


'''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 		a__				:





							'''simple docstring'''






							def __init__(		self					,    lowerCamelCase_					,    lowerCamelCase_=sys.maxsize   )       ->							Any:
														lowerCAmelCase__          =	"""bilinear"""
														lowerCAmelCase__          =	max_size
														lowerCAmelCase__          =	short_edge_length







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

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





							'''simple docstring'''






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







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_   )       ->							Union[str, Any]:
														lowerCAmelCase__          =	tuple(max(snake_case_   ) for s in zip(*[img.shape for img in images]   )   )
														lowerCAmelCase__          =	[im.shape[-2:] for im in images]
														lowerCAmelCase__          =	[
														    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   )       ->							Dict:
														with torch.no_grad():
																					if not isinstance(snake_case_					,    snake_case_   ):
																												lowerCAmelCase__          =	[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
																					lowerCAmelCase__          =	torch.tensor([im.shape[:2] for im in images]   )
																					lowerCAmelCase__          =	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
																					lowerCAmelCase__          =	[self.normalizer(snake_case_   ) for x in images]
																					# now pad them to do the following operations
																					lowerCAmelCase__          =	self.pad(snake_case_   )
																					# Normalize

																					if self.size_divisibility > 0:
																												raise NotImplementedError()
																					# pad
																					lowerCAmelCase__          =	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 					_snake_case   (     A ,    A       )						->    Dict:
							boxes[:, 0::2] *= scale_yx[:, 1]
							boxes[:, 1::2] *= scale_yx[:, 0]
							return boxes






def 					_snake_case   (     A ,    A       )						->    Tuple:
							assert torch.isfinite(A       ).all(), "Box tensor contains infinite or NaN!"
							lowerCAmelCase__          =	box_size
							tensor[:, 0].clamp_(min=0 ,    max=A       )
							tensor[:, 1].clamp_(min=0 ,    max=A       )
							tensor[:, 2].clamp_(min=0 ,    max=A       )
							tensor[:, 3].clamp_(min=0 ,    max=A       )

def 				A__    (  lowerCamelCase  )     ->		Optional[Any]:
				UpperCamelCase_:							Optional[int]		=  len(lowerCamelCase  )
				for _ in range(
				    0   ,			int(((no_of_elements - 1) / 2) + 1  )  ):  # we don't need to traverse to end of list as
								for j in range(0   ,			no_of_elements - 1  ):
												if (
												    lst[j + 1] < lst[j]
												):  # applying bubble sort algorithm from left to right (or forwards)
																UpperCamelCase_:							str		=  lst[j + 1]
																UpperCamelCase_:							Tuple		=  lst[j]
																UpperCamelCase_:							str		=  temp
												if (
												    lst[no_of_elements - 1 - j] < lst[no_of_elements - 2 - j]
												):  # applying bubble sort algorithm from right to left (or backwards)
																UpperCamelCase_:							Union[str, Any]		=  lst[no_of_elements - 1 - j]
																UpperCamelCase_:							Dict		=  lst[no_of_elements - 2 - j]
																UpperCamelCase_:							List[Any]		=  temp
				return lst


if __name__ == "__main__":
							print("""enter the list to be sorted""")
							lowerCamelCase_  :       Union[str, Any]					      =     [int(x) for x in input().split()]  # inputing elements of the list in one line
							lowerCamelCase_  :       Union[str, Any]					      =     double_sort(lst)
							print("""the sorted list is""")
							print(sorted_lst)






