"""This module implements an abstract base class (ABC) 'BaseDataset' for datasets.

It also includes common transformation functions (e.g., get_transform, __scale_width), which can be later used in subclasses.
"""
import random
import numpy as np
import torch.utils.data as data
from PIL import Image
import torchvision.transforms as transforms
from abc import ABC, abstractmethod
import torchvision.transforms.functional as F
import torch

class BaseDataset(data.Dataset, ABC):
    """This class is an abstract base class (ABC) for datasets.

    To create a subclass, you need to implement the following four functions:
    -- <__init__>:                      initialize the class, first call BaseDataset.__init__(self, opt).
    -- <__len__>:                       return the size of dataset.
    -- <__getitem__>:                   get a data point.
    -- <modify_commandline_options>:    (optionally) add dataset-specific options and set default options.
    """

    def __init__(self, opt):
        """Initialize the class; save the options in the class

        Parameters:
            opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOptions
        """
        self.opt = opt
        self.root = opt.dataroot

    @staticmethod
    def modify_commandline_options(parser, is_train):
        """Add new dataset-specific options, and rewrite default values for existing options.

        Parameters:
            parser          -- original option parser
            is_train (bool) -- whether training phase or test phase. You can use this flag to add training-specific or test-specific options.

        Returns:
            the modified parser.
        """
        return parser

    @abstractmethod
    def __len__(self):
        """Return the total number of images in the dataset."""
        return 0

    @abstractmethod
    def __getitem__(self, index):
        """Return a data point and its metadata information.

        Parameters:
            index - - a random integer for data indexing

        Returns:
            a dictionary of data with their names. It ususally contains the data itself and its metadata information.
        """
        pass


def get_params(opt, size):
    w, h = size
    new_h = h
    new_w = w
    if opt.preprocess == 'resize_and_crop':
        new_h = new_w = opt.load_size
    elif opt.preprocess == 'scale_width_and_crop':
        new_w = opt.load_size
        new_h = opt.load_size * h // w

    x = random.randint(0, np.maximum(0, new_w - opt.crop_size))
    y = random.randint(0, np.maximum(0, new_h - opt.crop_size))

    flip = random.random() > 0.5

    return {'crop_pos': (x, y), 'flip': flip}


def get_transform(opt, params=None, grayscale=False, method=Image.BICUBIC, convert=True,crop=True):
    transform_list = []
    if grayscale:
        transform_list.append(transforms.Grayscale(1))
    if 'resize' in opt.preprocess:
        osize = [opt.load_size, opt.load_size]
        transform_list.append(transforms.Resize(osize, method))
    elif 'scale_width' in opt.preprocess:
        transform_list.append(transforms.Lambda(lambda img: __scale_width(img, opt.load_size, method)))

    if 'crop' in opt.preprocess and crop:
        if params is None:
            transform_list.append(transforms.RandomCrop(opt.crop_size))
        else:
            transform_list.append(transforms.Lambda(lambda img: __crop(img, params['crop_pos'], opt.crop_size)))

    if opt.preprocess == 'none':
        transform_list.append(transforms.Lambda(lambda img: __make_power_2(img, base=4, method=method)))

    if not opt.no_flip:
        if params is None:
            transform_list.append(transforms.RandomHorizontalFlip())
        elif params['flip']:
            transform_list.append(transforms.Lambda(lambda img: __flip(img, params['flip'])))

    if convert:
        transform_list += [transforms.ToTensor()]
        if grayscale:
            transform_list += [transforms.Normalize((0.5,), (0.5,))]
        else:
            transform_list += [transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]
    return transforms.Compose(transform_list)


def __make_power_2(img, base, method=Image.BICUBIC):
    ow, oh = img.size
    h = int(round(oh / base) * base)
    w = int(round(ow / base) * base)
    if (h == oh) and (w == ow):
        return img

    __print_size_warning(ow, oh, w, h)
    return img.resize((w, h), method)


def __scale_width(img, target_width, method=Image.BICUBIC):
    ow, oh = img.size
    if (ow == target_width):
        return img
    w = target_width
    h = int(target_width * oh / ow)
    return img.resize((w, h), method)


def __crop(img, pos, size):
    ow, oh = img.size
    x1, y1 = pos
    tw = th = size
    if (ow > tw or oh > th):
        return img.crop((x1, y1, x1 + tw, y1 + th))
    return img


def __flip(img, flip):
    if flip:
        return img.transpose(Image.FLIP_LEFT_RIGHT)
    return img


def __print_size_warning(ow, oh, w, h):
    """Print warning information about image size(only print once)"""
    if not hasattr(__print_size_warning, 'has_printed'):
        print("The image size needs to be a multiple of 4. "
              "The loaded image size was (%d, %d), so it was adjusted to "
              "(%d, %d). This adjustment will be done to all images "
              "whose sizes are not multiples of 4" % (ow, oh, w, h))
        __print_size_warning.has_printed = True



