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       __snake_case :
 def __init__(	self		, snake_case__		, snake_case__=sys.maxsize    )						->				str:
       '''simple docstring'''





       UpperCAmelCase     :			List[Any]     	='''bilinear'''
       UpperCAmelCase     :			Tuple     	=max_size
       UpperCAmelCase     :			int     	=short_edge_length






 def __call__(	self		, snake_case__    )						->				Dict:
       '''simple docstring'''





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

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

       return img_augs







class       __snake_case :
 def __init__(	self		, snake_case__    )						->				Union[str, Any]:
       '''simple docstring'''





       UpperCAmelCase     :			Optional[Any]     	=ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]		, cfg.INPUT.MAX_SIZE_TEST    )
       UpperCAmelCase     :			Dict     	=cfg.INPUT.FORMAT
       UpperCAmelCase     :			Tuple     	=cfg.SIZE_DIVISIBILITY
       UpperCAmelCase     :			List[str]     	=cfg.PAD_VALUE
       UpperCAmelCase     :			Tuple     	=cfg.INPUT.MAX_SIZE_TEST
       UpperCAmelCase     :			Any     	=cfg.MODEL.DEVICE
       UpperCAmelCase     :			List[str]     	=torch.tensor(cfg.MODEL.PIXEL_STD    ).to(self.device    ).view(len(cfg.MODEL.PIXEL_STD    )		, 1		, 1    )
       UpperCAmelCase     :			Optional[Any]     	=torch.tensor(cfg.MODEL.PIXEL_MEAN    ).to(self.device    ).view(len(cfg.MODEL.PIXEL_STD    )		, 1		, 1    )
       UpperCAmelCase     :			Optional[Any]     	=lambda snake_case__    : (x - self.pixel_mean) / self.pixel_std
 def 						UpperCAmelCase__							(	self		, snake_case__    )						->				Optional[int]:
       '''simple docstring'''





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

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






 def __call__(	self		, snake_case__		, snake_case__=False    )						->				int:
       '''simple docstring'''





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

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





def 	lowerCAmelCase_			(					__lowerCAmelCase		,       __lowerCAmelCase    )->	Union[str, Any]:






      '''simple docstring'''





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





def 	lowerCAmelCase_			(					__lowerCAmelCase		,       __lowerCAmelCase    )->	List[str]:






      '''simple docstring'''





      assert torch.isfinite(lowerCAmelCase__    ).all(), "Box tensor contains infinite or NaN!"
      UpperCAmelCase	,    UpperCAmelCase     :			int     	=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__    )








def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

		if n < 0:
				return 0
		# table[i] represents the number of ways to get to amount i
		__A    =							[0] * (n + 1)

		# There is exactly 1 way to get to zero(You pick no coins).
		__A    =							1

		# Pick all coins one by one and update table[] values
		# after the index greater than or equal to the value of the
		# picked coin
		for coin_val in s:
				for j in range(lowerCAmelCase__					,     n + 1	):
						table[j] += table[j - coin_val]

		return table[n]


if __name__ == "__main__":
					import doctest

					doctest.testmod()

