"""
nnU-Net Integration Module
=========================

Integrates the state-of-the-art nnU-Net framework for medical image segmentation.
nnU-Net automatically configures itself to any dataset and consistently
outperforms other methods in medical imaging challenges.

Key Features:
- Self-configuring preprocessing and network architecture
- ResEnc (Residual Encoder) variants for improved performance
- Pretrained medical models
- Advanced data augmentation strategies
- Deep supervision with optimized loss functions

Author: Tumor Detection Segmentation Team
Reference: Isensee et al., Nature Methods, 2021
"""

import logging
import os
from pathlib import Path
from typing import Dict, List, Optional, Union

import numpy as np
import torch

# Configure logging
logger = logging.getLogger(__name__)


class NnUNetIntegration:
    """
    Integration wrapper for nnU-Net framework providing seamless
    integration with our tumor detection pipeline.
    """

    def __init__(self,
                 model_variant: str = "ResEnc_L",
                 use_pretrained: bool = True,
                 device: Union[str, torch.device] = "cuda"):
        """
        Initialize nnU-Net integration.

        Args:
            model_variant: Model variant to use (ResEnc_S, ResEnc_M, ResEnc_L, ResEnc_XL)
            use_pretrained: Whether to use pretrained weights
            device: Device for computation
        """
        self.model_variant = model_variant
        self.use_pretrained = use_pretrained
        self.device = torch.device(device) if isinstance(device, str) else device
        self.model = None
        self.predictor = None

        # nnU-Net configuration paths
        self.nnunet_raw = os.environ.get('nnUNet_raw', '/tmp/nnUNet_raw')
        self.nnunet_preprocessed = os.environ.get('nnUNet_preprocessed',
                                                 '/tmp/nnUNet_preprocessed')
        self.nnunet_results = os.environ.get('nnUNet_results',
                                           '/tmp/nnUNet_results')

        logger.info(f"Initialized nnU-Net integration with {model_variant}")

    def setup_environment(self) -> None:
        """Set up nnU-Net environment variables and directories."""
        # Set environment variables
        os.environ['nnUNet_raw'] = self.nnunet_raw
        os.environ['nnUNet_preprocessed'] = self.nnunet_preprocessed
        os.environ['nnUNet_results'] = self.nnunet_results

        # Create directories
        for path in [self.nnunet_raw, self.nnunet_preprocessed, self.nnunet_results]:
            Path(path).mkdir(parents=True, exist_ok=True)

        logger.info("nnU-Net environment configured successfully")

    def prepare_dataset(self,
                       images_dir: str,
                       masks_dir: str,
                       dataset_id: int = 999,
                       dataset_name: str = "TumorDetection") -> str:
        """
        Prepare dataset in nnU-Net format.

        Args:
            images_dir: Directory containing input images
            masks_dir: Directory containing segmentation masks
            dataset_id: Unique dataset identifier
            dataset_name: Name of the dataset

        Returns:
            Path to prepared dataset
        """
        # Create dataset directory structure
        dataset_path = Path(self.nnunet_raw) / f"Dataset{dataset_id:03d}_{dataset_name}"

        # Create subdirectories
        (dataset_path / "imagesTr").mkdir(parents=True, exist_ok=True)
        (dataset_path / "labelsTr").mkdir(parents=True, exist_ok=True)
        (dataset_path / "imagesTs").mkdir(parents=True, exist_ok=True)

        # Generate dataset.json with medical imaging metadata
        dataset_json = {
            "name": dataset_name,
            "description": "Tumor detection and segmentation dataset",
            "reference": "Tumor Detection Segmentation Project",
            "licence": "Internal Use",
            "release": "1.0",
            "tensorImageSize": "3D",
            "modality": {
                "0": "CT"  # Assuming CT scans, adjust as needed
            },
            "labels": {
                "background": 0,
                "tumor": 1
            },
            "numTraining": 0,  # Will be updated based on actual data
            "file_ending": ".nii.gz"
        }

        # Save dataset configuration
        import json
        with open(dataset_path / "dataset.json", "w") as f:
            json.dump(dataset_json, f, indent=2)

        logger.info(f"Prepared nnU-Net dataset: {dataset_path}")
        return str(dataset_path)

    def get_model_configurations(self) -> Dict[str, Dict]:
        """
        Get available nnU-Net model configurations with performance benchmarks.

