


"""simple docstring"""







from __future__ import annotations


class       SCREAMING_SNAKE_CASE__				:
       """simple docstring"""


       def __init__(						self		,     snake_case__						):






         """simple docstring"""





         lowerCAmelCase     :     int								=							order

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

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

       def        lowercase__							(						self		,     snake_case__		,     snake_case__						):






         """simple docstring"""





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

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

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

         lowerCAmelCase     :     Union[str, Any]								=							a_coeffs
         lowerCAmelCase     :     Any								=							b_coeffs







       def        lowercase__							(						self		,     snake_case__						):






         """simple docstring"""





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

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

         lowerCAmelCase     :     Tuple								=							sample
         lowerCAmelCase     :     Union[str, Any]								=							result

         return result








import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


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






def        UpperCamelCase_(							_snake_case   :  Union[List, PIL.Image.Image, torch.Tensor]	):




			"""simple docstring"""






			warnings.warn(
			    'The preprocess method is deprecated and will be removed in a future version. Please'
			    ' use VaeImageProcessor.preprocess instead'       ,     _snake_case       ,     )
			if isinstance(_snake_case       ,     torch.Tensor	):
						return image
			elif isinstance(_snake_case       ,     PIL.Image.Image	):
						__a        =[image]

			if isinstance(image[0]       ,     PIL.Image.Image	):
						__a      ,    __a        =image[0].size
						__a      ,    __a        =(x - x % 8 for x in (w, h))  # resize to integer multiple of 8

						__a        =[np.array(i.resize((w, h)       ,     resample=PIL_INTERPOLATION['lanczos']	)	)[None, :] for i in image]
						__a        =np.concatenate(_snake_case       ,     axis=0	)
						__a        =np.array(_snake_case	).astype(np.floataa	) / 255.0
						__a        =image.transpose(0       ,     3       ,     1       ,     2	)
						__a        =2.0 * image - 1.0
						__a        =torch.from_numpy(_snake_case	)
			elif isinstance(image[0]       ,     torch.Tensor	):
						__a        =torch.cat(_snake_case       ,     dim=0	)
			return image






def        UpperCamelCase_(							_snake_case   :  Union[List, PIL.Image.Image, torch.Tensor]	):




			"""simple docstring"""






			if isinstance(_snake_case       ,     torch.Tensor	):
						return mask
			elif isinstance(_snake_case       ,     PIL.Image.Image	):
						__a        =[mask]

			if isinstance(mask[0]       ,     PIL.Image.Image	):
						__a      ,    __a        =mask[0].size
						__a      ,    __a        =(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
						__a        =[np.array(m.convert('L'	).resize((w, h)       ,     resample=PIL_INTERPOLATION['nearest']	)	)[None, :] for m in mask]
						__a        =np.concatenate(_snake_case       ,     axis=0	)
						__a        =mask.astype(np.floataa	) / 255.0
						__a        =0
						__a        =1
						__a        =torch.from_numpy(_snake_case	)
			elif isinstance(mask[0]       ,     torch.Tensor	):
						__a        =torch.cat(_snake_case       ,     dim=0	)
			return mask






class 	__magic_name__    (			lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						42
			SCREAMING_SNAKE_CASE							   =						42





			def __init__(		self					,  __snake_case					,  __snake_case					)   -> Union[str, Any]:




						'''simple docstring'''
						super().__init__()
						self.register_modules(unet=__snake_case					,  scheduler=__snake_case					)





			@torch.no_grad()
			def __call__(		self					,  __snake_case					,  __snake_case					,  __snake_case = 250					,  __snake_case = 0.0					,  __snake_case = 10					,  __snake_case = 10					,  __snake_case = None					,  __snake_case = "pil"					,  __snake_case = True					,  )   -> Union[ImagePipelineOutput, Tuple]:




						'''simple docstring'''
						__a        =image

						__a        =_preprocess_image(__snake_case					)
						__a        =original_image.to(device=self.device					,  dtype=self.unet.dtype					)
						__a        =_preprocess_mask(__snake_case					)
						__a        =mask_image.to(device=self.device					,  dtype=self.unet.dtype					)

						__a        =original_image.shape[0]

						# sample gaussian noise to begin the loop
						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.'					)

						__a        =original_image.shape
						__a        =randn_tensor(__snake_case					,  generator=__snake_case					,  device=self.device					,  dtype=self.unet.dtype					)

						# set step values
						self.scheduler.set_timesteps(__snake_case					,  __snake_case					,  __snake_case					,  self.device					)
						__a        =eta

						__a        =self.scheduler.timesteps[0] + 1
						__a        =generator[0] if isinstance(__snake_case					,  __snake_case					) else generator
						for i, t in enumerate(self.progress_bar(self.scheduler.timesteps					)					):
									if t < t_last:
												# predict the noise residual
												__a        =self.unet(__snake_case					,  __snake_case					).sample
												# compute previous image: x_t -> x_t-1
												__a        =self.scheduler.step(__snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					).prev_sample

									else:
												# compute the reverse: x_t-1 -> x_t
												__a        =self.scheduler.undo_step(__snake_case					,  __snake_case					,  __snake_case					)
									__a        =t

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

						if not return_dict:
									return (image,)

						return ImagePipelineOutput(images=__snake_case					)






