# 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.
# ============================================================================

"""Cycle GAN test."""

import os
import mindspore
from src.models.cycle_gan import get_generator
from src.utils.args import get_args
from src.dataset.cyclegan_dataset import create_dataset
from src.utils.reporter import Reporter
from src.utils.tools import save_image, load_ckpt


def predict():
    """Predict function."""
    args = get_args("predict")
    mindspore.set_context(mode=0, device_target=args.platform, save_graphs=args.save_graphs, device_id=args.device_id,
                          jit_config={"jit_level": "O2"})
    args.rank = 0
    args.device_num = 1
    if args.platform == "GPU":
        mindspore.set_context(enable_graph_kernel=True)
    G_A = get_generator(args)
    G_B = get_generator(args)
    G_A.set_train(True)
    G_B.set_train(True)
    load_ckpt(args, G_A, G_B)
    imgs_out = os.path.join(args.outputs_dir, "predict")
    if not os.path.exists(imgs_out):
        os.makedirs(imgs_out)
    if not os.path.exists(os.path.join(imgs_out, "fake_A")):
        os.makedirs(os.path.join(imgs_out, "fake_A"))
    if not os.path.exists(os.path.join(imgs_out, "fake_B")):
        os.makedirs(os.path.join(imgs_out, "fake_B"))
    args.data_dir = 'testA'
    ds = create_dataset(args)
    reporter = Reporter(args)
    reporter.start_predict("A to B")
    for data in ds.create_dict_iterator(output_numpy=True):
        img_A = mindspore.Tensor(data["image"])
        path_A = data["image_name"][0]
        path_B = path_A[0:-4] + "_fake_B.jpg"
        fake_B = G_A(img_A)
        save_image(fake_B, os.path.join(imgs_out, "fake_B", path_B))
        save_image(img_A, os.path.join(imgs_out, "fake_B", path_A))
    reporter.info('save fake_B at %s', os.path.join(imgs_out, "fake_B", path_A))
    reporter.end_predict()
    args.data_dir = 'testB'
    ds = create_dataset(args)
    reporter.dataset_size = args.dataset_size
    reporter.start_predict("B to A")
    for data in ds.create_dict_iterator(output_numpy=True):
        img_B = mindspore.Tensor(data["image"])
        path_B = data["image_name"][0]
        path_A = path_B[0:-4] + "_fake_A.jpg"
        fake_A = G_B(img_B)
        save_image(fake_A, os.path.join(imgs_out, "fake_A", path_A))
        save_image(img_B, os.path.join(imgs_out, "fake_A", path_B))
    reporter.info('save fake_A at %s', os.path.join(imgs_out, "fake_A", path_B))
    reporter.end_predict()

if __name__ == "__main__":
    predict()