
'''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				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE=sys.maxsize		):


											"""simple docstring"""





											lowercase_	:     str													=       '''bilinear'''
											lowercase_	:     int													=       max_size
											lowercase_	:     int													=       short_edge_length






					def __call__(   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Tuple													=       []
											for img in imgs:
																	lowercase_						, lowercase_	:     int													=       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_	:     str													=       size * 1.0 / min(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)
																	if h < w:
																							lowercase_						, lowercase_	:     Tuple													=       size, scale * w
																	else:
																							lowercase_						, lowercase_	:     str													=       scale * h, size
																	if max(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		) > self.max_size:
																							lowercase_	:     Union[str, Any]													=       self.max_size * 1.0 / max(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)
																							lowercase_	:     Optional[Any]													=       newh * scale
																							lowercase_	:     Union[str, Any]													=       neww * scale
																	lowercase_	:     Tuple													=       int(neww + 0.5		)
																	lowercase_	:     List[str]													=       int(newh + 0.5		)

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

											return img_augs







class  lowerCAmelCase__   :






					def __init__(   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





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






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





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

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






					def __call__(   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE=False		):


											"""simple docstring"""





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

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







def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: Union[str, Any]      ,      __SCREAMING_SNAKE_CASE					: Dict							):



						"""simple docstring"""





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




def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: int      ,      __SCREAMING_SNAKE_CASE					: Tuple[int, int]							):



						"""simple docstring"""





						assert torch.isfinite(__SCREAMING_SNAKE_CASE							).all(), "Box tensor contains infinite or NaN!"
						lowercase_						, lowercase_	:     Union[str, Any]													=       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 math import isqrt, loga
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: int							):



						"""simple docstring"""





						lowercase_	:     Any													=       [True] * max_number
						for i in range(2      ,      isqrt(max_number - 1							) + 1							):
												if is_prime[i]:
																		for j in range(i**2      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE							):
																								lowercase_	:     Optional[Any]													=       False

						return [i for i in range(2      ,      __SCREAMING_SNAKE_CASE							) if is_prime[i]]
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: int = 800800      ,      __SCREAMING_SNAKE_CASE					: int = 800800							):



						"""simple docstring"""





						lowercase_	:     Union[str, Any]													=       degree * loga(__SCREAMING_SNAKE_CASE							)
						lowercase_	:     Any													=       int(__SCREAMING_SNAKE_CASE							)
						lowercase_	:     Optional[Any]													=       calculate_prime_numbers(__SCREAMING_SNAKE_CASE							)

						lowercase_	:     Optional[Any]													=       0
						lowercase_	:     List[Any]													=       0
						lowercase_	:     Union[str, Any]													=       len(__SCREAMING_SNAKE_CASE							) - 1
						while left < right:
												while (
												    prime_numbers[right] * loga(prime_numbers[left]							)
												    + prime_numbers[left] * loga(prime_numbers[right]							)
												    > upper_bound
												):
																		right -= 1
												hybrid_integers_count += right - left
												left += 1

						return hybrid_integers_count


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



