"""
Ensemble Framework Module
=========================

Combines nnU-Net, MONAI, and Detectron2 models into a unified
ensemble framework for enhanced tumor detection and segmentation.

Key Features:
- Multi-model ensemble strategies
- Weighted voting and fusion
- Confidence-based selection
- Performance optimization
- Unified inference pipeline

Author: Tumor Detection Segmentation Team
Phase: 4 - Advanced External Integrations
"""

import logging
from typing import Any, Dict, List, Union

import numpy as np
import torch

from .detectron2_integration import Detectron2Integration
from .monai_integration import MonaiIntegration
from .nnunet_integration import NnUNetIntegration

# Configure logging
logger = logging.getLogger(__name__)


class EnsembleFramework:
    """
    Unified ensemble framework combining multiple state-of-the-art
    medical imaging models for enhanced tumor detection.
    """

    def __init__(self,
                 device: Union[str, torch.device] = "cuda",
                 ensemble_strategy: str = "weighted_voting"):
        """
        Initialize ensemble framework.

        Args:
            device: Device for computation
            ensemble_strategy: Strategy for combining models
        """
        self.device = torch.device(device) if isinstance(device, str) else device
        self.ensemble_strategy = ensemble_strategy

        # Initialize integrations
        self.nnunet = None
        self.monai = None
        self.detectron2 = None

        # Ensemble weights and configurations
        self.model_weights = {
            "nnunet": 0.4,    # High weight for medical specialization
            "monai": 0.35,    # Strong medical imaging capabilities
            "detectron2": 0.25  # Computer vision enhancement
        }

        self.confidence_thresholds = {
            "nnunet": 0.8,
            "monai": 0.75,
            "detectron2": 0.7
        }

        logger.info(f"Initialized ensemble framework with {ensemble_strategy}")

    def add_nnunet_model(self,
                        model_variant: str = "ResEnc_L",
                        use_pretrained: bool = True) -> None:
        """
        Add nnU-Net model to ensemble.

        Args:
            model_variant: nnU-Net model variant
            use_pretrained: Whether to use pretrained weights
        """
        self.nnunet = NnUNetIntegration(
            model_variant=model_variant,
            use_pretrained=use_pretrained,
            device=self.device
        )
        self.nnunet.setup_environment()
        logger.info("Added nnU-Net to ensemble")

    def add_monai_model(self,
                       model_name: str = "UNETR",
                       use_pretrained: bool = True) -> None:
        """
        Add MONAI model to ensemble.

        Args:
            model_name: MONAI model name
            use_pretrained: Whether to use pretrained weights
        """
        self.monai = MonaiIntegration(
            model_name=model_name,
            use_pretrained=use_pretrained,
            device=self.device
        )
        self.monai.setup_transforms()
        self.monai.load_model()
        logger.info("Added MONAI model to ensemble")

    def add_detectron2_model(self,
                           model_name: str = "mask_rcnn",
                           backbone: str = "ResNet50_FPN") -> None:
        """
        Add Detectron2 model to ensemble.

        Args:
            model_name: Detectron2 model name
            backbone: Backbone architecture
        """
        self.detectron2 = Detectron2Integration(
            model_name=model_name,
            backbone=backbone,
            use_pretrained=True,
            device=self.device
        )
        self.detectron2.setup_config()
        self.detectron2.load_model()
        logger.info("Added Detectron2 model to ensemble")

    def get_ensemble_strategies(self) -> Dict[str, str]:
        """
        Get available ensemble strategies.

        Returns:
            Dictionary of strategy names and descriptions
        """
        strategies = {
            "weighted_voting": "Weighted average based on model confidence",
            "max_confidence": "Select prediction with highest confidence",
            "majority_voting": "Simple majority vote across models",
            "adaptive_weighting": "Dynamic weights based on input characteristics",
            "hierarchical": "Sequential refinement from detection to segmentation",
            "uncertainty_based": "Weight by prediction uncertainty"
        }
        return strategies

    def set_model_weights(self, weights: Dict[str, float]) -> None:
        """
        Set custom weights for ensemble models.

