


import math
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 KarrasDiffusionSchedulers, SchedulerMixin


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



@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class 							A					( UpperCAmelCase__							):
					'''simple docstring'''






					A__    =				42
					A__    =				None






def 				UpperCamelCase      (			__magic_name__  :							Union[str, Any]					,					__magic_name__  :							Tuple=0.9_9_9					,					__magic_name__  :							str="cosine"					,					)     ->						Tuple:
		"""simple docstring"""




		if alpha_transform_type == "cosine":

				def alpha_bar_fn(__magic_name__  :							Any  ):
						return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2  ) ** 2

		elif alpha_transform_type == "exp":

				def alpha_bar_fn(__magic_name__  :							Optional[int]  ):
						return math.exp(t * -1_2.0  )

		else:
				raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}'''  )

		lowercase__      =	[]
		for i in range(__magic_name__  ):
				lowercase__      =	i / num_diffusion_timesteps
				lowercase__      =	(i + 1) / num_diffusion_timesteps
				betas.append(min(1 - alpha_bar_fn(__magic_name__  ) / alpha_bar_fn(__magic_name__  )					,					__magic_name__  )  )
		return torch.tensor(__magic_name__					,					dtype=torch.floataa  )







class 							A					( UpperCAmelCase__				,						UpperCAmelCase__							):
					'''simple docstring'''






					A__    =				[e.name for e in KarrasDiffusionSchedulers]
					A__    =				1

					@register_to_config
					def __init__(self					:		int   ,      _UpperCAmelCase					:		int = 1000   ,      _UpperCAmelCase					:		float = 0.0_001   ,      _UpperCAmelCase					:		float = 0.02   ,      _UpperCAmelCase					:		str = "linear"   ,      _UpperCAmelCase					:		Optional[Union[np.ndarray, List[float]]] = None   ,      _UpperCAmelCase					:		str = "epsilon"   ,      _UpperCAmelCase					:		str = "linear"   ,      _UpperCAmelCase					:		Optional[bool] = False   ,      _UpperCAmelCase					:		str = "linspace"   ,      _UpperCAmelCase					:		int = 0   ,      )		->     str:




							"""simple docstring"""


							if trained_betas is not None:
									lowercase__      =	torch.tensor(_UpperCAmelCase   ,      dtype=torch.floataa	)
							elif beta_schedule == "linear":
									lowercase__      =	torch.linspace(_UpperCAmelCase   ,      _UpperCAmelCase   ,      _UpperCAmelCase   ,      dtype=torch.floataa	)
							elif beta_schedule == "scaled_linear":
									# this schedule is very specific to the latent diffusion model.
									lowercase__      =	(
									    torch.linspace(beta_start**0.5   ,      beta_end**0.5   ,      _UpperCAmelCase   ,      dtype=torch.floataa	) ** 2
									)
							elif beta_schedule == "squaredcos_cap_v2":
									# Glide cosine schedule
									lowercase__      =	betas_for_alpha_bar(_UpperCAmelCase	)
							else:
									raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''	)

							lowercase__      =	1.0 - self.betas
							lowercase__      =	torch.cumprod(self.alphas   ,      dim=0	)

							lowercase__      =	np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5	)
							lowercase__      =	np.concatenate([sigmas[::-1], [0.0]]	).astype(np.floataa	)
							lowercase__      =	torch.from_numpy(_UpperCAmelCase	)

							# setable values
							lowercase__      =	None
							lowercase__      =	np.linspace(0   ,      num_train_timesteps - 1   ,      _UpperCAmelCase   ,      dtype=_UpperCAmelCase	)[::-1].copy()
							lowercase__      =	torch.from_numpy(_UpperCAmelCase	)
							lowercase__      =	False
							lowercase__      =	use_karras_sigmas

					@property
					def 						lowerCamelCase__  (self					:		Any	)		->     Optional[int]:




							"""simple docstring"""


							if self.config.timestep_spacing in ["linspace", "trailing"]:
									return self.sigmas.max()

							return (self.sigmas.max() ** 2 + 1) ** 0.5

					def 						lowerCamelCase__  (self					:		List[Any]   ,      _UpperCAmelCase					:		torch.FloatTensor   ,      _UpperCAmelCase					:		Union[float, torch.FloatTensor]	)		->     torch.FloatTensor:




							"""simple docstring"""


							if isinstance(_UpperCAmelCase   ,      torch.Tensor	):
									lowercase__      =	timestep.to(self.timesteps.device	)
							lowercase__      =	(self.timesteps == timestep).nonzero().item()
							lowercase__      =	self.sigmas[step_index]

							lowercase__      =	sample / ((sigma**2 + 1) ** 0.5)

							lowercase__      =	True
							return sample

					def 						lowerCamelCase__  (self					:		int   ,      _UpperCAmelCase					:		int   ,      _UpperCAmelCase					:		Union[str, torch.device] = None	)		->     Optional[Any]:




							"""simple docstring"""


							lowercase__      =	num_inference_steps

							# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
							if self.config.timestep_spacing == "linspace":
									lowercase__      =	np.linspace(0   ,      self.config.num_train_timesteps - 1   ,      _UpperCAmelCase   ,      dtype=_UpperCAmelCase	)[
									    ::-1
									].copy()
							elif self.config.timestep_spacing == "leading":
									lowercase__      =	self.config.num_train_timesteps // self.num_inference_steps
									# creates integer timesteps by multiplying by ratio
									# casting to int to avoid issues when num_inference_step is power of 3
									lowercase__      =	(np.arange(0   ,      _UpperCAmelCase	) * step_ratio).round()[::-1].copy().astype(_UpperCAmelCase	)
									timesteps += self.config.steps_offset
							elif self.config.timestep_spacing == "trailing":
									lowercase__      =	self.config.num_train_timesteps / self.num_inference_steps
									# creates integer timesteps by multiplying by ratio
									# casting to int to avoid issues when num_inference_step is power of 3
									lowercase__      =	(np.arange(self.config.num_train_timesteps   ,      0   ,      -step_ratio	)).round().copy().astype(_UpperCAmelCase	)
									timesteps -= 1
							else:
									raise ValueError(
									    f'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''	)

							lowercase__      =	np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5	)
							lowercase__      =	np.log(_UpperCAmelCase	)

							if self.config.interpolation_type == "linear":
									lowercase__      =	np.interp(_UpperCAmelCase   ,      np.arange(0   ,      len(_UpperCAmelCase	)	)   ,      _UpperCAmelCase	)
							elif self.config.interpolation_type == "log_linear":
									lowercase__      =	torch.linspace(np.log(sigmas[-1]	)   ,      np.log(sigmas[0]	)   ,      num_inference_steps + 1	).exp()
							else:
									raise ValueError(
									    f'''{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either'''
									    """ 'linear' or 'log_linear'"""	)

							if self.use_karras_sigmas:
									lowercase__      =	self._convert_to_karras(in_sigmas=_UpperCAmelCase   ,      num_inference_steps=self.num_inference_steps	)
									lowercase__      =	np.array([self._sigma_to_t(_UpperCAmelCase   ,      _UpperCAmelCase	) for sigma in sigmas]	)

							lowercase__      =	np.concatenate([sigmas, [0.0]]	).astype(np.floataa	)
							lowercase__      =	torch.from_numpy(_UpperCAmelCase	).to(device=_UpperCAmelCase	)
							if str(_UpperCAmelCase	).startswith("""mps"""	):
									# mps does not support float64
									lowercase__      =	torch.from_numpy(_UpperCAmelCase	).to(_UpperCAmelCase   ,      dtype=torch.floataa	)
							else:
									lowercase__      =	torch.from_numpy(_UpperCAmelCase	).to(device=_UpperCAmelCase	)

					def 						lowerCamelCase__  (self					:		List[str]   ,      _UpperCAmelCase					:		Optional[int]   ,      _UpperCAmelCase					:		int	)		->     Tuple:




							"""simple docstring"""


							lowercase__      =	np.log(_UpperCAmelCase	)

							# get distribution
							lowercase__      =	log_sigma - log_sigmas[:, np.newaxis]

							# get sigmas range
							lowercase__      =	np.cumsum((dists >= 0)   ,      axis=0	).argmax(axis=0	).clip(max=log_sigmas.shape[0] - 2	)
							lowercase__      =	low_idx + 1

							lowercase__      =	log_sigmas[low_idx]
							lowercase__      =	log_sigmas[high_idx]

