




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




@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class     A					(				lowerCamelCase_       ):
							_SCREAMING_SNAKE_CASE  :		torch.FloatTensor
							_SCREAMING_SNAKE_CASE  :		Optional[torch.FloatTensor]			  =       None







def  a_		(							__snake_case     ,		__snake_case=0.9_99     ,		__snake_case="cosine"     ,		)	->			Dict:

			'''simple docstring'''


			if alpha_transform_type == "cosine":

						def alpha_bar_fn(__snake_case					):
									return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2					) ** 2

			elif alpha_transform_type == "exp":

						def alpha_bar_fn(__snake_case					):
									return math.exp(t * -12.0					)

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

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





class     A					(				lowerCamelCase_  ,						lowerCamelCase_       ):
							_SCREAMING_SNAKE_CASE  :		Dict			  =       [e.name for e in KarrasDiffusionSchedulers]
							_SCREAMING_SNAKE_CASE  :		List[str]			  =       1







							@register_to_config
							def __init__(    self   :	str   ,     __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   ,     )		->					Dict:

										"""simple docstring"""



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

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

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

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







							@property
							def        lowercase__							(    self   :	str				)		->					Any:

										"""simple docstring"""



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

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







							def        lowercase__							(    self   :	Tuple   ,     __UpperCAmelCase   :	torch.FloatTensor   ,     __UpperCAmelCase   :	Union[float, torch.FloatTensor]				)		->					torch.FloatTensor:

										"""simple docstring"""



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

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

										UpperCamelCase_     				= True
										return sample







							def        lowercase__							(    self   :	str   ,     __UpperCAmelCase   :	int   ,     __UpperCAmelCase   :	Union[str, torch.device] = None				)		->					int:

										"""simple docstring"""



										UpperCamelCase_     				= 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":
													UpperCamelCase_     				= np.linspace(0   ,     self.config.num_train_timesteps - 1   ,     __UpperCAmelCase   ,     dtype=__UpperCAmelCase				)[
													    ::-1
													].copy()
										elif self.config.timestep_spacing == "leading":
													UpperCamelCase_     				= 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
													UpperCamelCase_     				= (np.arange(0   ,     __UpperCAmelCase				) * step_ratio).round()[::-1].copy().astype(__UpperCAmelCase				)
													timesteps += self.config.steps_offset
										elif self.config.timestep_spacing == "trailing":
													UpperCamelCase_     				= 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
													UpperCamelCase_     				= (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\'.'''				)

										UpperCamelCase_     				= np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5				)
										UpperCamelCase_     				= np.log(__UpperCAmelCase				)

										if self.config.interpolation_type == "linear":
													UpperCamelCase_     				= np.interp(__UpperCAmelCase   ,     np.arange(0   ,     len(__UpperCAmelCase				)				)   ,     __UpperCAmelCase				)
										elif self.config.interpolation_type == "log_linear":
													UpperCamelCase_     				= 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:
													UpperCamelCase_     				= self._convert_to_karras(in_sigmas=__UpperCAmelCase   ,     num_inference_steps=self.num_inference_steps				)
													UpperCamelCase_     				= np.array([self._sigma_to_t(__UpperCAmelCase   ,     __UpperCAmelCase				) for sigma in sigmas]				)

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







							def        lowercase__							(    self   :	List[Any]   ,     __UpperCAmelCase   :	List[Any]   ,     __UpperCAmelCase   :	List[Any]				)		->					Dict:

										"""simple docstring"""



										UpperCamelCase_     				= np.log(__UpperCAmelCase				)

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

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

										UpperCamelCase_     				= log_sigmas[low_idx]
										UpperCamelCase_     				= log_sigmas[high_idx]

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

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







							def        lowercase__							(    self   :	Union[str, Any]   ,     __UpperCAmelCase   :	torch.FloatTensor   ,     __UpperCAmelCase   :	List[Any]				)		->					torch.FloatTensor:

										"""simple docstring"""



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

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







							def        lowercase__							(    self   :	List[Any]   ,     __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				):
													UpperCamelCase_     				= timestep.to(self.timesteps.device				)

