# Ultralytics YOLO 🚀, AGPL-3.0 license
import contextlib
from itertools import repeat
from multiprocessing.pool import ThreadPool
from pathlib import Path

import cv2
import numpy as np
import torch
import torchvision

from ultralytics.utils import LOCAL_RANK, NUM_THREADS, TQDM, colorstr, is_dir_writeable

from .augment import Compose, Format, Instances, LetterBox, classify_albumentations, classify_transforms, v8_transforms
from .base import BaseDataset
from .utils import HELP_URL, LOGGER, get_hash, img2label_paths, verify_image, verify_image_label

# Ultralytics dataset *.cache version, >= 1.0.0 for YOLOv8
DATASET_CACHE_VERSION = '1.0.3'


class YOLODataset(BaseDataset):
    """
    Dataset class for loading object detection and/or segmentation labels in YOLO format.

    Args:
        data (dict, optional): A dataset YAML dictionary. Defaults to None.
        use_segments (bool, optional): If True, segmentation masks are used as labels. Defaults to False.
        use_keypoints (bool, optional): If True, keypoints are used as labels. Defaults to False.

    Returns:
        (torch.utils.data.Dataset): A PyTorch dataset object that can be used for training an object detection model.
    """

    def __init__(self, *args, data=None, use_segments=False, use_keypoints=False, **kwargs):
        """Initializes the YOLODataset with optional configurations for segments and keypoints."""
        self.use_segments = use_segments
        self.use_keypoints = use_keypoints
        self.data = data
        assert not (self.use_segments and self.use_keypoints), 'Can not use both segments and keypoints.'
        super().__init__(*args, **kwargs)

    def cache_labels(self, path=Path('./labels.cache')):
        """
        Cache dataset labels, check images and read shapes.

        Args:
            path (Path): path where to save the cache file (default: Path('./labels.cache')).
        Returns:
            (dict): labels.
        """
        x = {'labels': []}
        nm, nf, ne, nc, msgs = 0, 0, 0, 0, []  # number missing, found, empty, corrupt, messages
        desc = f'{self.prefix}Scanning {path.parent / path.stem}...'
        total = len(self.im_files)
        nkpt, ndim = self.data.get('kpt_shape', (0, 0))
        if self.use_keypoints and (nkpt <= 0 or ndim not in (2, 3)):
            raise ValueError("'kpt_shape' in data.yaml missing or incorrect. Should be a list with [number of "
                             "keypoints, number of dims (2 for x,y or 3 for x,y,visible)], i.e. 'kpt_shape: [17, 3]'")
        with ThreadPool(NUM_THREADS) as pool:
            results = pool.imap(func=verify_image_label,
                                iterable=zip(self.im_files, self.label_files, repeat(self.prefix),
                                             repeat(self.use_keypoints), repeat(len(self.data['names'])), repeat(nkpt),
                                             repeat(ndim)))
            pbar = TQDM(results, desc=desc, total=total)
            for im_file, lb, shape, segments, keypoint, nm_f, nf_f, ne_f, nc_f, msg in pbar:
                nm += nm_f
                nf += nf_f
                ne += ne_f
                nc += nc_f
                if im_file:
                    x['labels'].append(
                        dict(
                            im_file=im_file,
                            shape=shape,
                            cls=lb[:, 0:1],  # n, 1
                            bboxes=lb[:, 1:],  # n, 4
                            segments=segments,
                            keypoints=keypoint,
                            normalized=True,
                            bbox_format='xywh'))
                if msg:
                    msgs.append(msg)
                pbar.desc = f'{desc} {nf} images, {nm + ne} backgrounds, {nc} corrupt'
            pbar.close()

