





from __future__ import annotations





def lowerCAmelCase__(__snake_case	,__snake_case							)    ->				list[int]:







	'''simple docstring'''







	assert len(__snake_case							) == len(__snake_case							)

	lowerCamelCase__        =		len(__snake_case							)
	lowerCamelCase__        =		list(range(__snake_case							)							)
	lowerCamelCase__        =		[-1] * n  # who the donor has donated to
	lowerCamelCase__        =		[-1] * n  # who the recipient has received from
	lowerCamelCase__        =		[0] * n

	while unmatched_donors:
		lowerCamelCase__        =		unmatched_donors[0]
		lowerCamelCase__        =		donor_pref[donor]
		lowerCamelCase__        =		donor_preference[num_donations[donor]]
		num_donations[donor] += 1
		lowerCamelCase__        =		recipient_pref[recipient]
		lowerCamelCase__        =		rec_record[recipient]

		if prev_donor != -1:
			if rec_preference.index(__snake_case							) > rec_preference.index(__snake_case							):
				lowerCamelCase__        =		donor
				lowerCamelCase__        =		recipient
				unmatched_donors.append(__snake_case							)
				unmatched_donors.remove(__snake_case							)
		else:
			lowerCamelCase__        =		donor
			lowerCamelCase__        =		recipient
			unmatched_donors.remove(__snake_case							)
	return donor_record










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  __A   (   lowerCAmelCase			):

			'''simple docstring'''







			lowerCAmelCase_												=						42






class  __A   (   lowerCAmelCase      ,    lowerCAmelCase			):

			'''simple docstring'''







			lowerCAmelCase_												=						1





			@register_to_config
			def __init__(   self	,  __lowerCAmelCase = 4_0	,  __lowerCAmelCase = 0.002	,  __lowerCAmelCase = 80.0	,  __lowerCAmelCase = 0.5	,  __lowerCAmelCase = 1.0	,  __lowerCAmelCase = 7.0	,  __lowerCAmelCase = True	,  ):



				'''simple docstring'''







				lowerCamelCase__        =		sigma_max

				lowerCamelCase__        =		np.linspace(0	,  1	,  __lowerCAmelCase							)
				lowerCamelCase__        =		self._convert_to_karras(__lowerCAmelCase							)
				lowerCamelCase__        =		self.sigma_to_t(__lowerCAmelCase							)

				# setable values
				lowerCamelCase__        =		None
				lowerCamelCase__        =		torch.from_numpy(__lowerCAmelCase							)
				lowerCamelCase__        =		torch.from_numpy(__lowerCAmelCase							)
				lowerCamelCase__        =		False
				lowerCamelCase__        =		False





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase	,  __lowerCAmelCase=None							):



				'''simple docstring'''







				if schedule_timesteps is None:
					lowerCamelCase__        =		self.timesteps

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





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase	,  __lowerCAmelCase							):



				'''simple docstring'''







				if isinstance(__lowerCAmelCase	,  torch.Tensor							):
					lowerCamelCase__        =		timestep.to(self.timesteps.device							)
				lowerCamelCase__        =		self.index_for_timestep(__lowerCAmelCase							)
				lowerCamelCase__        =		self.sigmas[step_idx]

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

				lowerCamelCase__        =		True
				return sample





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase							):



				'''simple docstring'''







				if not isinstance(__lowerCAmelCase	,  np.ndarray							):
					lowerCamelCase__        =		np.array(__lowerCAmelCase	,  dtype=np.floataa							)

				lowerCamelCase__        =		1_0_0_0 * 0.25 * np.log(sigmas + 1E-44							)

				return timesteps





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase = None	,  __lowerCAmelCase = None	,  __lowerCAmelCase = 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}.'							)

					lowerCamelCase__        =		np.array(__lowerCAmelCase	,  dtype=np.intaa							)
					lowerCamelCase__        =		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.'							)

					lowerCamelCase__        =		num_inference_steps

					lowerCamelCase__        =		self.config.num_train_timesteps // self.num_inference_steps
					lowerCamelCase__        =		(np.arange(0	,  __lowerCAmelCase							) * step_ratio).round()[::-1].copy().astype(np.intaa							)
					lowerCamelCase__        =		False

