




"""simple docstring"""





from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


A   =       transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)

def 					_UpperCamelCase    (			UpperCamelCase							)			->			Union[str, Any]:


			"""simple docstring"""
			if isinstance(UpperCamelCase				,       torch.Tensor							):
						return image
			elif isinstance(UpperCamelCase				,       PIL.Image.Image							):
						__UpperCAmelCase	: int		=					[image]

			__UpperCAmelCase	: int		=					[trans(img.convert("RGB"							)							) for img in image]
			__UpperCAmelCase	: Dict		=					torch.stack(UpperCamelCase							)
			return image





class      a__				(	snake_case__   ):


			def __init__(      self      :    List[Any]      ,  UpperCamelCase_      :    int      ,  UpperCamelCase_      :    Any):


						"""simple docstring"""




						super().__init__()

						# make sure scheduler can always be converted to DDIM
						__UpperCAmelCase	: Tuple		=					DDIMScheduler.from_config(scheduler.config)

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


			def       a_     (      self      :    Any      ,  UpperCamelCase_      :    Dict):


						"""simple docstring"""




						if strength < 0 or strength > 1:
									raise ValueError(F"The value of strength should in [0.0, 1.0] but is {strength}")


			def       a_     (      self      :    Any      ,  UpperCamelCase_      :    List[Any]      ,  UpperCamelCase_      :    Optional[int]      ,  UpperCamelCase_      :    List[Any]):


						"""simple docstring"""




						__UpperCAmelCase	: Optional[int]		=					min(int(num_inference_steps * strength)      ,  _A)

						__UpperCAmelCase	: str		=					max(num_inference_steps - init_timestep      ,  0)
						__UpperCAmelCase	: Optional[int]		=					self.scheduler.timesteps[t_start:]

						return timesteps, num_inference_steps - t_start


			def       a_     (      self      :    Union[str, Any]      ,  UpperCamelCase_      :    Optional[Any]      ,  UpperCamelCase_      :    List[Any]      ,  UpperCamelCase_      :    Optional[Any]      ,  UpperCamelCase_      :    List[Any]      ,  UpperCamelCase_      :    List[Any]      ,  UpperCamelCase_      :    List[Any]=None):


						"""simple docstring"""




						if not isinstance(_A      ,  (torch.Tensor, PIL.Image.Image, list)):
									raise ValueError(
									    F"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(_A)}")

						__UpperCAmelCase	: Union[str, Any]		=					image.to(device=_A      ,  dtype=_A)

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

						__UpperCAmelCase	: Dict		=					init_latents.shape
						__UpperCAmelCase	: Optional[int]		=					randn_tensor(_A      ,  generator=_A      ,  device=_A      ,  dtype=_A)

						# get latents
						print("add noise to latents at timestep"      ,  _A)
						__UpperCAmelCase	: List[Any]		=					self.scheduler.add_noise(_A      ,  _A      ,  _A)
						__UpperCAmelCase	: Optional[int]		=					init_latents

						return latents


			@torch.no_grad()
			def __call__(      self      :    Any      ,  UpperCamelCase_      :    Optional[Any] = None      ,  UpperCamelCase_      :    Optional[Any] = 0.8      ,  UpperCamelCase_      :    Union[str, Any] = 1      ,  UpperCamelCase_      :    Optional[Any] = None      ,  UpperCamelCase_      :    List[str] = 0.0      ,  UpperCamelCase_      :    Optional[int] = 50      ,  UpperCamelCase_      :    Any = None      ,  UpperCamelCase_      :    Union[str, Any] = "pil"      ,  UpperCamelCase_      :    Union[str, Any] = True      ,  ):


						"""simple docstring"""




						self.check_inputs(_A)

						# 2. Preprocess image
						__UpperCAmelCase	: int		=					preprocess(_A)

						# 3. set timesteps
						self.scheduler.set_timesteps(_A      ,  device=self.device)
						__UpperCAmelCase	: Tuple		=					self.get_timesteps(_A      ,  _A      ,  self.device)
						__UpperCAmelCase	: int		=					timesteps[:1].repeat(_A)

						# 4. Prepare latent variables
						__UpperCAmelCase	: Dict		=					self.prepare_latents(_A      ,  _A      ,  _A      ,  self.unet.dtype      ,  self.device      ,  _A)
						__UpperCAmelCase	: List[Any]		=					latents

