



from typing import List, Optional, Tuple, Union

import torch

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




class 					snake_case__						(       lowerCAmelCase_     ):






     """simple docstring"""


     def __init__(       self  :  int, _snake_case  :  str, _snake_case  :  Tuple					)   ->Tuple:
            super().__init__()

            # make sure scheduler can always be converted to DDIM
            snake_case__							:	Optional[int]   	=	DDIMScheduler.from_config(scheduler.config					)

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






     @torch.no_grad()
     def __call__(       self  :  str, _snake_case  :  int = 1, _snake_case  :  Optional[Union[torch.Generator, List[torch.Generator]]] = None, _snake_case  :  float = 0.0, _snake_case  :  int = 5_0, _snake_case  :  Optional[bool] = None, _snake_case  :  Optional[str] = "pil", _snake_case  :  bool = True, )   ->Union[ImagePipelineOutput, Tuple]:

            # Sample gaussian noise to begin loop
            if isinstance(self.unet.config.sample_size, _snake_case					):
                   snake_case__							:	List[Any]   	=	(
                       batch_size,
                       self.unet.config.in_channels,
                       self.unet.config.sample_size,
                       self.unet.config.sample_size,
                   )
            else:
                   snake_case__							:	Dict   	=	(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

            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.'''					)

            snake_case__							:	Optional[Any]   	=	randn_tensor(_snake_case, generator=_snake_case, device=self.device, dtype=self.unet.dtype					)

            # set step values
            self.scheduler.set_timesteps(_snake_case					)

            for t in self.progress_bar(self.scheduler.timesteps					):
                   # 1. predict noise model_output
                   snake_case__							:	Dict   	=	self.unet(_snake_case, _snake_case					).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
                   snake_case__							:	Dict   	=	self.scheduler.step(
                       _snake_case, _snake_case, _snake_case, eta=_snake_case, use_clipped_model_output=_snake_case, generator=_snake_case					).prev_sample

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

            if not return_dict:
                   return (image,)

            return ImagePipelineOutput(images=_snake_case					)











def        lowercase_							(A : Union[str, Any]		,					A : List[str]		,					A : int		,					A : Optional[int]      ):
       global f  # a global dp table for knapsack
       if f[i][j] < 0:
              if j < wt[i - 1]:
                     snake_case__							:	Union[str, Any]   	=	mf_knapsack(i - 1		,					A		,					A		,					A      )
              else:
                     snake_case__							:	Any   	=	max(
                         mf_knapsack(i - 1		,					A		,					A		,					A      )		,					mf_knapsack(i - 1		,					A		,					A		,					j - wt[i - 1]      ) + val[i - 1]		,					)
              snake_case__							:	Optional[int]   	=	val
       return f[i][j]
def        lowercase_							(A : Optional[int]		,					A : Union[str, Any]		,					A : str		,					A : Dict      ):
       snake_case__							:	int   	=	[[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_:
                            snake_case__							:	Union[str, Any]   	=	max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]]		,					dp[i - 1][w_]      )
                     else:
                            snake_case__							:	str   	=	dp[i - 1][w_]

       return dp[n][w_], dp
def        lowercase_							(A : int		,					A : list		,					A : list      ):
       if not (isinstance(A		,					(list, tuple)      ) and isinstance(A		,					(list, tuple)      )):
              raise ValueError(
                  'Both the weights and values vectors must be either lists or tuples'      )

       snake_case__							:	Dict   	=	len(A      )
       if num_items != len(A      ):
              snake_case__							:	str   	=	(
                  'The number of weights must be the same as the number of values.\n'
                  F'''But got {num_items} weights and {len(A      )} values'''
              )
              raise ValueError(A      )
       for i in range(A      ):
              if not isinstance(wt[i]		,					A      ):
                     snake_case__							:	Optional[int]   	=	(
                         'All weights must be integers but got weight of '
                         F'''type {type(wt[i]      )} at index {i}'''
                     )
                     raise TypeError(A      )

       snake_case__	,				snake_case__							:	Optional[int]   	=	knapsack(A		,					A		,					A		,					A      )
       snake_case__							:	set   	=	set()
       _construct_solution(A		,					A		,					A		,					A		,					A      )

       return optimal_val, example_optional_set
def        lowercase_							(A : list		,					A : list		,					A : int		,					A : int		,					A : set      ):
       # 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(A		,					A		,					i - 1		,					A		,					A      )
              else:
                     optimal_set.add(A      )
                     _construct_solution(A		,					A		,					i - 1		,					j - wt[i - 1]		,					A      )


if __name__ == "__main__":
 a_   :Any        =      [3, 2, 4, 4]
 a_   :List[Any]        =      [4, 3, 2, 3]
 a_   :Union[str, Any]        =      4
 a_   :List[str]        =      6
 a_   :Union[str, Any]        =      [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
 a_  ,						a_   :List[Any]        =      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_   :Any        =      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)