        Args:
            weights: Dictionary of model weights
        """
        # Normalize weights to sum to 1
        total_weight = sum(weights.values())
        self.model_weights = {k: v/total_weight for k, v in weights.items()}
        logger.info(f"Updated model weights: {self.model_weights}")

    def predict_ensemble(self,
                        input_data: Any,
                        return_individual: bool = False) -> Dict[str, Any]:
        """
        Run ensemble prediction on input data.

        Args:
            input_data: Input medical image data
            return_individual: Whether to return individual model predictions

        Returns:
            Ensemble predictions and optionally individual predictions
        """
        individual_predictions = {}
        confidences = {}

        # Get nnU-Net predictions
        if self.nnunet is not None:
            try:
                nnunet_pred = self._run_nnunet_prediction(input_data)
                individual_predictions["nnunet"] = nnunet_pred
                confidences["nnunet"] = self._calculate_confidence(nnunet_pred)
                logger.info("nnU-Net prediction completed")
            except Exception as e:
                logger.warning(f"nnU-Net prediction failed: {e}")

        # Get MONAI predictions
        if self.monai is not None:
            try:
                monai_pred = self._run_monai_prediction(input_data)
                individual_predictions["monai"] = monai_pred
                confidences["monai"] = self._calculate_confidence(monai_pred)
                logger.info("MONAI prediction completed")
            except Exception as e:
                logger.warning(f"MONAI prediction failed: {e}")

        # Get Detectron2 predictions
        if self.detectron2 is not None:
            try:
                det2_pred = self._run_detectron2_prediction(input_data)
                individual_predictions["detectron2"] = det2_pred
                confidences["detectron2"] = self._calculate_confidence(det2_pred)
                logger.info("Detectron2 prediction completed")
            except Exception as e:
                logger.warning(f"Detectron2 prediction failed: {e}")

        # Combine predictions using selected strategy
        ensemble_pred = self._combine_predictions(
            individual_predictions,
            confidences
        )

        result = {
            "ensemble_prediction": ensemble_pred,
            "confidences": confidences,
            "ensemble_confidence": np.mean(list(confidences.values()))
        }

        if return_individual:
            result["individual_predictions"] = individual_predictions

        logger.info("Ensemble prediction completed")
        return result

    def _run_nnunet_prediction(self, input_data: Any) -> np.ndarray:
        """Run nnU-Net prediction."""
        # Implementation would depend on nnU-Net's prediction interface
        # This is a placeholder for the actual prediction logic
        prediction = np.random.rand(256, 256, 128)  # Placeholder
        return prediction

    def _run_monai_prediction(self, input_data: Any) -> torch.Tensor:
        """Run MONAI prediction."""
        if isinstance(input_data, str):
            # Load and preprocess if input is path
            # Placeholder - actual implementation would load and preprocess
            input_tensor = torch.randn(1, 1, 96, 96, 96).to(self.device)
        else:
            input_tensor = input_data

        prediction = self.monai.run_inference(input_tensor)
        return prediction

    def _run_detectron2_prediction(self, input_data: Any) -> Dict[str, Any]:
        """Run Detectron2 prediction."""
        # Convert medical image format for Detectron2
        if isinstance(input_data, str):
            # Load image - placeholder
            image = np.random.randint(0, 255, (512, 512, 3), dtype=np.uint8)
        else:
            image = input_data

        prediction = self.detectron2.run_inference(image)
        return prediction

    def _calculate_confidence(self, prediction: Any) -> float:
        """Calculate prediction confidence score."""
        if isinstance(prediction, torch.Tensor):
            # For segmentation masks, use entropy or max probability
            probs = torch.softmax(prediction, dim=1)
            confidence = torch.max(probs).item()
        elif isinstance(prediction, dict) and "scores" in prediction:
            # For detection results, use average score
            scores = prediction["scores"]
            confidence = np.mean(scores) if len(scores) > 0 else 0.0
        else:
            # Default confidence calculation
            confidence = 0.8

        return float(confidence)

    def _combine_predictions(self,
                           predictions: Dict[str, Any],
                           confidences: Dict[str, float]) -> Any:
        """
        Combine predictions using the selected ensemble strategy.

