#   Copyright (c) 2020 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 print_function

import os
import io
import tarfile
import numpy as np
import scipy.io as scio
from PIL import Image

from paddle.io import Dataset
from .utils import _check_exists_and_download

__all__ = ["Flowers"]

DATA_URL = 'http://paddlemodels.bj.bcebos.com/flowers/102flowers.tgz'
LABEL_URL = 'http://paddlemodels.bj.bcebos.com/flowers/imagelabels.mat'
SETID_URL = 'http://paddlemodels.bj.bcebos.com/flowers/setid.mat'
DATA_MD5 = '52808999861908f626f3c1f4e79d11fa'
LABEL_MD5 = 'e0620be6f572b9609742df49c70aed4d'
SETID_MD5 = 'a5357ecc9cb78c4bef273ce3793fc85c'

# In official 'readme', tstid is the flag of test data
# and trnid is the flag of train data. But test data is more than train data.
# So we exchange the train data and test data.
MODE_FLAG_MAP = {'train': 'tstid', 'test': 'trnid', 'valid': "valid"}


class Flowers(Dataset):
    """
    Implement of flowers dataset

    Args:
        data_file(str): path to data file, can be set None if
            :attr:`download` is True. Default None
        label_file(str): path to label file, can be set None if
            :attr:`download` is True. Default None
        setid_file(str): path to subset index file, can be set
            None if :attr:`download` is True. Default None
        mode(str): 'train', 'valid' or 'test' mode. Default 'train'.
        download(bool): whether auto download mnist dataset if
            :attr:`image_path`/:attr:`label_path` unset. Default
            True

    Examples:
        
        .. code-block:: python

            from paddle.incubate.hapi.datasets import Flowers

            flowers = Flowers(mode='test')

            for i in range(len(flowers)):
                sample = flowers[i]
                print(sample[0].shape, sample[1])

    """

    def __init__(self,
                 data_file=None,
                 label_file=None,
                 setid_file=None,
                 mode='train',
                 transform=None,
                 download=True):
        assert mode.lower() in ['train', 'valid', 'test'], \
                "mode should be 'train', 'valid' or 'test', but got {}".format(mode)
        self.flag = MODE_FLAG_MAP[mode.lower()]

        self.data_file = data_file
        if self.data_file is None:
            assert download, "data_file not set and auto download disabled"
            self.data_file = _check_exists_and_download(
                data_file, DATA_URL, DATA_MD5, 'flowers', download)

        self.label_file = label_file
        if self.label_file is None:
            assert download, "label_file not set and auto download disabled"
            self.label_file = _check_exists_and_download(
                label_file, LABEL_URL, LABEL_MD5, 'flowers', download)

        self.setid_file = setid_file
        if self.setid_file is None:
            assert download, "setid_file not set and auto download disabled"
            self.setid_file = _check_exists_and_download(
                setid_file, SETID_URL, SETID_MD5, 'flowers', download)

        self.transform = transform

        # read dataset into memory
        self._load_anno()

    def _load_anno(self):
        self.name2mem = {}
        self.data_tar = tarfile.open(self.data_file)
        for ele in self.data_tar.getmembers():
            self.name2mem[ele.name] = ele

        self.labels = scio.loadmat(self.label_file)['labels'][0]
        self.indexes = scio.loadmat(self.setid_file)[self.flag][0]

    def __getitem__(self, idx):
        index = self.indexes[idx]
        label = np.array([self.labels[index - 1]])
        img_name = "jpg/image_%05d.jpg" % index
        img_ele = self.name2mem[img_name]
        image = self.data_tar.extractfile(img_ele).read()
        image = np.array(Image.open(io.BytesIO(image)))

        if self.transform is not None:
            image = self.transform(image)

        return image, label.astype('int64')

    def __len__(self):
        return len(self.indexes)