# Copyright (c) 2026 webAI, Inc.
"""
YOLO26 Validator - Pure MLX Implementation

Validation pipeline for YOLO26 models with mAP calculation.
"""

import logging
from pathlib import Path

import mlx.core as mx
import mlx.nn as nn
import numpy as np
import yaml

logger = logging.getLogger(__name__)


class Validator:
    """YOLO26 Validation class - Pure MLX.

    Implements COCO-style evaluation metrics.
    """

    def __init__(self, model: nn.Module, task: str = "detect"):
        """Initialize validator.

        Args:
            model: YOLO26 model to validate
            task: Task type - 'detect', 'segment', 'pose', or 'obb'
        """
        self.model = model
        self.task = task
        self.metrics = {}

        # IoU thresholds for mAP calculation
        self.iou_thresholds = np.linspace(0.5, 0.95, 10)

        # Statistics
        self.stats = []
        self.seen = 0

        # Compiled inference function
        self._compiled_infer = None

    def __call__(
        self,
        data: str | None = None,
        imgsz: int = 640,
        batch: int = 16,
        conf: float = 0.001,
        iou: float = 0.6,
        verbose: bool = True,
    ) -> dict[str, float]:
        """Run validation.

        Args:
            data: Path to data configuration file
            imgsz: Input image size
            batch: Batch size
            conf: Confidence threshold for NMS/filtering
            iou: IoU threshold
            verbose: Print progress

        Returns:
            Dictionary of validation metrics
        """
        # Load data config
        if data is not None:
            self._data_cfg = self._load_data_config(data)
        else:
            self._data_cfg = {}

        if verbose:
            logger.info(f"\nValidating YOLO26 ({self.task})")
            logger.info(f"  Data: {data}")
            logger.info(f"  Image size: {imgsz}")
            logger.info(f"  Batch size: {batch}")
            logger.info(f"  Confidence: {conf}")
            logger.info(f"  IoU threshold: {iou}")

        # Set model to evaluation mode
        if self.model is not None:
            self.model.eval()

        # Reset statistics
        self.stats = []
        self.seen = 0

        # TODO: Implement actual data loading and validation
        # For now, return placeholder metrics

        # Process batches
        num_batches = 10  # Placeholder

        for _batch_idx in range(num_batches):
            # Generate dummy data
            images = mx.random.uniform(shape=(batch, imgsz, imgsz, 3))

            # Run inference
            preds = self._infer(images)
            mx.eval(preds)

            # Update statistics (placeholder)
            self.seen += batch

        # Compute final metrics
        self.metrics = self._compute_metrics()

        if verbose:
            self._print_results()

        return self.metrics

    def _load_data_config(self, data: str) -> dict:
        """Load data configuration from YAML.

        Args:
            data: Path to the YAML data configuration file.

        Returns:
            Parsed configuration dict, or empty dict if file not found.
        """
        data_path = Path(data)
        if not data_path.exists():
            return {}

        with open(data_path) as f:
            return yaml.safe_load(f)

    def _infer(self, images: mx.array) -> mx.array:
        """Run inference on images.

        Automatically enables compile_for_inference() on the model for
        27-50% faster inference via JIT compilation.

        Args:
            images: Input images (B, H, W, C)

        Returns:
            Model predictions
        """
        if self.model is None:
            raise ValueError("Model not loaded")

        # Enable compiled inference on first call (27-50% faster)
        if self._compiled_infer is None:
            if hasattr(self.model, "compile_for_inference"):
                self.model.compile_for_inference()
            self._compiled_infer = True  # Mark as initialized

        return self.model(images)

    def _compute_metrics(self) -> dict[str, float]:
        """Compute validation metrics from accumulated statistics.

        Returns:
            Dictionary with mAP50, mAP50-95, precision, recall
        """
        # TODO: Implement actual mAP calculation
        # This requires:
        # 1. Matching predictions to ground truth by IoU
        # 2. Computing precision-recall curves
        # 3. Calculating AP at different IoU thresholds

        if len(self.stats) == 0:
            return {
                "mAP50": 0.0,
                "mAP50-95": 0.0,
                "precision": 0.0,
                "recall": 0.0,
                "f1": 0.0,
            }

        # Placeholder metrics
        metrics = {
            "mAP50": 0.0,
            "mAP50-95": 0.0,
            "precision": 0.0,
            "recall": 0.0,
            "f1": 0.0,
        }

        return metrics

    def _process_batch(self, preds: np.ndarray) -> tuple:
        """Process a batch of predictions for metric accumulation.

