#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
SimNICT Dataset Batch Downloader
Download complete SimNICT datasets from Internet Archive

IMPORTANT: This downloader provides access to 8 out of 10 original SimNICT datasets.
AutoPET and HECKTOR22 are excluded from public release due to licensing restrictions.

Usage:
    python download_simnict.py --datasets AMOS COVID_19_NY_SBU --output_dir ./data
    python download_simnict.py --all --output_dir ./data
    python download_simnict.py --list  # Show available datasets

Author: TAMP Research Group
Version: 1.0
"""

import os
import sys
import argparse
import time
from pathlib import Path
from typing import List, Dict, Optional
import logging

try:
    import internetarchive as ia
except ImportError:
    print("❌ Error: internetarchive library not found")
    print("Please install it using: pip install internetarchive")
    sys.exit(1)

# =============================================================================
# Dataset Configuration
# =============================================================================

SIMNICT_DATASETS = {
    "AMOS": {
        "identifier": "simnict-amos",
        "description": "Abdominal multi-organ segmentation dataset",
        "volumes": 500,
        "files": 504,
        "size_gb": "~22 GB"
    },
    "COVID_19_NY_SBU": {
        "identifier": "simnict-covid-19-ny-sbu", 
        "description": "COVID-19 NY-SBU chest CT dataset",
        "volumes": 459,
        "files": 463,
        "size_gb": "~30 GB"
    },
    "CT_Images_COVID19": {
        "identifier": "simnict-ct-images-in-covid-19",
        "description": "CT Images in COVID-19 dataset", 
        "volumes": 771,
        "files": 775,
        "size_gb": "~13 GB"
    },
    "CT_COLONOGRAPHY": {
        "identifier": "simnict-ct-colonography",
        "description": "CT colonography screening dataset",
        "volumes": 1730,
        "files": 1734, 
        "size_gb": "~271 GB"
    },
    "LNDb": {
        "identifier": "simnict-lndb",
        "description": "Lung nodule database",
        "volumes": 294,
        "files": 298,
        "size_gb": "~34 GB"
    },
    "LUNA": {
        "identifier": "simnict-luna",
        "description": "Lung nodule analysis dataset", 
        "volumes": 888,
        "files": 892,
        "size_gb": "~63 GB"
    },
    "MELA": {
        "identifier": "simnict-mela",
        "description": "Melanoma detection dataset",
        "volumes": 1100, 
        "files": 1104,
        "size_gb": "~147 GB"
    },
    "STOIC": {
        "identifier": "simnict-stoic",
        "description": "COVID-19 AI challenge dataset",
        "volumes": 2000,
        "files": 2004,
        "size_gb": "~243 GB"
    }
}

# =============================================================================
# Logging Configuration  
# =============================================================================

logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(levelname)s - %(message)s',
    handlers=[
        logging.FileHandler('simnict_download.log'),
        logging.StreamHandler()
    ]
)
logger = logging.getLogger(__name__)

# =============================================================================
# SimNICT Downloader Class
# =============================================================================

class SimNICTDownloader:
    def __init__(self, output_dir: str = "./simnict_data", 
                 max_retries: int = 3, chunk_size: int = 1024*1024):
        """
        Initialize SimNICT downloader
        
        Args:
            output_dir: Directory to save downloaded datasets
            max_retries: Maximum retry attempts for failed downloads
            chunk_size: Download chunk size in bytes (default 1MB)
        """
        self.output_dir = Path(output_dir)
        self.max_retries = max_retries
        self.chunk_size = chunk_size
        
        # Create output directory
        self.output_dir.mkdir(parents=True, exist_ok=True)
        logger.info(f"📁 Output directory: {self.output_dir.absolute()}")
    
    def list_available_datasets(self) -> None:
        """Display all available SimNICT datasets"""
        print("\n" + "="*80)
        print("📋 Available SimNICT Datasets (8 out of 10 original datasets)")
        print("="*80)
        print("ℹ️  Note: AutoPET and HECKTOR22 excluded due to licensing restrictions")
        print("="*80)
        
        total_size = 0
        total_volumes = 0
        
        for name, info in SIMNICT_DATASETS.items():
            print(f"\n🔹 {name}")
            print(f"   📝 Description: {info['description']}")
            print(f"   📊 Volumes: {info['volumes']:,}")  
            print(f"   📄 Files: {info['files']:,}")
            print(f"   💾 Size: {info['size_gb']}")
            print(f"   🏷️  ID: {info['identifier']}")
            print(f"   🔗 URL: https://archive.org/details/{info['identifier']}")
            
