#!/usr/bin/env python3
"""
Sync BitTransformerLM repository to HuggingFace Hub for OS launch.
Uploads all cleaned documentation and code with proper commit message.
"""

import os
import logging
from pathlib import Path
from huggingface_hub import HfApi, login
from typing import Optional, List

# Setup logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)

def sync_repository_to_hf(
    repo_id: str = "WCNegentropy/BitTransformerLM",
    token: Optional[str] = None,
    commit_message: str = "🚀 OS Launch: Clean documentation and refined licensing"
):
    """
    Sync the entire cleaned BitTransformerLM repository to HuggingFace Hub.
    
    Args:
        repo_id: HuggingFace repository ID
        token: HF token (defaults to HF_TOKEN environment variable)
        commit_message: Commit message for the upload
    """
    
    # Get token from environment if not provided
    if token is None:
        token = os.environ.get('HF_TOKEN')
        if not token:
            logger.error("HF_TOKEN environment variable not set and no token provided")
            return False
    
    try:
        # Login to HuggingFace
        login(token=token)
        api = HfApi()
        logger.info("Successfully authenticated with HuggingFace Hub")
        
        # Get the repository root directory
        repo_root = Path(__file__).parent
        logger.info(f"Repository root: {repo_root}")
        
        # Files and directories to upload (excluding unnecessary files)
        include_patterns = [
            # Core code
            "bit_transformer/**/*.py",
            "tests/**/*.py",
            "*.py",  # Root level Python files
            
            # Documentation (cleaned)
            "README.md",
            "MODEL_CARD.md", 
            "RESEARCH_STATUS.md",
            "EMPIRICAL_VALIDATION.md",
            "OPEN_SOURCE_LAUNCH.md",
            "AGENTS.md",
            
            # Configuration
            "requirements.txt",
            "pyproject.toml",
            "Dockerfile",
            "start.sh",
            
            # License files (cleaned)
            "LICENSE/**/*.txt",
        ]
        
        # Files to exclude
        exclude_patterns = [
            "__pycache__/**",
            "*.pyc",
            ".git/**",
            ".pytest_cache/**",
            "weights/**",
            "checkpoints/**",
            "*.log",
            # Outdated documentation  
            "BitTransformerLM_full_assessment.md",
            "FORENSIC_*.md",
            "state_of_the_repo_audit.md",
            # Old upload script
            "upload_to_hf.py",
        ]
        
        # Get all files to upload
        files_to_upload = []
        for pattern in include_patterns:
            for file_path in repo_root.glob(pattern):
                if file_path.is_file():
                    # Check if file should be excluded
                    relative_path = file_path.relative_to(repo_root)
                    should_exclude = any(
                        relative_path.match(exclude) 
                        for exclude in exclude_patterns
                    )
                    if not should_exclude:
                        files_to_upload.append(file_path)
        
        logger.info(f"Found {len(files_to_upload)} files to upload")
        
        # Upload files in batches
        uploaded_count = 0
        for file_path in files_to_upload:
            try:
                relative_path = file_path.relative_to(repo_root)
                logger.info(f"Uploading: {relative_path}")
                
                api.upload_file(
                    path_or_fileobj=str(file_path),
                    path_in_repo=str(relative_path),
                    repo_id=repo_id,
                    repo_type="model",
                    commit_message=commit_message,
                    commit_description="""
This OS launch commit includes:

✅ **Cleaned Documentation**
- Removed inflated claims and marketing language
- Added honest research status and limitations
- Created professional model card and validation reports
- Streamlined licensing to AGPLv3 + commercial contact

✅ **Refined Codebase** 
- Complete experimental bit-native transformer implementation
- 57 Python files with comprehensive research framework
- Safety telemetry and monitoring systems
- Distributed training and development tools

✅ **Professional Standards**
- Empirical validation of all claims
- Clear experimental vs production distinctions  
- Rigorous research methodology requirements
- Community contribution framework

Ready for serious research evaluation and academic investigation.
                    """.strip()
                )
                
                uploaded_count += 1
                if uploaded_count % 10 == 0:
                    logger.info(f"Progress: {uploaded_count}/{len(files_to_upload)} files uploaded")
                    
            except Exception as e:
                logger.warning(f"Failed to upload {relative_path}: {e}")
                continue
        
        logger.info(f"✅ Successfully uploaded {uploaded_count}/{len(files_to_upload)} files")
        logger.info(f"🎉 Repository synced to: https://huggingface.co/{repo_id}")
        
        return True
        
    except Exception as e:
        logger.error(f"❌ Failed to sync repository: {e}")
        return False

def create_release_info():
    """Create a release information file for the OS launch."""
    release_info = """# BitTransformerLM v0.1.0 - Experimental Research Release

**Release Date:** August 2025  
**Status:** Open Source Research Implementation  
**License:** AGPLv3 + Commercial Licensing Available  

## What's Included

This release provides a complete experimental framework for bit-native language modeling research:

- **Core Architecture:** 57 Python files implementing bit-native transformer with reversible layers
- **Safety Systems:** Real-time K/C/S telemetry and monitoring
- **Research Tools:** Interactive dashboard, distributed training, comprehensive testing
- **Documentation:** Professional model card, research status, and validation reports

## Important Notes

⚠️ **Experimental Status:** This is research code requiring rigorous baseline validation  
⚠️ **Not Production Ready:** Needs extensive evaluation vs standard transformers  
⚠️ **Research Use Only:** Intended for academic investigation and experimentation  

## Licensing

- **Open Source:** AGPLv3 for research and open source use
- **Commercial:** Contact contact@wcnegentropy.com for commercial licensing

## Next Steps

The research community is invited to:
1. Conduct rigorous baseline comparisons vs standard transformers
2. Evaluate on established language modeling benchmarks  
3. Validate (or refute) claimed memory efficiency benefits
4. Share findings openly to advance the field

**Research responsibly. Validate rigorously. Share openly.**
"""
    
    release_file = Path(__file__).parent / "RELEASE_INFO.md"
    with open(release_file, 'w') as f:
        f.write(release_info)
    
    logger.info("Created RELEASE_INFO.md")
    return release_file

if __name__ == "__main__":
    # Create release info file
    create_release_info()
    
    # Sync to HuggingFace
    success = sync_repository_to_hf()
    
    if success:
        print("\n🚀 BitTransformerLM OS Launch Sync Complete!")
        print("📍 Repository: https://huggingface.co/WCNegentropy/BitTransformerLM")
        print("📧 Commercial inquiries: contact@wcnegentropy.com")
        print("\nReady for research community evaluation! 🧪✨")
    else:
        print("\n❌ Sync failed. Please check logs and try again.")