        Args:
            preds: Model predictions array with boxes, scores, and class indices

        Returns:
            Tuple of (correct predictions, prediction info), or (None, None) if not yet implemented
        """
        # Filter by confidence
        if isinstance(preds, mx.array):
            preds = np.array(preds)

        # TODO: Implement matching logic
        # 1. Filter predictions by confidence
        # 2. Match with ground truth by IoU
        # 3. Track TP, FP for each class

        return None, None

    def _box_iou(self, boxes1: np.ndarray, boxes2: np.ndarray) -> np.ndarray:
        """Compute IoU between two sets of boxes.

        Args:
            boxes1: Boxes array (N, 4) in xyxy format
            boxes2: Boxes array (M, 4) in xyxy format

        Returns:
            IoU matrix (N, M)
        """
        # Intersection
        inter_x1 = np.maximum(boxes1[:, None, 0], boxes2[None, :, 0])
        inter_y1 = np.maximum(boxes1[:, None, 1], boxes2[None, :, 1])
        inter_x2 = np.minimum(boxes1[:, None, 2], boxes2[None, :, 2])
        inter_y2 = np.minimum(boxes1[:, None, 3], boxes2[None, :, 3])

        inter_w = np.maximum(inter_x2 - inter_x1, 0)
        inter_h = np.maximum(inter_y2 - inter_y1, 0)
        inter_area = inter_w * inter_h

        # Areas
        area1 = (boxes1[:, 2] - boxes1[:, 0]) * (boxes1[:, 3] - boxes1[:, 1])
        area2 = (boxes2[:, 2] - boxes2[:, 0]) * (boxes2[:, 3] - boxes2[:, 1])

        # Union
        union_area = area1[:, None] + area2[None, :] - inter_area

        return inter_area / (union_area + 1e-7)

    def _ap_per_class(
        self, tp: np.ndarray, conf: np.ndarray, pred_cls: np.ndarray, target_cls: np.ndarray
    ) -> tuple:
        """Calculate AP per class.

        Args:
            tp: True positive flags (N,)
            conf: Confidence scores (N,)
            pred_cls: Predicted classes (N,)
            target_cls: Target classes

        Returns:
            Tuple of (precision, recall, AP, F1, unique classes)
        """
        # Sort by confidence
        sort_idx = np.argsort(-conf)
        tp = tp[sort_idx]
        conf = conf[sort_idx]
        pred_cls = pred_cls[sort_idx]

        # Find unique classes
        unique_classes = np.unique(target_cls)
        nc = len(unique_classes)

        # Initialize arrays
        precision = np.zeros((nc,))
        recall = np.zeros((nc,))
        ap = np.zeros((nc,))

        for i, c in enumerate(unique_classes):
            # Mask for this class
            cls_mask = pred_cls == c
            n_gt = np.sum(target_cls == c)
            n_pred = np.sum(cls_mask)

            if n_pred == 0 or n_gt == 0:
                continue

            # Cumulative TP and FP
            tp_cumsum = np.cumsum(tp[cls_mask])
            fp_cumsum = np.cumsum(~tp[cls_mask])

            # Precision and recall
            prec = tp_cumsum / (tp_cumsum + fp_cumsum + 1e-7)
            rec = tp_cumsum / (n_gt + 1e-7)

            # Store final values
            precision[i] = prec[-1] if len(prec) > 0 else 0
            recall[i] = rec[-1] if len(rec) > 0 else 0

            # AP (area under PR curve)
            # Use all-point interpolation
            mrec = np.concatenate([[0], rec, [1]])
            mpre = np.concatenate([[1], prec, [0]])

            # Make precision monotonically decreasing
            for j in range(len(mpre) - 1, 0, -1):
                mpre[j - 1] = max(mpre[j - 1], mpre[j])

            # Calculate AP
            idx = np.where(mrec[1:] != mrec[:-1])[0]
            ap[i] = np.sum((mrec[idx + 1] - mrec[idx]) * mpre[idx + 1])

        # F1 score
        f1 = 2 * precision * recall / (precision + recall + 1e-7)

        return precision, recall, ap, f1, unique_classes

    def _print_results(self):
        """Print validation results (mAP, precision, recall, F1) to logger."""
        logger.info("\nValidation Results:")
        logger.info(f"  mAP@0.5: {self.metrics['mAP50']:.4f}")
        logger.info(f"  mAP@0.5:0.95: {self.metrics['mAP50-95']:.4f}")
        logger.info(f"  Precision: {self.metrics['precision']:.4f}")
        logger.info(f"  Recall: {self.metrics['recall']:.4f}")
        logger.info(f"  F1: {self.metrics['f1']:.4f}")