						# 5. Denoising loop
						for t in self.progress_bar(_A):
									# 1. predict noise model_output
									__UpperCAmelCase	: Optional[Any]		=					self.unet(_A      ,  _A).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
									__UpperCAmelCase	: List[Any]		=					self.scheduler.step(
									    _A      ,  _A      ,  _A      ,  eta=_A      ,  use_clipped_model_output=_A      ,  generator=_A      ,  ).prev_sample

						__UpperCAmelCase	: List[Any]		=					(image / 2 + 0.5).clamp(0      ,  1)
						__UpperCAmelCase	: List[str]		=					image.cpu().permute(0      ,  2      ,  3      ,  1).numpy()
						if output_type == "pil":
									__UpperCAmelCase	: Optional[int]		=					self.numpy_to_pil(_A)

						if not return_dict:
									return (image, latent_timestep.item())

						return ImagePipelineOutput(images=_A)








"""simple docstring"""





def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)			->			Tuple:


			"""simple docstring"""
			global f  # a global dp table for knapsack
			if f[i][j] < 0:
						if j < wt[i - 1]:
									__UpperCAmelCase	: Optional[int]		=					mf_knapsack(i - 1				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)
						else:
									__UpperCAmelCase	: Any		=					max(
									    mf_knapsack(i - 1				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)				,       mf_knapsack(i - 1				,       UpperCamelCase				,       UpperCamelCase				,       j - wt[i - 1]							) + val[i - 1]				,       )
						__UpperCAmelCase	: str		=					val
			return f[i][j]

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)			->			List[Any]:


			"""simple docstring"""
			__UpperCAmelCase	: List[Any]		=					[[0] * (w + 1) for _ in range(n + 1							)]

			for i in range(1				,       n + 1							):
						for w_ in range(1				,       w + 1							):
									if wt[i - 1] <= w_:
												__UpperCAmelCase	: Tuple		=					max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]]				,       dp[i - 1][w_]							)
									else:
												__UpperCAmelCase	: int		=					dp[i - 1][w_]

			return dp[n][w_], dp

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)			->			int:


			"""simple docstring"""
			if not (isinstance(UpperCamelCase				,       (list, tuple)							) and isinstance(UpperCamelCase				,       (list, tuple)							)):
						raise ValueError(
						    "Both the weights and values vectors must be either lists or tuples"							)

			__UpperCAmelCase	: List[Any]		=					len(UpperCamelCase							)
			if num_items != len(UpperCamelCase							):
						__UpperCAmelCase	: int		=					(
						    "The number of weights must be the same as the number of values.\n"
						    f"But got {num_items} weights and {len(UpperCamelCase							)} values"
						)
						raise ValueError(UpperCamelCase							)
			for i in range(UpperCamelCase							):
						if not isinstance(wt[i]				,       UpperCamelCase							):
									__UpperCAmelCase	: Tuple		=					(
									    "All weights must be integers but got weight of "
									    f"type {type(wt[i]							)} at index {i}"
									)
									raise TypeError(UpperCamelCase							)

			__UpperCAmelCase	,     __UpperCAmelCase	: Tuple		=					knapsack(UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)
			__UpperCAmelCase	: set		=					set()
			_construct_solution(UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)

			return optimal_val, example_optional_set

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)			->			Dict:


			"""simple docstring"""
			# for the current item i at a maximum weight j to be part of an optimal subset,
			# the optimal value at (i, j) must be greater than the optimal value at (i-1, j).
			# where i - 1 means considering only the previous items at the given maximum weight
			if i > 0 and j > 0:
						if dp[i - 1][j] == dp[i][j]:
									_construct_solution(UpperCamelCase				,       UpperCamelCase				,       i - 1				,       UpperCamelCase				,       UpperCamelCase							)
						else:
									optimal_set.add(UpperCamelCase							)
									_construct_solution(UpperCamelCase				,       UpperCamelCase				,       i - 1				,       j - wt[i - 1]				,       UpperCamelCase							)


if __name__ == "__main__":
					A   =       [3, 2, 4, 4]
					A   =       [4, 3, 2, 3]
					A   =       4
					A   =       6
					A   =       [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
					A ,						A   =       knapsack(w, wt, val, n)
					print(optimal_solution)
					print(mf_knapsack(n, wt, val, w))  # switched the n and w

					# testing the dynamic programming problem with example
					# the optimal subset for the above example are items 3 and 4
					A ,						A   =       knapsack_with_example_solution(w, wt, val)
					assert optimal_solution == 8
					assert optimal_subset == {3, 4}
					print("""optimal_value = """, optimal_solution)
					print("""An optimal subset corresponding to the optimal value""", optimal_subset)


