"""
Detectron2 Integration Module
============================

Integrates Facebook's Detectron2 framework for advanced computer vision
capabilities in medical image analysis.

Key Features:
- Mask R-CNN for instance segmentation
- DeepLabV3+ for semantic segmentation
- Panoptic-DeepLab for unified segmentation
- Advanced backbone architectures (ResNet, RegNet, EfficientNet)
- Pretrained COCO weights adaptable to medical domains
- State-of-the-art object detection and segmentation

Author: Tumor Detection Segmentation Team
Reference: facebookresearch/detectron2 on GitHub
"""

import logging
from pathlib import Path
from typing import Any, Dict, Union

import torch

# Configure logging
logger = logging.getLogger(__name__)


class Detectron2Integration:
    """
    Integration wrapper for Detectron2 framework providing advanced
    computer vision capabilities for medical image analysis.
    """

    def __init__(self,
                 model_name: str = "mask_rcnn",
                 backbone: str = "ResNet50_FPN",
                 use_pretrained: bool = True,
                 device: Union[str, torch.device] = "cuda"):
        """
        Initialize Detectron2 integration.

        Args:
            model_name: Model architecture (mask_rcnn, deeplabv3plus, etc.)
            backbone: Backbone architecture
            use_pretrained: Whether to use pretrained weights
            device: Device for computation
        """
        self.model_name = model_name
        self.backbone = backbone
        self.use_pretrained = use_pretrained
        self.device = torch.device(device) if isinstance(device, str) else device
        self.model = None
        self.cfg = None

        logger.info(f"Initialized Detectron2 integration with {model_name}")

    def get_available_models(self) -> Dict[str, Dict]:
        """
        Get available Detectron2 models and configurations.

        Returns:
            Dictionary of available models and their specifications
        """
        models = {
            "mask_rcnn": {
                "description": "Mask R-CNN for instance segmentation",
                "backbone_options": ["ResNet50_FPN", "ResNet101_FPN", "ResNeXt101_FPN"],
                "use_case": "Tumor instance detection and segmentation",
                "pretrained": True,
                "performance": "High accuracy, slower inference"
            },
            "deeplabv3plus": {
                "description": "DeepLabV3+ for semantic segmentation",
                "backbone_options": ["ResNet50", "ResNet101", "Xception"],
                "use_case": "Dense tumor segmentation",
                "pretrained": True,
                "performance": "Excellent boundary delineation"
            },
            "panoptic_deeplab": {
                "description": "Panoptic-DeepLab unified segmentation",
                "backbone_options": ["ResNet50", "ResNet101"],
                "use_case": "Combined semantic and instance segmentation",
                "pretrained": True,
                "performance": "Best of both worlds"
            },
            "retinanet": {
                "description": "RetinaNet object detection",
                "backbone_options": ["ResNet50_FPN", "ResNet101_FPN"],
                "use_case": "Tumor detection bounding boxes",
                "pretrained": True,
                "performance": "Fast inference, good for screening"
            }
        }
        return models

    def get_backbone_options(self) -> Dict[str, Dict]:
        """
        Get available backbone architectures.

        Returns:
            Dictionary of backbone options and their properties
        """
        backbones = {
            "ResNet50_FPN": {
                "parameters_M": 32.0,
                "depth": 50,
                "fpn": True,
                "description": "Standard ResNet-50 with Feature Pyramid Network"
            },
            "ResNet101_FPN": {
                "parameters_M": 60.2,
                "depth": 101,
                "fpn": True,
                "description": "Deeper ResNet-101 with FPN"
            },
            "RegNetX_4GF": {
                "parameters_M": 22.1,
                "depth": "variable",
                "fpn": True,
                "description": "Efficient RegNet architecture"
            },
            "EfficientNet_B3": {
                "parameters_M": 12.0,
                "depth": "variable",
                "fpn": True,
                "description": "Efficient compound scaling"
            }
        }
        return backbones

    def setup_config(self,
                    num_classes: int = 2,
                    roi_heads_batch_size: int = 512,
                    learning_rate: float = 0.00025) -> None:
        """
        Set up Detectron2 configuration.

