import torch
import torchvision
import os
from PIL import Image
from typing import Callable, Optional, Tuple, Any, List


class BerkeleyDataset(object):
    """
    This class represents the semantic Berkeley dataset. Annotations are similar to Cityscapes annotation, therefore
    same id transformations can be used.

    Berkeley dataset is required to have following folder structure:
    bgg/
        /gtFine
            /train
                /city
            /val
                /city
        /leftImg8bit
            /train
                /city
            /val
                /city

    """
    from collections import namedtuple

    # a label and all meta information
    # Code inspired by Cityscapes https://github.com/mcordts/cityscapesScripts
    Label = namedtuple('Label', [

        'name',  # The identifier of this label, e.g. 'car', 'person', ... .
        # We use them to uniquely name a class

        'id',  # An integer ID that is associated with this label.
        # The IDs are used to represent the label in ground truth images
        # An ID of -1 means that this label does not have an ID and thus
        # is ignored when creating ground truth images (e.g. license plate).
        # Do not modify these IDs, since exactly these IDs are expected by the
        # evaluation server.

        'trainId',
        # Feel free to modify these IDs as suitable for your method. Then create
        # ground truth images with train IDs, using the tools provided in the
        # 'preparation' folder. However, make sure to validate or submit results
        # to our evaluation server using the regular IDs above!
        # For trainIds, multiple labels might have the same ID. Then, these labels
        # are mapped to the same class in the ground truth images. For the inverse
        # mapping, we use the label that is defined first in the list below.
        # For example, mapping all void-type classes to the same ID in training,
        # might make sense for some approaches.
        # Max value is 255!

        'category',  # The name of the category that this label belongs to

        'categoryId',
        # The ID of this category. Used to create ground truth images
        # on category level.

        'hasInstances',
        # Whether this label distinguishes between single instances or not

        'ignoreInEval',
        # Whether pixels having this class as ground truth label are ignored
        # during evaluations or not

        'color',  # The color of this label
    ])

    # Our extended list of label types. Our train id is compatible with Cityscapes
    labels = [
        Label('unlabeled', 0, 255, 'void', 0, False, True, (0, 0, 0)),
        Label('dynamic', 1, 255, 'void', 0, False, True, (111, 74, 0)),
        Label('ego vehicle', 2, 255, 'void', 0, False, True, (0, 0, 0)),
        Label('ground', 3, 255, 'void', 0, False, True, (81, 0, 81)),
        Label('static', 4, 255, 'void', 0, False, True, (0, 0, 0)),
        Label('parking', 5, 255, 'flat', 1, False, True, (250, 170, 160)),
        Label('rail track', 6, 255, 'flat', 1, False, True, (230, 150, 140)),
        Label('road', 7, 0, 'flat', 1, False, False, (128, 64, 128)),
        Label('sidewalk', 8, 1, 'flat', 1, False, False, (244, 35, 232)),
        Label('bridge', 9, 255, 'construction', 2, False, True, (150, 100, 100)),
        Label('building', 10, 2, 'construction', 2, False, False, (70, 70, 70)),
        Label('fence', 11, 4, 'construction', 2, False, False, (190, 153, 153)),
        Label('garage', 12, 255, 'construction', 2, False, True, (180, 100, 180)),
        Label('guard rail', 13, 255, 'construction', 2, False, True, (180, 165, 180)),
        Label('tunnel', 14, 255, 'construction', 2, False, True, (150, 120, 90)),
        Label('wall', 15, 3, 'construction', 2, False, False, (102, 102, 156)),
        Label('banner', 16, 255, 'object', 3, False, True, (250, 170, 100)),
        Label('billboard', 17, 255, 'object', 3, False, True, (220, 220, 250)),
        Label('lane divider', 18, 255, 'object', 3, False, True, (255, 165, 0)),
        Label('parking sign', 19, 255, 'object', 3, False, False, (220, 20, 60)),
        Label('pole', 20, 5, 'object', 3, False, False, (153, 153, 153)),
        Label('polegroup', 21, 255, 'object', 3, False, True, (153, 153, 153)),
        Label('street light', 22, 255, 'object', 3, False, True, (220, 220, 100)),
        Label('traffic cone', 23, 255, 'object', 3, False, True, (255, 70, 0)),
        Label('traffic device', 24, 255, 'object', 3, False, True, (220, 220, 220)),
        Label('traffic light', 25, 6, 'object', 3, False, False, (250, 170, 30)),
        Label('traffic sign', 26, 7, 'object', 3, False, False, (220, 220, 0)),
        Label('traffic sign frame', 27, 255, 'object', 3, False, True, (250, 170, 250)),
        Label('terrain', 28, 9, 'nature', 4, False, False, (152, 251, 152)),
        Label('vegetation', 29, 8, 'nature', 4, False, False, (107, 142, 35)),
        Label('sky', 30, 10, 'sky', 5, False, False, (70, 130, 180)),
        Label('person', 31, 11, 'human', 6, True, False, (220, 20, 60)),
        Label('rider', 32, 12, 'human', 6, True, False, (255, 0, 0)),
        Label('bicycle', 33, 18, 'vehicle', 7, True, False, (119, 11, 32)),
        Label('bus', 34, 15, 'vehicle', 7, True, False, (0, 60, 100)),
        Label('car', 35, 13, 'vehicle', 7, True, False, (0, 0, 142)),
        Label('caravan', 36, 255, 'vehicle', 7, True, True, (0, 0, 90)),
        Label('motorcycle', 37, 17, 'vehicle', 7, True, False, (0, 0, 230)),
        Label('trailer', 38, 255, 'vehicle', 7, True, True, (0, 0, 110)),
        Label('train', 39, 16, 'vehicle', 7, True, False, (0, 80, 100)),
        Label('truck', 40, 14, 'vehicle', 7, True, False, (0, 0, 70)),
    ]

    def __init__(self,
                 root,
                 split,
                 transforms: Optional[Callable] = None):

        super(BerkeleyDataset, self).__init__()

        self.mode = 'gtFine'
        self.images_dir = os.path.join(root, 'leftImg8bit', split)
        self.targets_dir = os.path.join(root, self.mode, split)
        self.split = split
        self.images = []
        self.targets = []
        self.transforms = transforms

        for city in os.listdir(self.images_dir):
            img_dir = os.path.join(self.images_dir, city)
            target_dir = os.path.join(self.targets_dir, city)
            for file_name in os.listdir(img_dir):
                target_id = os.path.splitext(file_name)[0]
                target_suffix = "_train_id"
                target_ext = ".png"
                target_name = target_id + target_suffix + target_ext

                self.images.append(os.path.join(img_dir, file_name))
                self.targets.append(os.path.join(target_dir, target_name))

    def __getitem__(self, index: int) -> Tuple[Any, Any]:
        """
        Args:
            index (int): Index
        Returns:
            tuple: (image, target) where target is a tuple of all target types if target_type is a list with more
            than one item. Otherwise target is a json object if target_type="polygon", else the image segmentation.
        """

        image = Image.open(self.images[index]).convert('RGB')
        target = Image.open(self.targets[index])

        if self.transforms is not None:
            image, target = self.transforms(image, target)

        return image, target

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


def bdd(root: str,
        split: str,
        transforms: List[Callable]):
    return BerkeleyDataset(root=root,
                           split=split,
                           transforms=transforms)
