
"""simple docstring"""







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 							__magic_name__     :





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





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

																			if img.dtype == np.uinta:
																										__snake_case     			=       Image.fromarray(snake_case_    )
																										__snake_case     			=       pil_image.resize((neww, newh)							,						PILImageResampling.BILINEAR    )
																										__snake_case     			=       np.asarray(snake_case_    )
																			else:
																										__snake_case     			=       img.permute(2							,						0							,						1    ).unsqueeze(0    )  # 3, 0, 1)  # hw(c) -> nchw
																										__snake_case     			=       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 							__magic_name__     :





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





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

																			if self.size_divisibility > 0:
																										raise NotImplementedError()
																			# pad
																			__snake_case     			=       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   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     List[str]:







							"""simple docstring"""


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



def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     Tuple:







							"""simple docstring"""


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


"""simple docstring"""







from __future__ import annotations

from itertools import permutations
from random import randint
from timeit import repeat
def   __UpperCamelCase   (					)  ->     tuple[list[int], int]:







							"""simple docstring"""


							__snake_case     			=       [randint(-10_00							,    10_00 ) for i in range(10 )]
							__snake_case     			=       randint(-50_00							,    50_00 )
							return (arr, r)


_SCREAMING_SNAKE_CASE	       =      make_dataset()
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     tuple[int, ...]:







							"""simple docstring"""


							for triplet in permutations(SCREAMING_SNAKE_CASE							,    3 ):
														if sum(SCREAMING_SNAKE_CASE ) == target:
																					return tuple(sorted(SCREAMING_SNAKE_CASE ) )
							return (0, 0, 0)
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     tuple[int, int, int]:







							"""simple docstring"""


							arr.sort()
							__snake_case     			=       len(SCREAMING_SNAKE_CASE )
							for i in range(n - 1 ):
														__snake_case			,   __snake_case     			=       i + 1, n - 1
														while left < right:
																					if arr[i] + arr[left] + arr[right] == target:
																												return (arr[i], arr[left], arr[right])
																					elif arr[i] + arr[left] + arr[right] < target:
																												left += 1
																					elif arr[i] + arr[left] + arr[right] > target:
																												right -= 1
							return (0, 0, 0)
def   __UpperCamelCase   (					)  ->     tuple[float, float]:







							"""simple docstring"""


							__snake_case     			=       "\nfrom __main__ import dataset, triplet_sum1, triplet_sum2\n"
							__snake_case     			=       "\ntriplet_sum1(*dataset)\n"
							__snake_case     			=       "\ntriplet_sum2(*dataset)\n"
							__snake_case     			=       repeat(setup=SCREAMING_SNAKE_CASE							,    stmt=SCREAMING_SNAKE_CASE							,    repeat=5							,    number=1_00_00 )
							__snake_case     			=       repeat(setup=SCREAMING_SNAKE_CASE							,    stmt=SCREAMING_SNAKE_CASE							,    repeat=5							,    number=1_00_00 )
							return (min(SCREAMING_SNAKE_CASE ), min(SCREAMING_SNAKE_CASE ))


if __name__ == "__main__":
		from doctest import testmod

		testmod()
		_SCREAMING_SNAKE_CASE	       =      solution_times()
		print(F"""The time for naive implementation is {times[0]}.""")
		print(F"""The time for optimized implementation is {times[1]}.""")
