


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						,       UpperCAmelCase						,       UpperCAmelCase=sys.maxsize  )     ->       int:






								'''simple docstring'''






								lowercase_			 = "bilinear"
								lowercase_			 = max_size
								lowercase_			 = short_edge_length




							def __call__(  self						,       UpperCAmelCase  )     ->       Dict:






								'''simple docstring'''






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

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

								return img_augs







class     __lowerCamelCase     :


							"""simple docstring"""

							def __init__(  self						,       UpperCAmelCase  )     ->       Dict:






								'''simple docstring'''






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




							def 				A__      (  self						,       UpperCAmelCase  )     ->       str:






								'''simple docstring'''






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

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




							def __call__(  self						,       UpperCAmelCase						,       UpperCAmelCase=False  )     ->       Optional[int]:






								'''simple docstring'''






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

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







def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		List[Any]  ,			__lowerCamelCase:		int  ):







	'''simple docstring'''



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




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		List[str]  ,			__lowerCamelCase:		Tuple[int, int]  ):







	'''simple docstring'''



	assert torch.isfinite(__lowerCamelCase  ).all(), "Box tensor contains infinite or NaN!"
	lowercase_ ,	lowercase_			 = 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  )





from __future__ import annotations

from random import randrange




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		list  ):







	'''simple docstring'''



	if len(__lowerCamelCase  ) < 2:
		return collection
	lowercase_			 = randrange(len(__lowerCamelCase  )  )  # Use random element as pivot
	lowercase_			 = collection[pivot_index]
	lowercase_			 = []  # All elements greater than pivot
	lowercase_			 = []  # All elements less than or equal to pivot

	for element in collection[:pivot_index]:
		(greater if element > pivot else lesser).append(__lowerCamelCase  )

	for element in collection[pivot_index + 1 :]:
		(greater if element > pivot else lesser).append(__lowerCamelCase  )

	return [*quick_sort(__lowerCamelCase  ), pivot, *quick_sort(__lowerCamelCase  )]


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__       			=				input("""Enter numbers separated by a comma:\n""").strip()
							SCREAMING_SNAKE_CASE__       			=				[int(item) for item in user_input.split(""",""")]
							print(quick_sort(unsorted))

