# Copyright 2020-2022 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 FiveCrop in DE
"""
import pytest
import numpy as np

import mindspore.dataset as ds
import mindspore.dataset.transforms
import mindspore.dataset.vision as vision
from mindspore import log as logger
from util import visualize_list, save_and_check_md5_pil

DATA_DIR = ["../data/dataset/test_tf_file_3_images/train-0000-of-0001.data"]
SCHEMA_DIR = "../data/dataset/test_tf_file_3_images/datasetSchema.json"

GENERATE_GOLDEN = False


def METHOD_NAME(plot=False):
    """
    Feature: FiveCrop op
    Description: Test FiveCrop op basic usage
    Expectation: Output is the same as expected output
    """
    logger.info("test_five_crop")

    # First dataset
    data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
    transforms_1 = [
        vision.Decode(True),
        vision.ToTensor(),
    ]
    transform_1 = mindspore.dataset.transforms.Compose(transforms_1)
    data1 = data1.map(operations=transform_1, input_columns=["image"])

    # Second dataset
    data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
    transforms_2 = [
        vision.Decode(True),
        vision.FiveCrop(200),
        lambda *images: np.stack([vision.ToTensor()(image) for image in images])  # 4D stack of 5 images
    ]
    transform_2 = mindspore.dataset.transforms.Compose(transforms_2)
    data2 = data2.map(operations=transform_2, input_columns=["image"])

    num_iter = 0
    for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1, output_numpy=True),
                            data2.create_dict_iterator(num_epochs=1, output_numpy=True)):
        num_iter += 1
        image_1 = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
        image_2 = item2["image"]

        logger.info("shape of image_1: {}".format(image_1.shape))
        logger.info("shape of image_2: {}".format(image_2.shape))

        logger.info("dtype of image_1: {}".format(image_1.dtype))
        logger.info("dtype of image_2: {}".format(image_2.dtype))
        if plot:
            visualize_list(np.array([image_1] * 5), (image_2 * 255).astype(np.uint8).transpose(0, 2, 3, 1))

        # The output data should be of a 4D tensor shape, a stack of 5 images.
        assert len(image_2.shape) == 4
        assert image_2.shape[0] == 5


def test_five_crop_error_msg():
    """
    Feature: FiveCrop op
    Description: Test FiveCrop op when the input image is not in the correct format.
    Expectation: Invalid input is detected
    """

    logger.info("test_five_crop_error_msg")

    data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
    transforms = [
        vision.Decode(True),
        vision.FiveCrop(200),
        vision.ToTensor()
    ]
    transform = mindspore.dataset.transforms.Compose(transforms)
    data = data.map(operations=transform, input_columns=["image"])

    with pytest.raises(RuntimeError) as info:
        for _ in data:
            pass
    error_msg = \
        "map operation: [ToTensor] failed. The op is OneToOne, can only accept one tensor as input."
    assert error_msg in str(info.value)


def test_five_crop_md5():
    """
    Feature: FiveCrop op
    Description: Test FiveCrop op with md5 check
    Expectation: Passes the md5 check test
    """
    logger.info("test_five_crop_md5")

    # First dataset
    data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
    transforms = [
        vision.Decode(True),
        vision.FiveCrop(100),
        lambda *images: np.stack([vision.ToTensor()(image) for image in images])  # 4D stack of 5 images
    ]
    transform = mindspore.dataset.transforms.Compose(transforms)
    data = data.map(operations=transform, input_columns=["image"])
    # Compare with expected md5 from images
    filename = "five_crop_01_result.npz"
    save_and_check_md5_pil(data, filename, generate_golden=GENERATE_GOLDEN)


if __name__ == "__main__":
    METHOD_NAME(plot=True)
    test_five_crop_error_msg()
    test_five_crop_md5()