        if msgs:
            LOGGER.info('\n'.join(msgs))
        if nf == 0:
            LOGGER.warning(f'{self.prefix}WARNING ⚠️ No labels found in {path}. {HELP_URL}')
        x['hash'] = get_hash(self.label_files + self.im_files)
        x['results'] = nf, nm, ne, nc, len(self.im_files)
        x['msgs'] = msgs  # warnings
        save_dataset_cache_file(self.prefix, path, x)
        return x

    def get_labels(self):
        """Returns dictionary of labels for YOLO training."""
        self.label_files = img2label_paths(self.im_files)
        cache_path = Path(self.label_files[0]).parent.with_suffix('.cache')
        try:
            cache, exists = load_dataset_cache_file(cache_path), True  # attempt to load a *.cache file
            assert cache['version'] == DATASET_CACHE_VERSION  # matches current version
            assert cache['hash'] == get_hash(self.label_files + self.im_files)  # identical hash
        except (FileNotFoundError, AssertionError, AttributeError):
            cache, exists = self.cache_labels(cache_path), False  # run cache ops

        # Display cache
        nf, nm, ne, nc, n = cache.pop('results')  # found, missing, empty, corrupt, total
        if exists and LOCAL_RANK in (-1, 0):
            d = f'Scanning {cache_path}... {nf} images, {nm + ne} backgrounds, {nc} corrupt'
            TQDM(None, desc=self.prefix + d, total=n, initial=n)  # display results
            if cache['msgs']:
                LOGGER.info('\n'.join(cache['msgs']))  # display warnings

        # Read cache
        [cache.pop(k) for k in ('hash', 'version', 'msgs')]  # remove items
        labels = cache['labels']
        if not labels:
            LOGGER.warning(f'WARNING ⚠️ No images found in {cache_path}, training may not work correctly. {HELP_URL}')
        self.im_files = [lb['im_file'] for lb in labels]  # update im_files

        # Check if the dataset is all boxes or all segments
        lengths = ((len(lb['cls']), len(lb['bboxes']), len(lb['segments'])) for lb in labels)
        len_cls, len_boxes, len_segments = (sum(x) for x in zip(*lengths))
        if len_segments and len_boxes != len_segments:
            LOGGER.warning(
                f'WARNING ⚠️ Box and segment counts should be equal, but got len(segments) = {len_segments}, '
                f'len(boxes) = {len_boxes}. To resolve this only boxes will be used and all segments will be removed. '
                'To avoid this please supply either a detect or segment dataset, not a detect-segment mixed dataset.')
            for lb in labels:
                lb['segments'] = []
        if len_cls == 0:
            LOGGER.warning(f'WARNING ⚠️ No labels found in {cache_path}, training may not work correctly. {HELP_URL}')
        return labels

    def build_transforms(self, hyp=None):
        """Builds and appends transforms to the list."""
        if self.augment:
            hyp.mosaic = hyp.mosaic if self.augment and not self.rect else 0.0
            hyp.mixup = hyp.mixup if self.augment and not self.rect else 0.0
            transforms = v8_transforms(self, self.imgsz, hyp)
        else:
            transforms = Compose([LetterBox(new_shape=(self.imgsz, self.imgsz), scaleup=False)])
        transforms.append(
            Format(bbox_format='xywh',
                   normalize=True,
                   return_mask=self.use_segments,
                   return_keypoint=self.use_keypoints,
                   batch_idx=True,
                   mask_ratio=hyp.mask_ratio,
                   mask_overlap=hyp.overlap_mask))
        return transforms

    def close_mosaic(self, hyp):
        """Sets mosaic, copy_paste and mixup options to 0.0 and builds transformations."""
        hyp.mosaic = 0.0  # set mosaic ratio=0.0
        hyp.copy_paste = 0.0  # keep the same behavior as previous v8 close-mosaic
        hyp.mixup = 0.0  # keep the same behavior as previous v8 close-mosaic
        self.transforms = self.build_transforms(hyp)

    def update_labels_info(self, label):
        """Custom your label format here."""
        # NOTE: cls is not with bboxes now, classification and semantic segmentation need an independent cls label
        # We can make it also support classification and semantic segmentation by add or remove some dict keys there.
        bboxes = label.pop('bboxes')
        segments = label.pop('segments')
        keypoints = label.pop('keypoints', None)
        bbox_format = label.pop('bbox_format')
        normalized = label.pop('normalized')
        label['instances'] = Instances(bboxes, segments, keypoints, bbox_format=bbox_format, normalized=normalized)
        return label