				# Map timesteps to Karras sigmas directly for multistep sampling
				# See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
				lowerCamelCase__        =		self.config.num_train_timesteps
				lowerCamelCase__        =		timesteps[::-1].copy()
				lowerCamelCase__        =		ramp / (num_train_timesteps - 1)
				lowerCamelCase__        =		self._convert_to_karras(__lowerCAmelCase							)
				lowerCamelCase__        =		self.sigma_to_t(__lowerCAmelCase							)

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

				if str(__lowerCAmelCase							).startswith('''mps'''							):
					# mps does not support float64
					lowerCamelCase__        =		torch.from_numpy(__lowerCAmelCase							).to(__lowerCAmelCase	,  dtype=torch.floataa							)
				else:
					lowerCamelCase__        =		torch.from_numpy(__lowerCAmelCase							).to(device=__lowerCAmelCase							)





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__        =		self.config.sigma_min
				lowerCamelCase__        =		self.config.sigma_max

				lowerCamelCase__        =		self.config.rho
				lowerCamelCase__        =		sigma_min ** (1 / rho)
				lowerCamelCase__        =		sigma_max ** (1 / rho)
				lowerCamelCase__        =		(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
				return sigmas





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__        =		self.config.sigma_data

				lowerCamelCase__        =		sigma_data**2 / (sigma**2 + sigma_data**2)
				lowerCamelCase__        =		sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
				return c_skip, c_out





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__        =		self.config.sigma_min
				lowerCamelCase__        =		self.config.sigma_data

				lowerCamelCase__        =		sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
				lowerCamelCase__        =		(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
				return c_skip, c_out





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase = None	,  __lowerCAmelCase = 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							):
					lowerCamelCase__        =		timestep.to(self.timesteps.device							)

				lowerCamelCase__        =		self.config.sigma_min
				lowerCamelCase__        =		self.config.sigma_max

				lowerCamelCase__        =		self.index_for_timestep(__lowerCAmelCase							)

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

				# Get scalings for boundary conditions
				lowerCamelCase__    ,     lowerCamelCase__        =		self.get_scalings_for_boundary_condition(__lowerCAmelCase							)

				# 1. Denoise model output using boundary conditions
				lowerCamelCase__        =		c_out * model_output + c_skip * sample
				if self.config.clip_denoised:
					lowerCamelCase__        =		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:
					lowerCamelCase__        =		randn_tensor(
					    model_output.shape	,  dtype=model_output.dtype	,  device=model_output.device	,  generator=__lowerCAmelCase							)
				else:
					lowerCamelCase__        =		torch.zeros_like(__lowerCAmelCase							)
				lowerCamelCase__        =		noise * self.config.s_noise

				lowerCamelCase__        =		sigma_next.clamp(min=__lowerCAmelCase	,  max=__lowerCAmelCase							)

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

				if not return_dict:
					return (prev_sample,)

				return CMStochasticIterativeSchedulerOutput(prev_sample=__lowerCAmelCase							)





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase	,  ):



				'''simple docstring'''







				lowerCamelCase__        =		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
					lowerCamelCase__        =		self.timesteps.to(original_samples.device	,  dtype=torch.floataa							)
					lowerCamelCase__        =		timesteps.to(original_samples.device	,  dtype=torch.floataa							)
				else:
					lowerCamelCase__        =		self.timesteps.to(original_samples.device							)
					lowerCamelCase__        =		timesteps.to(original_samples.device							)

				lowerCamelCase__        =		[(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

				lowerCamelCase__        =		original_samples + noise * sigma
				return noisy_samples





			def __len__(   self							):



				'''simple docstring'''







				return self.config.num_train_timesteps