        Returns:
            Dictionary of model configurations and their performance
        """
        configs = {
            "ResEnc_S": {
                "params_M": 1.7,
                "gpu_memory_GB": 8,
                "dice_score": 82.7,  # Average medical benchmark performance
                "description": "Small ResEnc variant, fast training"
            },
            "ResEnc_M": {
                "params_M": 9.1,
                "gpu_memory_GB": 12,
                "dice_score": 83.3,
                "description": "Medium ResEnc variant, balanced performance"
            },
            "ResEnc_L": {
                "params_M": 22.7,
                "gpu_memory_GB": 35,
                "dice_score": 83.4,
                "description": "Large ResEnc variant, high performance"
            },
            "ResEnc_XL": {
                "params_M": 36.6,
                "gpu_memory_GB": 66,
                "dice_score": 83.3,
                "description": "Extra Large ResEnc variant, maximum capacity"
            }
        }
        return configs

    def load_pretrained_model(self,
                             model_path: Optional[str] = None,
                             fold: Union[int, str] = "all") -> None:
        """
        Load pretrained nnU-Net model.

        Args:
            model_path: Path to trained model folder
            fold: Fold to use for prediction ("all" or specific fold number)
        """
        try:
            # Import nnU-Net components (will be available after pip install)
            from nnunetv2.inference.predict_from_raw_data import \
                nnUNetPredictor

            # Initialize predictor
            self.predictor = nnUNetPredictor(
                tile_step_size=0.5,
                use_gaussian=True,
                use_mirroring=True,
                perform_everything_on_device=True,
                device=self.device,
                verbose=False,
                verbose_preprocessing=False,
                allow_tqdm=True
            )

            if model_path and Path(model_path).exists():
                # Load from trained model folder
                self.predictor.initialize_from_trained_model_folder(
                    model_path,
                    use_folds=(fold,) if fold != "all" else None,
                    checkpoint_name='checkpoint_final.pth'
                )
                logger.info(f"Loaded pretrained nnU-Net model from {model_path}")
            else:
                logger.warning("No pretrained model path provided or path doesn't exist")

        except ImportError:
            logger.error("nnU-Net not installed. Install with: pip install nnunetv2")
            raise

    def predict(self,
                input_folder: str,
                output_folder: str,
                save_probabilities: bool = False) -> Dict[str, np.ndarray]:
        """
        Run nnU-Net inference on input images.

        Args:
            input_folder: Folder containing input images
            output_folder: Folder to save predictions
            save_probabilities: Whether to save probability maps

        Returns:
            Dictionary mapping filenames to prediction arrays
        """
        if self.predictor is None:
            raise ValueError("Model not loaded. Call load_pretrained_model() first.")

        # Create output directory
        Path(output_folder).mkdir(parents=True, exist_ok=True)

        # Run prediction
        self.predictor.predict_from_files(
            input_folder,
            output_folder,
            save_probabilities=save_probabilities,
            overwrite=False,
            num_processes_preprocessing=2,
            num_processes_segmentation_export=2,
            folder_with_segs_from_prev_stage=None,
            num_parts=1,
            part_id=0
        )

        # Load and return predictions
        predictions = {}
        for pred_file in Path(output_folder).glob("*.nii.gz"):
            # Load prediction (would need medical image reading library)
            predictions[pred_file.name] = pred_file  # Placeholder

        logger.info(f"nnU-Net predictions saved to {output_folder}")
        return predictions

    def train_model(self,
                   dataset_id: int,
                   configuration: str = "3d_fullres",
                   fold: int = 0,
                   trainer: str = "nnUNetTrainer",
                   plans: str = "nnUNetPlans") -> None:
        """
        Train nnU-Net model on prepared dataset.

        Args:
            dataset_id: Dataset ID to train on
            configuration: Training configuration
            fold: Cross-validation fold
            trainer: Trainer class to use
            plans: Plans identifier
        """
        try:
            # This would call nnU-Net training command
            train_command = (
                f"nnUNetv2_train {dataset_id} {configuration} {fold} "
                f"-tr {trainer} -p {plans}"
            )

            if self.model_variant.startswith("ResEnc"):
                # Use ResEnc planner for residual encoder variants
                train_command += " -pl nnUNetPlannerResEnc"

            logger.info(f"Training command: {train_command}")
            # os.system(train_command)  # Uncomment when ready to train

        except Exception as e:
            logger.error(f"Training failed: {e}")
            raise

    def evaluate_model(self,
                      test_folder: str,
                      reference_folder: str) -> Dict[str, float]:
        """
        Evaluate model performance using medical metrics.

        Args:
            test_folder: Folder with test predictions
            reference_folder: Folder with ground truth

        Returns:
            Dictionary of evaluation metrics
        """
        # This would implement medical evaluation metrics
        metrics = {
            "dice_score": 0.0,
            "hausdorff_distance": 0.0,
            "sensitivity": 0.0,
            "specificity": 0.0,
            "precision": 0.0,
            "recall": 0.0
        }

        # Implementation would go here
        logger.info("Model evaluation completed")
        return metrics

    def get_preprocessing_transforms(self) -> List:
        """Get nnU-Net's automatic preprocessing transforms."""
        # This would return nnU-Net's preprocessing pipeline
        transforms = [
            "Resampling to target spacing",
            "Intensity normalization",
            "Cropping to non-zero region",
            "Z-score normalization"
        ]
        return transforms
        return transforms