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

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

										UpperCamelCase_     				= randn_tensor(
										    model_output.shape   ,     dtype=model_output.dtype   ,     device=model_output.device   ,     generator=__UpperCAmelCase				)

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

										if gamma > 0:
													UpperCamelCase_     				= 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":
													UpperCamelCase_     				= model_output
										elif self.config.prediction_type == "epsilon":
													UpperCamelCase_     				= sample - sigma_hat * model_output
										elif self.config.prediction_type == "v_prediction":
													# * c_out + input * c_skip
													UpperCamelCase_     				= 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
										UpperCamelCase_     				= (sample - pred_original_sample) / sigma_hat

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

										UpperCamelCase_     				= sample + derivative * dt

										if not return_dict:
													return (prev_sample,)

										return EulerDiscreteSchedulerOutput(prev_sample=__UpperCAmelCase   ,     pred_original_sample=__UpperCAmelCase				)







							def        lowercase__							(    self   :	Tuple   ,     __UpperCAmelCase   :	torch.FloatTensor   ,     __UpperCAmelCase   :	torch.FloatTensor   ,     __UpperCAmelCase   :	torch.FloatTensor   ,     )		->					torch.FloatTensor:

										"""simple docstring"""



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

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

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

										UpperCamelCase_     				= original_samples + noise * sigma
										return noisy_samples







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

										"""simple docstring"""



										return self.config.num_train_timesteps






import numpy as np




def  a_		(							__snake_case     ,		__snake_case					)	->			List[str]:

			'''simple docstring'''


			UpperCamelCase_     				= np.array(__snake_case					)
			UpperCamelCase_     				= np.array(__snake_case					)

			UpperCamelCase_     				= abs(predict - actual					)
			UpperCamelCase_     				= difference.mean()

			return score




def  a_		(							__snake_case     ,		__snake_case					)	->			Optional[Any]:

			'''simple docstring'''


			UpperCamelCase_     				= np.array(__snake_case					)
			UpperCamelCase_     				= np.array(__snake_case					)

			UpperCamelCase_     				= predict - actual
			UpperCamelCase_     				= np.square(__snake_case					)

			UpperCamelCase_     				= square_diff.mean()
			return score




def  a_		(							__snake_case     ,		__snake_case					)	->			Optional[Any]:

			'''simple docstring'''


			UpperCamelCase_     				= np.array(__snake_case					)
			UpperCamelCase_     				= np.array(__snake_case					)

			UpperCamelCase_     				= predict - actual
			UpperCamelCase_     				= np.square(__snake_case					)
			UpperCamelCase_     				= square_diff.mean()
			UpperCamelCase_     				= np.sqrt(__snake_case					)
			return score




def  a_		(							__snake_case     ,		__snake_case					)	->			Optional[int]:

			'''simple docstring'''


			UpperCamelCase_     				= np.array(__snake_case					)
			UpperCamelCase_     				= np.array(__snake_case					)

			UpperCamelCase_     				= np.log(predict + 1					)
			UpperCamelCase_     				= np.log(actual + 1					)

			UpperCamelCase_     				= log_predict - log_actual
			UpperCamelCase_     				= np.square(__snake_case					)
			UpperCamelCase_     				= square_diff.mean()

			UpperCamelCase_     				= np.sqrt(__snake_case					)

			return score




def  a_		(							__snake_case     ,		__snake_case					)	->			int:

			'''simple docstring'''


			UpperCamelCase_     				= np.array(__snake_case					)
			UpperCamelCase_     				= np.array(__snake_case					)

			UpperCamelCase_     				= predict - actual
			UpperCamelCase_     				= np.sum(__snake_case					) / len(__snake_case					)
			UpperCamelCase_     				= np.sum(__snake_case					) / len(__snake_case					)
			# print(numerator, denumerator)
			UpperCamelCase_     				= float(__snake_case					) / denumerator * 1_0_0

			return score





def  a_		(							__snake_case     ,		__snake_case					)	->			Optional[Any]:

			'''simple docstring'''


			return np.mean(np.array(__snake_case					) == np.array(__snake_case					)					)
