# Copyright 2021 Huawei Technologies Co., Ltd
#
# 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.
# ==============================================================================
"""
Testing AutoAugment in DE
"""
import numpy as np

import mindspore.dataset as ds
from mindspore.dataset.vision.c_transforms import Decode, AutoAugment, Resize
from mindspore.dataset.vision.utils import AutoAugmentPolicy, Inter
from mindspore import log as logger
from util import visualize_image, visualize_list, diff_mse

image_file = "../data/dataset/testImageNetData/train/class1/1_1.jpg"
data_dir = "../data/dataset/testImageNetData/train/"


def test_auto_augment_pipeline(plot=False):
    """
    Feature: AutoAugment
    Description: test AutoAugment pipeline
    Expectation: pass without error
    """
    logger.info("Test AutoAugment pipeline")

    # Original Images
    data_set = ds.ImageFolderDataset(dataset_dir=data_dir, shuffle=False)
    transforms_original = [Decode(), Resize(size=[224, 224])]
    ds_original = data_set.map(operations=transforms_original, input_columns="image")
    ds_original = ds_original.batch(512)

    for idx, (image, _) in enumerate(ds_original):
        if idx == 0:
            images_original = image.asnumpy()
        else:
            images_original = np.append(images_original,
                                        image.asnumpy(),
                                        axis=0)

    # Auto Augmented Images with ImageNet policy
    data_set1 = ds.ImageFolderDataset(dataset_dir=data_dir, shuffle=False)
    auto_augment_op = AutoAugment(AutoAugmentPolicy.IMAGENET, Inter.BICUBIC, 20)
    transforms = [Decode(), Resize(size=[224, 224]), auto_augment_op]
    ds_auto_augment = data_set1.map(operations=transforms, input_columns="image")
    ds_auto_augment = ds_auto_augment.batch(512)
    for idx, (image, _) in enumerate(ds_auto_augment):
        if idx == 0:
            images_auto_augment = image.asnumpy()
        else:
            images_auto_augment = np.append(images_auto_augment,
                                            image.asnumpy(),
                                            axis=0)
    assert images_original.shape[0] == images_auto_augment.shape[0]
    if plot:
        visualize_list(images_original, images_auto_augment)

    num_samples = images_original.shape[0]
    mse = np.zeros(num_samples)
    for i in range(num_samples):
        mse[i] = diff_mse(images_auto_augment[i], images_original[i])
    logger.info("MSE= {}".format(str(np.mean(mse))))

    # Auto Augmented Images with Cifar10 policy
    data_set2 = ds.ImageFolderDataset(dataset_dir=data_dir, shuffle=False)
    auto_augment_op = AutoAugment(AutoAugmentPolicy.CIFAR10, Inter.BILINEAR, 20)
    transforms = [Decode(), Resize(size=[224, 224]), auto_augment_op]
    ds_auto_augment = data_set2.map(operations=transforms, input_columns="image")
    ds_auto_augment = ds_auto_augment.batch(512)
    for idx, (image, _) in enumerate(ds_auto_augment):
        if idx == 0:
            images_auto_augment = image.asnumpy()
        else:
            images_auto_augment = np.append(images_auto_augment,
                                            image.asnumpy(),
                                            axis=0)
    assert images_original.shape[0] == images_auto_augment.shape[0]
    if plot:
        visualize_list(images_original, images_auto_augment)

    mse = np.zeros(num_samples)
    for i in range(num_samples):
        mse[i] = diff_mse(images_auto_augment[i], images_original[i])
    logger.info("MSE= {}".format(str(np.mean(mse))))