							# interpolate sigmas
							lowercase__      =	(low - log_sigma) / (low - high)
							lowercase__      =	np.clip(_UpperCAmelCase   ,      0   ,      1	)

							# transform interpolation to time range
							lowercase__      =	(1 - w) * low_idx + w * high_idx
							lowercase__      =	t.reshape(sigma.shape	)
							return t

					def 						lowerCamelCase__  (self					:		Optional[Any]   ,      _UpperCAmelCase					:		torch.FloatTensor   ,      _UpperCAmelCase					:		Optional[int]	)		->     torch.FloatTensor:




							"""simple docstring"""


							lowercase__      =	in_sigmas[-1].item()
							lowercase__      =	in_sigmas[0].item()

							lowercase__      =	7.0  # 7.0 is the value used in the paper
							lowercase__      =	np.linspace(0   ,      1   ,      _UpperCAmelCase	)
							lowercase__      =	sigma_min ** (1 / rho)
							lowercase__      =	sigma_max ** (1 / rho)
							lowercase__      =	(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
							return sigmas

					def 						lowerCamelCase__  (self					:		List[str]   ,      _UpperCAmelCase					:		torch.FloatTensor   ,      _UpperCAmelCase					:		Union[float, torch.FloatTensor]   ,      _UpperCAmelCase					:		torch.FloatTensor   ,      _UpperCAmelCase					:		float = 0.0   ,      _UpperCAmelCase					:		float = 0.0   ,      _UpperCAmelCase					:		float = float("""inf"""	)   ,      _UpperCAmelCase					:		float = 1.0   ,      _UpperCAmelCase					:		Optional[torch.Generator] = None   ,      _UpperCAmelCase					:		bool = True   ,      )		->     Union[EulerDiscreteSchedulerOutput, Tuple]:




							"""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"""
									        """ `EulerDiscreteScheduler.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	):
									lowercase__      =	timestep.to(self.timesteps.device	)

							lowercase__      =	(self.timesteps == timestep).nonzero().item()
							lowercase__      =	self.sigmas[step_index]

							lowercase__      =	min(s_churn / (len(self.sigmas	) - 1)   ,      2**0.5 - 1	) if s_tmin <= sigma <= s_tmax else 0.0

							lowercase__      =	randn_tensor(
							    model_output.shape   ,      dtype=model_output.dtype   ,      device=model_output.device   ,      generator=_UpperCAmelCase	)

							lowercase__      =	noise * s_noise
							lowercase__      =	sigma * (gamma + 1)

							if gamma > 0:
									lowercase__      =	sample + eps * (sigma_hat**2 - sigma**2) ** 0.5

							# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
							# NOTE: "original_sample" should not be an expected prediction_type but is left in for
							# backwards compatibility
							if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
									lowercase__      =	model_output
							elif self.config.prediction_type == "epsilon":
									lowercase__      =	sample - sigma_hat * model_output
							elif self.config.prediction_type == "v_prediction":
									# * c_out + input * c_skip
									lowercase__      =	model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
							else:
									raise ValueError(
									    f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''	)

							# 2. Convert to an ODE derivative
							lowercase__      =	(sample - pred_original_sample) / sigma_hat

							lowercase__      =	self.sigmas[step_index + 1] - sigma_hat

							lowercase__      =	sample + derivative * dt

							if not return_dict:
									return (prev_sample,)

							return EulerDiscreteSchedulerOutput(prev_sample=_UpperCAmelCase   ,      pred_original_sample=_UpperCAmelCase	)

					def 						lowerCamelCase__  (self					:		List[str]   ,      _UpperCAmelCase					:		torch.FloatTensor   ,      _UpperCAmelCase					:		torch.FloatTensor   ,      _UpperCAmelCase					:		torch.FloatTensor   ,      )		->     torch.FloatTensor:




							"""simple docstring"""


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

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

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

							lowercase__      =	original_samples + noise * sigma
							return noisy_samples


					def __len__(self					:		Optional[Any]	)		->     Any:




							"""simple docstring"""


							return self.config.num_train_timesteps





from __future__ import annotations






def 				UpperCamelCase      (			__magic_name__  :							list[list[int]]					,					__magic_name__  :							list[list[int]]  )     ->						list[int]:
		"""simple docstring"""




		assert len(__magic_name__  ) == len(__magic_name__  )

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

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

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