        Args:
            predictions: Individual model predictions
            confidences: Model confidence scores

        Returns:
            Combined ensemble prediction
        """
        if self.ensemble_strategy == "weighted_voting":
            return self._weighted_voting(predictions, confidences)
        elif self.ensemble_strategy == "max_confidence":
            return self._max_confidence_selection(predictions, confidences)
        elif self.ensemble_strategy == "majority_voting":
            return self._majority_voting(predictions)
        elif self.ensemble_strategy == "adaptive_weighting":
            return self._adaptive_weighting(predictions, confidences)
        else:
            # Default to weighted voting
            return self._weighted_voting(predictions, confidences)

    def _weighted_voting(self,
                        predictions: Dict[str, Any],
                        confidences: Dict[str, float]) -> Any:
        """Combine predictions using weighted voting."""
        # Initialize ensemble prediction
        ensemble_pred = None
        total_weight = 0.0

        for model_name, prediction in predictions.items():
            if model_name in self.model_weights:
                weight = self.model_weights[model_name] * confidences[model_name]

                if ensemble_pred is None:
                    # Initialize with first prediction
                    if isinstance(prediction, torch.Tensor):
                        ensemble_pred = prediction * weight
                    else:
                        ensemble_pred = prediction  # For complex predictions
                else:
                    # Add weighted prediction
                    if isinstance(prediction, torch.Tensor):
                        ensemble_pred += prediction * weight

                total_weight += weight

        # Normalize by total weight
        if isinstance(ensemble_pred, torch.Tensor) and total_weight > 0:
            ensemble_pred = ensemble_pred / total_weight

        return ensemble_pred

    def _max_confidence_selection(self,
                                 predictions: Dict[str, Any],
                                 confidences: Dict[str, float]) -> Any:
        """Select prediction with highest confidence."""
        max_conf_model = max(confidences.keys(), key=lambda x: confidences[x])
        return predictions[max_conf_model]

    def _majority_voting(self, predictions: Dict[str, Any]) -> Any:
        """Simple majority voting."""
        # Implementation would depend on prediction format
        # For now, return first available prediction
        return list(predictions.values())[0] if predictions else None

    def _adaptive_weighting(self,
                           predictions: Dict[str, Any],
                           confidences: Dict[str, float]) -> Any:
        """Adaptive weighting based on input characteristics."""
        # This would analyze input characteristics and adjust weights
        # For now, use standard weighted voting
        return self._weighted_voting(predictions, confidences)

    def evaluate_ensemble(self,
                         test_data: List[Any],
                         ground_truth: List[Any]) -> Dict[str, float]:
        """
        Evaluate ensemble performance.

        Args:
            test_data: List of test inputs
            ground_truth: List of ground truth annotations

        Returns:
            Dictionary of evaluation metrics
        """
        metrics = {
            "ensemble_dice": 0.0,
            "ensemble_iou": 0.0,
            "ensemble_accuracy": 0.0,
            "improvement_over_best_single": 0.0
        }

        # Run evaluation on test data
        for i, (test_input, gt) in enumerate(zip(test_data, ground_truth)):
            pred_result = self.predict_ensemble(test_input, return_individual=True)

            # Calculate metrics (placeholder implementation)
            # Actual implementation would compute proper medical metrics

        logger.info(f"Ensemble evaluation completed: {metrics}")
        return metrics

    def save_ensemble_config(self, config_path: str) -> None:
        """Save ensemble configuration."""
        config = {
            "ensemble_strategy": self.ensemble_strategy,
            "model_weights": self.model_weights,
            "confidence_thresholds": self.confidence_thresholds,
            "models": {
                "nnunet": self.nnunet.model_variant if self.nnunet else None,
                "monai": self.monai.model_name if self.monai else None,
                "detectron2": self.detectron2.model_name if self.detectron2 else None
            }
        }

        import json
        with open(config_path, 'w') as f:
            json.dump(config, f, indent=2)

        logger.info(f"Ensemble configuration saved to {config_path}")

    def load_ensemble_config(self, config_path: str) -> None:
        """Load ensemble configuration."""
        import json
        with open(config_path, 'r') as f:
            config = json.load(f)

        self.ensemble_strategy = config["ensemble_strategy"]
        self.model_weights = config["model_weights"]
        self.confidence_thresholds = config["confidence_thresholds"]

        logger.info(f"Ensemble configuration loaded from {config_path}")
        logger.info(f"Ensemble configuration loaded from {config_path}")