def get_transform_seg(opt, params=None, grayscale=False, method=Image.BICUBIC):
    
    transform_list = []

    if grayscale:
        transform_list.append(transforms.GrayscaleMask(1))

    if 'resize' in opt.preprocess:
        osize = [opt.load_size, opt.load_size]
        transform_list.append(ResizeMask(osize, method))
        
    if 'crop' in opt.preprocess:
        transform_list.append(RandomCropMask(opt.crop_size))

    if not opt.no_flip:
        transform_list.append(RandomHorizontalFlipMask())

    if not opt.no_rotate:
        transform_list.append(RandomRotationMask(degrees=0))
        
    transform_list += [ToTensorMask()]

    if grayscale:
        transform_list += [NormalizeMask((0.5,), (0.5,))]
    else:
        transform_list += [NormalizeMask((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]

    return ComposeMask(transform_list)


class ComposeMask(transforms.Compose):
    """Composes several transforms together.

    Args:
        transforms (list of ``Transform`` objects): list of transforms to compose.

    Example:
        >>> transforms.Compose([
        >>>     transforms.CenterCrop(10),
        >>>     transforms.ToTensor(),
        >>> ])
    """

    def __call__(self, img,mask):
        for t in self.transforms:
            img,mask = t(img,mask)
        return img,mask


class GrayscaleMask(transforms.Grayscale):
    """Convert image to grayscale.

    Args:
        num_output_channels (int): (1 or 3) number of channels desired for output image

    Returns:
        PIL Image: Grayscale version of the input.
        - If num_output_channels == 1 : returned image is single channel
        - If num_output_channels == 3 : returned image is 3 channel with r == g == b

    """

    def __init__(self, num_output_channels=1):
        self.num_output_channels = num_output_channels

    def __call__(self, img, mask):
        """
        Args:
            img (PIL Image): Image to be converted to grayscale.

        Returns:
            PIL Image: Randomly grayscaled image.
        """
        return F.to_grayscale(img, num_output_channels=self.num_output_channels),mask

    def __repr__(self):
        return self.__class__.__name__ + '(num_output_channels={0})'.format(self.num_output_channels)


class ResizeMask(transforms.Resize):
    """Resize the input PIL Image to the given size.

    Args:
        size (sequence or int): Desired output size. If size is a sequence like
            (h, w), output size will be matched to this. If size is an int,
            smaller edge of the image will be matched to this number.
            i.e, if height > width, then image will be rescaled to
            (size * height / width, size)
        interpolation (int, optional): Desired interpolation. Default is
            ``PIL.Image.BILINEAR``
    """
    def __call__(self, img,mask):
        """
        Args:
            img (PIL Image): Image to be scaled.

        Returns:
            PIL Image: Rescaled image.
        """
        return F.resize(img, self.size, self.interpolation),F.resize(mask, self.size, Image.NEAREST)


class RandomCropMask(transforms.RandomCrop):
    """Crop the given PIL Image at a random location.

    Args:
        size (sequence or int): Desired output size of the crop. If size is an
            int instead of sequence like (h, w), a square crop (size, size) is
            made.
        padding (int or sequence, optional): Optional padding on each border
            of the image. Default is None, i.e no padding. If a sequence of length
            4 is provided, it is used to pad left, top, right, bottom borders
            respectively. If a sequence of length 2 is provided, it is used to
            pad left/right, top/bottom borders, respectively.
        pad_if_needed (boolean): It will pad the image if smaller than the
            desired size to avoid raising an exception. Since cropping is done
            after padding, the padding seems to be done at a random offset.
        fill: Pixel fill value for constant fill. Default is 0. If a tuple of
            length 3, it is used to fill R, G, B channels respectively.
            This value is only used when the padding_mode is constant
        padding_mode: Type of padding. Should be: constant, edge, reflect or symmetric. Default is constant.

             - constant: pads with a constant value, this value is specified with fill

             - edge: pads with the last value on the edge of the image

             - reflect: pads with reflection of image (without repeating the last value on the edge)

                padding [1, 2, 3, 4] with 2 elements on both sides in reflect mode
                will result in [3, 2, 1, 2, 3, 4, 3, 2]

             - symmetric: pads with reflection of image (repeating the last value on the edge)

                padding [1, 2, 3, 4] with 2 elements on both sides in symmetric mode
                will result in [2, 1, 1, 2, 3, 4, 4, 3]

    """
    def __call__(self, img,mask):
        """
        Args:
            img (PIL Image): Image to be cropped.

