"""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  _lowerCamelCase			:







				def __init__( self    :       str			,					lowerCamelCase_    :       List[str]			,					lowerCamelCase_    :       str=sys.maxsize						):

					"""simple docstring"""






					_lowercase :    Optional[int]							=							'bilinear'
					_lowercase :    str							=							max_size
					_lowercase :    Optional[Any]							=							short_edge_length







				def __call__( self    :       Optional[Any]			,					lowerCamelCase_    :       Union[str, Any]						):

					"""simple docstring"""






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

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

					return img_augs



class  _lowerCamelCase			:







				def __init__( self    :       Union[str, Any]			,					lowerCamelCase_    :       int						):

					"""simple docstring"""






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







				def 							__UpperCAmelCase					( self    :       Any			,					lowerCamelCase_    :       str						):

					"""simple docstring"""






					_lowercase :    Optional[int]							=							tuple(max(_lowercase						) for s in zip(*[img.shape for img in images]						)						)
					_lowercase :    Union[str, Any]							=							[im.shape[-2:] for im in images]
					_lowercase :    Optional[Any]							=							[
					    nn.functional.pad(
					        _lowercase			,					[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]			,					value=self.pad_value			,					)
					    for size, im in zip(_lowercase			,					_lowercase						)
					]

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







				def __call__( self    :       Tuple			,					lowerCamelCase_    :       Optional[int]			,					lowerCamelCase_    :       Dict=False						):

					"""simple docstring"""






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

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



def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





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







def      __lowerCAmelCase(		__UpperCAmelCase     ,__UpperCAmelCase			):






	"""simple docstring"""





	assert torch.isfinite(UpperCamelCase__			).all(), "Box tensor contains infinite or NaN!"
	_lowercase				,      _lowercase :    List[Any]							=							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__			)


"""simple docstring"""







from __future__ import annotations


def      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





	_lowercase :    Any							=							[True] * limit
	_lowercase :    Union[str, Any]							=							False
	_lowercase :    Any							=							False
	_lowercase :    List[Any]							=							True

	for i in range(3     ,int(limit**0.5 + 1			)     ,2			):
		_lowercase :    Optional[Any]							=							i * 2
		while index < limit:
			_lowercase :    int							=							False
			_lowercase :    Tuple							=							index + i

	_lowercase :    Optional[Any]							=							[2]

	for i in range(3     ,__UpperCAmelCase     ,2			):
		if is_prime[i]:
			primes.append(__UpperCAmelCase			)

	return primes


def      __lowerCAmelCase(		__UpperCAmelCase = 1_000_000			):






	"""simple docstring"""





	_lowercase :    Optional[Any]							=							prime_sieve(__UpperCAmelCase			)
	_lowercase :    int							=							0
	_lowercase :    List[Any]							=							0

	for i in range(len(__UpperCAmelCase			)			):
		for j in range(i + length     ,len(__UpperCAmelCase			)			):
			_lowercase :    List[Any]							=							sum(primes[i:j]			)
			if sol >= ceiling:
				break

			if sol in primes:
				_lowercase :    Optional[int]							=							j - i
				_lowercase :    Tuple							=							sol

	return largest


if __name__ == "__main__":
						print(f"""{solution() = }""")



