
"""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 			__magic_name__       :
       def __init__(     self ,				A_ ,				A_=sys.maxsize       )     ->	int:





         """simple docstring"""




         _lowercase:			Dict       =     '''bilinear'''
         _lowercase:			Optional[int]       =     max_size
         _lowercase:			Any       =     short_edge_length



       def __call__(     self ,				A_       )     ->	Union[str, Any]:





         """simple docstring"""




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

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

         return img_augs


class 			__magic_name__       :
       def __init__(     self ,				A_       )     ->	Tuple:





         """simple docstring"""




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





         """simple docstring"""




         _lowercase:			Dict       =     tuple(max(A_       ) for s in zip(*[img.shape for img in images]       )       )
         _lowercase:			Tuple       =     [im.shape[-2:] for im in images]
         _lowercase:			Dict       =     [
             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 ,				A_ ,				A_=False       )     ->	Optional[int]:





         """simple docstring"""




         with torch.no_grad():
           if not isinstance(A_ ,				A_       ):
             _lowercase:			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
           _lowercase:			Any       =     torch.tensor([im.shape[:2] for im in images]       )
           _lowercase:			str       =     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
           _lowercase:			Optional[int]       =     [self.normalizer(A_       ) for x in images]
           # now pad them to do the following operations
           _lowercase:			str       =     self.pad(A_       )
           # Normalize

           if self.size_divisibility > 0:
             raise NotImplementedError()
           # pad
           _lowercase:			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   _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase						):






  """simple docstring"""



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







def   _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase						):






  """simple docstring"""



  assert torch.isfinite(_UpperCamelCase						).all(), "Box tensor contains infinite or NaN!"
  _lowercase:			Any       =     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						)


"""simple docstring"""




def  _lowerCAmelCase		(		_UpperCamelCase						):






  """simple docstring"""



  if not isinstance(_UpperCamelCase ,				_UpperCamelCase						):
    raise ValueError('''Input series is not valid, valid series - [2, 4, 6]'''						)
  if len(_UpperCamelCase						) == 0:
    raise ValueError('''Input list must be a non empty list'''						)
  if len(_UpperCamelCase						) == 1:
    return True
  _lowercase:			Any       =     series[1] - series[0]
  for index in range(len(_UpperCamelCase						) - 1						):
    if series[index + 1] - series[index] != common_diff:
      return False
  return True




def  _lowerCAmelCase		(		_UpperCamelCase						):






  """simple docstring"""



  if not isinstance(_UpperCamelCase ,				_UpperCamelCase						):
    raise ValueError('''Input series is not valid, valid series - [2, 4, 6]'''						)
  if len(_UpperCamelCase						) == 0:
    raise ValueError('''Input list must be a non empty list'''						)
  _lowercase:			Tuple       =     0
  for val in series:
    answer += val
  return answer / len(_UpperCamelCase						)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
