




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			,						lowerCamelCase__			,						lowerCamelCase__=sys.maxsize							)    ->      List[str]:



							'''simple docstring'''
							__lowerCamelCase		       =    'bilinear'
							__lowerCamelCase		       =    max_size
							__lowerCamelCase		       =    short_edge_length






			def __call__(      self			,						lowerCamelCase__							)    ->      Optional[int]:



							'''simple docstring'''
							__lowerCamelCase		       =    []
							for img in imgs:
											__lowerCamelCase       ,						__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(lowerCamelCase__			,						lowerCamelCase__							)
											if h < w:
															__lowerCamelCase       ,						__lowerCamelCase		       =    size, scale * w
											else:
															__lowerCamelCase       ,						__lowerCamelCase		       =    scale * h, size
											if max(lowerCamelCase__			,						lowerCamelCase__							) > self.max_size:
															__lowerCamelCase		       =    self.max_size * 1.0 / max(lowerCamelCase__			,						lowerCamelCase__							)
															__lowerCamelCase		       =    newh * scale
															__lowerCamelCase		       =    neww * scale
											__lowerCamelCase		       =    int(neww + 0.5							)
											__lowerCamelCase		       =    int(newh + 0.5							)

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

							return img_augs







class     __lowerCAmelCase :

			"""simple docstring"""







			def __init__(      self			,						lowerCamelCase__							)    ->      Optional[int]:



							'''simple docstring'''
							__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       lowercase_    (      self			,						lowerCamelCase__							)    ->      Tuple:



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

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






			def __call__(      self			,						lowerCamelCase__			,						lowerCamelCase__=False							)    ->      List[Any]:



							'''simple docstring'''
							with torch.no_grad():
											if not isinstance(lowerCamelCase__			,						lowerCamelCase__							):
															__lowerCamelCase		       =    [images]
											if single_image:
															assert len(lowerCamelCase__							) == 1
											for i in range(len(lowerCamelCase__							)							):
															if isinstance(images[i]			,						torch.Tensor							):
																			images.insert(lowerCamelCase__			,						images.pop(lowerCamelCase__							).to(self.device							).float()							)
															elif not isinstance(images[i]			,						torch.Tensor							):
																			images.insert(
																			    lowerCamelCase__			,						torch.as_tensor(img_tensorize(images.pop(lowerCamelCase__							)			,						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(lowerCamelCase__							)
											# 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(lowerCamelCase__							) for x in images]
											# now pad them to do the following operations
											__lowerCamelCase       ,						__lowerCamelCase		       =    self.pad(lowerCamelCase__							)
											# Normalize

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







def 			lowerCamelCase_		(       UpperCamelCase__				:      List[str] ,     UpperCamelCase__				:      str      )     ->       List[Any]:





				"""simple docstring"""


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







def 			lowerCamelCase_		(       UpperCamelCase__				:      List[str] ,     UpperCamelCase__				:      Tuple[int, int]      )     ->       Dict:





				"""simple docstring"""


				assert torch.isfinite(UpperCamelCase__      ).all(), "Box tensor contains infinite or NaN!"
				__lowerCamelCase       ,						__lowerCamelCase		       =    box_size
				tensor[:, 0].clamp_(min=0 ,     max=UpperCamelCase__      )
				tensor[:, 1].clamp_(min=0 ,     max=UpperCamelCase__      )
				tensor[:, 2].clamp_(min=0 ,     max=UpperCamelCase__      )
				tensor[:, 3].clamp_(min=0 ,     max=UpperCamelCase__      )








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.
__A        =    10







def 			lowerCamelCase_		(       UpperCamelCase__				:      int ,     UpperCamelCase__				:      int ,     UpperCamelCase__				:      list[int] ,     UpperCamelCase__				:      int      )     ->       int:





				"""simple docstring"""


				for i in range(UpperCamelCase__ ,     UpperCamelCase__      ):
								if array[i] == target:
												return i
				return -1







def 			lowerCamelCase_		(       UpperCamelCase__				:      list[int] ,     UpperCamelCase__				:      int      )     ->       int:





				"""simple docstring"""


				__lowerCamelCase		       =    0
				__lowerCamelCase		       =    len(UpperCamelCase__      )
				while left <= right:
								if right - left < precision:
												return lin_search(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__      )

								__lowerCamelCase		       =    (left + right) // 3 + 1
								__lowerCamelCase		       =    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]:
												__lowerCamelCase		       =    one_third - 1
								elif array[two_third] < target:
												__lowerCamelCase		       =    two_third + 1

								else:
												__lowerCamelCase		       =    one_third + 1
												__lowerCamelCase		       =    two_third - 1
				else:
								return -1







def 			lowerCamelCase_		(       UpperCamelCase__				:      int ,     UpperCamelCase__				:      int ,     UpperCamelCase__				:      list[int] ,     UpperCamelCase__				:      int      )     ->       int:





				"""simple docstring"""


				if left < right:
								if right - left < precision:
												return lin_search(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__      )
								__lowerCamelCase		       =    (left + right) // 3 + 1
								__lowerCamelCase		       =    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(UpperCamelCase__ ,     one_third - 1 ,     UpperCamelCase__ ,     UpperCamelCase__      )
								elif array[two_third] < target:
												return rec_ternary_search(two_third + 1 ,     UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__      )
								else:
												return rec_ternary_search(one_third + 1 ,     two_third - 1 ,     UpperCamelCase__ ,     UpperCamelCase__      )
				else:
								return -1


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					__A        =    input("Enter numbers separated by comma:\n").strip()
					__A        =    [int(item.strip()) for item in user_input.split(",")]
					assert collection == sorted(collection), f"List must be ordered.\n{collection}."
					__A        =    int(input("Enter the number to be found in the list:\n").strip())
					__A        =    ite_ternary_search(collection, target)
					__A        =    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")


