




"""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     A__      :




 def __init__(   self  :	str      ,    lowercase  :	Any      ,    lowercase  :	Optional[int]=sys.maxsize	)					-> Any:
      __snake_case          =	"bilinear"
      __snake_case          =	max_size
      __snake_case          =	short_edge_length




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

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

      return img_augs

class     A__      :




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




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

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




 def __call__(   self  :	List[str]      ,    lowercase  :	Dict      ,    lowercase  :	Optional[int]=False	)					-> Dict:






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

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




def       snake_case__	(       lowerCAmelCase   :		List[str]				,					lowerCAmelCase   :		Tuple		):
     boxes[:, 0::2] *= scale_yx[:, 1]
     boxes[:, 1::2] *= scale_yx[:, 0]
     return boxes




def       snake_case__	(       lowerCAmelCase   :		Union[str, Any]				,					lowerCAmelCase   :		Tuple[int, int]		):
     assert torch.isfinite(lowerCAmelCase		).all(), "Box tensor contains infinite or NaN!"
     __snake_case          =	box_size
     tensor[:, 0].clamp_(min=0				,					max=lowerCAmelCase		)
     tensor[:, 1].clamp_(min=0				,					max=lowerCAmelCase		)
     tensor[:, 2].clamp_(min=0				,					max=lowerCAmelCase		)
     tensor[:, 3].clamp_(min=0				,					max=lowerCAmelCase		)








"""simple docstring"""

from itertools import combinations_with_replacement




def       snake_case__	(       lowerCAmelCase   :		int = 100		):
					__snake_case          =	[*list(range(1				,					21		)		), 25]
					__snake_case          =	[2 * x for x in range(1				,					21		)] + [50]
					__snake_case          =	[3 * x for x in range(1				,					21		)]
					__snake_case          =	singles + doubles + triples + [0]

					__snake_case          =	0
					__snake_case          =	42
					__snake_case          =	42
					__snake_case          =	42
					__snake_case          =	42

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

					return num_checkouts


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


