"""
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals

from functools import partial
import six
import math
import random
import cv2
import numpy as np
from PIL import Image
from paddle.vision.transforms import ColorJitter as RawColorJitter

from .autoaugment import ImageNetPolicy
from .functional import augmentations
from ppcls.utils import logger


class UnifiedResize(object):
    def __init__(self, interpolation=None, backend="cv2"):
        _cv2_interp_from_str = {
            'nearest': cv2.INTER_NEAREST,
            'bilinear': cv2.INTER_LINEAR,
            'area': cv2.INTER_AREA,
            'bicubic': cv2.INTER_CUBIC,
            'lanczos': cv2.INTER_LANCZOS4
        }
        _pil_interp_from_str = {
            'nearest': Image.NEAREST,
            'bilinear': Image.BILINEAR,
            'bicubic': Image.BICUBIC,
            'box': Image.BOX,
            'lanczos': Image.LANCZOS,
            'hamming': Image.HAMMING
        }

        def _pil_resize(src, size, resample):
            pil_img = Image.fromarray(src)
            pil_img = pil_img.resize(size, resample)
            return np.asarray(pil_img)

        if backend.lower() == "cv2":
            if isinstance(interpolation, str):
                interpolation = _cv2_interp_from_str[interpolation.lower()]
            # compatible with opencv < version 4.4.0
            elif not interpolation:
                interpolation = cv2.INTER_LINEAR
            self.resize_func = partial(cv2.resize, interpolation=interpolation)
        elif backend.lower() == "pil":
            if isinstance(interpolation, str):
                interpolation = _pil_interp_from_str[interpolation.lower()]
            self.resize_func = partial(_pil_resize, resample=interpolation)
        else:
            logger.warning(
                f"The backend of Resize only support \"cv2\" or \"PIL\". \"f{backend}\" is unavailable. Use \"cv2\" instead."
            )
            self.resize_func = cv2.resize

    def __call__(self, src, size):
        return self.resize_func(src, size)


class OperatorParamError(ValueError):
    """ OperatorParamError
    """
    pass


class DecodeImage(object):
    """ decode image """

    def __init__(self, to_rgb=True, to_np=False, channel_first=False):
        self.to_rgb = to_rgb
        self.to_np = to_np  # to numpy
        self.channel_first = channel_first  # only enabled when to_np is True

    def __call__(self, img):
        if six.PY2:
            assert type(img) is str and len(
                img) > 0, "invalid input 'img' in DecodeImage"
        else:
            assert type(img) is bytes and len(
                img) > 0, "invalid input 'img' in DecodeImage"
        data = np.frombuffer(img, dtype='uint8')
        img = cv2.imdecode(data, 1)
        if self.to_rgb:
            assert img.shape[2] == 3, 'invalid shape of image[%s]' % (
                img.shape)
            img = img[:, :, ::-1]

        if self.channel_first:
            img = img.transpose((2, 0, 1))

        return img


class ResizeImage(object):
    """ resize image """

    def __init__(self,
                 size=None,
                 resize_short=None,
                 interpolation=None,
                 backend="cv2"):
        if resize_short is not None and resize_short > 0:
            self.resize_short = resize_short
            self.w = None
            self.h = None
        elif size is not None:
            self.resize_short = None
            self.w = size if type(size) is int else size[0]
            self.h = size if type(size) is int else size[1]
        else:
            raise OperatorParamError("invalid params for ReisizeImage for '\
                'both 'size' and 'resize_short' are None")

        self._resize_func = UnifiedResize(
            interpolation=interpolation, backend=backend)

    def __call__(self, img):
        img_h, img_w = img.shape[:2]
        if self.resize_short is not None:
            percent = float(self.resize_short) / min(img_w, img_h)
            w = int(round(img_w * percent))
            h = int(round(img_h * percent))
        else:
            w = self.w
            h = self.h
        return self._resize_func(img, (w, h))


class CropImage(object):
    """ crop image """

    def __init__(self, size):
        if type(size) is int:
            self.size = (size, size)
        else:
            self.size = size  # (h, w)

    def __call__(self, img):
        w, h = self.size
        img_h, img_w = img.shape[:2]
        w_start = (img_w - w) // 2
        h_start = (img_h - h) // 2

        w_end = w_start + w
        h_end = h_start + h
        return img[h_start:h_end, w_start:w_end, :]


class RandCropImage(object):
    """ random crop image """

    def __init__(self,
                 size,
                 scale=None,
                 ratio=None,
                 interpolation=None,
                 backend="cv2"):
        if type(size) is int:
            self.size = (size, size)  # (h, w)
        else:
            self.size = size

        self.scale = [0.08, 1.0] if scale is None else scale
        self.ratio = [3. / 4., 4. / 3.] if ratio is None else ratio

        self._resize_func = UnifiedResize(
            interpolation=interpolation, backend=backend)

    def __call__(self, img):
        size = self.size
        scale = self.scale
        ratio = self.ratio

        aspect_ratio = math.sqrt(random.uniform(*ratio))
        w = 1. * aspect_ratio
        h = 1. / aspect_ratio

        img_h, img_w = img.shape[:2]

        bound = min((float(img_w) / img_h) / (w**2),
                    (float(img_h) / img_w) / (h**2))
        scale_max = min(scale[1], bound)
        scale_min = min(scale[0], bound)

