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#!/usr/bin/env python3
"""
WrinkleBrane Dataset Creation Script
Usage:
python create_wrinklebrane_dataset.py --token YOUR_HF_TOKEN --repo-id YOUR_REPO_NAME
This script creates a comprehensive dataset for WrinkleBrane associative memory
training and uploads it to HuggingFace Hub with proper metadata and organization.
"""
import argparse
import sys
from pathlib import Path
# Add the current directory to path
sys.path.insert(0, str(Path(__file__).parent))
from wrinklebrane_dataset_builder import create_wrinklebrane_dataset
def main():
parser = argparse.ArgumentParser(description="Create WrinkleBrane Dataset")
parser.add_argument("--token", required=True, help="HuggingFace access token")
parser.add_argument("--repo-id", default="WrinkleBrane", help="Dataset repository ID")
parser.add_argument("--private", action="store_true", default=True, help="Make dataset private")
parser.add_argument("--samples", type=int, default=20000, help="Total number of samples")
args = parser.parse_args()
print("🧠 Starting WrinkleBrane Dataset Creation")
print(f"Repository: {args.repo_id}")
print(f"Private: {args.private}")
print(f"Target samples: {args.samples}")
print("-" * 50)
try:
dataset_url = create_wrinklebrane_dataset(
hf_token=args.token,
repo_id=args.repo_id
)
print("\n" + "=" * 50)
print("🎉 SUCCESS! Dataset created and uploaded")
print(f"📍 URL: {dataset_url}")
print("=" * 50)
print("\n📋 Next Steps:")
print("1. View your dataset on HuggingFace Hub")
print("2. Test loading with: `from datasets import load_dataset`")
print("3. Integrate with WrinkleBrane training pipeline")
print("4. Monitor associative memory performance metrics")
except Exception as e:
print(f"\n❌ ERROR: {e}")
print("Please check your token and repository permissions.")
sys.exit(1)
if __name__ == "__main__":
main() |