

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      UpperCamelCase :

    def __init__(			self :      Dict							,    snake_case__ :      int							,    snake_case__ :      Tuple=sys.maxsize    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			'bilinear'
     SCREAMING_SNAKE_CASE								=			max_size
     SCREAMING_SNAKE_CASE								=			short_edge_length




    def __call__(			self :      Tuple							,    snake_case__ :      Union[str, Any]    ):

     """simple docstring"""






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

      if img.dtype == np.uinta:
       SCREAMING_SNAKE_CASE								=			Image.fromarray(snake_case__    )
       SCREAMING_SNAKE_CASE								=			pil_image.resize((neww, newh)							,    PILImageResampling.BILINEAR    )
       SCREAMING_SNAKE_CASE								=			np.asarray(snake_case__    )
      else:
       SCREAMING_SNAKE_CASE								=			img.permute(2							,    0							,    1    ).unsqueeze(0    )  # 3, 0, 1)  # hw(c) -> nchw
       SCREAMING_SNAKE_CASE								=			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      UpperCamelCase :

    def __init__(			self :      int							,    snake_case__ :      Union[str, Any]    ):

     """simple docstring"""






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

    def UpperCamelCase			(			self :      Tuple							,    snake_case__ :      Union[str, Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			tuple(max(snake_case__    ) for s in zip(*[img.shape for img in images]    )    )
     SCREAMING_SNAKE_CASE								=			[im.shape[-2:] for im in images]
     SCREAMING_SNAKE_CASE								=			[
         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 :      Optional[int]							,    snake_case__ :      Tuple							,    snake_case__ :      Optional[Any]=False    ):

     """simple docstring"""






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

      if self.size_divisibility > 0:
       raise NotImplementedError()
      # pad
      SCREAMING_SNAKE_CASE								=			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        __lowerCAmelCase    (       _UpperCamelCase		:  int		,							_UpperCamelCase		:  Optional[int]	)		->							Optional[int]:
 '''simple docstring'''


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




def        __lowerCAmelCase    (       _UpperCamelCase		:  List[str]		,							_UpperCamelCase		:  Tuple[int, int]	)		->							int:
 '''simple docstring'''


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




def        __lowerCAmelCase    (       _UpperCamelCase		:  str		,							_UpperCamelCase		:  int	)		->							list[str]:
 '''simple docstring'''


 return [sentence[i : i + ngram_size] for i in range(len(_UpperCamelCase	) - ngram_size + 1	)]


if __name__ == "__main__":
      from doctest import testmod

      testmod()

