"""
YOLO model wrapper for the microscopy object detection application.
Provides a clean interface to YOLOv8 for training, validation, and inference.
"""

from ultralytics import YOLO
from pathlib import Path
from typing import Optional, List, Dict, Callable, Any
import torch
from src.utils.logger import get_logger


logger = get_logger(__name__)


class YOLOWrapper:
    """Wrapper for YOLOv8 model operations."""

    def __init__(self, model_path: str = "yolov8s-seg.pt"):
        """
        Initialize YOLO model.

        Args:
            model_path: Path to model weights (.pt file)
        """
        self.model_path = model_path
        self.model = None
        self.device = "cuda" if torch.cuda.is_available() else "cpu"
        logger.info(f"YOLOWrapper initialized with device: {self.device}")

    def load_model(self) -> bool:
        """
        Load YOLO model from path.

        Returns:
            True if loaded successfully, False otherwise
        """
        try:
            logger.info(f"Loading YOLO model from {self.model_path}")
            self.model = YOLO(self.model_path)
            self.model.to(self.device)
            logger.info("Model loaded successfully")
            return True
        except Exception as e:
            logger.error(f"Error loading model: {e}")
            return False

    def train(
        self,
        data_yaml: str,
        epochs: int = 100,
        imgsz: int = 640,
        batch: int = 16,
        patience: int = 50,
        save_dir: str = "data/models",
        name: str = "custom_model",
        resume: bool = False,
        callbacks: Optional[Dict[str, Callable]] = None,
        **kwargs,
    ) -> Dict[str, Any]:
        """
        Train the YOLO model.

        Args:
            data_yaml: Path to data.yaml configuration file
            epochs: Number of training epochs
            imgsz: Input image size
            batch: Batch size
            patience: Early stopping patience
            save_dir: Directory to save trained model
            name: Name for the training run
            resume: Resume training from last checkpoint
            callbacks: Optional Ultralytics callback dictionary
            **kwargs: Additional training arguments

        Returns:
            Dictionary with training results
        """
        if self.model is None:
            self.load_model()

        try:
            logger.info(f"Starting training: {name}")
            logger.info(
                f"Data: {data_yaml}, Epochs: {epochs}, Batch: {batch}, ImgSz: {imgsz}"
            )

            # Train the model
            results = self.model.train(
                data=data_yaml,
                epochs=epochs,
                imgsz=imgsz,
                batch=batch,
                patience=patience,
                project=save_dir,
                name=name,
                device=self.device,
                resume=resume,
                **kwargs,
            )

            logger.info("Training completed successfully")
            return self._format_training_results(results)

        except Exception as e:
            logger.error(f"Error during training: {e}")
            raise

    def validate(self, data_yaml: str, split: str = "val", **kwargs) -> Dict[str, Any]:
        """
        Validate the model.

        Args:
            data_yaml: Path to data.yaml configuration file
            split: Dataset split to validate on ('val' or 'test')
            **kwargs: Additional validation arguments

        Returns:
            Dictionary with validation metrics
        """
        if self.model is None:
            self.load_model()

        try:
            logger.info(f"Starting validation on {split} split")
            results = self.model.val(
                data=data_yaml, split=split, device=self.device, **kwargs
            )

            logger.info("Validation completed successfully")
            return self._format_validation_results(results)

        except Exception as e:
            logger.error(f"Error during validation: {e}")
            raise

    def predict(
        self,
        source: str,
        conf: float = 0.25,
        iou: float = 0.45,
        save: bool = False,
        save_txt: bool = False,
        save_conf: bool = False,
        **kwargs,
    ) -> List[Dict]:
        """
        Perform inference on image(s).

        Args:
            source: Path to image or directory
            conf: Confidence threshold
            iou: IoU threshold for NMS
            save: Whether to save annotated images
            save_txt: Whether to save labels to .txt files
            save_conf: Whether to save confidence in labels
            **kwargs: Additional prediction arguments

        Returns:
            List of detection dictionaries
        """
        if self.model is None:
            self.load_model()

        try:
            logger.info(f"Running inference on {source}")
            results = self.model.predict(
                source=source,
                conf=conf,
                iou=iou,
                save=save,
                save_txt=save_txt,
                save_conf=save_conf,
                device=self.device,
                **kwargs,
            )

            detections = self._format_prediction_results(results)
            logger.info(f"Inference complete: {len(detections)} detections")
            return detections

        except Exception as e:
            logger.error(f"Error during inference: {e}")
            raise

    def export(
        self, format: str = "onnx", output_path: Optional[str] = None, **kwargs
    ) -> str:
        """
        Export model to different format.