        target_area = img_w * img_h * random.uniform(scale_min, scale_max)
        target_size = math.sqrt(target_area)
        w = int(target_size * w)
        h = int(target_size * h)

        i = random.randint(0, img_w - w)
        j = random.randint(0, img_h - h)

        img = img[j:j + h, i:i + w, :]

        return self._resize_func(img, size)


class RandFlipImage(object):
    """ random flip image
        flip_code:
            1: Flipped Horizontally
            0: Flipped Vertically
            -1: Flipped Horizontally & Vertically
    """

    def __init__(self, flip_code=1):
        assert flip_code in [-1, 0, 1
                             ], "flip_code should be a value in [-1, 0, 1]"
        self.flip_code = flip_code

    def __call__(self, img):
        if random.randint(0, 1) == 1:
            return cv2.flip(img, self.flip_code)
        else:
            return img


class AutoAugment(object):
    def __init__(self):
        self.policy = ImageNetPolicy()

    def __call__(self, img):
        from PIL import Image
        img = np.ascontiguousarray(img)
        img = Image.fromarray(img)
        img = self.policy(img)
        img = np.asarray(img)


class NormalizeImage(object):
    """ normalize image such as substract mean, divide std
    """

    def __init__(self,
                 scale=None,
                 mean=None,
                 std=None,
                 order='chw',
                 output_fp16=False,
                 channel_num=3):
        if isinstance(scale, str):
            scale = eval(scale)
        assert channel_num in [
            3, 4
        ], "channel number of input image should be set to 3 or 4."
        self.channel_num = channel_num
        self.output_dtype = 'float16' if output_fp16 else 'float32'
        self.scale = np.float32(scale if scale is not None else 1.0 / 255.0)
        self.order = order
        mean = mean if mean is not None else [0.485, 0.456, 0.406]
        std = std if std is not None else [0.229, 0.224, 0.225]

        shape = (3, 1, 1) if self.order == 'chw' else (1, 1, 3)
        self.mean = np.array(mean).reshape(shape).astype('float32')
        self.std = np.array(std).reshape(shape).astype('float32')

    def __call__(self, img):
        from PIL import Image
        if isinstance(img, Image.Image):
            img = np.array(img)

        assert isinstance(img,
                          np.ndarray), "invalid input 'img' in NormalizeImage"

        img = (img.astype('float32') * self.scale - self.mean) / self.std

        if self.channel_num == 4:
            img_h = img.shape[1] if self.order == 'chw' else img.shape[0]
            img_w = img.shape[2] if self.order == 'chw' else img.shape[1]
            pad_zeros = np.zeros(
                (1, img_h, img_w)) if self.order == 'chw' else np.zeros(
                    (img_h, img_w, 1))
            img = (np.concatenate(
                (img, pad_zeros), axis=0)
                   if self.order == 'chw' else np.concatenate(
                       (img, pad_zeros), axis=2))
        return img.astype(self.output_dtype)


class ToCHWImage(object):
    """ convert hwc image to chw image
    """

    def __init__(self):
        pass

    def __call__(self, img):
        from PIL import Image
        if isinstance(img, Image.Image):
            img = np.array(img)

        return img.transpose((2, 0, 1))


class AugMix(object):
    """ Perform AugMix augmentation and compute mixture.
    """

    def __init__(self,
                 prob=0.5,
                 aug_prob_coeff=0.1,
                 mixture_width=3,
                 mixture_depth=1,
                 aug_severity=1):
        """
        Args:
            prob: Probability of taking augmix
            aug_prob_coeff: Probability distribution coefficients.
            mixture_width: Number of augmentation chains to mix per augmented example.
            mixture_depth: Depth of augmentation chains. -1 denotes stochastic depth in [1, 3]'
            aug_severity: Severity of underlying augmentation operators (between 1 to 10).
        """
        # fmt: off
        self.prob = prob
        self.aug_prob_coeff = aug_prob_coeff
        self.mixture_width = mixture_width
        self.mixture_depth = mixture_depth
        self.aug_severity = aug_severity
        self.augmentations = augmentations
        # fmt: on

    def __call__(self, image):
        """Perform AugMix augmentations and compute mixture.
        Returns:
          mixed: Augmented and mixed image.
        """
        if random.random() > self.prob:
            # Avoid the warning: the given NumPy array is not writeable
            return np.asarray(image).copy()

        ws = np.float32(
            np.random.dirichlet([self.aug_prob_coeff] * self.mixture_width))
        m = np.float32(
            np.random.beta(self.aug_prob_coeff, self.aug_prob_coeff))

        # image = Image.fromarray(image)
        mix = np.zeros([image.shape[1], image.shape[0], 3])
        for i in range(self.mixture_width):
            image_aug = image.copy()
            image_aug = Image.fromarray(image_aug)
            depth = self.mixture_depth if self.mixture_depth > 0 else np.random.randint(
                1, 4)
            for _ in range(depth):
                op = np.random.choice(self.augmentations)
                image_aug = op(image_aug, self.aug_severity)
            mix += ws[i] * np.asarray(image_aug)

        mixed = (1 - m) * image + m * mix
        return mixed.astype(np.uint8)


class ColorJitter(RawColorJitter):
    """ColorJitter.
    """

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

    def __call__(self, img):
        if not isinstance(img, Image.Image):
            img = np.ascontiguousarray(img)
            img = Image.fromarray(img)
        img = super()._apply_image(img)
        if isinstance(img, Image.Image):
            img = np.asarray(img)
        return img