# code adapted from:

# https://github.com/kakaobrain/fast-autoaugment

# https://github.com/rpmcruz/autoaugment

import math

import random

import numpy as np

from collections import defaultdict

import PIL, PIL.ImageOps, PIL.ImageEnhance, PIL.ImageDraw

from PIL import Image

random_mirror = True

RESAMPLE_MODE=Image.BICUBIC

def ShearX(img, v):  # [-0.3, 0.3]

    assert -0.3 <= v <= 0.3

    if random_mirror and random.random() > 0.5:

        v = -v

    return img.transform(img.size, Image.AFFINE, (1, v, 0, 0, 1, 0),

                         RESAMPLE_MODE)

def ShearY(img, v):  # [-0.3, 0.3]

    assert -0.3 <= v <= 0.3

    if random_mirror and random.random() > 0.5:

        v = -v

    return img.transform(img.size, Image.AFFINE, (1, 0, 0, v, 1, 0),

                         RESAMPLE_MODE)

def TranslateX(img, v):  # [-150, 150] => percentage: [-0.45, 0.45]

    assert -0.45 <= v <= 0.45

    if random_mirror and random.random() > 0.5:

        v = -v

    v = v * img.size[0]

    return img.transform(img.size, Image.AFFINE, (1, 0, v, 0, 1, 0),

                         RESAMPLE_MODE)

def TranslateY(img, v):  # [-150, 150] => percentage: [-0.45, 0.45]

    assert -0.45 <= v <= 0.45

    if random_mirror and random.random() > 0.5:

        v = -v

    v = v * img.size[1]

    return img.transform(img.size, Image.AFFINE, (1, 0, 0, 0, 1, v),

                         RESAMPLE_MODE)

def TranslateXabs(img, v):  # [-150, 150] => percentage: [-0.45, 0.45]

    assert 0 <= v

    if random.random() > 0.5:

        v = -v

    return img.transform(img.size, Image.AFFINE, (1, 0, v, 0, 1, 0),

                         RESAMPLE_MODE)

def TranslateYabs(img, v):  # [-150, 150] => percentage: [-0.45, 0.45]

    assert 0 <= v

    if random.random() > 0.5:

        v = -v

    return img.transform(img.size, Image.AFFINE, (1, 0, 0, 0, 1, v),

                         RESAMPLE_MODE)

def Rotate(img, v):  # [-30, 30]

    assert -30 <= v <= 30

    if random_mirror and random.random() > 0.5:

        v = -v

    return img.rotate(v)

def AutoContrast(img, _):

    return PIL.ImageOps.autocontrast(img)

def Invert(img, _):

    return PIL.ImageOps.invert(img)

def Equalize(img, _):

    return PIL.ImageOps.equalize(img)

def Flip(img, _):  # not from the paper

    return PIL.ImageOps.mirror(img)

def Solarize(img, v):  # [0, 256]

    assert 0 <= v <= 256

    return PIL.ImageOps.solarize(img, v)

def SolarizeAdd(img, addition=0, threshold=128):

    img_np = np.array(img).astype(np.int)

    img_np = img_np + addition

    img_np = np.clip(img_np, 0, 255)

    img_np = img_np.astype(np.uint8)

    img = Image.fromarray(img_np)

    return PIL.ImageOps.solarize(img, threshold)

def Posterize(img, v):  # [4, 8]

    #assert 4 <= v <= 8

    v = int(v)

    return PIL.ImageOps.posterize(img, v)

def Contrast(img, v):  # [0.1,1.9]

    assert 0.1 <= v <= 1.9

    return PIL.ImageEnhance.Contrast(img).enhance(v)

def Color(img, v):  # [0.1,1.9]

    assert 0.1 <= v <= 1.9

    return PIL.ImageEnhance.Color(img).enhance(v)

def Brightness(img, v):  # [0.1,1.9]

    assert 0.1 <= v <= 1.9

    return PIL.ImageEnhance.Brightness(img).enhance(v)

def Sharpness(img, v):  # [0.1,1.9]

    assert 0.1 <= v <= 1.9

    return PIL.ImageEnhance.Sharpness(img).enhance(v)

def CutoutAbs(img, v):  # [0, 60] => percentage: [0, 0.2]

    # assert 0 <= v <= 20

    if v < 0:

        return img

    w, h = img.size

    x0 = np.random.uniform(w)

    y0 = np.random.uniform(h)

    x0 = int(max(0, x0 - v / 2.))

    y0 = int(max(0, y0 - v / 2.))

    x1 = min(w, x0 + v)

    y1 = min(h, y0 + v)

    xy = (x0, y0, x1, y1)

    color = (125, 123, 114)

    # color = (0, 0, 0)

    img = img.copy()