    # Auto Augmented Images with SVHN policy
    data_set3 = ds.ImageFolderDataset(dataset_dir=data_dir, shuffle=False)
    auto_augment_op = AutoAugment(AutoAugmentPolicy.SVHN, Inter.NEAREST, 20)
    transforms = [Decode(), Resize(size=[224, 224]), auto_augment_op]
    ds_auto_augment = data_set3.map(operations=transforms, input_columns="image")
    ds_auto_augment = ds_auto_augment.batch(512)
    for idx, (image, _) in enumerate(ds_auto_augment):
        if idx == 0:
            images_auto_augment = image.asnumpy()
        else:
            images_auto_augment = np.append(images_auto_augment,
                                            image.asnumpy(),
                                            axis=0)
    assert images_original.shape[0] == images_auto_augment.shape[0]
    if plot:
        visualize_list(images_original, images_auto_augment)

    mse = np.zeros(num_samples)
    for i in range(num_samples):
        mse[i] = diff_mse(images_auto_augment[i], images_original[i])
    logger.info("MSE= {}".format(str(np.mean(mse))))


def test_auto_augment_eager(plot=False):
    """
    Feature: AutoAugment
    Description: test AutoAugment eager
    Expectation: pass without error
    """
    img = np.fromfile(image_file, dtype=np.uint8)
    logger.info("Image.type: {}, Image.shape: {}".format(type(img), img.shape))

    img = Decode()(img)
    img_auto_augmented = AutoAugment()(img)
    if plot:
        visualize_image(img, img_auto_augmented)
    logger.info("Image.type: {}, Image.shape: {}".format(type(img_auto_augmented), img_auto_augmented.shape))
    mse = diff_mse(img_auto_augmented, img)
    logger.info("MSE= {}".format(str(mse)))


def test_auto_augment_invalid_policy():
    """
    Feature: AutoAugment
    Description: test AutoAugment with invalid policy
    Expectation: throw TypeError
    """
    logger.info("test_auto_augment_invalid_policy")
    dataset = ds.ImageFolderDataset(data_dir, 1, shuffle=False, decode=True)
    try:
        auto_augment_op = AutoAugment(policy="invalid")
        dataset.map(operations=auto_augment_op, input_columns=['image'])
    except TypeError as e:
        logger.info("Got an exception in DE: {}".format(str(e)))
        assert "Argument policy with value invalid is not of type [<enum 'AutoAugmentPolicy'>]" in str(e)


def test_auto_augment_invalid_interpolation():
    """
    Feature: AutoAugment
    Description: test AutoAugment with invalid interpolation
    Expectation: throw TypeError
    """
    logger.info("test_auto_augment_invalid_interpolation")
    dataset = ds.ImageFolderDataset(data_dir, 1, shuffle=False, decode=True)
    try:
        auto_augment_op = AutoAugment(interpolation="invalid")
        dataset.map(operations=auto_augment_op, input_columns=['image'])
    except TypeError as e:
        logger.info("Got an exception in DE: {}".format(str(e)))
        assert "Argument interpolation with value invalid is not of type [<enum 'Inter'>]" in str(e)


def test_auto_augment_invalid_fill_value():
    """
    Feature: AutoAugment
    Description: test AutoAugment with invalid fill_value
    Expectation: throw TypeError or ValueError
    """
    logger.info("test_auto_augment_invalid_fill_value")
    dataset = ds.ImageFolderDataset(data_dir, 1, shuffle=False, decode=True)
    try:
        auto_augment_op = AutoAugment(fill_value=(10, 10))
        dataset.map(operations=auto_augment_op, input_columns=['image'])
    except TypeError as e:
        logger.info("Got an exception in DE: {}".format(str(e)))
        assert "fill_value should be a single integer or a 3-tuple." in str(e)
    try:
        auto_augment_op = AutoAugment(fill_value=300)
        dataset.map(operations=auto_augment_op, input_columns=['image'])
    except ValueError as e:
        logger.info("Got an exception in DE: {}".format(str(e)))
        assert "is not within the required interval of [0, 255]." in str(e)


if __name__ == "__main__":
    test_auto_augment_pipeline(plot=True)
    test_auto_augment_eager(plot=True)
    test_auto_augment_invalid_policy()
    test_auto_augment_invalid_interpolation()
    test_auto_augment_invalid_fill_value()