    @staticmethod
    def collate_fn(batch):
        """Collates data samples into batches."""
        new_batch = {}
        keys = batch[0].keys()
        values = list(zip(*[list(b.values()) for b in batch]))
        for i, k in enumerate(keys):
            value = values[i]
            if k == 'img':
                value = torch.stack(value, 0)
            if k in ['masks', 'keypoints', 'bboxes', 'cls']:
                value = torch.cat(value, 0)
            new_batch[k] = value
        new_batch['batch_idx'] = list(new_batch['batch_idx'])
        for i in range(len(new_batch['batch_idx'])):
            new_batch['batch_idx'][i] += i  # add target image index for build_targets()
        new_batch['batch_idx'] = torch.cat(new_batch['batch_idx'], 0)
        return new_batch


# Classification dataloaders -------------------------------------------------------------------------------------------
class ClassificationDataset(torchvision.datasets.ImageFolder):
    """
    YOLO Classification Dataset.

    Args:
        root (str): Dataset path.

    Attributes:
        cache_ram (bool): True if images should be cached in RAM, False otherwise.
        cache_disk (bool): True if images should be cached on disk, False otherwise.
        samples (list): List of samples containing file, index, npy, and im.
        torch_transforms (callable): torchvision transforms applied to the dataset.
        album_transforms (callable, optional): Albumentations transforms applied to the dataset if augment is True.
    """

    def __init__(self, root, args, augment=False, cache=False, prefix=''):
        """
        Initialize YOLO object with root, image size, augmentations, and cache settings.

        Args:
            root (str): Dataset path.
            args (Namespace): Argument parser containing dataset related settings.
            augment (bool, optional): True if dataset should be augmented, False otherwise. Defaults to False.
            cache (bool | str | optional): Cache setting, can be True, False, 'ram' or 'disk'. Defaults to False.
        """
        super().__init__(root=root)
        if augment and args.fraction < 1.0:  # reduce training fraction
            self.samples = self.samples[:round(len(self.samples) * args.fraction)]
        self.prefix = colorstr(f'{prefix}: ') if prefix else ''
        self.cache_ram = cache is True or cache == 'ram'
        self.cache_disk = cache == 'disk'
        self.samples = self.verify_images()  # filter out bad images
        self.samples = [list(x) + [Path(x[0]).with_suffix('.npy'), None] for x in self.samples]  # file, index, npy, im
        self.torch_transforms = classify_transforms(args.imgsz, rect=args.rect)
        self.album_transforms = classify_albumentations(
            augment=augment,
            size=args.imgsz,
            scale=(1.0 - args.scale, 1.0),  # (0.08, 1.0)
            hflip=args.fliplr,
            vflip=args.flipud,
            hsv_h=args.hsv_h,  # HSV-Hue augmentation (fraction)
            hsv_s=args.hsv_s,  # HSV-Saturation augmentation (fraction)
            hsv_v=args.hsv_v,  # HSV-Value augmentation (fraction)
            mean=(0.0, 0.0, 0.0),  # IMAGENET_MEAN
            std=(1.0, 1.0, 1.0),  # IMAGENET_STD
            auto_aug=False) if augment else None

