





import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize







class 				__lowerCAmelCase       :







      def __init__(    self		,      lowerCAmelCase__		,      lowerCAmelCase__=sys.maxsize							)   ->						Any:



        '''simple docstring'''




        a__	:					Any										='bilinear'
        a__	:					Optional[int]										=max_size
        a__	:					Union[str, Any]										=short_edge_length







      def __call__(    self		,      lowerCAmelCase__							)   ->						List[Any]:



        '''simple docstring'''




        a__	:					List[str]										=[]
        for img in imgs:
          a__	:					Union[str, Any]										=img.shape[:2]
          # later: provide list and randomly choose index for resize
          a__	:					Dict										=np.random.randint(self.short_edge_length[0]		,      self.short_edge_length[1] + 1							)
          if size == 0:
            return img
          a__	:					Any										=size * 1.0 / min(_snake_case		,      _snake_case							)
          if h < w:
            a__	:					str										=size, scale * w
          else:
            a__	:					Optional[int]										=scale * h, size
          if max(_snake_case		,      _snake_case							) > self.max_size:
            a__	:					Union[str, Any]										=self.max_size * 1.0 / max(_snake_case		,      _snake_case							)
            a__	:					Optional[int]										=newh * scale
            a__	:					Dict										=neww * scale
          a__	:					List[str]										=int(neww + 0.5							)
          a__	:					Dict										=int(newh + 0.5							)

          if img.dtype == np.uinta:
            a__	:					str										=Image.fromarray(_snake_case							)
            a__	:					List[Any]										=pil_image.resize((neww, newh)		,      PILImageResampling.BILINEAR							)
            a__	:					List[Any]										=np.asarray(_snake_case							)
          else:
            a__	:					Dict										=img.permute(2		,      0		,      1							).unsqueeze(0							)  # 3, 0, 1)  # hw(c) -> nchw
            a__	:					int										=nn.functional.interpolate(
                _snake_case		,      (newh, neww)		,      mode=self.interp_method		,      align_corners=_snake_case							).squeeze(0							)
          img_augs.append(_snake_case							)

        return img_augs







class 				__lowerCAmelCase       :







      def __init__(    self		,      lowerCAmelCase__							)   ->						Union[str, Any]:



        '''simple docstring'''




        a__	:					Optional[int]										=ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]		,      cfg.INPUT.MAX_SIZE_TEST							)
        a__	:					Optional[Any]										=cfg.INPUT.FORMAT
        a__	:					Optional[Any]										=cfg.SIZE_DIVISIBILITY
        a__	:					int										=cfg.PAD_VALUE
        a__	:					Optional[int]										=cfg.INPUT.MAX_SIZE_TEST
        a__	:					Optional[int]										=cfg.MODEL.DEVICE
        a__	:					List[str]										=torch.tensor(cfg.MODEL.PIXEL_STD							).to(self.device							).view(len(cfg.MODEL.PIXEL_STD							)		,      1		,      1							)
        a__	:					Dict										=torch.tensor(cfg.MODEL.PIXEL_MEAN							).to(self.device							).view(len(cfg.MODEL.PIXEL_STD							)		,      1		,      1							)
        a__	:					Any										=lambda lowerCAmelCase__							: (x - self.pixel_mean) / self.pixel_std







      def    _lowercase  (    self		,      lowerCAmelCase__							)   ->						List[Any]:



        '''simple docstring'''




        a__	:					str										=tuple(max(_snake_case							) for s in zip(*[img.shape for img in images]							)							)
        a__	:					Any										=[im.shape[-2:] for im in images]
        a__	:					Any										=[
            nn.functional.pad(
                _snake_case		,      [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]		,      value=self.pad_value		,      )
            for size, im in zip(_snake_case		,      _snake_case							)
        ]

        return torch.stack(_snake_case							), torch.tensor(_snake_case							)







      def __call__(    self		,      lowerCAmelCase__		,      lowerCAmelCase__=False							)   ->						int:



        '''simple docstring'''




        with torch.no_grad():
          if not isinstance(_snake_case		,      _snake_case							):
            a__	:					Optional[Any]										=[images]
          if single_image:
            assert len(_snake_case							) == 1
          for i in range(len(_snake_case							)							):
            if isinstance(images[i]		,      torch.Tensor							):
              images.insert(_snake_case		,      images.pop(_snake_case							).to(self.device							).float()							)
            elif not isinstance(images[i]		,      torch.Tensor							):
              images.insert(
                  _snake_case		,      torch.as_tensor(img_tensorize(images.pop(_snake_case							)		,      input_format=self.input_format							)							)
                  .to(self.device							)
                  .float()		,      )
            # resize smallest edge
          a__	:					Union[str, Any]										=torch.tensor([im.shape[:2] for im in images]							)
          a__	:					str										=self.aug(_snake_case							)
          # transpose images and convert to torch tensors
          # images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
          # now normalize before pad to avoid useless arithmetic
          a__	:					str										=[self.normalizer(_snake_case							) for x in images]
          # now pad them to do the following operations
          a__	:					List[str]										=self.pad(_snake_case							)
          # Normalize

          if self.size_divisibility > 0:
            raise NotImplementedError()
          # pad
          a__	:					Tuple										=torch.true_divide(_snake_case		,      _snake_case							)
          if single_image:
            return images[0], sizes[0], scales_yx[0]
          else:
            return images, sizes, scales_yx
def      _A       (		SCREAMING_SNAKE_CASE     :   List[str]       ,       SCREAMING_SNAKE_CASE     :   Any						):





  """simple docstring"""



  boxes[:, 0::2] *= scale_yx[:, 1]
  boxes[:, 1::2] *= scale_yx[:, 0]
  return boxes





def      _A       (		SCREAMING_SNAKE_CASE     :   List[str]       ,       SCREAMING_SNAKE_CASE     :   Tuple[int, int]						):





  """simple docstring"""



  assert torch.isfinite(SCREAMING_SNAKE_CASE						).all(), "Box tensor contains infinite or NaN!"
  a__	:					Dict										=box_size
  tensor[:, 0].clamp_(min=0       ,       max=SCREAMING_SNAKE_CASE						)
  tensor[:, 1].clamp_(min=0       ,       max=SCREAMING_SNAKE_CASE						)
  tensor[:, 2].clamp_(min=0       ,       max=SCREAMING_SNAKE_CASE						)
  tensor[:, 3].clamp_(min=0       ,       max=SCREAMING_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)