    PIL.ImageDraw.Draw(img).rectangle(xy, color)

    return img

def Cutout(img, v):  # [0, 60] => percentage: [0, 0.2]

    assert 0.0 <= v <= 0.2

    if v <= 0.:

        return img

    v = v * img.size[0]

    return CutoutAbs(img, v)

def TranslateYAbs(img, v):  # [-150, 150] => percentage: [-0.45, 0.45]

    assert 0 <= v <= 10

    if random.random() > 0.5:

        v = -v

    return img.transform(img.size, Image.AFFINE, (1, 0, 0, 0, 1, v),

                         resample=RESAMPLE_MODE)

def TranslateXAbs(img, v):  # [-150, 150] => percentage: [-0.45, 0.45]

    assert 0 <= v <= 10

    if random.random() > 0.5:

        v = -v

    return img.transform(img.size, Image.AFFINE, (1, 0, v, 0, 1, 0),

                         resample=RESAMPLE_MODE)

def Posterize2(img, v):  # [0, 4]

    assert 0 <= v <= 4

    v = int(v)

    return PIL.ImageOps.posterize(img, v)

def SamplePairing(imgs):  # [0, 0.4]

    def f(img1, v):

        i = np.random.choice(len(imgs))

        img2 = Image.fromarray(imgs[i])

        return Image.blend(img1, img2, v)

    return f

def augment_list(for_autoaug=True):  # 16 oeprations and their ranges

    l = [

        (ShearX, -0.3, 0.3),  # 0

        (ShearY, -0.3, 0.3),  # 1

        (TranslateX, -0.45, 0.45),  # 2

        (TranslateY, -0.45, 0.45),  # 3

        (Rotate, -30, 30),  # 4

        (AutoContrast, 0, 1),  # 5

        (Invert, 0, 1),  # 6

        (Equalize, 0, 1),  # 7

        (Solarize, 0, 256),  # 8

        (Posterize, 4, 8),  # 9

        (Contrast, 0.1, 1.9),  # 10

        (Color, 0.1, 1.9),  # 11

        (Brightness, 0.1, 1.9),  # 12

        (Sharpness, 0.1, 1.9),  # 13

        (Cutout, 0, 0.2),  # 14

        # (SamplePairing(imgs), 0, 0.4),  # 15

    ]

    if for_autoaug:

        l += [

            (CutoutAbs, 0, 20),  # compatible with auto-augment

            (Posterize2, 0, 4),  # 9

            (TranslateXAbs, 0, 10),  # 9

            (TranslateYAbs, 0, 10),  # 9

        ]

    return l

augment_dict = {fn.__name__: (fn, v1, v2) for fn, v1, v2 in augment_list()}

PARAMETER_MAX = 10

def float_parameter(level, maxval):

    return float(level) * maxval / PARAMETER_MAX

def int_parameter(level, maxval):

    return int(float_parameter(level, maxval))

def autoaug2fastaa(f):

    def autoaug():

        mapper = defaultdict(lambda: lambda x: x)

        mapper.update({

            'ShearX': lambda x: float_parameter(x, 0.3),

            'ShearY': lambda x: float_parameter(x, 0.3),

            'TranslateX': lambda x: int_parameter(x, 10),

            'TranslateY': lambda x: int_parameter(x, 10),

            'Rotate': lambda x: int_parameter(x, 30),

            'Solarize': lambda x: 256 - int_parameter(x, 256),

            'Posterize2': lambda x: 4 - int_parameter(x, 4),

            'Contrast': lambda x: float_parameter(x, 1.8) + .1,

            'Color': lambda x: float_parameter(x, 1.8) + .1,

            'Brightness': lambda x: float_parameter(x, 1.8) + .1,

            'Sharpness': lambda x: float_parameter(x, 1.8) + .1,

            'CutoutAbs': lambda x: int_parameter(x, 20)

        })

        def low_high(name, prev_value):

            _, low, high = get_augment(name)

            return float(prev_value - low) / (high - low)

        policies = f()

        new_policies = []

        for policy in policies:

            new_policies.append([(name, pr, low_high(name, mapper[name](level))) for name, pr, level in policy])

        return new_policies

    return autoaug

@autoaug2fastaa

def autoaug_imagenet_policies():