        Args:
            num_classes: Number of classes (including background)
            roi_heads_batch_size: Batch size for ROI heads
            learning_rate: Learning rate for training
        """
        try:
            from detectron2.config import get_cfg
            from detectron2.model_zoo import model_zoo

            self.cfg = get_cfg()

            # Model configuration based on selected architecture
            if self.model_name == "mask_rcnn":
                if self.backbone == "ResNet50_FPN":
                    config_file = "COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml"
                elif self.backbone == "ResNet101_FPN":
                    config_file = "COCO-InstanceSegmentation/mask_rcnn_R_101_FPN_3x.yaml"
                else:
                    config_file = "COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml"

            elif self.model_name == "deeplabv3plus":
                # DeepLab configuration
                config_file = "COCO-PanopticSegmentation/panoptic_fpn_R_50_3x.yaml"

            else:
                # Default to Mask R-CNN
                config_file = "COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml"

            self.cfg.merge_from_file(model_zoo.get_config_file(config_file))

            # Customize for medical imaging
            self.cfg.MODEL.ROI_HEADS.NUM_CLASSES = num_classes - 1  # Exclude background
            self.cfg.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = roi_heads_batch_size
            self.cfg.SOLVER.IMS_PER_BATCH = 2
            self.cfg.SOLVER.BASE_LR = learning_rate
            self.cfg.SOLVER.WARMUP_ITERS = 1000
            self.cfg.SOLVER.MAX_ITER = 10000
            self.cfg.SOLVER.STEPS = (7000,)
            self.cfg.SOLVER.GAMMA = 0.1

            # Input format for medical images
            self.cfg.INPUT.FORMAT = "RGB"  # Can be adapted for grayscale
            self.cfg.INPUT.MIN_SIZE_TRAIN = (800,)
            self.cfg.INPUT.MAX_SIZE_TRAIN = 1333
            self.cfg.INPUT.MIN_SIZE_TEST = 800
            self.cfg.INPUT.MAX_SIZE_TEST = 1333

            # Device configuration
            self.cfg.MODEL.DEVICE = str(self.device)

            if self.use_pretrained:
                self.cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url(config_file)

            logger.info("Detectron2 configuration set up successfully")

        except ImportError:
            logger.error("Detectron2 not installed. Install with: pip install detectron2")
            raise

    def load_model(self) -> Any:
        """
        Load Detectron2 model.

        Returns:
            Loaded Detectron2 model
        """
        try:
            from detectron2.checkpoint import DetectionCheckpointer
            from detectron2.engine import DefaultPredictor
            from detectron2.modeling import build_model

            if self.cfg is None:
                self.setup_config()

            # Build model
            self.model = build_model(self.cfg)

            if self.use_pretrained:
                # Load pretrained weights
                checkpointer = DetectionCheckpointer(self.model)
                checkpointer.load(self.cfg.MODEL.WEIGHTS)

            # Set up predictor for inference
            self.predictor = DefaultPredictor(self.cfg)

            logger.info(f"Loaded Detectron2 model: {self.model_name}")
            return self.model

        except ImportError:
            logger.error("Detectron2 not installed")
            raise

    def run_inference(self, image: Any) -> Dict[str, Any]:
        """
        Run inference on input image.

        Args:
            image: Input image (numpy array or PIL Image)

        Returns:
            Dictionary containing predictions
        """
        if self.predictor is None:
            raise ValueError("Model not loaded. Call load_model() first.")

        # Run prediction
        outputs = self.predictor(image)

        # Extract predictions
        predictions = {
            "instances": outputs["instances"].to("cpu"),
            "pred_boxes": outputs["instances"].pred_boxes.tensor.cpu().numpy(),
            "scores": outputs["instances"].scores.cpu().numpy(),
            "pred_classes": outputs["instances"].pred_classes.cpu().numpy()
        }

        # Add masks if available (for instance segmentation)
        if hasattr(outputs["instances"], "pred_masks"):
            predictions["pred_masks"] = outputs["instances"].pred_masks.cpu().numpy()

        logger.info("Inference completed")
        return predictions

    def setup_training(self,
                      train_dataset: str,
                      val_dataset: str,
                      output_dir: str) -> None:
        """
        Set up training configuration.

