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 						_SCREAMING_SNAKE_CASE				:






		"""simple docstring"""







		def __init__(      self     :					Dict	,       __SCREAMING_SNAKE_CASE     :					int	,       __SCREAMING_SNAKE_CASE     :					Any=sys.maxsize ):






					"""simple docstring"""
					_snake_case														=   "bilinear"
					_snake_case														=   max_size
					_snake_case														=   short_edge_length







		def __call__(      self     :					str	,       __SCREAMING_SNAKE_CASE     :					int ):






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

								if img.dtype == np.uinta:
											_snake_case														=   Image.fromarray(__SCREAMING_SNAKE_CASE )
											_snake_case														=   pil_image.resize((neww, newh)	,       PILImageResampling.BILINEAR )
											_snake_case														=   np.asarray(__SCREAMING_SNAKE_CASE )
								else:
											_snake_case														=   img.permute(2	,       0	,       1 ).unsqueeze(0 )  # 3, 0, 1)  # hw(c) -> nchw
											_snake_case														=   nn.functional.interpolate(
											    __SCREAMING_SNAKE_CASE	,       (newh, neww)	,       mode=self.interp_method	,       align_corners=__SCREAMING_SNAKE_CASE ).squeeze(0 )
								img_augs.append(__SCREAMING_SNAKE_CASE )

					return img_augs

class 						_SCREAMING_SNAKE_CASE				:






		"""simple docstring"""







		def __init__(      self     :					List[Any]	,       __SCREAMING_SNAKE_CASE     :					str ):






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



		def 					_a					(      self     :					Union[str, Any]	,       __SCREAMING_SNAKE_CASE     :					str ):






					"""simple docstring"""
					_snake_case														=   tuple(max(__SCREAMING_SNAKE_CASE ) for s in zip(*[img.shape for img in images] ) )
					_snake_case														=   [im.shape[-2:] for im in images]
					_snake_case														=   [
					    nn.functional.pad(
					        __SCREAMING_SNAKE_CASE	,       [0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]	,       value=self.pad_value	,       )
					    for size, im in zip(__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE )
					]

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







		def __call__(      self     :					Optional[int]	,       __SCREAMING_SNAKE_CASE     :					str	,       __SCREAMING_SNAKE_CASE     :					List[Any]=False ):






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

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






def  UpperCAmelCase__(    lowerCamelCase_				,						lowerCamelCase_	):



			"""simple docstring"""





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







def  UpperCAmelCase__(    lowerCamelCase_				,						lowerCamelCase_	):



			"""simple docstring"""





			assert torch.isfinite(lowerCamelCase_	).all(), "Box tensor contains infinite or NaN!"
			_snake_case							,			_snake_case														=   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_ = 1000	):



			"""simple docstring"""





			return sum(i for i in range(lowerCamelCase_	) if i % 3 == 0 or i % 5 == 0	)


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

