


'''simple docstring'''





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

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: Dict ):






			"""simple docstring"""




			__A=	image.size
			__A=	(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
			__A=	image.resize((w, h),resample=PIL_INTERPOLATION['lanczos'] )
			__A=	np.array(__UpperCAmelCase ).astype(np.floataa ) / 2_55.0
			__A=	image[None].transpose(0,3,1,2 )
			__A=	torch.from_numpy(__UpperCAmelCase )
			return 2.0 * image - 1.0






class 						a__					(					a_   ):





			'''simple docstring'''

			def __init__(  self							:			int			,    lowerCAmelCase_							:			VQModel			,    lowerCAmelCase_							:			UNetaDModel			,    lowerCAmelCase_							:			Union[
			        DDIMScheduler,
			        PNDMScheduler,
			        LMSDiscreteScheduler,
			        EulerDiscreteScheduler,
			        EulerAncestralDiscreteScheduler,
			        DPMSolverMultistepScheduler,
			    ]			,    )      ->		str:
						super().__init__()
						self.register_modules(vqvae=a_			,    unet=a_			,    scheduler=a_	)

			@torch.no_grad()
			def __call__(  self							:			str			,    lowerCAmelCase_							:			Union[torch.Tensor, PIL.Image.Image] = None			,    lowerCAmelCase_							:			Optional[int] = 1			,    lowerCAmelCase_							:			Optional[int] = 100			,    lowerCAmelCase_							:			Optional[float] = 0.0			,    lowerCAmelCase_							:			Optional[Union[torch.Generator, List[torch.Generator]]] = None			,    lowerCAmelCase_							:			Optional[str] = "pil"			,    lowerCAmelCase_							:			bool = True			,    )      ->		Union[Tuple, ImagePipelineOutput]:
						if isinstance(a_			,    PIL.Image.Image	):
									__A=	1
						elif isinstance(a_			,    torch.Tensor	):
									__A=	image.shape[0]
						else:
									raise ValueError(F"""`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(a_	)}"""	)

						if isinstance(a_			,    PIL.Image.Image	):
									__A=	preprocess(a_	)

						__A=	image.shape[-2:]

						# in_channels should be 6: 3 for latents, 3 for low resolution image
						__A=	(batch_size, self.unet.config.in_channels // 2, height, width)
						__A=	next(self.unet.parameters()	).dtype

						__A=	randn_tensor(a_			,    generator=a_			,    device=self.device			,    dtype=a_	)

						__A=	image.to(device=self.device			,    dtype=a_	)

						# set timesteps and move to the correct device
						self.scheduler.set_timesteps(a_			,    device=self.device	)
						__A=	self.scheduler.timesteps

						# scale the initial noise by the standard deviation required by the scheduler
						__A=	latents * self.scheduler.init_noise_sigma

						# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
						# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
						# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
						# and should be between [0, 1]
						__A=	'eta' in set(inspect.signature(self.scheduler.step	).parameters.keys()	)
						__A=	{}
						if accepts_eta:
									__A=	eta

						for t in self.progress_bar(a_	):
									# concat latents and low resolution image in the channel dimension.
									__A=	torch.cat([latents, image]			,    dim=1	)
									__A=	self.scheduler.scale_model_input(a_			,    a_	)
									# predict the noise residual
									__A=	self.unet(a_			,    a_	).sample
									# compute the previous noisy sample x_t -> x_t-1
									__A=	self.scheduler.step(a_			,    a_			,    a_			,    **a_	).prev_sample

						# decode the image latents with the VQVAE
						__A=	self.vqvae.decode(a_	).sample
						__A=	torch.clamp(a_			,    -1.0			,    1.0	)
						__A=	image / 2 + 0.5
						__A=	image.cpu().permute(0			,    2			,    3			,    1	).numpy()

						if output_type == "pil":
									__A=	self.numpy_to_pil(a_	)

						if not return_dict:
									return (image,)

						return ImagePipelineOutput(images=a_	)








'''simple docstring'''

import unittest

from knapsack import knapsack as k






class 						a_		(     unittest.TestCase    ):


							def    UpperCamelCase     (				self   :						str			)			->					List[Any]:
										snake_case:    Optional[Any]      							=0
										snake_case:    List[str]      							=[0]
										snake_case:    List[str]      							=[0]
										snake_case:    Union[str, Any]      							=len(a_			)
										self.assertEqual(k.knapsack(a_      ,						a_      ,						a_      ,						a_			)      ,						0			)

										snake_case:    Union[str, Any]      							=[6_0]
										snake_case:    str      							=[1_0]
										snake_case:    List[str]      							=len(a_			)
										self.assertEqual(k.knapsack(a_      ,						a_      ,						a_      ,						a_			)      ,						0			)


							def    UpperCamelCase     (				self   :						int			)			->					Optional[Any]:
										snake_case:    Optional[int]      							=3
										snake_case:    Dict      							=[1, 2, 3]
										snake_case:    List[Any]      							=[3, 2, 1]
										snake_case:    Tuple      							=len(a_			)
										self.assertEqual(k.knapsack(a_      ,						a_      ,						a_      ,						a_			)      ,						5			)




							def    UpperCamelCase     (				self   :						List[str]			)			->					str:
										snake_case:    Any      							=5_0
										snake_case:    str      							=[6_0, 1_0_0, 1_2_0]
										snake_case:    Any      							=[1_0, 2_0, 3_0]
										snake_case:    Tuple      							=len(a_			)
										self.assertEqual(k.knapsack(a_      ,						a_      ,						a_      ,						a_			)      ,						2_2_0			)


if __name__ == "__main__":
	unittest.main()




