# Copyright 2020 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.
# ============================================================================
"""
create train or eval dataset.
"""
import os
from tqdm import tqdm
import numpy as np

from mindspore import Tensor
from mindspore.train.model import Model
import mindspore.common.dtype as mstype
import mindspore.dataset.engine as de
import mindspore.dataset.vision.c_transforms as C
import mindspore.dataset.transforms.c_transforms as C2


def create_dataset(dataset_path, do_train, config, repeat_num=1):
    """
    create a train or eval dataset

    Args:
        dataset_path(string): the path of dataset.
        do_train(bool): whether dataset is used for train or eval.
        config(struct): the config of train and eval in diffirent platform.
        repeat_num(int): the repeat times of dataset. Default: 1.


    Returns:
        dataset
    """
    if config.platform == "Ascend":
        rank_size = int(os.getenv("RANK_SIZE", '1'))
        rank_id = int(os.getenv("RANK_ID", '0'))
        if rank_size == 1:
            ds = de.ImageFolderDataset(dataset_path, num_parallel_workers=8, shuffle=True)
        else:
            ds = de.ImageFolderDataset(dataset_path, num_parallel_workers=8, shuffle=True,
                                       num_shards=rank_size, shard_id=rank_id)
    elif config.platform == "GPU":
        if do_train:
            from mindspore.communication.management import get_rank, get_group_size
            ds = de.ImageFolderDataset(dataset_path, num_parallel_workers=8, shuffle=True,
                                       num_shards=get_group_size(), shard_id=get_rank())
        else:
            ds = de.ImageFolderDataset(dataset_path, num_parallel_workers=8, shuffle=True)
    elif config.platform == "CPU":
        ds = de.ImageFolderDataset(dataset_path, num_parallel_workers=8, shuffle=True)

    resize_height = config.image_height
    resize_width = config.image_width
    buffer_size = 1000

    # define map operations
    decode_op = C.Decode()
    resize_crop_op = C.RandomCropDecodeResize(resize_height, scale=(0.08, 1.0), ratio=(0.75, 1.333))
    horizontal_flip_op = C.RandomHorizontalFlip(prob=0.5)

    resize_op = C.Resize((256, 256))
    center_crop = C.CenterCrop(resize_width)
    rescale_op = C.RandomColorAdjust(brightness=0.4, contrast=0.4, saturation=0.4)
    normalize_op = C.Normalize(mean=[0.485 * 255, 0.456 * 255, 0.406 * 255],
                               std=[0.229 * 255, 0.224 * 255, 0.225 * 255])
    change_swap_op = C.HWC2CHW()

    if do_train:
        trans = [resize_crop_op, horizontal_flip_op, rescale_op, normalize_op, change_swap_op]
    else:
        trans = [decode_op, resize_op, center_crop, normalize_op, change_swap_op]

    type_cast_op = C2.TypeCast(mstype.int32)

    ds = ds.map(input_columns="image", operations=trans, num_parallel_workers=8)
    ds = ds.map(input_columns="label", operations=type_cast_op, num_parallel_workers=8)

    # apply shuffle operations
    ds = ds.shuffle(buffer_size=buffer_size)

    # apply batch operations
    ds = ds.batch(config.batch_size, drop_remainder=True)

    # apply dataset repeat operation
    ds = ds.repeat(repeat_num)

    return ds


def extract_features(net, dataset_path, config):
    features_folder = dataset_path + '_features'
    if not os.path.exists(features_folder):
        os.makedirs(features_folder)
    dataset = create_dataset(dataset_path=dataset_path,
                             do_train=False,
                             config=config,
                             repeat_num=1)
    step_size = dataset.get_dataset_size()
    pbar = tqdm(list(dataset.create_dict_iterator()))
    model = Model(net)
    i = 0
    for data in pbar:
        features_path = os.path.join(features_folder, f"feature_{i}.npy")
        label_path = os.path.join(features_folder, f"label_{i}.npy")
        if not (os.path.exists(features_path) and os.path.exists(label_path)):
            image = data["image"]
            label = data["label"]
            features = model.predict(Tensor(image))
            np.save(features_path, features.asnumpy())
            np.save(label_path, label)
        pbar.set_description("Process dataset batch: %d" % (i + 1))
        i += 1

    return step_size