        Returns:
            PIL Image: Cropped image.
        """
        if self.padding is not None:
            img = F.pad(img, self.padding, self.fill, self.padding_mode)

        # pad the width if needed
        if self.pad_if_needed and img.size[0] < self.size[1]:
            img = F.pad(img, (self.size[1] - img.size[0], 0), self.fill, self.padding_mode)
        # pad the height if needed
        if self.pad_if_needed and img.size[1] < self.size[0]:
            img = F.pad(img, (0, self.size[0] - img.size[1]), self.fill, self.padding_mode)

        i, j, h, w = self.get_params(img, self.size)

        return F.crop(img, i, j, h, w),F.crop(mask, i, j, h, w)


class RandomHorizontalFlipMask(transforms.RandomHorizontalFlip):
    """Horizontally flip the given PIL Image randomly with a given probability.

    Args:
        p (float): probability of the image being flipped. Default value is 0.5
    """

    def __call__(self, img, mask):
        """
        Args:
            img (PIL Image): Image to be flipped.

        Returns:
            PIL Image: Randomly flipped image.
        """
        if random.random() < self.p:
            return F.hflip(img),F.hflip(mask)
        return img,mask

class ToTensorMask(transforms.ToTensor):
    """Convert a ``PIL Image`` or ``numpy.ndarray`` to tensor.

    Converts a PIL Image or numpy.ndarray (H x W x C) in the range
    [0, 255] to a torch.FloatTensor of shape (C x H x W) in the range [0.0, 1.0]
    if the PIL Image belongs to one of the modes (L, LA, P, I, F, RGB, YCbCr, RGBA, CMYK, 1)
    or if the numpy.ndarray has dtype = np.uint8

    In the other cases, tensors are returned without scaling.
    """

    def __call__(self, img,mask):
        """
        Args:
            pic (PIL Image or numpy.ndarray): Image to be converted to tensor.

        Returns:
            Tensor: Converted image.
        """
        return F.to_tensor(img),torch.from_numpy(np.array(mask,dtype=np.int64)).unsqueeze(0)

class RandomRotationMask(transforms.RandomRotation):
    """Rotate the image by angle.

    Args:
        degrees (sequence or float or int): Range of degrees to select from.
            If degrees is a number instead of sequence like (min, max), the range of degrees
            will be (-degrees, +degrees).
        resample ({PIL.Image.NEAREST, PIL.Image.BILINEAR, PIL.Image.BICUBIC}, optional):
            An optional resampling filter. See `filters`_ for more information.
            If omitted, or if the image has mode "1" or "P", it is set to PIL.Image.NEAREST.
        expand (bool, optional): Optional expansion flag.
            If true, expands the output to make it large enough to hold the entire rotated image.
            If false or omitted, make the output image the same size as the input image.
            Note that the expand flag assumes rotation around the center and no translation.
        center (2-tuple, optional): Optional center of rotation.
            Origin is the upper left corner.
            Default is the center of the image.
        fill (3-tuple or int): RGB pixel fill value for area outside the rotated image.
            If int, it is used for all channels respectively.

    .. _filters: https://pillow.readthedocs.io/en/latest/handbook/concepts.html#filters

    """
    @staticmethod
    def get_params(degrees):
        """Get parameters for ``rotate`` for a random rotation.

        Returns:
            sequence: params to be passed to ``rotate`` for random rotation.
        """
        angle = random.uniform(degrees[0], degrees[1])

        return angle

    def __call__(self, img,mask):
        """
        Args:
            img (PIL Image): Image to be rotated.

        Returns:
            PIL Image: Rotated image.
        """
        angle = random.choice([0,90,180,270])
        
        return F.rotate(img, angle),F.rotate(mask, angle,fill=(0,))

class NormalizeMask(transforms.Normalize):
    """Normalize a tensor image with mean and standard deviation.
    Given mean: ``(M1,...,Mn)`` and std: ``(S1,..,Sn)`` for ``n`` channels, this transform
    will normalize each channel of the input ``torch.*Tensor`` i.e.
    ``input[channel] = (input[channel] - mean[channel]) / std[channel]``

    .. note::
        This transform acts out of place, i.e., it does not mutates the input tensor.

    Args:
        mean (sequence): Sequence of means for each channel.
        std (sequence): Sequence of standard deviations for each channel.
        inplace(bool,optional): Bool to make this operation in-place.

    """

    def __call__(self, tensor_img,tensor_mask):
        """
        Args:
            tensor (Tensor): Tensor image of size (C, H, W) to be normalized.

        Returns:
            Tensor: Normalized Tensor image.
        """
        return F.normalize(tensor_img, self.mean, self.std, self.inplace),tensor_mask


    def __repr__(self):
        return self.__class__.__name__ + '(mean={0}, std={1})'.format(self.mean, self.std)
