# Ultralytics YOLO 🚀, AGPL-3.0 license

from itertools import repeat
from multiprocessing.pool import ThreadPool
from pathlib import Path
import joblib

import cv2
import numpy as np
import torch
import torchvision
from tqdm import tqdm

from ultralytics.utils import LOCAL_RANK, NUM_THREADS, TQDM_BAR_FORMAT, 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_label


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.
    """
    cache_version = '1.0.2'  # dataset labels *.cache version, >= 1.0.0 for YOLOv8

    def __init__(self, *args, data=None, use_segments=False, use_keypoints=False, **kwargs):
        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]'")
        NUM_THREADS = 32
        print(f'[debug] dataset.py cache_labels(): scanning label directory with {NUM_THREADS} threads')
        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, bar_format=TQDM_BAR_FORMAT)
            for im_file, lb, shape, segments, keypoint, nm_f, nf_f, ne_f, nc_f, msg in tqdm(results, total=total, desc=desc):
                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()
        print(f'[debug] dataset.py cache_labels(): {nf} images, {nm + ne} backgrounds, {nc} corrupt')
        
        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
        x['version'] = self.cache_version  # cache version
        if is_dir_writeable(path.parent):
            if path.exists():
                path.unlink()  # remove *.cache file if exists
            print('[debug] caching with joblib dump')
            joblib.dump(x, str(path))  # save the cache with joblib
            print(f'[debug] cache created at {path}')
            #np.save(str(path), x)  # save cache for next time
            #path.with_suffix('.cache.npy').rename(path)  # remove .npy suffix
            LOGGER.info(f'{self.prefix}New cache created: {path}')
        else:
            LOGGER.warning(f'{self.prefix}WARNING ⚠️ Cache directory {path.parent} is not writeable, cache not saved.')
        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:
            print('[debug] dataset.py get_labels(): loading labels from cache')
            import gc
            gc.disable()  # reduce pickle load time https://github.com/ultralytics/ultralytics/pull/1585
            cache, exists = joblib.load(str(cache_path)), True
            #cache, exists = np.load(str(cache_path), allow_pickle=True).item(), True  # load dict
            gc.enable()
            assert cache['version'] == self.cache_version  # matches current version
            assert cache['hash'] == get_hash(self.label_files + self.im_files)  # identical hash
            print('[debug] dataset.py get_labels(): labels has been loaded from cache')
        except (FileNotFoundError, AssertionError, AttributeError):
            cache, exists = self.cache_labels(cache_path), False  # run cache ops

        # Display cache
        print('[debug] base.py get_labels(): displaying 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, bar_format=TQDM_BAR_FORMAT)  # display cache results
            if cache['msgs']:
                LOGGER.info('\n'.join(cache['msgs']))  # display warnings
        if nf == 0:  # number of labels found
            raise FileNotFoundError(f'{self.prefix}No labels found in {cache_path}, can not start training. {HELP_URL}')
        print('[debug] base.py get_labels(): displaying cache: done')

        # Read cache
        print('[debug] base.py get_labels(): reading cache')
        [cache.pop(k) for k in ('hash', 'version', 'msgs')]  # remove items
        labels = cache['labels']
        assert len(labels), f'No valid labels found, please check your dataset. {HELP_URL}'
        self.im_files = [lb['im_file'] for lb in labels]  # update im_files
        print('[debug] base.py get_labels(): reading cache done')

        # 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:
            raise ValueError(f'All labels empty in {cache_path}, can not start training without labels. {HELP_URL}')
        print('[debug] base.py get_labels(): returning the labels')

        return labels

    # TODO: use hyp config to set all these augmentations
    def build_transforms(self, hyp=None):
        """Builds and appends transforms to the list."""
        # disable augmentations for RGB in order to train with gray-scale
        self.augment = False

        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):
        """
        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.cache_ram = cache is True or cache == 'ram'
        self.cache_disk = cache == 'disk'
        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)
        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))
            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 len(self.samples)


# TODO: support semantic segmentation
class SemanticDataset(BaseDataset):

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