


'''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       :


     """simple docstring"""






     def __init__(       self  :	Tuple							,				snake_case  :	Optional[Any]							,				snake_case  :	Optional[int]=sys.maxsize    )		->							Optional[Any]:
          __UpperCAmelCase			:				Union[str, Any]													=    '''bilinear'''
          __UpperCAmelCase			:				str													=    max_size
          __UpperCAmelCase			:				List[str]													=    short_edge_length






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

               if img.dtype == np.uinta:
                    __UpperCAmelCase			:				Tuple													=    Image.fromarray(snake_case    )
                    __UpperCAmelCase			:				Dict													=    pil_image.resize((neww, newh)							,				PILImageResampling.BILINEAR    )
                    __UpperCAmelCase			:				Union[str, Any]													=    np.asarray(snake_case    )
               else:
                    __UpperCAmelCase			:				List[str]													=    img.permute(2							,				0							,				1    ).unsqueeze(0    )  # 3, 0, 1)  # hw(c) -> nchw
                    __UpperCAmelCase			:				Dict													=    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 			a       :


     """simple docstring"""






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

     def 				lowerCamelCase__				(       self  :	Optional[int]							,				snake_case  :	int    )		->							int:
          __UpperCAmelCase			:				Dict													=    tuple(max(snake_case    ) for s in zip(*[img.shape for img in images]    )    )
          __UpperCAmelCase			:				Union[str, Any]													=    [im.shape[-2:] for im in images]
          __UpperCAmelCase			:				str													=    [
              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  :	int							,				snake_case  :	str							,				snake_case  :	List[Any]=False    )		->							Optional[Any]:
          with torch.no_grad():
               if not isinstance(snake_case							,				snake_case    ):
                    __UpperCAmelCase			:				Dict													=    [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
               __UpperCAmelCase			:				Optional[int]													=    torch.tensor([im.shape[:2] for im in images]    )
               __UpperCAmelCase			:				Dict													=    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
               __UpperCAmelCase			:				Dict													=    [self.normalizer(snake_case    ) for x in images]
               # now pad them to do the following operations
               __UpperCAmelCase							,     __UpperCAmelCase			:				Dict													=    self.pad(snake_case    )
               # Normalize

               if self.size_divisibility > 0:
                    raise NotImplementedError()
               # pad
               __UpperCAmelCase			:				Tuple													=    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 			_a			(		_lowercase      :			str    ,      _lowercase      :			Optional[Any]						):





     '''simple docstring'''






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




def 			_a			(		_lowercase      :			List[str]    ,      _lowercase      :			Tuple[int, int]						):





     '''simple docstring'''






     assert torch.isfinite(_lowercase						).all(), "Box tensor contains infinite or NaN!"
     __UpperCAmelCase							,     __UpperCAmelCase			:				List[Any]													=    box_size
     tensor[:, 0].clamp_(min=0    ,      max=_lowercase						)
     tensor[:, 1].clamp_(min=0    ,      max=_lowercase						)
     tensor[:, 2].clamp_(min=0    ,      max=_lowercase						)
     tensor[:, 3].clamp_(min=0    ,      max=_lowercase						)



'''simple docstring'''



from __future__ import annotations




def 			_a			(		_lowercase      :			int						):





     '''simple docstring'''






     __UpperCAmelCase			:				str													=    [True] * limit
     __UpperCAmelCase			:				Tuple													=    False
     __UpperCAmelCase			:				List[str]													=    False
     __UpperCAmelCase			:				List[Any]													=    True

     for i in range(3    ,      int(limit**0.5 + 1						)    ,      2						):
          __UpperCAmelCase			:				Dict													=    i * 2
          while index < limit:
               __UpperCAmelCase			:				Optional[int]													=    False
               __UpperCAmelCase			:				Union[str, Any]													=    index + i

     __UpperCAmelCase			:				Optional[Any]													=    [2]

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

     return primes




def 			_a			(		_lowercase      :			int = 1000000						):





     '''simple docstring'''






     __UpperCAmelCase			:				List[str]													=    prime_sieve(_lowercase						)
     __UpperCAmelCase			:				Optional[Any]													=    0
     __UpperCAmelCase			:				int													=    0

     for i in range(len(_lowercase						)						):
          for j in range(i + length    ,      len(_lowercase						)						):
               __UpperCAmelCase			:				Optional[Any]													=    sum(primes[i:j]						)
               if sol >= ceiling:
                    break

               if sol in primes:
                    __UpperCAmelCase			:				Any													=    j - i
                    __UpperCAmelCase			:				List[Any]													=    sol

     return largest


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