



'''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_					:
  """simple docstring"""




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






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

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

    return img_augs






class       lowerCamelCase_					:
  """simple docstring"""




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




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

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






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

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




def  a_  (							_lowerCAmelCase ,_lowerCAmelCase )   ->   int:
  boxes[:, 0::2] *= scale_yx[:, 1]
  boxes[:, 1::2] *= scale_yx[:, 0]
  return boxes


def  a_  (							_lowerCAmelCase ,_lowerCAmelCase )   ->   Optional[Any]:
  assert torch.isfinite(_lowerCAmelCase ).all(), "Box tensor contains infinite or NaN!"
  __lowerCamelCase : List[Any]					 =				box_size
  tensor[:, 0].clamp_(min=0 ,max=_lowerCAmelCase )
  tensor[:, 1].clamp_(min=0 ,max=_lowerCAmelCase )
  tensor[:, 2].clamp_(min=0 ,max=_lowerCAmelCase )
  tensor[:, 3].clamp_(min=0 ,max=_lowerCAmelCase )











'''simple docstring'''





def  a_  (							_lowerCAmelCase = 1000 )   ->   int:
  return sum(i for i in range(_lowerCAmelCase ) if i % 3 == 0 or i % 5 == 0 )


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






