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import gc
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from glob import glob
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from io import BytesIO
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from pathlib import Path
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import clip
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import pandas as pd
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import torch
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import ujson
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import webdataset as wds
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from PIL import Image
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from sentence_transformers import SentenceTransformer
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from torchvision.transforms import (CenterCrop, Compose, InterpolationMode,
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Normalize, Resize, ToTensor)
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from tqdm import tqdm
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torch.multiprocessing.set_sharing_strategy('file_system')
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def load_image(jpg):
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return jpg, Image.open(BytesIO(jpg))
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def load_json(json):
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return ujson.loads(json)
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load_preprocess_map = {
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'jpg': load_image,
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'json': load_json,
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}
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def convert_image_to_rgb(im):
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return im.convert("RGB")
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image_transforms = Compose([
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Resize(224, interpolation=InterpolationMode.BICUBIC),
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CenterCrop(224),
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convert_image_to_rgb,
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ToTensor(),
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Normalize((0.48145466, 0.4578275, 0.40821073),
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(0.26862954, 0.26130258, 0.27577711)),
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])
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def image_preprocess(jpgs):
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jpg_orig, im = jpgs
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im = image_transforms(im)
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return jpg_orig, im
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texts_to_check = [
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'page_title',
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'section_title',
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'hierarchical_section_title',
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'caption',
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'caption_attribution_description',
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'caption_alt_text_description',
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'context_page_description',
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'context_section_description'
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]
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def meta_preprocess(meta: dict):
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return {
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'captions': [meta[text] for text in texts_to_check if text in meta and meta[text]],
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'orig': meta
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}
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mclip_preprocess_map = {
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'jpg': image_preprocess,
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'json': meta_preprocess
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}
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def log(msg):
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print(msg, end='\n\n\n\n')
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return msg
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def func(wds_dataset_str, device=None, batch_size=4, **kwargs):
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nocap = 0
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if device is None:
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device = 'cuda' if torch.cuda.is_available() else 'cpu'
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print('Loading models:')
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model, _ = clip.load('ViT-B/32', device=device, jit=False)
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mclip = SentenceTransformer(
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'sentence-transformers/clip-ViT-B-32-multilingual-v1', device=device)
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cosine_similarity = torch.nn.CosineSimilarity(dim=1, eps=1e-6)
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print('Finished loading models')
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ds = wds.WebDataset(wds_dataset_str, shardshuffle=False).map_dict(
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**load_preprocess_map).map_dict(**mclip_preprocess_map).to_tuple('jpg', 'json').batched(batch_size)
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dl = wds.WebLoader(ds, batch_size=None, shuffle=False, **kwargs)
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writer = wds.ShardWriter('%05d.tar', 10000)
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for i, batch in enumerate(tqdm(dl)):
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try:
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imss, metas = batch
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orig_jpgs, ims = zip(*imss)
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ims = torch.stack(ims)
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captionss = [meta['captions'] for meta in metas]
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with torch.no_grad():
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image_features = torch.unbind(
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model.encode_image(ims.to(device)).float())
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text_featuress = [mclip.encode(captions, convert_to_tensor=True).to(
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device).float() for captions in captionss]
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similarities = [
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cosine_similarity(image_feature.repeat(
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len(text_features), 1), text_features).tolist()
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for image_feature, text_features in zip(image_features, text_featuress)
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]
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captionss = [[cap for cap, sim in zip(
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captions, similarity) if sim > 0.26] for captions, similarity in zip(captionss, similarities)]
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for orig_jpg, captions, meta in zip(orig_jpgs, captionss, metas):
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if len(captions) == 0:
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nocap += 1
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tqdm.write(f'No captions: {nocap}')
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continue
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sample = {
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'__key__': f'{writer.count:08}',
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'jpg': orig_jpg,
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'txt': ''.join(captions),
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'json': ujson.dumps(meta['orig'])
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}
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writer.write(sample)
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if i % 25 == 0:
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gc.collect()
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torch.cuda.empty_cache()
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except Exception as e:
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print(f'Error: {e}')
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raise e
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writer.close()
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