    return [

        [('Posterize2', 0.4, 8), ('Rotate', 0.6, 9)],

        [('Solarize', 0.6, 5), ('AutoContrast', 0.6, 5)],

        [('Equalize', 0.8, 8), ('Equalize', 0.6, 3)],

        [('Posterize2', 0.6, 7), ('Posterize2', 0.6, 6)],

        [('Equalize', 0.4, 7), ('Solarize', 0.2, 4)],

        [('Equalize', 0.4, 4), ('Rotate', 0.8, 8)],

        [('Solarize', 0.6, 3), ('Equalize', 0.6, 7)],

        [('Posterize2', 0.8, 5), ('Equalize', 1.0, 2)],

        [('Rotate', 0.2, 3), ('Solarize', 0.6, 8)],

        [('Equalize', 0.6, 8), ('Posterize2', 0.4, 6)],

        [('Rotate', 0.8, 8), ('Color', 0.4, 0)],

        [('Rotate', 0.4, 9), ('Equalize', 0.6, 2)],

        [('Equalize', 0.0, 7), ('Equalize', 0.8, 8)],

        [('Invert', 0.6, 4), ('Equalize', 1.0, 8)],

        [('Color', 0.6, 4), ('Contrast', 1.0, 8)],

        [('Rotate', 0.8, 8), ('Color', 1.0, 0)],

        [('Color', 0.8, 8), ('Solarize', 0.8, 7)],

        [('Sharpness', 0.4, 7), ('Invert', 0.6, 8)],

        [('ShearX', 0.6, 5), ('Equalize', 1.0, 9)],

        [('Color', 0.4, 0), ('Equalize', 0.6, 3)],

        [('Equalize', 0.4, 7), ('Solarize', 0.2, 4)],

        [('Solarize', 0.6, 5), ('AutoContrast', 0.6, 5)],

        [('Invert', 0.6, 4), ('Equalize', 1.0, 8)],

        [('Color', 0.6, 4), ('Contrast', 1.0, 8)],

        [('Equalize', 0.8, 8), ('Equalize', 0.6, 3)],

    ]

def get_augment(name):

    return augment_dict[name]

def apply_augment(img, name, level):

    augment_fn, low, high = get_augment(name)

    return augment_fn(img.copy(), level * (high - low) + low)

def rand_augment_list():  # 16 oeprations and their ranges

    l = [

        (AutoContrast, 0, 1),

        (Equalize, 0, 1),

        (Invert, 0, 1),

        (Rotate, 0, 30),

        (Posterize, 0, 4),

        (Solarize, 0, 256),

        (SolarizeAdd, 0, 110),

        (Color, 0.1, 1.9),

        (Contrast, 0.1, 1.9),

        (Brightness, 0.1, 1.9),

        (Sharpness, 0.1, 1.9),

        (ShearX, 0., 0.3),

        (ShearY, 0., 0.3),

        (CutoutAbs, 0, 40),

        (TranslateXabs, 0., 100),

        (TranslateYabs, 0., 100),

    ]

    return l

class ERandomCrop:

    # pylint: disable=misplaced-comparison-constant

    def __init__(self, imgsize, min_covered=0.1, aspect_ratio_range=(3./4, 4./3),

                 area_range=(0.1, 1.0), max_attempts=10):

        assert 0.0 < min_covered

        assert 0 < aspect_ratio_range[0] <= aspect_ratio_range[1]

        assert 0 < area_range[0] <= area_range[1]

        assert 1 <= max_attempts

        self.min_covered = min_covered

        self.aspect_ratio_range = aspect_ratio_range

        self.area_range = area_range

        self.max_attempts = max_attempts

        self._fallback = ECenterCrop(imgsize)

    def __call__(self, img):

        # https://github.com/tensorflow/tensorflow/blob/9274bcebb31322370139467039034f8ff852b004/tensorflow/core/kernels/sample_distorted_bounding_box_op.cc#L111

        original_width, original_height = img.size

        min_area = self.area_range[0] * (original_width * original_height)

        max_area = self.area_range[1] * (original_width * original_height)

        for _ in range(self.max_attempts):

            aspect_ratio = random.uniform(*self.aspect_ratio_range)

            height = int(round(math.sqrt(min_area / aspect_ratio)))

            max_height = int(round(math.sqrt(max_area / aspect_ratio)))

            if max_height * aspect_ratio > original_width:

                max_height = (original_width + 0.5 - 1e-7) / aspect_ratio

                max_height = int(max_height)

                if max_height * aspect_ratio > original_width:

                    max_height -= 1

            if max_height > original_height:

                max_height = original_height

            if height >= max_height:

                height = max_height

            height = int(round(random.uniform(height, max_height)))

            width = int(round(height * aspect_ratio))

            area = width * height

            if area < min_area or area > max_area:

                continue

            if width > original_width or height > original_height:

                continue

            if area < self.min_covered * (original_width * original_height):

                continue

            if width == original_width and height == original_height:

                return self._fallback(img)

            x = random.randint(0, original_width - width)

            y = random.randint(0, original_height - height)

            return img.crop((x, y, x + width, y + height))

        return self._fallback(img)

class ECenterCrop:

    """Crop the given PIL Image and resize it to desired size.

    Args:

        img (PIL Image): Image to be cropped. (0,0) denotes the top left corner of the image.

        output_size (sequence or int): (height, width) of the crop box. If int,

            it is used for both directions

    Returns:

        PIL Image: Cropped image.

    """

    def __init__(self, imgsize):

        self.imgsize = imgsize

        import torchvision.transforms as pth_transforms

        self.resize_method = pth_transforms.Resize((imgsize, imgsize), interpolation=RESAMPLE_MODE)

    def __call__(self, img):

        image_width, image_height = img.size

        image_short = min(image_width, image_height)

        crop_size = float(self.imgsize) / (self.imgsize + 32) * image_short

        crop_height, crop_width = crop_size, crop_size

        crop_top = int(round((image_height - crop_height) / 2.))

        crop_left = int(round((image_width - crop_width) / 2.))

        img = img.crop((crop_left, crop_top, crop_left + crop_width, crop_top + crop_height))

        return self.resize_method(img)