
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__  :
			def __init__(self			: Optional[Any] ,    snake_case_			: List[Any] ,    snake_case_			: Union[str, Any]=sys.maxsize       ):
				__a			:  List[str]		    =  '''bilinear'''
				__a			:  List[str]		    =  max_size
				__a			:  List[Any]		    =  short_edge_length







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

					if img.dtype == np.uinta:
						__a			:  Optional[Any]		    =  Image.fromarray(snake_case_       )
						__a			:  str		    =  pil_image.resize((neww, newh) ,    PILImageResampling.BILINEAR       )
						__a			:  Optional[Any]		    =  np.asarray(snake_case_       )
					else:
						__a			:  Tuple		    =  img.permute(2 ,    0 ,    1       ).unsqueeze(0       )  # 3, 0, 1)  # hw(c) -> nchw
						__a			:  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__  :
			def __init__(self			: int ,    snake_case_			: List[Any]       ):
				__a			:  Dict		    =  ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST] ,    cfg.INPUT.MAX_SIZE_TEST       )
				__a			:  Union[str, Any]		    =  cfg.INPUT.FORMAT
				__a			:  Dict		    =  cfg.SIZE_DIVISIBILITY
				__a			:  List[Any]		    =  cfg.PAD_VALUE
				__a			:  Dict		    =  cfg.INPUT.MAX_SIZE_TEST
				__a			:  List[Any]		    =  cfg.MODEL.DEVICE
				__a			:  Tuple		    =  torch.tensor(cfg.MODEL.PIXEL_STD       ).to(self.device       ).view(len(cfg.MODEL.PIXEL_STD       ) ,    1 ,    1       )
				__a			:  int		    =  torch.tensor(cfg.MODEL.PIXEL_MEAN       ).to(self.device       ).view(len(cfg.MODEL.PIXEL_STD       ) ,    1 ,    1       )
				__a			:  Union[str, Any]		    =  lambda snake_case_       : (x - self.pixel_mean) / self.pixel_std
			def 				lowerCAmelCase      (self			: Union[str, Any] ,    snake_case_			: Dict       ):
				__a			:  List[str]		    =  tuple(max(snake_case_       ) for s in zip(*[img.shape for img in images]       )       )
				__a			:  List[Any]		    =  [im.shape[-2:] for im in images]
				__a			:  Optional[int]		    =  [
				    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			: List[str] ,    snake_case_			: int ,    snake_case_			: Dict=False       ):
				with torch.no_grad():
					if not isinstance(snake_case_ ,    snake_case_       ):
						__a			:  str		    =  [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
					__a			:  Optional[Any]		    =  torch.tensor([im.shape[:2] for im in images]       )
					__a			:  int		    =  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
					__a			:  str		    =  [self.normalizer(snake_case_       ) for x in images]
					# now pad them to do the following operations
					__a			:  List[Any]		    =  self.pad(snake_case_       )
					# Normalize

					if self.size_divisibility > 0:
						raise NotImplementedError()
					# pad
					__a			:  str		    =  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        __UpperCamelCase					(    lowerCAmelCase__							:    int  ,       lowerCAmelCase__							:    Union[str, Any]  ):
	boxes[:, 0::2] *= scale_yx[:, 1]
	boxes[:, 1::2] *= scale_yx[:, 0]
	return boxes




def        __UpperCamelCase					(    lowerCAmelCase__							:    List[str]  ,       lowerCAmelCase__							:    Tuple[int, int]  ):
	assert torch.isfinite(lowerCAmelCase__  ).all(), "Box tensor contains infinite or NaN!"
	__a			:  Optional[Any]		    =  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

# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
lowercase__          =10




def        __UpperCamelCase					(    lowerCAmelCase__							:    int  ,       lowerCAmelCase__							:    int  ,       lowerCAmelCase__							:    list[int]  ,       lowerCAmelCase__							:    int  ):
 for i in range(lowerCAmelCase__  ,       lowerCAmelCase__  ):
  if array[i] == target:
   return i
 return -1




def        __UpperCamelCase					(    lowerCAmelCase__							:    list[int]  ,       lowerCAmelCase__							:    int  ):
 __a			:  List[Any]		    =  0
 __a			:  Union[str, Any]		    =  len(lowerCAmelCase__  )
 while left <= right:
  if right - left < precision:
   return lin_search(lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  )

  __a			:  str		    =  (left + right) // 3 + 1
  __a			:  List[Any]		    =  2 * (left + right) // 3 + 1

  if array[one_third] == target:
   return one_third
  elif array[two_third] == target:
   return two_third

  elif target < array[one_third]:
   __a			:  List[str]		    =  one_third - 1
  elif array[two_third] < target:
   __a			:  List[str]		    =  two_third + 1

  else:
   __a			:  Dict		    =  one_third + 1
   __a			:  Any		    =  two_third - 1
 else:
  return -1




def        __UpperCamelCase					(    lowerCAmelCase__							:    int  ,       lowerCAmelCase__							:    int  ,       lowerCAmelCase__							:    list[int]  ,       lowerCAmelCase__							:    int  ):
 if left < right:
  if right - left < precision:
   return lin_search(lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  )
  __a			:  Union[str, Any]		    =  (left + right) // 3 + 1
  __a			:  Optional[Any]		    =  2 * (left + right) // 3 + 1

  if array[one_third] == target:
   return one_third
  elif array[two_third] == target:
   return two_third

  elif target < array[one_third]:
   return rec_ternary_search(lowerCAmelCase__  ,       one_third - 1  ,       lowerCAmelCase__  ,       lowerCAmelCase__  )
  elif array[two_third] < target:
   return rec_ternary_search(two_third + 1  ,       lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  )
  else:
   return rec_ternary_search(one_third + 1  ,       two_third - 1  ,       lowerCAmelCase__  ,       lowerCAmelCase__  )
 else:
  return -1


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      lowercase__          =input('Enter numbers separated by comma:\n').strip()
      lowercase__          =[int(item.strip()) for item in user_input.split(',')]
      assert collection == sorted(collection), F"List must be ordered.\n{collection}."
      lowercase__          =int(input('Enter the number to be found in the list:\n').strip())
      lowercase__          =ite_ternary_search(collection, target)
      lowercase__          =rec_ternary_search(0, len(collection) - 1, collection, target)
      if resulta != -1:
            print(F"""Iterative search: {target} found at positions: {resulta}""")
            print(F"""Recursive search: {target} found at positions: {resulta}""")
      else:
            print('Not found')
