'''simple docstring'''




from __future__ import annotations






class 							a__:


			'''simple docstring'''






			def __init__(  self			,       __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase     =    order

					# a_{0} ... a_{k}
					lowerCAmelCase     =    [1.0] + [0.0] * order
					# b_{0} ... b_{k}
					lowerCAmelCase     =    [1.0] + [0.0] * order

					# x[n-1] ... x[n-k]
					lowerCAmelCase     =    [0.0] * self.order
					# y[n-1] ... y[n-k]
					lowerCAmelCase     =    [0.0] * self.order



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase):




					"""simple docstring"""


					if len(__lowerCAmelCase) < self.order:
							lowerCAmelCase     =    [1.0, *a_coeffs]

					if len(__lowerCAmelCase) != self.order + 1:
							lowerCAmelCase     =    (
							    f"Expected a_coeffs to have {self.order + 1} elements "
							    f"for {self.order}-order filter, got {len(__lowerCAmelCase)}"
							)
							raise ValueError(__lowerCAmelCase)

					if len(__lowerCAmelCase) != self.order + 1:
							lowerCAmelCase     =    (
							    f"Expected b_coeffs to have {self.order + 1} elements "
							    f"for {self.order}-order filter, got {len(__lowerCAmelCase)}"
							)
							raise ValueError(__lowerCAmelCase)

					lowerCAmelCase     =    a_coeffs
					lowerCAmelCase     =    b_coeffs







			def 				a_			(  self			,       __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase     =    0.0

					# Start at index 1 and do index 0 at the end.
					for i in range(1			,       self.order + 1):
							result += (
							    self.b_coeffs[i] * self.input_history[i - 1]
							    - self.a_coeffs[i] * self.output_history[i - 1]
							)

					lowerCAmelCase     =    (result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

					lowerCAmelCase     =    self.input_history[:-1]
					lowerCAmelCase     =    self.output_history[:-1]

					lowerCAmelCase     =    sample
					lowerCAmelCase     =    result

					return result












"""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      UpperCamelCase		(__snake_case     ):






						def __init__(		self  :Any			, __magic_name__  :Union[str, Any]			, __magic_name__  :Optional[Any]				)      ->Dict:
										super().__init__()

										# make sure scheduler can always be converted to DDIM
										lowercase    :      Optional[Any]       = DDIMScheduler.from_config(scheduler.config				)

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






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

										# Sample gaussian noise to begin loop
										if isinstance(self.unet.config.sample_size			, __magic_name__				):
														lowercase    :      int       = (
														    batch_size,
														    self.unet.config.in_channels,
														    self.unet.config.sample_size,
														    self.unet.config.sample_size,
														)
										else:
														lowercase    :      Tuple       = (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

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

										lowercase    :      Dict       = randn_tensor(__magic_name__			, generator=__magic_name__			, device=self.device			, dtype=self.unet.dtype				)

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

										for t in self.progress_bar(self.scheduler.timesteps				):
														# 1. predict noise model_output
														lowercase    :      Union[str, Any]       = self.unet(__magic_name__			, __magic_name__				).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
														lowercase    :      int       = self.scheduler.step(
														    __magic_name__			, __magic_name__			, __magic_name__			, eta=__magic_name__			, use_clipped_model_output=__magic_name__			, generator=__magic_name__				).prev_sample

										lowercase    :      Optional[Any]       = (image / 2 + 0.5).clamp(0			, 1				)
										lowercase    :      Tuple       = image.cpu().permute(0			, 2			, 3			, 1				).numpy()
										if output_type == "pil":
														lowercase    :      Any       = self.numpy_to_pil(__magic_name__				)

										if not return_dict:
														return (image,)

										return ImagePipelineOutput(images=__magic_name__				)
