


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     __SCREAMING_SNAKE_CASE							(       lowercase_      )		->     Any:




						'''simple docstring'''







						__UpperCAmelCase    ,      __UpperCAmelCase			:							Dict	    =	image.size
						__UpperCAmelCase    ,      __UpperCAmelCase			:							Union[str, Any]	    =	(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
						__UpperCAmelCase			:							Optional[int]	    =	image.resize((w, h)      ,       resample=PIL_INTERPOLATION['''lanczos''']      )
						__UpperCAmelCase			:							Optional[Any]	    =	np.array(lowercase_      ).astype(np.floataa      ) / 2_5_5.0
						__UpperCAmelCase			:							Dict	    =	image[None].transpose(0      ,       3      ,       1      ,       2      )
						__UpperCAmelCase			:							Any	    =	torch.from_numpy(lowercase_      )
						return 2.0 * image - 1.0





class 							lowerCamelCase   (  _UpperCamelCase ):


	def __init__(    self				,     lowercase__				,     lowercase__				,     lowercase__				,     ):
							super().__init__()
							self.register_modules(vqvae=lowercase__				,     unet=lowercase__				,     scheduler=lowercase__)


	@torch.no_grad()
	def __call__(    self				,     lowercase__ = None				,     lowercase__ = 1				,     lowercase__ = 1_0_0				,     lowercase__ = 0.0				,     lowercase__ = None				,     lowercase__ = "pil"				,     lowercase__ = True				,     ):
							if isinstance(lowercase__				,     PIL.Image.Image):
													__UpperCAmelCase			:							Optional[int]	    =	1
							elif isinstance(lowercase__				,     torch.Tensor):
													__UpperCAmelCase			:							Tuple	    =	image.shape[0]
							else:
													raise ValueError(F"`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(lowercase__)}")

							if isinstance(lowercase__				,     PIL.Image.Image):
													__UpperCAmelCase			:							List[str]	    =	preprocess(lowercase__)

							__UpperCAmelCase    ,      __UpperCAmelCase			:							Optional[int]	    =	image.shape[-2:]

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

							__UpperCAmelCase			:							Dict	    =	randn_tensor(lowercase__				,     generator=lowercase__				,     device=self.device				,     dtype=lowercase__)

							__UpperCAmelCase			:							int	    =	image.to(device=self.device				,     dtype=lowercase__)

							# set timesteps and move to the correct device
							self.scheduler.set_timesteps(lowercase__				,     device=self.device)
							__UpperCAmelCase			:							Optional[int]	    =	self.scheduler.timesteps

							# scale the initial noise by the standard deviation required by the scheduler
							__UpperCAmelCase			:							Optional[Any]	    =	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]
							__UpperCAmelCase			:							Optional[Any]	    =	'''eta''' in set(inspect.signature(self.scheduler.step).parameters.keys())
							__UpperCAmelCase			:							Optional[Any]	    =	{}
							if accepts_eta:
													__UpperCAmelCase			:							List[str]	    =	eta

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

							# decode the image latents with the VQVAE
							__UpperCAmelCase			:							Dict	    =	self.vqvae.decode(lowercase__).sample
							__UpperCAmelCase			:							Optional[Any]	    =	torch.clamp(lowercase__				,     -1.0				,     1.0)
							__UpperCAmelCase			:							Optional[int]	    =	image / 2 + 0.5
							__UpperCAmelCase			:							Optional[int]	    =	image.cpu().permute(0				,     2				,     3				,     1).numpy()

							if output_type == "pil":
													__UpperCAmelCase			:							List[str]	    =	self.numpy_to_pil(lowercase__)

							if not return_dict:
													return (image,)

							return ImagePipelineOutput(images=lowercase__)





import unittest

from knapsack import greedy_knapsack as kp




class 							lowerCamelCase   (  unittest.TestCase ):


	def  A(    self):
							__UpperCAmelCase			:							Optional[Any]	    =	[1_0, 2_0, 3_0, 4_0, 5_0, 6_0]
							__UpperCAmelCase			:							str	    =	[2, 4, 6, 8, 1_0, 1_2]
							__UpperCAmelCase			:							List[Any]	    =	1_0_0
							self.assertEqual(kp.calc_profit(lowercase__				,     lowercase__				,     lowercase__)				,     2_1_0)


	def  A(    self):
							self.assertRaisesRegex(lowercase__				,     '''max_weight must greater than zero.''')


	def  A(    self):
							self.assertRaisesRegex(lowercase__				,     '''Weight can not be negative.''')


	def  A(    self):
							self.assertRaisesRegex(lowercase__				,     '''Profit can not be negative.''')


	def  A(    self):
							self.assertRaisesRegex(lowercase__				,     '''max_weight must greater than zero.''')


	def  A(    self):
							self.assertRaisesRegex(
							    lowercase__				,     '''The length of profit and weight must be same.''')


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

