





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        SCREAMING_SNAKE_CASE__					:



 def __init__(				self,__lowerCamelCase,__lowerCamelCase=sys.maxsize					):
        A__		   =		'''bilinear'''
        A__		   =		max_size
        A__		   =		short_edge_length




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

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

        return img_augs




class        SCREAMING_SNAKE_CASE__					:



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



 def 							UpperCamelCase     (				self,__lowerCamelCase					):
        A__		   =		tuple(max(__lowerCamelCase					) for s in zip(*[img.shape for img in images]					)					)
        A__		   =		[im.shape[-2:] for im in images]
        A__		   =		[
            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					):
        with torch.no_grad():
               if not isinstance(__lowerCamelCase,__lowerCamelCase					):
                      A__		   =		[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
               A__		   =		torch.tensor([im.shape[:2] for im in images]					)
               A__		   =		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
               A__		   =		[self.normalizer(__lowerCamelCase					) for x in images]
               # now pad them to do the following operations
               A__   ,      A__		   =		self.pad(__lowerCamelCase					)
               # Normalize

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


def       UpperCamelCase__(			UpperCamelCase__     :			Dict					,	UpperCamelCase__     :			Dict    )->Dict:
       boxes[:, 0::2] *= scale_yx[:, 1]
       boxes[:, 1::2] *= scale_yx[:, 0]
       return boxes


def       UpperCamelCase__(			UpperCamelCase__     :			Optional[Any]					,	UpperCamelCase__     :			Tuple[int, int]    )->Optional[Any]:
       assert torch.isfinite(UpperCamelCase__    ).all(), "Box tensor contains infinite or NaN!"
       A__   ,      A__		   =		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__    )












import math


def       UpperCamelCase__(			UpperCamelCase__     :			int    )->list:
       A__		   =		[True] * n
       A__		   =		False
       A__		   =		False
       A__		   =		True

       for i in range(3					,	int(n**0.5 + 1    )					,	2    ):
              A__		   =		i * 2
              while index < n:
                     A__		   =		False
                     A__		   =		index + i

       A__		   =		[2]

       for i in range(3					,	UpperCamelCase__					,	2    ):
              if is_prime[i]:
                     primes.append(UpperCamelCase__    )

       return primes


def       UpperCamelCase__(			UpperCamelCase__     :			int = 99_99_66_66_33_33    )->int:
       A__		   =		math.floor(math.sqrt(UpperCamelCase__    )    ) + 1_00
       A__		   =		prime_sieve(UpperCamelCase__    )

       A__		   =		0
       A__		   =		0
       A__		   =		primes[prime_index]

       while (last_prime**2) <= limit:
              A__		   =		primes[prime_index + 1]

              A__		   =		last_prime**2
              A__		   =		next_prime**2

              # Get numbers divisible by lps(current)
              A__		   =		lower_bound + last_prime
              while upper_bound > current <= limit:
                     matches_sum += current
                     current += last_prime

              # Reset the upper_bound
              while (upper_bound - next_prime) > limit:
                     upper_bound -= next_prime

              # Add the numbers divisible by ups(current)
              A__		   =		upper_bound - next_prime
              while current > lower_bound:
                     matches_sum += current
                     current -= next_prime

              # Remove the numbers divisible by both ups and lps
              A__		   =		0
              while upper_bound > current <= limit:
                     if current <= lower_bound:
                            # Increment the current number
                            current += last_prime * next_prime
                            continue

                     if current > limit:
                            break

                     # Remove twice since it was added by both ups and lps
                     matches_sum -= current * 2

                     # Increment the current number
                     current += last_prime * next_prime

              # Setup for next pair
              A__		   =		next_prime
              prime_index += 1

       return matches_sum


if __name__ == "__main__":
       print(solution())





