# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license

import torch

from ultralytics.data import ClassificationDataset, build_dataloader
from ultralytics.engine.validator import BaseValidator
from ultralytics.utils import LOGGER
from ultralytics.utils.metrics import ClassifyMetrics, ConfusionMatrix
from ultralytics.utils.plotting import plot_images


class ClassificationValidator(BaseValidator):
    """
    A class extending the BaseValidator class for validation based on a classification model.

    This validator handles the validation process for classification models, including metrics calculation,
    confusion matrix generation, and visualization of results.

    Attributes:
        targets (List[torch.Tensor]): Ground truth class labels.
        pred (List[torch.Tensor]): Model predictions.
        metrics (ClassifyMetrics): Object to calculate and store classification metrics.
        names (Dict): Mapping of class indices to class names.
        nc (int): Number of classes.
        confusion_matrix (ConfusionMatrix): Matrix to evaluate model performance across classes.

    Methods:
        get_desc: Return a formatted string summarizing classification metrics.
        init_metrics: Initialize confusion matrix, class names, and tracking containers.
        preprocess: Preprocess input batch by moving data to device.
        update_metrics: Update running metrics with model predictions and batch targets.
        finalize_metrics: Finalize metrics including confusion matrix and processing speed.
        postprocess: Extract the primary prediction from model output.
        get_stats: Calculate and return a dictionary of metrics.
        build_dataset: Create a ClassificationDataset instance for validation.
        get_dataloader: Build and return a data loader for classification validation.
        print_results: Print evaluation metrics for the classification model.
        plot_val_samples: Plot validation image samples with their ground truth labels.
        plot_predictions: Plot images with their predicted class labels.

    Examples:
        >>> from ultralytics.models.yolo.classify import ClassificationValidator
        >>> args = dict(model="yolo11n-cls.pt", data="imagenet10")
        >>> validator = ClassificationValidator(args=args)
        >>> validator()

    Notes:
        Torchvision classification models can also be passed to the 'model' argument, i.e. model='resnet18'.
    """

    def __init__(self, dataloader=None, save_dir=None, pbar=None, args=None, _callbacks=None):
        """Initialize ClassificationValidator with dataloader, save directory, and other parameters."""
        super().__init__(dataloader, save_dir, pbar, args, _callbacks)
        self.targets = None
        self.pred = None
        self.args.task = "classify"
        self.metrics = ClassifyMetrics()

    def get_desc(self):
        """Return a formatted string summarizing classification metrics."""
        return ("%22s" + "%11s" * 2) % ("classes", "top1_acc", "top5_acc")

    def init_metrics(self, model):
        """Initialize confusion matrix, class names, and tracking containers for predictions and targets."""
        self.names = model.names
        self.nc = len(model.names)
        self.confusion_matrix = ConfusionMatrix(nc=self.nc, conf=self.args.conf, task="classify")
        self.pred = []
        self.targets = []

    def preprocess(self, batch):
        """Preprocess input batch by moving data to device and converting to appropriate dtype."""
        batch["img"] = batch["img"].to(self.device, non_blocking=True)
        batch["img"] = batch["img"].half() if self.args.half else batch["img"].float()
        batch["cls"] = batch["cls"].to(self.device)
        return batch

    def update_metrics(self, preds, batch):
        """Update running metrics with model predictions and batch targets."""
        n5 = min(len(self.names), 5)
        self.pred.append(preds.argsort(1, descending=True)[:, :n5].type(torch.int32).cpu())
        self.targets.append(batch["cls"].type(torch.int32).cpu())

    def finalize_metrics(self, *args, **kwargs):
        """Finalize metrics including confusion matrix and processing speed."""
        self.confusion_matrix.process_cls_preds(self.pred, self.targets)
        if self.args.plots:
            for normalize in True, False:
                self.confusion_matrix.plot(
                    save_dir=self.save_dir, names=self.names.values(), normalize=normalize, on_plot=self.on_plot
                )
        self.metrics.speed = self.speed
        self.metrics.confusion_matrix = self.confusion_matrix
        self.metrics.save_dir = self.save_dir

    def postprocess(self, preds):
        """Extract the primary prediction from model output if it's in a list or tuple format."""
        return preds[0] if isinstance(preds, (list, tuple)) else preds

    def get_stats(self):
        """Calculate and return a dictionary of metrics by processing targets and predictions."""
        self.metrics.process(self.targets, self.pred)
        return self.metrics.results_dict

    def build_dataset(self, img_path):
        """Create a ClassificationDataset instance for validation."""
        return ClassificationDataset(root=img_path, args=self.args, augment=False, prefix=self.args.split)

    def get_dataloader(self, dataset_path, batch_size):
        """Build and return a data loader for classification validation."""
        dataset = self.build_dataset(dataset_path)
        return build_dataloader(dataset, batch_size, self.args.workers, rank=-1)

    def print_results(self):
        """Print evaluation metrics for the classification model."""
        pf = "%22s" + "%11.3g" * len(self.metrics.keys)  # print format
        LOGGER.info(pf % ("all", self.metrics.top1, self.metrics.top5))

    def plot_val_samples(self, batch, ni):
        """Plot validation image samples with their ground truth labels."""
        plot_images(
            images=batch["img"],
            batch_idx=torch.arange(len(batch["img"])),
            cls=batch["cls"].view(-1),  # warning: use .view(), not .squeeze() for Classify models
            fname=self.save_dir / f"val_batch{ni}_labels.jpg",
            names=self.names,
            on_plot=self.on_plot,
        )

    def plot_predictions(self, batch, preds, ni):
        """Plot images with their predicted class labels and save the visualization."""
        plot_images(
            batch["img"],
            batch_idx=torch.arange(len(batch["img"])),
            cls=torch.argmax(preds, dim=1),
            fname=self.save_dir / f"val_batch{ni}_pred.jpg",
            names=self.names,
            on_plot=self.on_plot,
        )  # pred
