



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   ,       lowerCamelCase   ,       lowerCamelCase=sys.maxsize       ):



				'''simple docstring'''


				a__					=  "bilinear"
				a__					=  max_size
				a__					=  short_edge_length







	def __call__(	self   ,       lowerCamelCase       ):



				'''simple docstring'''


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

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







	'''simple docstring'''




	def __init__(	self   ,       lowerCamelCase       ):



				'''simple docstring'''


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

	def 				_A			(	self   ,       lowerCamelCase       ):



				'''simple docstring'''


				a__					=  tuple(max(snake_case_       ) for s in zip(*[img.shape for img in images]       )       )
				a__					=  [im.shape[-2:] for im in images]
				a__					=  [
				    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   ,       lowerCamelCase   ,       lowerCamelCase=False       ):



				'''simple docstring'''


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

							if self.size_divisibility > 0:
										raise NotImplementedError()
							# pad
							a__					=  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    UpperCAmelCase					(  lowercase__						:					Any				,					lowercase__						:					Optional[int]	):



			'''simple docstring'''





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





def    UpperCAmelCase					(  lowercase__						:					Union[str, Any]				,					lowercase__						:					Tuple[int, int]	):



			'''simple docstring'''





			assert torch.isfinite(_lowerCAmelCase	).all(), "Box tensor contains infinite or NaN!"
			a__					=  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        UpperCAmelCase__   (				_lowerCAmelCase    :			int = 400_0000)    ->		int:



						"""simple docstring"""

						snake_case :       List[Any]				=    1
						snake_case :       int				=    2
						snake_case :       List[str]				=    0
						while j <= n:
												if j % 2 == 0:
																		total += j
												snake_case				,					snake_case :       Any				=    j, i + j

						return total


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





