import random
import shutil
from pathlib import Path

from ultralytics.data.utils import IMG_FORMATS, img2label_paths
from ultralytics.utils import DATASETS_DIR, LOGGER, TQDM


def split_classify_dataset(source_dir, train_ratio=0.8):
    """
    Split dataset into train and val directories in a new directory.

    Creates a new directory '{source_dir}_split' with train/val subdirectories, preserving the original class
    structure with an 80/20 split by default.

    Directory structure:
        Before:
            caltech/
            ├── class1/
            │   ├── img1.jpg
            │   ├── img2.jpg
            │   └── ...
            ├── class2/
            │   ├── img1.jpg
            │   └── ...
            └── ...

        After:
            caltech_split/
            ├── train/
            │   ├── class1/
            │   │   ├── img1.jpg
            │   │   └── ...
            │   ├── class2/
            │   │   ├── img1.jpg
            │   │   └── ...
            │   └── ...
            └── val/
                ├── class1/
                │   ├── img2.jpg
                │   └── ...
                ├── class2/
                │   └── ...
                └── ...

    Args:
        source_dir (str | Path): Path to Caltech dataset root directory.
        train_ratio (float): Ratio for train split, between 0 and 1.

    Examples:
        >>> # Split dataset with default 80/20 ratio
        >>> split_classify_dataset("path/to/caltech")
        >>> # Split with custom ratio
        >>> split_classify_dataset("path/to/caltech", 0.75)
    """
    source_path = Path(source_dir)
    split_path = Path(f"{source_path}_split")
    train_path, val_path = split_path / "train", split_path / "val"

    # Create directory structure
    split_path.mkdir(exist_ok=True)
    train_path.mkdir(exist_ok=True)
    val_path.mkdir(exist_ok=True)

    # Process class directories
    class_dirs = [d for d in source_path.iterdir() if d.is_dir()]
    total_images = sum(len(list(d.glob("*.*"))) for d in class_dirs)
    stats = f"{len(class_dirs)} classes, {total_images} images"
    LOGGER.info(f"Splitting {source_path} ({stats}) into {train_ratio:.0%} train, {1 - train_ratio:.0%} val...")

    for class_dir in class_dirs:
        # Create class directories
        (train_path / class_dir.name).mkdir(exist_ok=True)
        (val_path / class_dir.name).mkdir(exist_ok=True)

        # Split and copy files
        image_files = list(class_dir.glob("*.*"))
        random.shuffle(image_files)
        split_idx = int(len(image_files) * train_ratio)

        for img in image_files[:split_idx]:
            shutil.copy2(img, train_path / class_dir.name / img.name)

        for img in image_files[split_idx:]:
            shutil.copy2(img, val_path / class_dir.name / img.name)

    LOGGER.info(f"Split complete in {split_path} ✅")
    return split_path


def autosplit(path=DATASETS_DIR / "coco8/images", weights=(0.9, 0.1, 0.0), annotated_only=False):
    """
    Automatically split a dataset into train/val/test splits and save the resulting splits into autosplit_*.txt files.

    Args:
        path (Path, optional): Path to images directory.
        weights (list | tuple, optional): Train, validation, and test split fractions.
        annotated_only (bool, optional): If True, only images with an associated txt file are used.

    Examples:
        >>> from ultralytics.data.split import autosplit
        >>> autosplit()
    """
    path = Path(path)  # images dir
    files = sorted(x for x in path.rglob("*.*") if x.suffix[1:].lower() in IMG_FORMATS)  # image files only
    n = len(files)  # number of files
    random.seed(0)  # for reproducibility
    indices = random.choices([0, 1, 2], weights=weights, k=n)  # assign each image to a split

    txt = ["autosplit_train.txt", "autosplit_val.txt", "autosplit_test.txt"]  # 3 txt files
    for x in txt:
        if (path.parent / x).exists():
            (path.parent / x).unlink()  # remove existing

    LOGGER.info(f"Autosplitting images from {path}" + ", using *.txt labeled images only" * annotated_only)
    for i, img in TQDM(zip(indices, files), total=n):
        if not annotated_only or Path(img2label_paths([str(img)])[0]).exists():  # check label
            with open(path.parent / txt[i], "a", encoding="utf-8") as f:
                f.write(f"./{img.relative_to(path.parent).as_posix()}" + "\n")  # add image to txt file


if __name__ == "__main__":
    split_classify_dataset("../datasets/caltech101")