            total_volumes += info['volumes']
            # Extract numeric size for total calculation
            size_str = info['size_gb'].replace('~', '').replace(' GB', '')
            try:
                total_size += float(size_str)
            except:
                pass
        
        print(f"\n📈 Total Statistics:")
        print(f"   🗂️  Datasets: {len(SIMNICT_DATASETS)}")
        print(f"   📊 Total Volumes: {total_volumes:,}")
        print(f"   💾 Total Size: ~{total_size:.0f} GB")
        print("="*80)
    
    def check_dataset_exists(self, identifier: str) -> bool:
        """Check if dataset exists on Internet Archive"""
        try:
            item = ia.get_item(identifier)
            return item.exists
        except Exception as e:
            logger.error(f"Error checking dataset {identifier}: {e}")
            return False
    
    def get_dataset_files(self, identifier: str) -> List[str]:
        """Get list of files in a dataset"""
        try:
            item = ia.get_item(identifier)
            if not item.exists:
                return []
            
            files = []
            for file_obj in item.files:
                if isinstance(file_obj, dict) and 'name' in file_obj:
                    # Only include .nii.gz files (skip metadata)
                    filename = file_obj['name']
                    if filename.endswith('.nii.gz'):
                        files.append(filename)
            
            return sorted(files)
        except Exception as e:
            logger.error(f"Error getting files for {identifier}: {e}")
            return []
    
    def download_dataset(self, dataset_name: str, 
                        resume: bool = True, 
                        verify_checksum: bool = True) -> bool:
        """
        Download a specific SimNICT dataset
        
        Args:
            dataset_name: Name of dataset to download
            resume: Whether to resume partial downloads
            verify_checksum: Whether to verify file checksums
            
        Returns:
            True if download successful, False otherwise
        """
        if dataset_name not in SIMNICT_DATASETS:
            logger.error(f"❌ Unknown dataset: {dataset_name}")
            logger.info(f"Available datasets: {list(SIMNICT_DATASETS.keys())}")
            return False
        
        dataset_info = SIMNICT_DATASETS[dataset_name]
        identifier = dataset_info['identifier']
        
        logger.info(f"\n{'='*60}")
        logger.info(f"📤 Starting download: {dataset_name}")
        logger.info(f"🏷️  Identifier: {identifier}")
        logger.info(f"📊 Expected volumes: {dataset_info['volumes']}")
        logger.info(f"💾 Estimated size: {dataset_info['size_gb']}")
        logger.info(f"{'='*60}")
        
        # Check if dataset exists
        if not self.check_dataset_exists(identifier):
            logger.error(f"❌ Dataset not found on Internet Archive: {identifier}")
            return False
        
        # Create dataset directory
        dataset_dir = self.output_dir / dataset_name
        dataset_dir.mkdir(exist_ok=True)
        
        # Get files to download
        files_to_download = self.get_dataset_files(identifier)
        if not files_to_download:
            logger.error(f"❌ No files found for dataset: {dataset_name}")
            return False
        
        logger.info(f"📋 Found {len(files_to_download)} files to download")
        
        # Check existing files if resuming
        existing_files = set()
        if resume:
            for file_path in dataset_dir.iterdir():
                if file_path.is_file() and file_path.suffix == '.gz':
                    existing_files.add(file_path.name)
            
            if existing_files:
                logger.info(f"📂 Found {len(existing_files)} existing files (resume mode)")
        
        # Download files
        successful_downloads = 0
        failed_downloads = 0
        skipped_files = 0
        
        for i, filename in enumerate(files_to_download, 1):
            file_path = dataset_dir / filename
            
            # Skip if file exists and resuming
            if resume and filename in existing_files:
                logger.info(f"⏭️  Skipping existing file [{i}/{len(files_to_download)}]: {filename}")
                skipped_files += 1
                continue
            
            logger.info(f"📥 Downloading [{i}/{len(files_to_download)}]: {filename}")
            
            success = self._download_file_with_retry(
                identifier, filename, file_path, verify_checksum
            )
            
            if success:
                successful_downloads += 1
                logger.info(f"✅ Downloaded: {filename}")
            else:
                failed_downloads += 1
                logger.error(f"❌ Failed: {filename}")
            
