






"""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					, UpperCAmelCase_					, UpperCAmelCase_=sys.maxsize)			->   Optional[Any]:




          '''simple docstring'''




          lowercase__:							str         =     "bilinear"
          lowercase__:							Optional[Any]         =     max_size
          lowercase__:							int         =     short_edge_length





   def __call__(       self					, UpperCAmelCase_)			->   Tuple:




          '''simple docstring'''




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

                 if img.dtype == np.uinta:
                        lowercase__:							Dict         =     Image.fromarray(UpperCAmelCase_)
                        lowercase__:							Optional[Any]         =     pil_image.resize((neww, newh)					, PILImageResampling.BILINEAR)
                        lowercase__:							Union[str, Any]         =     np.asarray(UpperCAmelCase_)
                 else:
                        lowercase__:							str         =     img.permute(2					, 0					, 1).unsqueeze(0)  # 3, 0, 1)  # hw(c) -> nchw
                        lowercase__:							Tuple         =     nn.functional.interpolate(
                            UpperCAmelCase_					, (newh, neww)					, mode=self.interp_method					, align_corners=UpperCAmelCase_).squeeze(0)
                 img_augs.append(UpperCAmelCase_)

          return img_augs







class 						_a  :





   '''simple docstring'''






   def __init__(       self					, UpperCAmelCase_)			->   str:




          '''simple docstring'''




          lowercase__:							List[str]         =     ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]					, cfg.INPUT.MAX_SIZE_TEST)
          lowercase__:							List[Any]         =     cfg.INPUT.FORMAT
          lowercase__:							Tuple         =     cfg.SIZE_DIVISIBILITY
          lowercase__:							int         =     cfg.PAD_VALUE
          lowercase__:							Optional[Any]         =     cfg.INPUT.MAX_SIZE_TEST
          lowercase__:							Tuple         =     cfg.MODEL.DEVICE
          lowercase__:							str         =     torch.tensor(cfg.MODEL.PIXEL_STD).to(self.device).view(len(cfg.MODEL.PIXEL_STD)					, 1					, 1)
          lowercase__:							int         =     torch.tensor(cfg.MODEL.PIXEL_MEAN).to(self.device).view(len(cfg.MODEL.PIXEL_STD)					, 1					, 1)
          lowercase__:							Any         =     lambda UpperCAmelCase_: (x - self.pixel_mean) / self.pixel_std





   def __lowercase							(       self					, UpperCAmelCase_)			->   str:




          '''simple docstring'''




          lowercase__:							Dict         =     tuple(max(UpperCAmelCase_) for s in zip(*[img.shape for img in images]))
          lowercase__:							Union[str, Any]         =     [im.shape[-2:] for im in images]
          lowercase__:							Optional[int]         =     [
              nn.functional.pad(
                  UpperCAmelCase_					, [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]					, value=self.pad_value					, )
              for size, im in zip(UpperCAmelCase_					, UpperCAmelCase_)
          ]

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





   def __call__(       self					, UpperCAmelCase_					, UpperCAmelCase_=False)			->   Optional[int]:




          '''simple docstring'''




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

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







def 							A(    snake_case_		,  snake_case_						):
       """simple docstring"""





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







def 							A(    snake_case_		,  snake_case_						):
       """simple docstring"""





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








from math import sqrt


def  A__ (       __lowerCamelCase						):
   SCREAMING_SNAKE_CASE_	=      [1]
   for i in range(2,  int(sqrt(__lowerCamelCase						)						) + 1,  1						):
      if number % i == 0:
         values.append(__lowerCamelCase						)
         if int(number // i						) != i:
            values.append(int(number // i						)						)
   return sorted(__lowerCamelCase						)


def  A__ (       __lowerCamelCase						):
   return sum(factors(__lowerCamelCase						)						) > n


def  A__ (       __lowerCamelCase						):
   SCREAMING_SNAKE_CASE_	=      factors(__lowerCamelCase						)
   SCREAMING_SNAKE_CASE_	=      len(__lowerCamelCase						)
   SCREAMING_SNAKE_CASE_	=      [[0 for i in range(number + 1						)] for j in range(r + 1						)]
   for i in range(r + 1						):
      SCREAMING_SNAKE_CASE_	=      True

   for i in range(1,  number + 1						):
      SCREAMING_SNAKE_CASE_	=      False

   for i in range(1,  r + 1						):
      for j in range(1,  number + 1						):
         if j < values[i - 1]:
            SCREAMING_SNAKE_CASE_	=      subset[i - 1][j]
         else:
            SCREAMING_SNAKE_CASE_	=      subset[i - 1][j] or subset[i - 1][j - values[i - 1]]

   return subset[r][number] != 0


def  A__ (       __lowerCamelCase						):
   return abundant(__lowerCamelCase						) and not semi_perfect(__lowerCamelCase						)


if __name__ == "__main__":
   import doctest

   doctest.testmod(verbose=True)
   for number in (69, 70, 71):
      print(F"""{number} is {'' if weird(number) else 'not '}weird.""")


