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import numpy as np
import cv2
from albumentations.core.transforms_interface import DualTransform

class TopBiasedRandomCrop(DualTransform):
    """
    Randomly crops an image so that the cropped region's width is at least min_width and its height is at least min_height 
    of the original. The vertical (y) offset is biased by sampling from a Beta distribution with parameters beta_alpha and beta_beta.
    
    This transform also adjusts bounding boxes in [x_min, y_min, x_max, y_max] format.

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# delete local tag '12345'
git tag -d <tagname>
# delete remote tag '12345' (eg, GitHub version too)
git push origin :<tagname>

# create local tag
git tag <tagname>
# push tag to remote
git push origin <tagname>

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import numpy as np
from collections import namedtuple

topk_namedtuple = namedtuple('topk_namedtuple', ['values', 'indices'])


def topk(array: np.ndarray, k: int, largest: bool = True) -> topk_namedtuple:
    """Returns the k largest/smallest elements and corresponding indices 
    from an array-like input.

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import os

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
from torchvision import transforms

from torch.ao.quantization import get_default_qat_qconfig_mapping
from torch.ao.quantization.quantize_fx import prepare_qat_fx, convert_fx

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