Upload hf_job_add_dimensions.py with huggingface_hub

hf_job_add_dimensions.py

@@ -0,0 +1,132 @@
+#!/usr/bin/env python3
+"""
+Add height/width columns to nastol-images-full dataset
+"""
+import argparse
+import os
+import logging
+import sys
+from pathlib import Path
+
+logging.basicConfig(
+    level=logging.INFO,
+    format='[%(asctime)s] %(levelname)s: %(message)s',
+    datefmt='%Y-%m-%d %H:%M:%S',
+    stream=sys.stdout,
+    force=True
+)
+logger = logging.getLogger(__name__)
+
+from datasets import load_dataset
+from huggingface_hub import HfApi
+import pyarrow as pa
+import pyarrow.parquet as pq
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument('--input-dataset', type=str, default='vlordier/nastol-images-full')
+    ap.add_argument('--output-dataset', type=str, default='vlordier/nastol-images-full')
+    ap.add_argument('--split', type=str, default='train')
+    ap.add_argument('--shard-index', type=int, default=0)
+    ap.add_argument('--num-shards', type=int, default=1)
+    ap.add_argument('--batch-size', type=int, default=1000)
+    args = ap.parse_args()
+    
+    logger.info("="*60)
+    logger.info("Add Height/Width Columns to Dataset")
+    logger.info("="*60)
+    logger.info(f"Arguments: {vars(args)}")
+    
+    token = os.environ.get('HF_TOKEN')
+    api = HfApi(token=token)
+    
+    # Load dataset
+    logger.info(f"Loading {args.input_dataset}...")
+    ds = load_dataset(args.input_dataset, split=args.split, streaming=True)
+    
+    if args.num_shards > 1:
+        ds = ds.shard(num_shards=args.num_shards, index=args.shard_index)
+        logger.info(f"Processing shard {args.shard_index+1}/{args.num_shards}")
+    
+    # Process in batches
+    buffer = []
+    batch_count = 0
+    upload_count = 0
+    
+    def flush_buffer():
+        nonlocal buffer, upload_count
+        if not buffer:
+            return
+        
+        # Build columns
+        image_paths = [b['image_path'] for b in buffer]
+        images_bytes = [b['image'] for b in buffer]
+        heights = [b['height'] for b in buffer]
+        widths = [b['width'] for b in buffer]
+        
+        table = pa.table({
+            'image_path': image_paths,
+            'image': images_bytes,
+            'height': heights,
+            'width': widths
+        })
+        
+        # Write parquet
+        local_dir = Path('dimension_batches')
+        local_dir.mkdir(parents=True, exist_ok=True)
+        file_name = f"shard-{args.shard_index:03d}-batch-{upload_count:04d}.parquet"
+        local_path = local_dir / file_name
+        pq.write_table(table, local_path)
+        
+        # Upload
+        path_in_repo = f"data/{file_name}"
+        logger.info(f"Uploading batch {upload_count} with {len(buffer)} images -> {path_in_repo}")
+        try:
+            api.upload_file(
+                path_or_fileobj=str(local_path),
+                path_in_repo=path_in_repo,
+                repo_id=args.output_dataset,
+                repo_type='dataset',
+                token=token
+            )
+            logger.info("✓ Uploaded")
+        except Exception as e:
+            logger.error(f"Upload failed: {e}")
+        
+        buffer.clear()
+        upload_count += 1
+    
+    logger.info("Processing images...")
+    for idx, sample in enumerate(ds):
+        image = sample['image']
+        image_path = sample.get('image_path', f'img_{idx:06d}')
+        
+        # Get dimensions
+        width, height = image.size
+        
+        # Store original image bytes
+        import io
+        buf = io.BytesIO()
+        image.save(buf, format='PNG')
+        image_bytes = buf.getvalue()
+        
+        buffer.append({
+            'image_path': image_path,
+            'image': image_bytes,
+            'height': height,
+            'width': width
+        })
+        
+        if len(buffer) >= args.batch_size:
+            flush_buffer()
+            batch_count += 1
+            logger.info(f"Processed {batch_count * args.batch_size} images")
+    
+    # Final flush
+    flush_buffer()
+    logger.info(f"✓ Completed shard {args.shard_index}: {batch_count * args.batch_size + len(buffer)} images")
+
+
+if __name__ == '__main__':
+    main()