from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class UpperCAmelCase						( __A  ):


				'''simple docstring'''






				lowerCamelCase_    = 42
				lowerCamelCase_    = 42
				def __init__(						self		,     lowercase		,     lowercase ):



						"""simple docstring"""





						super().__init__()
						self.register_modules(unet=lowercase		,     scheduler=lowercase )







				@torch.no_grad()
				def __call__(						self		,     lowercase = 1		,     lowercase = 5_0		,     lowercase = None		,     lowercase = "pil"		,     lowercase = True		,     **lowercase		,     ):



						"""simple docstring"""





						A_		:    Optional[Any]         =				self.unet.config.sample_size
						A_		:    Union[str, Any]         =				(batch_size, 3, img_size, img_size)

						A_		:    Dict         =				self.unet

						# sample x_0 ~ N(0, sigma_0^2 * I)
						A_		:    Optional[int]         =				randn_tensor(lowercase		,     generator=lowercase		,     device=self.device ) * self.scheduler.init_noise_sigma

						self.scheduler.set_timesteps(lowercase )

						for t in self.progress_bar(self.scheduler.timesteps ):
								# here sigma_t == t_i from the paper
								A_		:    int         =				self.scheduler.schedule[t]
								A_		:    Tuple         =				self.scheduler.schedule[t - 1] if t > 0 else 0

								# 1. Select temporarily increased noise level sigma_hat
								# 2. Add new noise to move from sample_i to sample_hat
								A_       ,      A_		:    List[str]         =				self.scheduler.add_noise_to_input(lowercase		,     lowercase		,     generator=lowercase )

								# 3. Predict the noise residual given the noise magnitude `sigma_hat`
								# The model inputs and output are adjusted by following eq. (213) in [1].
								A_		:    Optional[int]         =				(sigma_hat / 2) * model((sample_hat + 1) / 2		,     sigma_hat / 2 ).sample

								# 4. Evaluate dx/dt at sigma_hat
								# 5. Take Euler step from sigma to sigma_prev
								A_		:    Optional[int]         =				self.scheduler.step(lowercase		,     lowercase		,     lowercase		,     lowercase )

								if sigma_prev != 0:
										# 6. Apply 2nd order correction
										# The model inputs and output are adjusted by following eq. (213) in [1].
										A_		:    Union[str, Any]         =				(sigma_prev / 2) * model((step_output.prev_sample + 1) / 2		,     sigma_prev / 2 ).sample
										A_		:    List[str]         =				self.scheduler.step_correct(
										    lowercase		,     lowercase		,     lowercase		,     lowercase		,     step_output.prev_sample		,     step_output['derivative']		,     )
								A_		:    Optional[Any]         =				step_output.prev_sample

						A_		:    Any         =				(sample / 2 + 0.5).clamp(0		,     1 )
						A_		:    Dict         =				sample.cpu().permute(0		,     2		,     3		,     1 ).numpy()
						if output_type == "pil":
								A_		:    Dict         =				self.numpy_to_pil(lowercase )

						if not return_dict:
								return (image,)

						return ImagePipelineOutput(images=lowercase )







from ..utils import DummyObject, requires_backends





class UpperCAmelCase						( metaclass=__A  ):


				'''simple docstring'''






				lowerCamelCase_    = ['''onnx''']
				def __init__(						self		,     *lowercase		,     **lowercase ):



						"""simple docstring"""





						requires_backends(self		,     ['onnx'] )
				@classmethod
				def   lowerCAmelCase_       (						cls		,     *lowercase		,     **lowercase ):



						"""simple docstring"""





						requires_backends(cls		,     ['onnx'] )







				@classmethod
				def   lowerCAmelCase_       (						cls		,     *lowercase		,     **lowercase ):



						"""simple docstring"""





						requires_backends(cls		,     ['onnx'] )






