




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 	_UpperCamelCase					:
      def __init__(	self      :List[Any]       ,					lowerCamelCase      :int       ,					lowerCamelCase      :Union[str, Any]=sys.maxsize					)   ->	str:
       UpperCAmelCase__							       =  "bilinear"
       UpperCAmelCase__							       =  max_size
       UpperCAmelCase__							       =  short_edge_length







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

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

       return img_augs



class 	_UpperCamelCase					:
      def __init__(	self      :Optional[Any]       ,					lowerCamelCase      :Optional[int]					)   ->	Tuple:
       UpperCAmelCase__							       =  ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]       ,					cfg.INPUT.MAX_SIZE_TEST					)
       UpperCAmelCase__							       =  cfg.INPUT.FORMAT
       UpperCAmelCase__							       =  cfg.SIZE_DIVISIBILITY
       UpperCAmelCase__							       =  cfg.PAD_VALUE
       UpperCAmelCase__							       =  cfg.INPUT.MAX_SIZE_TEST
       UpperCAmelCase__							       =  cfg.MODEL.DEVICE
       UpperCAmelCase__							       =  torch.tensor(cfg.MODEL.PIXEL_STD					).to(self.device					).view(len(cfg.MODEL.PIXEL_STD					)       ,					1       ,					1					)
       UpperCAmelCase__							       =  torch.tensor(cfg.MODEL.PIXEL_MEAN					).to(self.device					).view(len(cfg.MODEL.PIXEL_STD					)       ,					1       ,					1					)
       UpperCAmelCase__							       =  lambda lowerCamelCase					: (x - self.pixel_mean) / self.pixel_std
      def 		UpperCAmelCase_  (	self      :int       ,					lowerCamelCase      :Tuple					)   ->	Tuple:
       UpperCAmelCase__							       =  tuple(max(__lowerCamelCase					) for s in zip(*[img.shape for img in images]					)					)
       UpperCAmelCase__							       =  [im.shape[-2:] for im in images]
       UpperCAmelCase__							       =  [
           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      :Optional[Any]       ,					lowerCamelCase      :Tuple       ,					lowerCamelCase      :Optional[int]=False					)   ->	str:
       with torch.no_grad():
        if not isinstance(__lowerCamelCase       ,					__lowerCamelCase					):
         UpperCAmelCase__							       =  [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
        UpperCAmelCase__							       =  torch.tensor([im.shape[:2] for im in images]					)
        UpperCAmelCase__							       =  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
        UpperCAmelCase__							       =  [self.normalizer(__lowerCamelCase					) for x in images]
        # now pad them to do the following operations
        UpperCAmelCase__       ,       UpperCAmelCase__							       =  self.pad(__lowerCamelCase					)
        # Normalize

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



def   lowerCAmelCase						(						_lowerCAmelCase							:      Any  ,							_lowerCAmelCase							:      Union[str, Any]		):




 """simple docstring"""







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



def   lowerCAmelCase						(						_lowerCAmelCase							:      Optional[int]  ,							_lowerCAmelCase							:      Tuple[int, int]		):




 """simple docstring"""







 assert torch.isfinite(UpperCamelCase__		).all(), "Box tensor contains infinite or NaN!"
 UpperCAmelCase__       ,       UpperCAmelCase__							       =  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__		)












from __future__ import annotations

import math


def 	UpperCamelCase__(       UpperCamelCase__			:       int  )->bool:

       if 1 < number < 4:
              # 2 and 3 are primes
              return True
       elif number < 2 or number % 2 == 0 or number % 3 == 0:
              # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
              return False

       # All primes number are in format of 6k +/- 1
       for i in range(5 ,     int(math.sqrt(UpperCamelCase__  ) + 1  ) ,     6  ):
              if number % i == 0 or number % (i + 2) == 0:
                     return False
       return True


def 	UpperCamelCase__(       UpperCamelCase__			:       int  )->list[int]:
       A__											=						str(UpperCamelCase__  )
       A__											=						[n]
       for i in range(1 ,     len(UpperCamelCase__  )  ):
              list_nums.append(int(str_num[i:]  )  )
              list_nums.append(int(str_num[:-i]  )  )
       return list_nums


def 	UpperCamelCase__(       UpperCamelCase__			:       int  )->bool:
       if len(str(UpperCamelCase__  )  ) > 3:
              if not is_prime(int(str(UpperCamelCase__  )[-3:]  )  ) or not is_prime(int(str(UpperCamelCase__  )[:3]  )  ):
                     return False
       return True


def 	UpperCamelCase__(       UpperCamelCase__			:       int = 11  )->list[int]:
       A__											=						[]
       A__											=						13
       while len(UpperCamelCase__  ) != count:
              if validate(UpperCamelCase__  ):
                     A__											=						list_truncated_nums(UpperCamelCase__  )
                     if all(is_prime(UpperCamelCase__  ) for i in list_nums  ):
                            list_truncated_primes.append(UpperCamelCase__  )
              num += 2
       return list_truncated_primes


def 	UpperCamelCase__(       )->int:
       return sum(compute_truncated_primes(11  )  )


if __name__ == "__main__":
       print(F"{sum(compute_truncated_primes(11)) = }")