            # Brief pause between downloads
            time.sleep(0.5)
        
        # Summary
        logger.info(f"\n📊 Download Summary for {dataset_name}:")
        logger.info(f"   ✅ Successful: {successful_downloads}")
        logger.info(f"   ⏭️  Skipped: {skipped_files}")  
        logger.info(f"   ❌ Failed: {failed_downloads}")
        logger.info(f"   📁 Location: {dataset_dir.absolute()}")
        
        return failed_downloads == 0
    
    def _download_file_with_retry(self, identifier: str, filename: str, 
                                 file_path: Path, verify_checksum: bool) -> bool:
        """Download single file with retry logic"""
        for attempt in range(self.max_retries):
            try:
                # Use internetarchive library to download
                item = ia.get_item(identifier)
                
                # Find the file object
                file_obj = None
                for f in item.files:
                    if isinstance(f, dict) and f.get('name') == filename:
                        file_obj = f
                        break
                
                if not file_obj:
                    logger.error(f"File not found in item: {filename}")
                    return False
                
                # Download the file
                success = item.download(
                    files=[filename],
                    destdir=file_path.parent,
                    verify=verify_checksum,
                    verbose=False,
                    retries=1  # Handle retries at our level
                )
                
                if success and file_path.exists():
                    return True
                else:
                    raise Exception("Download failed or file not created")
                    
            except Exception as e:
                logger.warning(f"⚠️  Attempt {attempt + 1}/{self.max_retries} failed for {filename}: {e}")
                
                if attempt < self.max_retries - 1:
                    wait_time = (attempt + 1) * 2  # Exponential backoff
                    logger.info(f"🔄 Retrying in {wait_time} seconds...")
                    time.sleep(wait_time)
                else:
                    logger.error(f"💔 All {self.max_retries} attempts failed for {filename}")
                    return False
        
        return False
    
    def download_multiple_datasets(self, dataset_names: List[str], 
                                 resume: bool = True) -> Dict[str, bool]:
        """
        Download multiple SimNICT datasets
        
        Args:
            dataset_names: List of dataset names to download
            resume: Whether to resume partial downloads
            
        Returns:
            Dictionary mapping dataset names to success status
        """
        if not dataset_names:
            logger.error("❌ No datasets specified")
            return {}
        
        logger.info(f"\n🚀 Starting batch download of {len(dataset_names)} datasets")
        logger.info(f"📋 Datasets: {', '.join(dataset_names)}")
        
        results = {}
        successful = 0
        
        for i, dataset_name in enumerate(dataset_names, 1):
            logger.info(f"\n{'🔄' * 20} Dataset {i}/{len(dataset_names)} {'🔄' * 20}")
            
            success = self.download_dataset(dataset_name, resume=resume)
            results[dataset_name] = success
            
            if success:
                successful += 1
                logger.info(f"🎉 Successfully downloaded: {dataset_name}")
            else:
                logger.error(f"💔 Failed to download: {dataset_name}")
        
        # Final summary
        logger.info(f"\n{'=' * 80}")
        logger.info(f"🏁 Batch Download Complete")
        logger.info(f"{'=' * 80}")
        logger.info(f"✅ Successful: {successful}/{len(dataset_names)}")
        logger.info(f"❌ Failed: {len(dataset_names) - successful}")
        
        for dataset_name, success in results.items():
            status = "✅" if success else "❌"
            logger.info(f"   {status} {dataset_name}")
        
        return results
    
    def validate_downloads(self, dataset_names: List[str]) -> Dict[str, Dict]:
        """
        Validate downloaded datasets
        
        Args:
            dataset_names: List of dataset names to validate
            
        Returns:
            Validation results for each dataset
        """
        logger.info(f"\n🔍 Validating {len(dataset_names)} datasets...")
        
        results = {}
        
        for dataset_name in dataset_names:
            if dataset_name not in SIMNICT_DATASETS:
                continue
                
            dataset_dir = self.output_dir / dataset_name
            expected_info = SIMNICT_DATASETS[dataset_name]
            
            if not dataset_dir.exists():
                results[dataset_name] = {
                    "status": "missing",
                    "message": "Dataset directory not found"
                }
                continue
            
            # Count downloaded files
            nii_files = list(dataset_dir.glob("*.nii.gz"))
            file_count = len(nii_files)
            
            expected_files = expected_info['files']
            completion_rate = (file_count / expected_files) * 100
            
            if file_count == expected_files:
                status = "complete"
                message = f"All {file_count} files downloaded successfully"
            elif file_count > 0:
                status = "partial"
                message = f"Partial download: {file_count}/{expected_files} files ({completion_rate:.1f}%)"
            else:
                status = "empty"
                message = "No files found"
            
            results[dataset_name] = {
                "status": status,
                "files_found": file_count,
                "files_expected": expected_files,
                "completion_rate": completion_rate,
                "message": message
            }
            
            logger.info(f"📊 {dataset_name}: {message}")
        
        return results

# =============================================================================
# Command Line Interface
# =============================================================================

def main():
    parser = argparse.ArgumentParser(
        description="Download SimNICT datasets from Internet Archive",
        formatter_class=argparse.RawDescriptionHelpFormatter,
        epilog="""
Examples:
  # List available datasets
  python download_simnict.py --list
  
