






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  _lowercase   :
      def __init__(     self				,			a				,			a=sys.maxsize   ):
       snake_case__  :    int							       ="""bilinear"""
       snake_case__  :    Optional[Any]							       =max_size
       snake_case__  :    List[str]							       =short_edge_length



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

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

       return img_augs







class  _lowercase   :
      def __init__(     self				,			a   ):
       snake_case__  :    Tuple							       =ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]				,			cfg.INPUT.MAX_SIZE_TEST   )
       snake_case__  :    str							       =cfg.INPUT.FORMAT
       snake_case__  :    Optional[Any]							       =cfg.SIZE_DIVISIBILITY
       snake_case__  :    Tuple							       =cfg.PAD_VALUE
       snake_case__  :    Any							       =cfg.INPUT.MAX_SIZE_TEST
       snake_case__  :    Union[str, Any]							       =cfg.MODEL.DEVICE
       snake_case__  :    Any							       =torch.tensor(cfg.MODEL.PIXEL_STD   ).to(self.device   ).view(len(cfg.MODEL.PIXEL_STD   )				,			1				,			1   )
       snake_case__  :    List[Any]							       =torch.tensor(cfg.MODEL.PIXEL_MEAN   ).to(self.device   ).view(len(cfg.MODEL.PIXEL_STD   )				,			1				,			1   )
       snake_case__  :    Optional[int]							       =lambda a   : (x - self.pixel_mean) / self.pixel_std
      def 						lowercase__					(     self				,			a   ):
       snake_case__  :    int							       =tuple(max(a   ) for s in zip(*[img.shape for img in images]   )   )
       snake_case__  :    Any							       =[im.shape[-2:] for im in images]
       snake_case__  :    List[Any]							       =[
           nn.functional.pad(
               a				,			[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]				,			value=self.pad_value				,			)
           for size, im in zip(a				,			a   )
       ]

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



      def __call__(     self				,			a				,			a=False   ):
       with torch.no_grad():
        if not isinstance(a				,			a   ):
         snake_case__  :    str							       =[images]
        if single_image:
         assert len(a   ) == 1
        for i in range(len(a   )   ):
         if isinstance(images[i]				,			torch.Tensor   ):
          images.insert(a				,			images.pop(a   ).to(self.device   ).float()   )
         elif not isinstance(images[i]				,			torch.Tensor   ):
          images.insert(
              a				,			torch.as_tensor(img_tensorize(images.pop(a   )				,			input_format=self.input_format   )   )
              .to(self.device   )
              .float()				,			)
            # resize smallest edge
        snake_case__  :    Union[str, Any]							       =torch.tensor([im.shape[:2] for im in images]   )
        snake_case__  :    str							       =self.aug(a   )
        # 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__  :    Optional[int]							       =[self.normalizer(a   ) for x in images]
        # now pad them to do the following operations
        snake_case__  :    Tuple							       =self.pad(a   )
        # Normalize

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







def 			A__							(      _a  :					Optional[Any]		,  _a  :					List[str]	):





 '''simple docstring'''







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







def 			A__							(      _a  :					List[str]		,  _a  :					Tuple[int, int]	):





 '''simple docstring'''







 assert torch.isfinite(_a	).all(), "Box tensor contains infinite or NaN!"
 snake_case__  :    str							       =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	)













import math







def 			A__							(      _a  :					list		,  _a  :					int = 0		,  _a  :					int = 0	):





 '''simple docstring'''







 snake_case__  :    Tuple							       =end or len(_a	)
 for i in range(_a		,  _a	):
  snake_case__  :    int							       =i
  snake_case__  :    int							       =array[i]
  while temp_index != start and temp_index_value < array[temp_index - 1]:
   snake_case__  :    Dict							       =array[temp_index - 1]
   temp_index -= 1
  snake_case__  :    Dict							       =temp_index_value
 return array







def 			A__							(      _a  :					list		,  _a  :					int		,  _a  :					int	):  # Max Heap





 '''simple docstring'''







 snake_case__  :    Tuple							       =index
 snake_case__  :    Dict							       =2 * index + 1  # Left Node
 snake_case__  :    Union[str, Any]							       =2 * index + 2  # Right Node

 if left_index < heap_size and array[largest] < array[left_index]:
  snake_case__  :    Dict							       =left_index

 if right_index < heap_size and array[largest] < array[right_index]:
  snake_case__  :    List[Any]							       =right_index

 if largest != index:
  snake_case__       ,   snake_case__  :    List[Any]							       =array[largest], array[index]
  heapify(_a		,  _a		,  _a	)







def 			A__							(      _a  :					list	):





 '''simple docstring'''







 snake_case__  :    List[str]							       =len(_a	)

 for i in range(n // 2		,  -1		,  -1	):
  heapify(_a		,  _a		,  _a	)

 for i in range(n - 1		,  0		,  -1	):
  snake_case__       ,   snake_case__  :    Tuple							       =array[0], array[i]
  heapify(_a		,  0		,  _a	)

 return array







def 			A__							(      _a  :					list		,  _a  :					int		,  _a  :					int		,  _a  :					int	):





 '''simple docstring'''







 if (array[first_index] > array[middle_index]) != (
     array[first_index] > array[last_index]
 ):
  return array[first_index]
 elif (array[middle_index] > array[first_index]) != (
     array[middle_index] > array[last_index]
 ):
  return array[middle_index]
 else:
  return array[last_index]







def 			A__							(      _a  :					list		,  _a  :					int		,  _a  :					int		,  _a  :					int	):





 '''simple docstring'''







 snake_case__  :    Dict							       =low
 snake_case__  :    Union[str, Any]							       =high
 while True:
  while array[i] < pivot:
   i += 1
  j -= 1
  while pivot < array[j]:
   j -= 1
  if i >= j:
   return i
  snake_case__       ,   snake_case__  :    List[Any]							       =array[j], array[i]
  i += 1







def 			A__							(      _a  :					list	):





 '''simple docstring'''







 if len(_a	) == 0:
  return array
 snake_case__  :    Optional[int]							       =2 * math.ceil(math.loga(len(_a	)	)	)
 snake_case__  :    Tuple							       =16
 return intro_sort(_a		,  0		,  len(_a	)		,  _a		,  _a	)







def 			A__							(      _a  :					list		,  _a  :					int		,  _a  :					int		,  _a  :					int		,  _a  :					int	):





 '''simple docstring'''







 while end - start > size_threshold:
  if max_depth == 0:
   return heap_sort(_a	)
  max_depth -= 1
  snake_case__  :    Dict							       =median_of_a(_a		,  _a		,  start + ((end - start) // 2) + 1		,  end - 1	)
  snake_case__  :    List[str]							       =partition(_a		,  _a		,  _a		,  _a	)
  intro_sort(_a		,  _a		,  _a		,  _a		,  _a	)
  snake_case__  :    Union[str, Any]							       =p
 return insertion_sort(_a		,  _a		,  _a	)


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       __lowerCamelCase	:		Optional[int]										=   input("""Enter numbers separated by a comma : """).strip()
       __lowerCamelCase	:		Dict										=   [float(item) for item in user_input.split(""",""")]
       print(sort(unsorted))





