#   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 annotations

from typing import TYPE_CHECKING, Any, Literal

if TYPE_CHECKING:
    import numpy.typing as npt

    from paddle.vision.transforms.transforms import _Transform

    from ..image import _ImageBackend, _ImageDataType

    _DatasetMode = Literal["train", "valid", "test"]

import os
import tarfile

import numpy as np
from PIL import Image

import paddle
from paddle.dataset.common import _check_exists_and_download
from paddle.io import Dataset
from paddle.utils import try_import

__all__ = []

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[tuple["_ImageDataType", "npt.NDArray[np.int64]"]]):
    """
    Implementation of `Flowers102 <https://www.robots.ox.ac.uk/~vgg/data/flowers/>`_
    dataset.

    Args:
        data_file (str|None, optional): Path to data file, can be set None if
            :attr:`download` is True. Default: None, default data path: ~/.cache/paddle/dataset/flowers/.
        label_file (str|None, optional): Path to label file, can be set None if
            :attr:`download` is True. Default: None, default data path: ~/.cache/paddle/dataset/flowers/.
        setid_file (str|None, optional): Path to subset index file, can be set
            None if :attr:`download` is True. Default: None, default data path: ~/.cache/paddle/dataset/flowers/.
        mode (str, optional): Either train or test mode. Default 'train'.
        transform (Callable|None, optional): transform to perform on image, None for no transform. Default: None.
        download (bool|None, optional): download dataset automatically if :attr:`data_file` is None. Default: True.
        backend (str|None, optional): Specifies which type of image to be returned:
            PIL.Image or numpy.ndarray. Should be one of {'pil', 'cv2'}.
            If this option is not set, will get backend from :ref:`paddle.vision.get_image_backend <api_paddle_vision_get_image_backend>`,
            default backend is 'pil'. Default: None.

    Returns:
        :ref:`api_paddle_io_Dataset`. An instance of Flowers dataset.

    Examples:

        .. code-block:: python

            >>> # doctest: +TIMEOUT(60)
            >>> import itertools
            >>> import paddle.vision.transforms as T
            >>> from paddle.vision.datasets import Flowers

            >>> flowers = Flowers()
            >>> print(len(flowers))
            6149

            >>> for i in range(5):  # only show first 5 images
            ...     img, label = flowers[i]
            ...     # do something with img and label
            ...     print(type(img), img.size, label)
            ...     # <class 'PIL.JpegImagePlugin.JpegImageFile'> (523, 500) [1]

            >>> transform = T.Compose(
            ...     [
            ...         T.Resize(64),
            ...         T.ToTensor(),
            ...         T.Normalize(
            ...             mean=[0.5, 0.5, 0.5],
            ...             std=[0.5, 0.5, 0.5],
            ...             to_rgb=True,
            ...         ),
            ...     ]
            ... )
            >>> flowers_test = Flowers(
            ...     mode="test",
            ...     transform=transform,  # apply transform to every image
            ...     backend="cv2",  # use OpenCV as image transform backend
            ... )
            >>> print(len(flowers_test))
            1020

            >>> for img, label in itertools.islice(iter(flowers_test), 5):  # only show first 5 images
            ...     # do something with img and label
            ...     print(type(img), img.shape, label) # type: ignore
            ...     # <class 'paddle.Tensor'> [3, 64, 96] [1]
    """

    backend: _ImageBackend
    data_file: str | None
    label_file: str | None
    setid_file: str | None
    mode: _DatasetMode
    transform: _Transform[Any, Any] | None

    def __init__(
        self,
        data_file: str | None = None,
        label_file: str | None = None,
        setid_file: str | None = None,
        mode: _DatasetMode = 'train',
        transform: _Transform[Any, Any] | None = None,
        download: bool = True,
        backend: _ImageBackend | None = None,
    ) -> None:
        assert mode.lower() in [
            'train',
            'valid',
            'test',
        ], f"mode should be 'train', 'valid' or 'test', but got {mode}"

        if backend is None:
            backend = paddle.vision.get_image_backend()
        if backend not in ['pil', 'cv2']:
            raise ValueError(
                f"Expected backend are one of ['pil', 'cv2'], but got {backend}"
            )
        self.backend = backend

        flag = MODE_FLAG_MAP[mode.lower()]

        if not data_file:
            assert download, (
                "data_file is not set and downloading automatically is disabled"
            )
            data_file = _check_exists_and_download(
                data_file, DATA_URL, DATA_MD5, 'flowers', download
            )

        if not label_file:
            assert download, (
                "label_file is not set and downloading automatically is disabled"
            )
            label_file = _check_exists_and_download(
                label_file, LABEL_URL, LABEL_MD5, 'flowers', download
            )

        if not setid_file:
            assert download, (
                "setid_file is not set and downloading automatically is disabled"
            )
            setid_file = _check_exists_and_download(
                setid_file, SETID_URL, SETID_MD5, 'flowers', download
            )

        self.transform = transform

        data_tar = tarfile.open(data_file)
        self.data_path = data_file.replace(".tgz", "/")
        if not os.path.exists(self.data_path):
            os.mkdir(self.data_path)
        jpg_path = os.path.join(self.data_path, "jpg")
        if not os.path.exists(jpg_path):
            data_tar.extractall(self.data_path)

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

    def __getitem__(
        self, idx: int
    ) -> tuple[_ImageDataType, npt.NDArray[np.int64]]:
        index = self.indexes[idx]
        label = np.array([self.labels[index - 1]])
        img_name = f"jpg/image_{index:05}.jpg"
        image = os.path.join(self.data_path, img_name)
        if self.backend == 'pil':
            image = Image.open(image)
        elif self.backend == 'cv2':
            image = np.array(Image.open(image))

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

        if self.backend == 'pil':
            return image, label.astype('int64')

        return image.astype(paddle.get_default_dtype()), label.astype('int64')

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