"""simple docstring"""






from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin

@dataclass
class lowerCamelCase		(_SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					a   	=		4_2
					a   	=		4_2
					a   	=		None



class lowerCamelCase		(_SCREAMING_SNAKE_CASE    ,    _SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					a   	=		2



					@register_to_config
					def __init__(						self			:				Any   , _snake_case			:				float = 0.02   , _snake_case			:				float = 100   , _snake_case			:				float = 1.007   , _snake_case			:				float = 80   , _snake_case			:				float = 0.05   , _snake_case			:				float = 50   , )							->       Optional[Any]:
									# standard deviation of the initial noise distribution
									SCREAMING_SNAKE_CASE__		  =				sigma_max

									# setable values
									SCREAMING_SNAKE_CASE__		  =				None
									SCREAMING_SNAKE_CASE__		  =				None
									SCREAMING_SNAKE_CASE__		  =				None  # sigma(t_i)



					def 			lowerCAmelCase_					(						self			:				Dict   , _snake_case			:				torch.FloatTensor   , _snake_case			:				Optional[int] = None		)							->       torch.FloatTensor:
									return sample



					def 			lowerCAmelCase_					(						self			:				Optional[Any]   , _snake_case			:				int   , _snake_case			:				Union[str, torch.device] = None		)							->       List[Any]:
									SCREAMING_SNAKE_CASE__		  =				num_inference_steps
									SCREAMING_SNAKE_CASE__		  =				np.arange(0   , self.num_inference_steps		)[::-1].copy()
									SCREAMING_SNAKE_CASE__		  =				torch.from_numpy(_snake_case		).to(_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				[
									    (
									        self.config.sigma_max**2
									        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
									    )
									    for i in self.timesteps
									]
									SCREAMING_SNAKE_CASE__		  =				torch.tensor(_snake_case   , dtype=torch.floataa   , device=_snake_case		)



					def 			lowerCAmelCase_					(						self			:				Tuple   , _snake_case			:				torch.FloatTensor   , _snake_case			:				float   , _snake_case			:				Optional[torch.Generator] = None		)							->       Tuple[torch.FloatTensor, float]:
									if self.config.s_min <= sigma <= self.config.s_max:
													SCREAMING_SNAKE_CASE__		  =				min(self.config.s_churn / self.num_inference_steps   , 2**0.5 - 1		)
									else:
													SCREAMING_SNAKE_CASE__		  =				0

									# sample eps ~ N(0, S_noise^2 * I)
									SCREAMING_SNAKE_CASE__		  =				self.config.s_noise * randn_tensor(sample.shape   , generator=_snake_case		).to(sample.device		)
									SCREAMING_SNAKE_CASE__		  =				sigma + gamma * sigma
									SCREAMING_SNAKE_CASE__		  =				sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

									return sample_hat, sigma_hat



					def 			lowerCAmelCase_					(						self			:				int   , _snake_case			:				torch.FloatTensor   , _snake_case			:				float   , _snake_case			:				float   , _snake_case			:				torch.FloatTensor   , _snake_case			:				bool = True   , )							->       Union[KarrasVeOutput, Tuple]:
									SCREAMING_SNAKE_CASE__		  =				sample_hat + sigma_hat * model_output
									SCREAMING_SNAKE_CASE__		  =				(sample_hat - pred_original_sample) / sigma_hat
									SCREAMING_SNAKE_CASE__		  =				sample_hat + (sigma_prev - sigma_hat) * derivative

									if not return_dict:
													return (sample_prev, derivative)

									return KarrasVeOutput(
									    prev_sample=_snake_case   , derivative=_snake_case   , pred_original_sample=_snake_case		)



					def 			lowerCAmelCase_					(						self			:				Dict   , _snake_case			:				torch.FloatTensor   , _snake_case			:				float   , _snake_case			:				float   , _snake_case			:				torch.FloatTensor   , _snake_case			:				torch.FloatTensor   , _snake_case			:				torch.FloatTensor   , _snake_case			:				bool = True   , )							->       Union[KarrasVeOutput, Tuple]:
									SCREAMING_SNAKE_CASE__		  =				sample_prev + sigma_prev * model_output
									SCREAMING_SNAKE_CASE__		  =				(sample_prev - pred_original_sample) / sigma_prev
									SCREAMING_SNAKE_CASE__		  =				sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

									if not return_dict:
													return (sample_prev, derivative)

									return KarrasVeOutput(
									    prev_sample=_snake_case   , derivative=_snake_case   , pred_original_sample=_snake_case		)



					def 			lowerCAmelCase_					(						self			:				Any   , _snake_case			:				Optional[int]   , _snake_case			:				Dict   , _snake_case			:				Any		)							->       Union[str, Any]:
									raise NotImplementedError()





"""simple docstring"""






from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class lowerCamelCase		(_SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					def __init__(						self			:				int   , _snake_case			:				Optional[int]   , _snake_case			:				Dict		)							->       Dict:
									super().__init__()

									# make sure scheduler can always be converted to DDIM
									SCREAMING_SNAKE_CASE__		  =				DDIMScheduler.from_config(scheduler.config		)

									self.register_modules(unet=_snake_case   , scheduler=_snake_case		)



					@torch.no_grad()
					def __call__(						self			:				int   , _snake_case			:				int = 1   , _snake_case			:				Optional[Union[torch.Generator, List[torch.Generator]]] = None   , _snake_case			:				float = 0.0   , _snake_case			:				int = 50   , _snake_case			:				Optional[bool] = None   , _snake_case			:				Optional[str] = "pil"   , _snake_case			:				bool = True   , )							->       Union[ImagePipelineOutput, Tuple]:

									# Sample gaussian noise to begin loop
									if isinstance(self.unet.config.sample_size   , _snake_case		):
													SCREAMING_SNAKE_CASE__		  =				(
													    batch_size,
													    self.unet.config.in_channels,
													    self.unet.config.sample_size,
													    self.unet.config.sample_size,
													)
									else:
													SCREAMING_SNAKE_CASE__		  =				(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

									if isinstance(_snake_case   , _snake_case		) and len(_snake_case		) != batch_size:
													raise ValueError(
													    F"""You have passed a list of generators of length {len(_snake_case		)}, but requested an effective batch"""
													    F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""		)

									SCREAMING_SNAKE_CASE__		  =				randn_tensor(_snake_case   , generator=_snake_case   , device=self.device   , dtype=self.unet.dtype		)

									# set step values
									self.scheduler.set_timesteps(_snake_case		)

									for t in self.progress_bar(self.scheduler.timesteps		):
													# 1. predict noise model_output
													SCREAMING_SNAKE_CASE__		  =				self.unet(_snake_case   , _snake_case		).sample

													# 2. predict previous mean of image x_t-1 and add variance depending on eta
													# eta corresponds to η in paper and should be between [0, 1]
													# do x_t -> x_t-1
													SCREAMING_SNAKE_CASE__		  =				self.scheduler.step(
													    _snake_case   , _snake_case   , _snake_case   , eta=_snake_case   , use_clipped_model_output=_snake_case   , generator=_snake_case		).prev_sample

									SCREAMING_SNAKE_CASE__		  =				(image / 2 + 0.5).clamp(0   , 1		)
									SCREAMING_SNAKE_CASE__		  =				image.cpu().permute(0   , 2   , 3   , 1		).numpy()
									if output_type == "pil":
													SCREAMING_SNAKE_CASE__		  =				self.numpy_to_pil(_snake_case		)

									if not return_dict:
													return (image,)

									return ImagePipelineOutput(images=_snake_case		)




