from PIL import Image
from os import path, listdir

import hydra
import numpy as np
import torch
from torch.utils.data import Dataset
from loguru import logger
from tqdm.rich import tqdm
import diskcache as dc
from typing import Union
from drawer import draw_bboxes
from data_augment import Compose, RandomHorizontalFlip, RandomVerticalFlip, Mosaic, MixUp


class YoloDataset(Dataset):
    def __init__(self, dataset_cfg: dict, phase: str = "train", image_size: int = 640, transform=None):
        phase_name = dataset_cfg.get(phase, phase)
        self.image_size = image_size

        self.transform = transform
        self.transform.get_more_data = self.get_more_data
        self.transform.image_size = self.image_size
        self.data = self.load_data(dataset_cfg.path, phase_name)

    def load_data(self, dataset_path, phase_name):
        """
        Loads data from a cache or generates a new cache for a specific dataset phase.

        Parameters:
            dataset_path (str): The root path to the dataset directory.
            phase_name (str): The specific phase of the dataset (e.g., 'train', 'test') to load or generate data for.

        Returns:
            dict: The loaded data from the cache for the specified phase.
        """
        cache_path = path.join(dataset_path, ".cache")
        cache = dc.Cache(cache_path)
        data = cache.get(phase_name)

        if data is None:
            logger.info("Generating {} cache", phase_name)
            images_path = path.join(dataset_path, phase_name, "images")
            labels_path = path.join(dataset_path, phase_name, "labels")
            data = self.filter_data(images_path, labels_path)
            cache[phase_name] = data

        cache.close()
        logger.info("Loaded {} cache", phase_name)
        data = cache[phase_name]
        return data

    def filter_data(self, images_path: str, labels_path: str) -> list:
        """
        Filters and collects dataset information by pairing images with their corresponding labels.

        Parameters:
            images_path (str): Path to the directory containing image files.
            labels_path (str): Path to the directory containing label files.

        Returns:
            list: A list of tuples, each containing the path to an image file and its associated labels as a tensor.
        """
        data = []
        valid_inputs = 0
        images_list = sorted(listdir(images_path))
        for image_name in tqdm(images_list, desc="Filtering data"):
            if not image_name.lower().endswith((".jpg", ".jpeg", ".png")):
                continue

            img_path = path.join(images_path, image_name)
            base_name, _ = path.splitext(image_name)
            label_path = path.join(labels_path, f"{base_name}.txt")

            if path.isfile(label_path):
                labels = self.load_valid_labels(label_path)
                if labels is not None:
                    data.append((img_path, labels))
                    valid_inputs += 1

        logger.info("Recorded {}/{} valid inputs", valid_inputs, len(images_list))
        return data

    def load_valid_labels(self, label_path: str) -> Union[torch.Tensor, None]:
        """
        Loads and validates bounding box data is [0, 1] from a label file.

        Parameters:
            label_path (str): The filepath to the label file containing bounding box data.

        Returns:
            torch.Tensor or None: A tensor of all valid bounding boxes if any are found; otherwise, None.
        """
        bboxes = []
        with open(label_path, "r") as file:
            for line in file:
                parts = list(map(float, line.strip().split()))
                cls = parts[0]
                points = np.array(parts[1:]).reshape(-1, 2)
                valid_points = points[(points >= 0) & (points <= 1)].reshape(-1, 2)
                if valid_points.size > 1:
                    bbox = torch.tensor([cls, *valid_points.min(axis=0), *valid_points.max(axis=0)])
                    bboxes.append(bbox)

        if bboxes:
            return torch.stack(bboxes)
        else:
            logger.warning("No valid BBox in {}", label_path)
            return None

    def get_data(self, idx):
        img_path, bboxes = self.data[idx]
        img = Image.open(img_path).convert("RGB")
        return img, bboxes

    def get_more_data(self, num: int = 1):
        indices = torch.randint(0, len(self), (num,))
        return [self.get_data(idx) for idx in indices]

    def __getitem__(self, idx) -> Union[Image.Image, torch.Tensor]:
        img, bboxes = self.get_data(idx)
        if self.transform:
            img, bboxes = self.transform(img, bboxes)
        return img, bboxes

    def __len__(self) -> int:
        return len(self.data)


@hydra.main(config_path="../config", config_name="config", version_base=None)
def main(cfg):
    transform = Compose([eval(aug)(prob) for aug, prob in cfg.augmentation.items()])
    dataset = YoloDataset(cfg.data, transform=transform)
    draw_bboxes(*dataset[0])


if __name__ == "__main__":
    import sys

    sys.path.append("./")
    from tools.log_helper import custom_logger

    custom_logger()
    main()