        Args:
            format: Export format (onnx, torchscript, tflite, etc.)
            output_path: Path for exported model
            **kwargs: Additional export arguments

        Returns:
            Path to exported model
        """
        if self.model is None:
            self.load_model()

        try:
            logger.info(f"Exporting model to {format} format")
            export_path = self.model.export(format=format, **kwargs)
            logger.info(f"Model exported to {export_path}")
            return str(export_path)

        except Exception as e:
            logger.error(f"Error exporting model: {e}")
            raise

    def _format_training_results(self, results) -> Dict[str, Any]:
        """Format training results into dictionary."""
        try:
            # Get the results dict
            results_dict = (
                results.results_dict if hasattr(results, "results_dict") else {}
            )

            formatted = {
                "success": True,
                "final_epoch": getattr(results, "epoch", 0),
                "metrics": {
                    "mAP50": float(results_dict.get("metrics/mAP50(B)", 0)),
                    "mAP50-95": float(results_dict.get("metrics/mAP50-95(B)", 0)),
                    "precision": float(results_dict.get("metrics/precision(B)", 0)),
                    "recall": float(results_dict.get("metrics/recall(B)", 0)),
                },
                "best_model_path": str(Path(results.save_dir) / "weights" / "best.pt"),
                "last_model_path": str(Path(results.save_dir) / "weights" / "last.pt"),
                "save_dir": str(results.save_dir),
            }

            return formatted

        except Exception as e:
            logger.error(f"Error formatting training results: {e}")
            return {"success": False, "error": str(e)}

    def _format_validation_results(self, results) -> Dict[str, Any]:
        """Format validation results into dictionary."""
        try:
            box_metrics = results.box

            formatted = {
                "success": True,
                "mAP50": float(box_metrics.map50),
                "mAP50-95": float(box_metrics.map),
                "precision": float(box_metrics.mp),
                "recall": float(box_metrics.mr),
                "fitness": (
                    float(results.fitness) if hasattr(results, "fitness") else 0.0
                ),
            }

            # Add per-class metrics if available
            if hasattr(box_metrics, "ap") and hasattr(results, "names"):
                class_metrics = {}
                for idx, name in results.names.items():
                    if idx < len(box_metrics.ap):
                        class_metrics[name] = {
                            "ap": float(box_metrics.ap[idx]),
                            "ap50": (
                                float(box_metrics.ap50[idx])
                                if hasattr(box_metrics, "ap50")
                                else 0.0
                            ),
                        }
                formatted["class_metrics"] = class_metrics

            return formatted

        except Exception as e:
            logger.error(f"Error formatting validation results: {e}")
            return {"success": False, "error": str(e)}

    def _format_prediction_results(self, results) -> List[Dict]:
        """Format prediction results into list of dictionaries."""
        detections = []

        try:
            for result in results:
                boxes = result.boxes
                image_path = str(result.path)
                orig_shape = result.orig_shape  # (height, width)
                height, width = orig_shape

                # Check if this is a segmentation model with masks
                has_masks = hasattr(result, "masks") and result.masks is not None

                for i in range(len(boxes)):
                    # Get normalized coordinates
                    xyxyn = boxes.xyxyn[i].cpu().numpy()  # Normalized [x1, y1, x2, y2]

                    detection = {
                        "image_path": image_path,
                        "class_id": int(boxes.cls[i]),
                        "class_name": result.names[int(boxes.cls[i])],
                        "confidence": float(boxes.conf[i]),
                        "bbox_normalized": [
                            float(v) for v in xyxyn
                        ],  # [x_min, y_min, x_max, y_max]
                        "bbox_absolute": [
                            float(v) for v in boxes.xyxy[i].cpu().numpy()
                        ],  # Absolute pixels
                    }

                    # Extract segmentation mask if available
                    if has_masks:
                        try:
                            # Get the mask for this detection
                            mask_data = result.masks.xy[
                                i
                            ]  # Polygon coordinates in absolute pixels

                            # Convert to normalized coordinates
                            if len(mask_data) > 0:
                                mask_normalized = []
                                for point in mask_data:
                                    x_norm = float(point[0]) / width
                                    y_norm = float(point[1]) / height
                                    mask_normalized.append([x_norm, y_norm])
                                detection["segmentation_mask"] = mask_normalized
                            else:
                                detection["segmentation_mask"] = None
                        except Exception as mask_error:
                            logger.warning(
                                f"Error extracting mask for detection {i}: {mask_error}"
                            )
                            detection["segmentation_mask"] = None
                    else:
                        detection["segmentation_mask"] = None

                    detections.append(detection)

            return detections

        except Exception as e:
            logger.error(f"Error formatting prediction results: {e}")
            return []

    @staticmethod
    def convert_bbox_format(
        bbox: List[float], format_from: str = "xywh", format_to: str = "xyxy"
    ) -> List[float]:
        """
        Convert bounding box between formats.

        Formats:
        - xywh: [x_center, y_center, width, height]
        - xyxy: [x_min, y_min, x_max, y_max]

        Args:
            bbox: Bounding box coordinates
            format_from: Source format
            format_to: Target format

        Returns:
            Converted bounding box
        """
        if format_from == "xywh" and format_to == "xyxy":
            x, y, w, h = bbox
            return [x - w / 2, y - h / 2, x + w / 2, y + h / 2]
        elif format_from == "xyxy" and format_to == "xywh":
            x1, y1, x2, y2 = bbox
            return [(x1 + x2) / 2, (y1 + y2) / 2, x2 - x1, y2 - y1]
        else:
            return bbox

    def get_model_info(self) -> Dict[str, Any]:
        """
        Get information about the loaded model.

        Returns:
            Dictionary with model information
        """
        if self.model is None:
            return {"error": "Model not loaded"}

        try:
            info = {
                "model_path": self.model_path,
                "device": self.device,
                "task": getattr(self.model, "task", "unknown"),
            }

            # Try to get class names
            if hasattr(self.model, "names"):
                info["classes"] = self.model.names
                info["num_classes"] = len(self.model.names)

            return info

        except Exception as e:
            logger.error(f"Error getting model info: {e}")
            return {"error": str(e)}