    def __getitem__(self, i):
        """Returns subset of data and targets corresponding to given indices."""
        f, j, fn, im = self.samples[i]  # filename, index, filename.with_suffix('.npy'), image
        if self.cache_ram and im is None:
            im = self.samples[i][3] = cv2.imread(f)
        elif self.cache_disk:
            if not fn.exists():  # load npy
                np.save(fn.as_posix(), cv2.imread(f), allow_pickle=False)
            im = np.load(fn)
        else:  # read image
            im = cv2.imread(f)  # BGR
        if self.album_transforms:
            sample = self.album_transforms(image=cv2.cvtColor(im, cv2.COLOR_BGR2RGB))['image']
        else:
            sample = self.torch_transforms(im)
        return {'img': sample, 'cls': j}

    def __len__(self) -> int:
        """Return the total number of samples in the dataset."""
        return len(self.samples)

    def verify_images(self):
        """Verify all images in dataset."""
        desc = f'{self.prefix}Scanning {self.root}...'
        path = Path(self.root).with_suffix('.cache')  # *.cache file path

        with contextlib.suppress(FileNotFoundError, AssertionError, AttributeError):
            cache = load_dataset_cache_file(path)  # attempt to load a *.cache file
            assert cache['version'] == DATASET_CACHE_VERSION  # matches current version
            assert cache['hash'] == get_hash([x[0] for x in self.samples])  # identical hash
            nf, nc, n, samples = cache.pop('results')  # found, missing, empty, corrupt, total
            if LOCAL_RANK in (-1, 0):
                d = f'{desc} {nf} images, {nc} corrupt'
                TQDM(None, desc=d, total=n, initial=n)
                if cache['msgs']:
                    LOGGER.info('\n'.join(cache['msgs']))  # display warnings
            return samples

        # Run scan if *.cache retrieval failed
        nf, nc, msgs, samples, x = 0, 0, [], [], {}
        with ThreadPool(NUM_THREADS) as pool:
            results = pool.imap(func=verify_image, iterable=zip(self.samples, repeat(self.prefix)))
            pbar = TQDM(results, desc=desc, total=len(self.samples))
            for sample, nf_f, nc_f, msg in pbar:
                if nf_f:
                    samples.append(sample)
                if msg:
                    msgs.append(msg)
                nf += nf_f
                nc += nc_f
                pbar.desc = f'{desc} {nf} images, {nc} corrupt'
            pbar.close()
        if msgs:
            LOGGER.info('\n'.join(msgs))
        x['hash'] = get_hash([x[0] for x in self.samples])
        x['results'] = nf, nc, len(samples), samples
        x['msgs'] = msgs  # warnings
        save_dataset_cache_file(self.prefix, path, x)
        return samples


def load_dataset_cache_file(path):
    """Load an Ultralytics *.cache dictionary from path."""
    import gc
    gc.disable()  # reduce pickle load time https://github.com/ultralytics/ultralytics/pull/1585
    cache = np.load(str(path), allow_pickle=True).item()  # load dict
    gc.enable()
    return cache


def save_dataset_cache_file(prefix, path, x):
    """Save an Ultralytics dataset *.cache dictionary x to path."""
    x['version'] = DATASET_CACHE_VERSION  # add cache version
    if is_dir_writeable(path.parent):
        if path.exists():
            path.unlink()  # remove *.cache file if exists
        np.save(str(path), x)  # save cache for next time
        path.with_suffix('.cache.npy').rename(path)  # remove .npy suffix
        LOGGER.info(f'{prefix}New cache created: {path}')
    else:
        LOGGER.warning(f'{prefix}WARNING ⚠️ Cache directory {path.parent} is not writeable, cache not saved.')


# TODO: support semantic segmentation
class SemanticDataset(BaseDataset):
    """
    Semantic Segmentation Dataset.

    This class is responsible for handling datasets used for semantic segmentation tasks. It inherits functionalities
    from the BaseDataset class.

    Note:
        This class is currently a placeholder and needs to be populated with methods and attributes for supporting
        semantic segmentation tasks.
    """

    def __init__(self):
        """Initialize a SemanticDataset object."""
        super().__init__()
