
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 :






			'''simple docstring'''

			def __init__(				self       :				Dict  ,		snake_case_       :				str  ,		snake_case_       :				str=sys.maxsize					):
							UpperCamelCase_:							Any		=  """bilinear"""
							UpperCamelCase_:							Dict		=  max_size
							UpperCamelCase_:							Dict		=  short_edge_length




			def __call__(				self       :				Optional[Any]  ,		snake_case_       :				Union[str, Any]					):
							UpperCamelCase_:							str		=  []
							for img in imgs:
											UpperCamelCase_,      UpperCamelCase_:							Optional[int]		=  img.shape[:2]
											# later: provide list and randomly choose index for resize
											UpperCamelCase_:							Optional[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(snake_case_  ,		snake_case_					)
											if h < w:
															UpperCamelCase_,      UpperCamelCase_:							Tuple		=  size, scale * w
											else:
															UpperCamelCase_,      UpperCamelCase_:							Union[str, Any]		=  scale * h, size
											if max(snake_case_  ,		snake_case_					) > self.max_size:
															UpperCamelCase_:							Optional[int]		=  self.max_size * 1.0 / max(snake_case_  ,		snake_case_					)
															UpperCamelCase_:							List[str]		=  newh * scale
															UpperCamelCase_:							Dict		=  neww * scale
											UpperCamelCase_:							int		=  int(neww + 0.5					)
											UpperCamelCase_:							List[Any]		=  int(newh + 0.5					)

											if img.dtype == np.uinta:
															UpperCamelCase_:							Any		=  Image.fromarray(snake_case_					)
															UpperCamelCase_:							Tuple		=  pil_image.resize((neww, newh)  ,		PILImageResampling.BILINEAR					)
															UpperCamelCase_:							Optional[int]		=  np.asarray(snake_case_					)
											else:
															UpperCamelCase_:							List[Any]		=  img.permute(2  ,		0  ,		1					).unsqueeze(0					)  # 3, 0, 1)  # hw(c) -> nchw
															UpperCamelCase_:							List[Any]		=  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 :






			'''simple docstring'''

			def __init__(				self       :				Optional[int]  ,		snake_case_       :				List[Any]					):
							UpperCamelCase_:							Union[str, Any]		=  ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]  ,		cfg.INPUT.MAX_SIZE_TEST					)
							UpperCamelCase_:							Dict		=  cfg.INPUT.FORMAT
							UpperCamelCase_:							Union[str, Any]		=  cfg.SIZE_DIVISIBILITY
							UpperCamelCase_:							Union[str, Any]		=  cfg.PAD_VALUE
							UpperCamelCase_:							List[Any]		=  cfg.INPUT.MAX_SIZE_TEST
							UpperCamelCase_:							List[str]		=  cfg.MODEL.DEVICE
							UpperCamelCase_:							List[str]		=  torch.tensor(cfg.MODEL.PIXEL_STD					).to(self.device					).view(len(cfg.MODEL.PIXEL_STD					)  ,		1  ,		1					)
							UpperCamelCase_:							List[Any]		=  torch.tensor(cfg.MODEL.PIXEL_MEAN					).to(self.device					).view(len(cfg.MODEL.PIXEL_STD					)  ,		1  ,		1					)
							UpperCamelCase_:							Tuple		=  lambda snake_case_					: (x - self.pixel_mean) / self.pixel_std

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

											if self.size_divisibility > 0:
															raise NotImplementedError()
											# pad
											UpperCamelCase_:							Optional[int]		=  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 				A__    (  lowerCamelCase   ,			lowerCamelCase  )     ->		Dict:
				boxes[:, 0::2] *= scale_yx[:, 1]
				boxes[:, 1::2] *= scale_yx[:, 0]
				return boxes







def 				A__    (  lowerCamelCase   ,			lowerCamelCase  )     ->		Tuple:
				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 				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)






