



'''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


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






@dataclass
class 						_UpperCamelCase  (							lowerCamelCase__   ):


			'''simple docstring'''

			_A				:		torch.FloatTensor






class 						_UpperCamelCase  (							lowerCamelCase__   , lowerCamelCase__   ):


			'''simple docstring'''

			_A				:		Any         =		1

			@register_to_config
			def __init__(							self					:			Any							,					lowerCAmelCase__					:			int = 4_0							,					lowerCAmelCase__					:			float = 0.0_02							,					lowerCAmelCase__					:			float = 80.0							,					lowerCAmelCase__					:			float = 0.5							,					lowerCAmelCase__					:			float = 1.0							,					lowerCAmelCase__					:			float = 7.0							,					lowerCAmelCase__					:			bool = True							,					):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Tuple   	=		sigma_max

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

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

			def UpperCamelCase__ (							self					:			Optional[Any]							,					lowerCAmelCase__					:			Any							,					lowerCAmelCase__					:			Tuple=None						):





					"""simple docstring"""






					if schedule_timesteps is None:
							__SCREAMING_SNAKE_CASE	:		int   	=		self.timesteps

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

			def UpperCamelCase__ (							self					:			int							,					lowerCAmelCase__					:			torch.FloatTensor							,					lowerCAmelCase__					:			Union[float, torch.FloatTensor]						):





					"""simple docstring"""






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

					__SCREAMING_SNAKE_CASE	:		str   	=		sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		True
					return sample

			def UpperCamelCase__ (							self					:			List[str]							,					lowerCAmelCase__					:			Union[float, np.ndarray]						):





					"""simple docstring"""






					if not isinstance(lowerCAmelCase__							,					np.ndarray						):
							__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		np.array(lowerCAmelCase__							,					dtype=np.floataa						)

					__SCREAMING_SNAKE_CASE	:		Any   	=		1_0_0_0 * 0.25 * np.log(sigmas + 1E-44						)

					return timesteps

			def UpperCamelCase__ (							self					:			Dict							,					lowerCAmelCase__					:			Optional[int] = None							,					lowerCAmelCase__					:			Union[str, torch.device] = None							,					lowerCAmelCase__					:			Optional[List[int]] = 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(lowerCAmelCase__						)						):
									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}."						)

							__SCREAMING_SNAKE_CASE	:		int   	=		np.array(lowerCAmelCase__							,					dtype=np.intaa						)
							__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		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."						)

							__SCREAMING_SNAKE_CASE	:		Tuple   	=		num_inference_steps

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

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

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

			def UpperCamelCase__ (							self					:			Dict							,					lowerCAmelCase__					:			Optional[int]						):





					"""simple docstring"""






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

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

			def UpperCamelCase__ (							self					:			List[str]							,					lowerCAmelCase__					:			List[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		self.config.sigma_data

					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		sigma_data**2 / (sigma**2 + sigma_data**2)
					__SCREAMING_SNAKE_CASE	:		str   	=		sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
					return c_skip, c_out

			def UpperCamelCase__ (							self					:			Union[str, Any]							,					lowerCAmelCase__					:			Optional[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Any   	=		self.config.sigma_min
					__SCREAMING_SNAKE_CASE	:		Dict   	=		self.config.sigma_data

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

			def UpperCamelCase__ (							self					:			Optional[Any]							,					lowerCAmelCase__					:			torch.FloatTensor							,					lowerCAmelCase__					:			Union[float, torch.FloatTensor]							,					lowerCAmelCase__					:			torch.FloatTensor							,					lowerCAmelCase__					:			Optional[torch.Generator] = None							,					lowerCAmelCase__					:			bool = True							,					):





					"""simple docstring"""






					if (
					    isinstance(lowerCAmelCase__							,					lowerCAmelCase__						)
					    or isinstance(lowerCAmelCase__							,					torch.IntTensor						)
					    or isinstance(lowerCAmelCase__							,					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(lowerCAmelCase__							,					torch.Tensor						):
							__SCREAMING_SNAKE_CASE	:		List[str]   	=		timestep.to(self.timesteps.device						)

					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		self.config.sigma_min
					__SCREAMING_SNAKE_CASE	:		int   	=		self.config.sigma_max

