



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

def   lowerCAmelCase__						(							_SCREAMING_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"""				,     __a				,     )
				if isinstance(__a				,     torch.Tensor    ):
								return image
				elif isinstance(__a				,     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(__a				,     axis=0    )
								__a       =		np.array(__a    ).astype(np.floataa    ) / 255.0
								__a       =		image.transpose(0				,     3				,     1				,     2    )
								__a       =		2.0 * image - 1.0
								__a       =		torch.from_numpy(__a    )
				elif isinstance(image[0]				,     torch.Tensor    ):
								__a       =		torch.cat(__a				,     dim=0    )
				return image

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Union[List, PIL.Image.Image, torch.Tensor]    ):
				"""simple docstring"""







				if isinstance(__a				,     torch.Tensor    ):
								return mask
				elif isinstance(__a				,     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(__a				,     axis=0    )
								__a       =		mask.astype(np.floataa    ) / 255.0
								__a       =		0
								__a       =		1
								__a       =		torch.from_numpy(__a    )
				elif isinstance(mask[0]				,     torch.Tensor    ):
								__a       =		torch.cat(__a				,     dim=0    )
				return mask






class    SCREAMING_SNAKE_CASE			(							__lowerCamelCase					):
							__lowerCamelCase	:	int    =42
							__lowerCamelCase	:	Any    =42





							def __init__(   self		:		List[Any]      ,				__lowercase		:		Union[str, Any]      ,				__lowercase		:		int	):

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







							@torch.no_grad()
							def __call__(   self		:		Union[str, Any]      ,				__lowercase		:		Optional[Any]      ,				__lowercase		:		List[Any]      ,				__lowercase		:		List[Any] = 250      ,				__lowercase		:		List[str] = 0.0      ,				__lowercase		:		Optional[Any] = 10      ,				__lowercase		:		Dict = 10      ,				__lowercase		:		str = None      ,				__lowercase		:		Any = "pil"      ,				__lowercase		:		Union[str, Any] = True      ,				):

											'''simple docstring'''
											__a       =		image

											__a       =		_preprocess_image(SCREAMING_SNAKE_CASE_	)
											__a       =		original_image.to(device=self.device      ,				dtype=self.unet.dtype	)
											__a       =		_preprocess_mask(SCREAMING_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(SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_	) and len(SCREAMING_SNAKE_CASE_	) != batch_size:
															raise ValueError(
															    F"You have passed a list of generators of length {len(SCREAMING_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(SCREAMING_SNAKE_CASE_      ,				generator=SCREAMING_SNAKE_CASE_      ,				device=self.device      ,				dtype=self.unet.dtype	)

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

											__a       =		self.scheduler.timesteps[0] + 1
											__a       =		generator[0] if isinstance(SCREAMING_SNAKE_CASE_      ,				SCREAMING_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(SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_	).sample
																			# compute previous image: x_t -> x_t-1
																			__a       =		self.scheduler.step(SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_	).prev_sample

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

											if not return_dict:
															return (image,)

											return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE_	)





def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			list				,     _SCREAMING_SNAKE_CASE       :			list				,     _SCREAMING_SNAKE_CASE       :			int				,     _SCREAMING_SNAKE_CASE       :			int				,     _SCREAMING_SNAKE_CASE       :			int    ):
				"""simple docstring"""







				if index == number_of_items:
								return 0
				__a       =		0
				__a       =		0
				__a       =		knapsack(_SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE				,     index + 1    )
				if weights[index] <= max_weight:
								__a       =		values[index] + knapsack(
								    _SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE				,     max_weight - weights[index]				,     index + 1    )
				return max(_SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE    )


if __name__ == "__main__":
						import doctest

						doctest.testmod()
