"""Create a hard multiple choice subset of the ImageNet validation split based on human accuracy annotation data."""

from typing import Dict
import argparse
import json
import pickle
import numpy as np
import scipy.io


def get_imagenet_labels() -> Dict[str, str]:
    """Return ground truth wnids."""
    with open("ILSVRC2012_devkit_t12/data/ILSVRC2012_validation_ground_truth.txt") as fp:
        ilsvrc_idxs = [int(line.strip()) for line in fp]
    ilsvrc_metadata = scipy.io.loadmat("ILSVRC2012_devkit_t12/data/meta.mat", simplify_cells=True)
    ilsvrc_idx2wnid = {
        synset['ILSVRC2012_ID']: synset['WNID']
        for synset in ilsvrc_metadata['synsets']
    }
    return {
        f"ILSVRC2012_val_{img_id:0>8}.JPEG": ilsvrc_idx2wnid[idx]
        for img_id, idx in enumerate(ilsvrc_idxs, start=1)
    }


def get_close_examples(num_choices: int = 4, seed: int | np.random.Generator = None):
    """
    Construct easy MCQA examples using ImageNet hierarchy.
    """
    rng = np.random.default_rng(seed)
    with open("imagenet_wnids.txt") as fp:
        wnids = [line.strip() for line in fp]
    gt_labels = get_imagenet_labels()
    
    with open("human_accuracy_annotations.pkl", "rb") as fp:
        annotation_data = pickle.load(fp)
    examples = []
    for imgname, annot in annotation_data['initial_annots'].items():
        if imgname.startswith("ILSVRC2012"):
            if gt_labels[imgname] not in annot.get('wrong', []):
                # Fill in answer choices from hierarchy
                other_choices = list(set(wnids) - {gt_labels[imgname]})
                wrong_choices = list(rng.choice(other_choices, size=num_choices - 1, replace=False))
                examples.append({
                    'image': imgname,
                    'choices': [gt_labels[imgname]] + list(wrong_choices),
                    'correct_answer': gt_labels[imgname]
                })
    return examples


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--n-choices", "-n", type=int, required=True)
    parser.add_argument("--output", "-o", type=str, default=None)
    parser.add_argument("--seed", type=int, default=42)
    args = parser.parse_args()
    dataset = get_close_examples(args.n_choices, seed=args.seed)
    print(f"No. of examples: {len(dataset)}")
    if args.output:
        with open(args.output, "w") as fp:
            json.dump(dataset, fp, indent=2)
        print(f"Saved to '{args.output}'")