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 							a				:







 """simple docstring"""






 def __init__(   self						,							lowerCAmelCase_						,							lowerCAmelCase_=sys.maxsize				)    ->   Union[str, Any]:
        _A	  =    'bilinear'
        _A	  =    max_size
        _A	  =    short_edge_length



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

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

        return img_augs



class 							a				:







 """simple docstring"""






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



 def 	UpperCAmelCase				(   self						,							lowerCAmelCase_				)    ->   str:
        _A	  =    tuple(max(UpperCamelCase_				) for s in zip(*[img.shape for img in images]				)				)
        _A	  =    [im.shape[-2:] for im in images]
        _A	  =    [
            nn.functional.pad(
                UpperCamelCase_						,							[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]						,							value=self.pad_value						,							)
            for size, im in zip(UpperCamelCase_						,							UpperCamelCase_				)
        ]

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



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

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






def 					snake_case    (			snake_case__     :Optional[int]  ,      snake_case__     :List[str])		->  Any:
       boxes[:, 0::2] *= scale_yx[:, 1]
       boxes[:, 1::2] *= scale_yx[:, 0]
       return boxes







def 					snake_case    (			snake_case__     :Dict  ,      snake_case__     :str)		->  Optional[int]:
       assert torch.isfinite(_snake_case).all(), "Box tensor contains infinite or NaN!"
       _A	  =    box_size
       tensor[:, 0].clamp_(min=0  ,      max=_snake_case)
       tensor[:, 1].clamp_(min=0  ,      max=_snake_case)
       tensor[:, 2].clamp_(min=0  ,      max=_snake_case)
       tensor[:, 3].clamp_(min=0  ,      max=_snake_case)






"""simple docstring"""



import random
def 					lowerCamelCase   (     _snake_case ):
    UpperCAmelCase__	:      Tuple     							=					num - 1
    UpperCAmelCase__	:      Dict     							=					0

    while s % 2 == 0:
        UpperCAmelCase__	:      Optional[int]     							=					s // 2
        t += 1

    for _ in range(5 ):
        UpperCAmelCase__	:      Optional[int]     							=					random.randrange(2    ,num - 1 )
        UpperCAmelCase__	:      List[str]     							=					pow(_snake_case    ,_snake_case    ,_snake_case )
        if v != 1:
            UpperCAmelCase__	:      List[Any]     							=					0
            while v != (num - 1):
                if i == t - 1:
                    return False
                else:
                    UpperCAmelCase__	:      Optional[Any]     							=					i + 1
                    UpperCAmelCase__	:      Any     							=					(v**2) % num
    return True
def 					lowerCamelCase   (     _snake_case ):
    if num < 2:
        return False

    UpperCAmelCase__	:      str     							=					[
        2,
        3,
        5,
        7,
        11,
        13,
        17,
        19,
        23,
        29,
        31,
        37,
        41,
        43,
        47,
        53,
        59,
        61,
        67,
        71,
        73,
        79,
        83,
        89,
        97,
        101,
        103,
        107,
        109,
        113,
        127,
        131,
        137,
        139,
        149,
        151,
        157,
        163,
        167,
        173,
        179,
        181,
        191,
        193,
        197,
        199,
        211,
        223,
        227,
        229,
        233,
        239,
        241,
        251,
        257,
        263,
        269,
        271,
        277,
        281,
        283,
        293,
        307,
        311,
        313,
        317,
        331,
        337,
        347,
        349,
        353,
        359,
        367,
        373,
        379,
        383,
        389,
        397,
        401,
        409,
        419,
        421,
        431,
        433,
        439,
        443,
        449,
        457,
        461,
        463,
        467,
        479,
        487,
        491,
        499,
        503,
        509,
        521,
        523,
        541,
        547,
        557,
        563,
        569,
        571,
        577,
        587,
        593,
        599,
        601,
        607,
        613,
        617,
        619,
        631,
        641,
        643,
        647,
        653,
        659,
        661,
        673,
        677,
        683,
        691,
        701,
        709,
        719,
        727,
        733,
        739,
        743,
        751,
        757,
        761,
        769,
        773,
        787,
        797,
        809,
        811,
        821,
        823,
        827,
        829,
        839,
        853,
        857,
        859,
        863,
        877,
        881,
        883,
        887,
        907,
        911,
        919,
        929,
        937,
        941,
        947,
        953,
        967,
        971,
        977,
        983,
        991,
        997,
    ]

    if num in low_primes:
        return True

    for prime in low_primes:
        if (num % prime) == 0:
            return False

    return rabin_miller(_snake_case )
def 					lowerCamelCase   (     _snake_case = 1024 ):
    while True:
        UpperCAmelCase__	:      List[Any]     							=					random.randrange(2 ** (keysize - 1)    ,2 ** (keysize) )
        if is_prime_low_num(_snake_case ):
            return num


if __name__ == "__main__":
    UpperCamelCase__          =	generate_large_prime()
    print(('Prime number:', num))
    print(('is_prime_low_num:', is_prime_low_num(num)))