					__SCREAMING_SNAKE_CASE	:		int   	=		self.index_for_timestep(lowerCAmelCase__						)

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

					# Get scalings for boundary conditions
					__SCREAMING_SNAKE_CASE	:		str   	=		self.get_scalings_for_boundary_condition(lowerCAmelCase__						)

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

					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		sigma_next.clamp(min=lowerCAmelCase__							,					max=lowerCAmelCase__						)

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

					if not return_dict:
							return (prev_sample,)

					return CMStochasticIterativeSchedulerOutput(prev_sample=lowerCAmelCase__						)

			def UpperCamelCase__ (							self					:			List[str]							,					lowerCAmelCase__					:			torch.FloatTensor							,					lowerCAmelCase__					:			torch.FloatTensor							,					lowerCAmelCase__					:			torch.FloatTensor							,					):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Any   	=		self.sigmas.to(device=original_samples.device							,					dtype=original_samples.dtype						)
					if original_samples.device.type == "mps" and torch.is_floating_point(lowerCAmelCase__						):
							# mps does not support float64
							__SCREAMING_SNAKE_CASE	:		str   	=		self.timesteps.to(original_samples.device							,					dtype=torch.floataa						)
							__SCREAMING_SNAKE_CASE	:		Tuple   	=		timesteps.to(original_samples.device							,					dtype=torch.floataa						)
					else:
							__SCREAMING_SNAKE_CASE	:		str   	=		self.timesteps.to(original_samples.device						)
							__SCREAMING_SNAKE_CASE	:		Tuple   	=		timesteps.to(original_samples.device						)

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

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

					__SCREAMING_SNAKE_CASE	:		str   	=		original_samples + noise * sigma
					return noisy_samples



			def __len__(							self					:			Optional[Any]						):





					"""simple docstring"""






					return self.config.num_train_timesteps





'''simple docstring'''




from __future__ import annotations

import random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar

UpperCamelCase__	:     str    =			TypeVar('''T''', bound=bool)






class 						_UpperCamelCase  (							Generic[T]   ):


			'''simple docstring'''

			def __init__(							self					:			str							,					lowerCAmelCase__					:			T						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		T   	=		value
					__SCREAMING_SNAKE_CASE	:		RandomizedHeapNode[T] | None   	=		None
					__SCREAMING_SNAKE_CASE	:		RandomizedHeapNode[T] | None   	=		None

			@property
			def UpperCamelCase__ (							self					:			Any						):





					"""simple docstring"""






					return self._value



			@staticmethod
			def UpperCamelCase__ (							lowerCAmelCase__					:			RandomizedHeapNode[T] | None							,					lowerCAmelCase__					:			RandomizedHeapNode[T] | None						):





					"""simple docstring"""






					if not roota:
							return roota

					if not roota:
							return roota

					if roota.value > roota.value:
							__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		List[str]   	=		roota, roota

					if random.choice([True, False]						):
							__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Tuple   	=		roota.right, roota.left

					__SCREAMING_SNAKE_CASE	:		Tuple   	=		RandomizedHeapNode.merge(roota.left							,					lowerCAmelCase__						)

					return roota






class 						_UpperCamelCase  (							Generic[T]   ):


			'''simple docstring'''

			def __init__(							self					:			Optional[int]							,					lowerCAmelCase__					:			Iterable[T] | None = ()						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		RandomizedHeapNode[T] | None   	=		None

					if data:
							for item in data:
									self.insert(lowerCAmelCase__						)

			def UpperCamelCase__ (							self					:			Dict							,					lowerCAmelCase__					:			T						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Dict   	=		RandomizedHeapNode.merge(self._root							,					RandomizedHeapNode(lowerCAmelCase__						)						)

			def UpperCamelCase__ (							self					:			Union[str, Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Tuple   	=		self.top()

					if self._root is None:
							return None

					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		RandomizedHeapNode.merge(self._root.left							,					self._root.right						)

					return result

			def UpperCamelCase__ (							self					:			Union[str, Any]						):





					"""simple docstring"""






					if not self._root:
							raise IndexError("""Can't get top element for the empty heap."""						)
					return self._root.value

			def UpperCamelCase__ (							self					:			List[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		List[str]   	=		None

			def UpperCamelCase__ (							self					:			Optional[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		int   	=		[]
					while self:
							result.append(self.pop()						)

					return result



			def __bool__(							self					:			List[Any]						):





					"""simple docstring"""






					return self._root is not None


if __name__ == "__main__":
						import doctest

						doctest.testmod()