"""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	, __lowerCAmelCase	, __lowerCAmelCase=sys.maxsize		):







            """simple docstring"""



            lowercase														=			"""bilinear"""
            lowercase														=			max_size
            lowercase														=			short_edge_length







      def __call__(		self	, __lowerCAmelCase		):







            """simple docstring"""



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

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

            return img_augs






class 				_A		:

      def __init__(		self	, __lowerCAmelCase		):







            """simple docstring"""



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

      def        A__ (		self	, __lowerCAmelCase		):







            """simple docstring"""



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

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







      def __call__(		self	, __lowerCAmelCase	, __lowerCAmelCase=False		):







            """simple docstring"""



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

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






def     UpperCAmelCase__ (       lowerCAmelCase__  :Any      ,	lowerCAmelCase__  :List[str]	)				->					Optional[Any]:
      '''simple docstring'''


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






def     UpperCAmelCase__ (       lowerCAmelCase__  :Tuple      ,	lowerCAmelCase__  :Tuple[int, int]	)				->					List[Any]:
      '''simple docstring'''


      assert torch.isfinite(lowerCAmelCase__	).all(), "Box tensor contains infinite or NaN!"
      lowercase							,							lowercase														=			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 __future__ import annotations


def     UpperCAmelCase__ (       lowerCAmelCase__  :list[float]      ,	lowerCAmelCase__  :list[float]	)				->					float:
      '''simple docstring'''


      lowercase														=			sorted(numsa + numsa	)
      lowercase							,							lowercase														=			divmod(len(lowerCAmelCase__	)      ,	2	)
      if mod == 1:
            return all_numbers[div]
      else:
            return (all_numbers[div] + all_numbers[div - 1]) / 2


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       __lowerCAmelCase					: List[Any]        =[float(x) for x in input("""Enter the elements of first array: """).split()]
       __lowerCAmelCase					: List[Any]        =[float(x) for x in input("""Enter the elements of second array: """).split()]
       print(F"""The median of two arrays is: {median_of_two_arrays(array_a, array_a)}""")



