma_to_t(UpperCamelCase_							)

					# setable values
					snake_case						:  Optional[int]        =			None
					snake_case						:  Union[str, Any]        =			torch.from_numpy(UpperCamelCase_							)
					snake_case						:  Optional[int]        =			torch.from_numpy(UpperCamelCase_							)
					snake_case						:  List[Any]        =			False
					snake_case						:  Optional[int]        =			False


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE=None							):



					"""simple docstring"""







					if schedule_timesteps is None:
							snake_case						:  Tuple        =			self.timesteps

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


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					if isinstance(UpperCamelCase_			,	torch.Tensor							):
							snake_case						:  Any        =			timestep.to(self.timesteps.device							)
					snake_case						:  str        =			self.index_for_timestep(UpperCamelCase_							)
					snake_case						:  Any        =			self.sigmas[step_idx]

					snake_case						:  List[Any]        =			sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

					snake_case						:  str        =			True
					return sample


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					if not isinstance(UpperCamelCase_			,	np.ndarray							):
							snake_case						:  int        =			np.array(UpperCamelCase_			,	dtype=np.floataa							)

					snake_case						:  Optional[int]        =			1_000 * 0.25 * np.log(sigmas + 1E-44							)

					return timesteps


			def 							lowerCamelCase_			(       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(UpperCamelCase_							)							):
									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}.'''							)

							snake_case						:  Optional[int]        =			np.array(UpperCamelCase_			,	dtype=np.intaa							)
							snake_case						:  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.'''							)

							snake_case						:  List[Any]        =			num_inference_steps

							snake_case						:  List[str]        =			self.config.num_train_timesteps // self.num_inference_steps
							snake_case						:  int        =			(np.arange(0			,	UpperCamelCase_							) * step_ratio).round()[::-1].copy().astype(np.intaa							)
							snake_case						:  List[str]        =			False

					# Map timesteps to Karras sigmas directly for multistep sampling
					# See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
					snake_case						:  List[str]        =			self.config.num_train_timesteps
					snake_case						:  Tuple        =			timesteps[::-1].copy()
					snake_case						:  Optional[int]        =			ramp / (num_train_timesteps - 1)
					snake_case						:  Dict        =			self._convert_to_karras(UpperCamelCase_							)
					snake_case						:  Optional[int]        =			self.sigma_to_t(UpperCamelCase_							)

					snake_case						:  Dict        =			np.concatenate([sigmas, [self.sigma_min]]							).astype(np.floataa							)
					snake_case						:  Dict        =			torch.from_numpy(UpperCamelCase_							).to(device=UpperCamelCase_							)

					if str(UpperCamelCase_							).startswith("mps"							):
							# mps does not support float64
							snake_case						:  Union[str, Any]        =			torch.from_numpy(UpperCamelCase_							).to(UpperCamelCase_			,	dtype=torch.floataa							)
					else:
							snake_case						:  Union[str, Any]        =			torch.from_numpy(UpperCamelCase_							).to(device=UpperCamelCase_							)


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case						:  Tuple        =			self.config.sigma_min
					snake_case						:  str        =			self.config.sigma_max

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


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case						:  str        =			self.config.sigma_data

					snake_case						:  Union[str, Any]        =			sigma_data**2 / (sigma**2 + sigma_data**2)
					snake_case						:  str        =			sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
					return c_skip, c_out


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case						:  Union[str, Any]        =			self.config.sigma_min
					snake_case						:  Union[str, Any]        =			self.config.sigma_data

					snake_case						:  List[str]        =			sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
					snake_case						:  Any        =			(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
					return c_skip, c_out


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE = None			,	SCREAMING_SNAKE_CASE = True			,	):



					"""simple docstring"""







					if (
					    isinstance(UpperCamelCase_			,	UpperCamelCase_							)
					    or isinstance(UpperCamelCase_			,	torch.IntTensor							)
					    or isinstance(UpperCamelCase_			,	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(UpperCamelCase_			,	torch.Tensor							):
							snake_case						:  Dict        =			timestep.to(self.timesteps.device							)

					snake_case						:  List[str]        =			self.config.sigma_min
					snake_case						:  List[str]        =			self.config.sigma_max

					snake_case						:  Any        =			self.index_for_timestep(UpperCamelCase_							)

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

					# Get scalings for boundary conditions
					snake_case     ,			snake_case						:  Any        =			self.get_scalings_for_boundary_condition(UpperCamelCase_							)

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

					snake_case						:  List[Any]        =			sigma_next.clamp(min=UpperCamelCase_			,	max=UpperCamelCase_							)

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

					if not return_dict:
							return (prev_sample,)

					return CMStochasticIterativeSchedulerOutput(prev_sample=UpperCamelCase_							)


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	):



					"""simple docstring"""







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

					snake_case						:  Union[str, Any]        =			[(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

					snake_case						:  str        =			original_samples + noise * sigma
					return noisy_samples



			def __len__(       self							):



					"""simple docstring"""







					return self.config.num_train_timesteps







"""simple docstring"""

def 					UpperCamelCase__       (						lowercase__					:       set    ,       lowercase__					:       set				):
		assert isinstance(lowercase__    ,       lowercase__				), F'''The input value of [set_a={set_a}] is not a set'''
		assert isinstance(lowercase__    ,       lowercase__				), F'''The input value of [set_b={set_b}] is not a set'''

		return {a + b for a in set_a for b in set_b}


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

							testmod()

