





'''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 				lowerCAmelCase_						:




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





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

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

     return img_augs







class 				lowerCAmelCase_						:




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




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

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







def 		__a(SCREAMING_SNAKE_CASE_					:   Tuple						,    SCREAMING_SNAKE_CASE_					:   List[str]      ):



    '''simple docstring'''





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





def 		__a(SCREAMING_SNAKE_CASE_					:   Union[str, Any]						,    SCREAMING_SNAKE_CASE_					:   Tuple[int, int]      ):



    '''simple docstring'''





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









'''simple docstring'''


def 		__a(SCREAMING_SNAKE_CASE_					:   int = 1000      ):



    '''simple docstring'''





    return sum(i for i in range(SCREAMING_SNAKE_CASE_      ) if i % 3 == 0 or i % 5 == 0      )


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