        Args:
            train_dataset: Training dataset name
            val_dataset: Validation dataset name
            output_dir: Directory to save training outputs
        """
        if self.cfg is None:
            self.setup_config()

        # Dataset configuration
        self.cfg.DATASETS.TRAIN = (train_dataset,)
        self.cfg.DATASETS.TEST = (val_dataset,)

        # Output directory
        self.cfg.OUTPUT_DIR = output_dir
        Path(output_dir).mkdir(parents=True, exist_ok=True)

        # Evaluation configuration
        self.cfg.TEST.EVAL_PERIOD = 1000

        logger.info("Training configuration set up")

    def train_model(self) -> None:
        """Train the Detectron2 model."""
        try:
            from detectron2.engine import DefaultTrainer
            from detectron2.evaluation import COCOEvaluator

            class MedicalTrainer(DefaultTrainer):
                @classmethod
                def build_evaluator(cls, cfg, dataset_name, output_folder=None):
                    if output_folder is None:
                        output_folder = Path(cfg.OUTPUT_DIR) / "inference"
                    return COCOEvaluator(dataset_name, cfg, True, output_folder)

            trainer = MedicalTrainer(self.cfg)
            trainer.resume_or_load(resume=False)
            trainer.train()

            logger.info("Training completed")

        except ImportError:
            logger.error("Detectron2 training components not available")
            raise

    def evaluate_model(self,
                      test_dataset: str,
                      output_dir: str) -> Dict[str, float]:
        """
        Evaluate model performance.

        Args:
            test_dataset: Test dataset name
            output_dir: Directory to save evaluation results

        Returns:
            Dictionary of evaluation metrics
        """
        try:
            from detectron2.data import build_detection_test_loader
            from detectron2.evaluation import (COCOEvaluator,
                                               inference_on_dataset)

            evaluator = COCOEvaluator(test_dataset, self.cfg, False, output_dir)
            test_loader = build_detection_test_loader(self.cfg, test_dataset)

            results = inference_on_dataset(self.predictor.model, test_loader, evaluator)

            # Extract key metrics
            metrics = {
                "bbox_AP": results.get("bbox", {}).get("AP", 0.0),
                "bbox_AP50": results.get("bbox", {}).get("AP50", 0.0),
                "segm_AP": results.get("segm", {}).get("AP", 0.0),
                "segm_AP50": results.get("segm", {}).get("AP50", 0.0)
            }

            logger.info(f"Evaluation metrics: {metrics}")
            return metrics

        except ImportError:
            logger.error("Detectron2 evaluation components not available")
            return {}

    def export_model(self, output_path: str) -> None:
        """
        Export model for deployment.

        Args:
            output_path: Path to save exported model
        """
        try:
            from detectron2.export import TracingAdapter, dump_torchscript_IR

            # Convert to TorchScript for deployment
            tracing_adapter = TracingAdapter(self.model, (1, 3, 800, 800))
            traced_model = torch.jit.trace(tracing_adapter, (torch.randn(1, 3, 800, 800),))

            # Save traced model
            torch.jit.save(traced_model, output_path)

            logger.info(f"Model exported to {output_path}")

        except ImportError:
            logger.error("Model export requires additional dependencies")
            raise

    def adapt_for_medical_imaging(self) -> None:
        """Adapt configuration specifically for medical imaging."""
        if self.cfg is None:
            self.setup_config()

        # Medical imaging specific adaptations
        self.cfg.INPUT.FORMAT = "L"  # Grayscale for many medical images
        self.cfg.MODEL.PIXEL_MEAN = [128.0]  # Adjust for medical image intensity
        self.cfg.MODEL.PIXEL_STD = [128.0]

        # Adjust anchor sizes for medical objects
        self.cfg.MODEL.ANCHOR_GENERATOR.SIZES = [[16, 32, 64, 128, 256]]

        # Medical-specific data augmentation
        self.cfg.INPUT.CROP.ENABLED = True
        self.cfg.INPUT.CROP.TYPE = "relative_range"
        self.cfg.INPUT.CROP.SIZE = [0.9, 0.9]

        logger.info("Configuration adapted for medical imaging")
        logger.info("Configuration adapted for medical imaging")
