





"""simple docstring"""





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				):




      '''simple docstring'''




      _lowerCAmelCase					     =		"""bilinear"""
      _lowerCAmelCase					     =		max_size
      _lowerCAmelCase					     =		short_edge_length







   def __call__(self       ,	lowerCamelCase				):




      '''simple docstring'''




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

         if img.dtype == np.uinta:
            _lowerCAmelCase					     =		Image.fromarray(__snake_case				)
            _lowerCAmelCase					     =		pil_image.resize((neww, newh)       ,	PILImageResampling.BILINEAR				)
            _lowerCAmelCase					     =		np.asarray(__snake_case				)
         else:
            _lowerCAmelCase					     =		img.permute(2       ,	0       ,	1				).unsqueeze(0				)  # 3, 0, 1)  # hw(c) -> nchw
            _lowerCAmelCase					     =		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				):




      '''simple docstring'''




      _lowerCAmelCase					     =		ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]       ,	cfg.INPUT.MAX_SIZE_TEST				)
      _lowerCAmelCase					     =		cfg.INPUT.FORMAT
      _lowerCAmelCase					     =		cfg.SIZE_DIVISIBILITY
      _lowerCAmelCase					     =		cfg.PAD_VALUE
      _lowerCAmelCase					     =		cfg.INPUT.MAX_SIZE_TEST
      _lowerCAmelCase					     =		cfg.MODEL.DEVICE
      _lowerCAmelCase					     =		torch.tensor(cfg.MODEL.PIXEL_STD				).to(self.device				).view(len(cfg.MODEL.PIXEL_STD				)       ,	1       ,	1				)
      _lowerCAmelCase					     =		torch.tensor(cfg.MODEL.PIXEL_MEAN				).to(self.device				).view(len(cfg.MODEL.PIXEL_STD				)       ,	1       ,	1				)
      _lowerCAmelCase					     =		lambda lowerCamelCase				: (x - self.pixel_mean) / self.pixel_std







   def 							A__						(self       ,	lowerCamelCase				):




      '''simple docstring'''




      _lowerCAmelCase					     =		tuple(max(__snake_case				) for s in zip(*[img.shape for img in images]				)				)
      _lowerCAmelCase					     =		[im.shape[-2:] for im in images]
      _lowerCAmelCase					     =		[
          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				):




      '''simple docstring'''




      with torch.no_grad():
         if not isinstance(__snake_case       ,	__snake_case				):
            _lowerCAmelCase					     =		[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
         _lowerCAmelCase					     =		torch.tensor([im.shape[:2] for im in images]				)
         _lowerCAmelCase					     =		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
         _lowerCAmelCase					     =		[self.normalizer(__snake_case				) for x in images]
         # now pad them to do the following operations
         _lowerCAmelCase     ,						_lowerCAmelCase					     =		self.pad(__snake_case				)
         # Normalize

         if self.size_divisibility > 0:
            raise NotImplementedError()
         # pad
         _lowerCAmelCase					     =		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 	__UpperCAmelCase					(       snake_case_  :  str   ,	snake_case_  :  Optional[int]   )							->	List[Any]:






   """simple docstring"""


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






def 	__UpperCAmelCase					(       snake_case_  :  Dict   ,	snake_case_  :  List[str]   )							->	List[str]:






   """simple docstring"""


   assert torch.isfinite(UpperCamelCase_   ).all(), "Box tensor contains infinite or NaN!"
   _lowerCAmelCase     ,						_lowerCAmelCase					     =		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"""





from itertools import combinations_with_replacement






def 	__UpperCAmelCase					(       snake_case_  :  int = 100   )							->	int:






   """simple docstring"""


   _lowerCAmelCase					     =		[*list(range(1   ,	21   )   ), 25]
   _lowerCAmelCase					     =		[2 * x for x in range(1   ,	21   )] + [50]
   _lowerCAmelCase					     =		[3 * x for x in range(1   ,	21   )]
   _lowerCAmelCase					     =		singles + doubles + triples + [0]

   _lowerCAmelCase					     =		0
   _lowerCAmelCase					     =		42
   _lowerCAmelCase					     =		42
   _lowerCAmelCase					     =		42
   _lowerCAmelCase					     =		42

   for double in doubles:
      for throwa, throwa in combinations_with_replacement(snake_case_   ,	2   ):
         _lowerCAmelCase					     =		double + throwa + throwa
         if checkout_total < limit:
            num_checkouts += 1

   return num_checkouts


if __name__ == "__main__":
  print(F'{solution() = }')