# Modified from [ViLD](https://github.com/tensorflow/tpu/tree/master/models/official/detection/projects/vild)
# TinyCLIP version for generating text embeddings
import numpy as np
import torch
import torch.nn.functional as F
from tqdm import tqdm
import sys
import os

# Add src to path
sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..', 'src'))
import open_clip


def article(name):
    return 'an' if name[0] in 'aeiou' else 'a'


def processed_name(name, rm_dot=False):
    # _ for lvis
    # / for obj365
    res = name.replace('_', ' ').replace('/', ' or ').lower()
    if rm_dot:
        res = res.rstrip('.')
    return res


single_template = [
    'a photo of {article} {}.'
]

multiple_templates = [
    'There is {article} {} in the scene.',
    'There is the {} in the scene.',
    'a photo of {article} {} in the scene.',
    'a photo of the {} in the scene.',
    'a photo of one {} in the scene.',

    'itap of {article} {}.',
    'itap of my {}.',  # itap: I took a picture of
    'itap of the {}.',
    'a photo of {article} {}.',
    'a photo of my {}.',
    'a photo of the {}.',
    'a photo of one {}.',
    'a photo of many {}.',

    'a good photo of {article} {}.',
    'a good photo of the {}.',
    'a bad photo of {article} {}.',
    'a bad photo of the {}.',
    'a photo of a nice {}.',
    'a photo of the nice {}.',
    'a photo of a cool {}.',
    'a photo of the cool {}.',
    'a photo of a weird {}.',
    'a photo of the weird {}.',

    'a photo of a small {}.',
    'a photo of the small {}.',
    'a photo of a large {}.',
    'a photo of the large {}.',

    'a photo of a clean {}.',
    'a photo of the clean {}.',
    'a photo of a dirty {}.',
    'a photo of the dirty {}.',

    'a bright photo of {article} {}.',
    'a bright photo of the {}.',
    'a dark photo of {article} {}.',
    'a dark photo of the {}.',

    'a photo of a hard to see {}.',
    'a photo of the hard to see {}.',
    'a low resolution photo of {article} {}.',
    'a low resolution photo of the {}.',
    'a cropped photo of {article} {}.',
    'a cropped photo of the {}.',
    'a close-up photo of {article} {}.',
    'a close-up photo of the {}.',
    'a jpeg corrupted photo of {article} {}.',
    'a jpeg corrupted photo of the {}.',
    'a blurry photo of {article} {}.',
    'a blurry photo of the {}.',
    'a pixelated photo of {article} {}.',
    'a pixelated photo of the {}.',

    'a black and white photo of the {}.',
    'a black and white photo of {article} {}.',

    'a plastic {}.',
    'the plastic {}.',

    'a toy {}.',
    'the toy {}.',
    'a plushie {}.',
    'the plushie {}.',
    'a cartoon {}.',
    'the cartoon {}.',

    'an embroidered {}.',
    'the embroidered {}.',

    'a painting of the {}.',
    'a painting of a {}.',
]


def build_text_embedding_lvis(categories, model, tokenizer):
    """Build text embeddings for categories using TinyCLIP"""
    templates = multiple_templates

    with torch.no_grad():
        all_text_embeddings = []
        for category in tqdm(categories, desc="Generating embeddings"):
            texts = [
                template.format(
                    processed_name(category, rm_dot=True), article=article(category)
                )
                for template in templates
            ]
            texts = [
                "This is " + text if text.startswith("a") or text.startswith("the") else text
                for text in texts
            ]
            # Tokenize texts - tokenizer returns tensor (same as inference.py)
            text_tokens = tokenizer(texts).cuda()

            text_embeddings = model.encode_text(text_tokens, normalized=True)
            # Average over all templates
            text_embedding = text_embeddings.mean(dim=0)
            text_embedding /= text_embedding.norm()

            all_text_embeddings.append(text_embedding)
        all_text_embeddings = torch.stack(all_text_embeddings, dim=0)

    return all_text_embeddings


import argparse
import json

if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='Generate text embeddings for TinyCLIP models')
    parser.add_argument('--model_version', 
                       default='TinyCLIP-auto-ViT-63M-32-Text-31M',
                       help='TinyCLIP model name')
    parser.add_argument('--ann', 
                       default='dataset/coco/annotations/panoptic_val2017.json',
                       help='Path to COCO panoptic annotation file')
    parser.add_argument('--out_path', 
                       default=None,
                       help='Output path for embeddings (auto-generated if not specified)')
    parser.add_argument('--pretrained', 
                       default='',
                       help='Path to pretrained checkpoint or pretrained tag')
    parser.add_argument('--cache_dir', 
                       default='checkpoints',
                       help='Cache directory for pretrained models')

    args = parser.parse_args()

    # Auto-generate output path if not specified
    if args.out_path is None:
        model_name_safe = args.model_version.replace('/', '-').replace(' ', '_')
        args.out_path = f'metadata/coco_panoptic_clip_hand_craft_{model_name_safe}.npy'

    print(f'Loading TinyCLIP model: {args.model_version}')
    print(f'Pretrained: {args.pretrained if args.pretrained else "None"}')
    
    # Create model and tokenizer using tiny_clip module directly
    from open_clip import tiny_clip
    model, _, _ = tiny_clip.create_model_and_transforms(
        args.model_version, 
        pretrained=args.pretrained if args.pretrained else None, 
        cache_dir=args.cache_dir
    )
    tokenizer = tiny_clip.get_tokenizer(args.model_version)
    model.cuda()
    model.eval()
    print('Loading', args.ann)
    data = json.load(open(args.ann, 'r'))
    cat_names = [x['name'] for x in 
                 sorted(data['categories'], key=lambda x: x['id'])]
    
    print(f'Found {len(cat_names)} categories')
    print(f'Generating embeddings...')
    
    text_embeddings = build_text_embedding_lvis(cat_names, model, tokenizer)
    
    print(f'Saving embeddings to {args.out_path}')
    np.save(args.out_path, text_embeddings.cpu().numpy())
    print(f'Done! Embeddings shape: {text_embeddings.shape}')