  # Download specific datasets
  python download_simnict.py --datasets AMOS COVID_19_NY_SBU --output_dir ./data
  
  # Download all datasets  
  python download_simnict.py --all --output_dir ./data
  
  # Resume interrupted downloads
  python download_simnict.py --datasets STOIC --resume --output_dir ./data
  
  # Validate existing downloads
  python download_simnict.py --validate AMOS LUNA --output_dir ./data
        """
    )
    
    parser.add_argument(
        "--datasets", nargs="+", metavar="DATASET",
        help="List of datasets to download (e.g., AMOS LUNA STOIC)"
    )
    
    parser.add_argument(
        "--all", action="store_true",
        help="Download all available SimNICT datasets"
    )
    
    parser.add_argument(
        "--list", action="store_true", 
        help="List available datasets and exit"
    )
    
    parser.add_argument(
        "--validate", nargs="*", metavar="DATASET",
        help="Validate downloaded datasets"
    )
    
    parser.add_argument(
        "--output_dir", default="./simnict_data",
        help="Output directory for downloads (default: ./simnict_data)"
    )
    
    parser.add_argument(
        "--resume", action="store_true",
        help="Resume interrupted downloads (skip existing files)"
    )
    
    parser.add_argument(
        "--no-checksum", action="store_true",
        help="Skip checksum verification (faster but less safe)"  
    )
    
    parser.add_argument(
        "--max-retries", type=int, default=3,
        help="Maximum retry attempts for failed downloads (default: 3)"
    )
    
    args = parser.parse_args()
    
    # Handle list command
    if args.list:
        downloader = SimNICTDownloader()
        downloader.list_available_datasets()
        return
    
    # Handle validation
    if args.validate is not None:
        datasets_to_validate = args.validate if args.validate else list(SIMNICT_DATASETS.keys())
        downloader = SimNICTDownloader(args.output_dir)
        results = downloader.validate_downloads(datasets_to_validate)
        return
    
    # Determine datasets to download
    if args.all:
        datasets = list(SIMNICT_DATASETS.keys())
    elif args.datasets:
        datasets = args.datasets
    else:
        parser.error("Must specify --datasets, --all, --list, or --validate")
    
    # Validate dataset names
    invalid_datasets = [d for d in datasets if d not in SIMNICT_DATASETS]
    if invalid_datasets:
        logger.error(f"❌ Invalid dataset names: {invalid_datasets}")
        logger.info(f"Available datasets: {list(SIMNICT_DATASETS.keys())}")
        return
    
    # Initialize downloader
    downloader = SimNICTDownloader(
        output_dir=args.output_dir,
        max_retries=args.max_retries
    )
    
    # Show download plan
    logger.info(f"\n📋 Download Plan:")
    total_size = 0
    for dataset in datasets:
        info = SIMNICT_DATASETS[dataset]
        logger.info(f"   🔹 {dataset}: {info['size_gb']} ({info['volumes']} volumes)")
        # Extract size for total calculation
        try:
            size_num = float(info['size_gb'].replace('~', '').replace(' GB', ''))
            total_size += size_num
        except:
            pass
    
    logger.info(f"   💾 Total estimated size: ~{total_size:.0f} GB")
    
    # Confirm download
    try:
        confirm = input(f"\nProceed with download? (y/N): ").strip().lower()
        if confirm != 'y':
            logger.info("❌ Download cancelled by user")
            return
    except KeyboardInterrupt:
        logger.info("\n❌ Download cancelled by user")
        return
    
    # Start downloads
    start_time = time.time()
    results = downloader.download_multiple_datasets(datasets, resume=args.resume)
    end_time = time.time()
    
    # Final report
    elapsed = end_time - start_time
    logger.info(f"\n⏱️  Total time: {elapsed:.1f} seconds ({elapsed/60:.1f} minutes)")
    
    # Validate downloads
    if any(results.values()):
        logger.info("\n🔍 Validating downloads...")
        validation_results = downloader.validate_downloads(list(results.keys()))

if __name__ == "__main__":
    main()