





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					,							__magic_name__					,							__magic_name__=sys.maxsize    )     ->   Optional[int]:


							'''simple docstring'''



							snake_case_						:    int				=  'bilinear'
							snake_case_						:    int				=  max_size
							snake_case_						:    Optional[int]				=  short_edge_length






			def __call__(self					,							__magic_name__    )     ->   Dict:


							'''simple docstring'''



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

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

							return img_augs




class 					__lowerCAmelCase       :






			def __init__(self					,							__magic_name__    )     ->   str:


							'''simple docstring'''



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






			def 			lowerCamelCase  (self					,							__magic_name__    )     ->   List[Any]:


							'''simple docstring'''



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

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






			def __call__(self					,							__magic_name__					,							__magic_name__=False    )     ->   Optional[int]:


							'''simple docstring'''



							with torch.no_grad():
											if not isinstance(SCREAMING_SNAKE_CASE_					,							SCREAMING_SNAKE_CASE_    ):
															snake_case_						:    Any				=  [images]
											if single_image:
															assert len(SCREAMING_SNAKE_CASE_    ) == 1
											for i in range(len(SCREAMING_SNAKE_CASE_    )    ):
															if isinstance(images[i]					,							torch.Tensor    ):
																			images.insert(SCREAMING_SNAKE_CASE_					,							images.pop(SCREAMING_SNAKE_CASE_    ).to(self.device    ).float()    )
															elif not isinstance(images[i]					,							torch.Tensor    ):
																			images.insert(
																			    SCREAMING_SNAKE_CASE_					,							torch.as_tensor(img_tensorize(images.pop(SCREAMING_SNAKE_CASE_    )					,							input_format=self.input_format    )    )
																			    .to(self.device    )
																			    .float()					,							)
            # resize smallest edge
											snake_case_						:    int				=  torch.tensor([im.shape[:2] for im in images]    )
											snake_case_						:    Any				=  self.aug(SCREAMING_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
											snake_case_						:    List[Any]				=  [self.normalizer(SCREAMING_SNAKE_CASE_    ) for x in images]
											# now pad them to do the following operations
											snake_case_						:    int				=  self.pad(SCREAMING_SNAKE_CASE_    )
											# Normalize

											if self.size_divisibility > 0:
															raise NotImplementedError()
											# pad
											snake_case_						:    Optional[int]				=  torch.true_divide(SCREAMING_SNAKE_CASE_					,							SCREAMING_SNAKE_CASE_    )
											if single_image:
															return images[0], sizes[0], scales_yx[0]
											else:
															return images, sizes, scales_yx






def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase )		->						Dict:






				"""simple docstring"""


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






def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase )		->						Optional[int]:






				"""simple docstring"""


				assert torch.isfinite(UpperCAmelCase_ ).all(), "Box tensor contains infinite or NaN!"
				snake_case_						:    List[Any]				=  box_size
				tensor[:, 0].clamp_(min=0 ,				max=UpperCAmelCase_ )
				tensor[:, 1].clamp_(min=0 ,				max=UpperCAmelCase_ )
				tensor[:, 2].clamp_(min=0 ,				max=UpperCAmelCase_ )
				tensor[:, 3].clamp_(min=0 ,				max=UpperCAmelCase_ )




'''simple docstring'''



import unittest

from knapsack import knapsack as k





class    UpperCAmelCase_  (unittest.TestCase  ):





    """simple docstring"""






    def       lowercase_				(							self	)				->     Optional[Any]:
         __lowerCamelCase   :	int								=		0
         __lowerCamelCase   :	Union[str, Any]								=		[0]
         __lowerCamelCase   :	Any								=		[0]
         __lowerCamelCase   :	Union[str, Any]								=		len(SCREAMING_SNAKE_CASE_	)
         self.assertEqual(k.knapsack(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)			,     0	)

         __lowerCamelCase   :	List[str]								=		[60]
         __lowerCamelCase   :	Union[str, Any]								=		[10]
         __lowerCamelCase   :	List[Any]								=		len(SCREAMING_SNAKE_CASE_	)
         self.assertEqual(k.knapsack(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)			,     0	)







    def       lowercase_				(							self	)				->     List[str]:
         __lowerCamelCase   :	Optional[int]								=		3
         __lowerCamelCase   :	int								=		[1, 2, 3]
         __lowerCamelCase   :	str								=		[3, 2, 1]
         __lowerCamelCase   :	Union[str, Any]								=		len(SCREAMING_SNAKE_CASE_	)
         self.assertEqual(k.knapsack(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)			,     5	)







    def       lowercase_				(							self	)				->     int:
         __lowerCamelCase   :	Optional[int]								=		50
         __lowerCamelCase   :	List[str]								=		[60, 1_00, 1_20]
         __lowerCamelCase   :	List[str]								=		[10, 20, 30]
         __lowerCamelCase   :	List[str]								=		len(SCREAMING_SNAKE_CASE_	)
         self.assertEqual(k.knapsack(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)			,     2_20	)


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






