"""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 						__UpperCAmelCase    :







			'''simple docstring'''





			def __init__(			self   ,      snake_case_   ,      snake_case_=sys.maxsize							):







										'''simple docstring'''


										A__       :     List[Any]								=			"""bilinear"""
										A__       :     str								=			max_size
										A__       :     Tuple								=			short_edge_length





			def __call__(			self   ,      snake_case_							):







										'''simple docstring'''


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

																	if img.dtype == np.uinta:
																								A__       :     Any								=			Image.fromarray(snake_case_							)
																								A__       :     int								=			pil_image.resize((neww, newh)   ,      PILImageResampling.BILINEAR							)
																								A__       :     int								=			np.asarray(snake_case_							)
																	else:
																								A__       :     List[str]								=			img.permute(2   ,      0   ,      1							).unsqueeze(0							)  # 3, 0, 1)  # hw(c) -> nchw
																								A__       :     List[str]								=			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    :







			'''simple docstring'''





			def __init__(			self   ,      snake_case_							):







										'''simple docstring'''


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





			def 		lowerCamelCase					(			self   ,      snake_case_							):







										'''simple docstring'''


										A__       :     Tuple								=			tuple(max(snake_case_							) for s in zip(*[img.shape for img in images]							)							)
										A__       :     Union[str, Any]								=			[im.shape[-2:] for im in images]
										A__       :     Union[str, Any]								=			[
										    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   ,      snake_case_   ,      snake_case_=False							):







										'''simple docstring'''


										with torch.no_grad():
																	if not isinstance(snake_case_   ,      snake_case_							):
																								A__       :     List[Any]								=			[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[int]								=			torch.tensor([im.shape[:2] for im in images]							)
																	A__       :     Optional[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__       :     Union[str, Any]								=			[self.normalizer(snake_case_							) for x in images]
																	# now pad them to do the following operations
																	A__				,							A__       :     Optional[int]								=			self.pad(snake_case_							)
																	# Normalize

																	if self.size_divisibility > 0:
																								raise NotImplementedError()
																	# pad
																	A__       :     List[Any]								=			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  _A(		lowerCAmelCase			,  lowerCAmelCase							):
							boxes[:, 0::2] *= scale_yx[:, 1]
							boxes[:, 1::2] *= scale_yx[:, 0]
							return boxes



def  _A(		lowerCAmelCase			,  lowerCAmelCase							):
							assert torch.isfinite(lowerCAmelCase							).all(), "Box tensor contains infinite or NaN!"
							A__				,							A__       :     List[str]								=			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							)






"""simple docstring"""



import math
from collections.abc import Iterator
from itertools import takewhile

def  _A(		lowerCAmelCase							):

							if 1 < number < 4:
														# 2 and 3 are primes
														return True
							elif number < 2 or number % 2 == 0 or number % 3 == 0:
														# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
														return False

							# All primes number are in format of 6k +/- 1
							for i in range(5			,  int(math.sqrt(lowerCAmelCase							) + 1							)			,  6							):
														if number % i == 0 or number % (i + 2) == 0:
																					return False
							return True

def  _A(		):
							A__       :     List[str]								=			2
							while True:
														if is_prime(lowerCAmelCase							):
																					yield num
														num += 1

def  _A(		lowerCAmelCase = 200_0000							):
							return sum(takewhile(lambda lowerCAmelCase							: x < n			,  prime_generator()							)							)


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





