#   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

import io
import tarfile
from typing import TYPE_CHECKING, Any, Literal

import numpy as np
from PIL import Image

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

if TYPE_CHECKING:
    import numpy.typing as npt

    from paddle._typing import DTypeLike
    from paddle.vision.transforms.transforms import _Transform

    from ..image import _ImageDataType

    _ImageBackend = Literal["cv2", "pil"]

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

__all__ = []

VOC_URL = 'https://dataset.bj.bcebos.com/voc/VOCtrainval_11-May-2012.tar'

VOC_MD5 = '6cd6e144f989b92b3379bac3b3de84fd'
SET_FILE = 'VOCdevkit/VOC2012/ImageSets/Segmentation/{}.txt'
DATA_FILE = 'VOCdevkit/VOC2012/JPEGImages/{}.jpg'
LABEL_FILE = 'VOCdevkit/VOC2012/SegmentationClass/{}.png'

CACHE_DIR = 'voc2012'

MODE_FLAG_MAP = {'train': 'trainval', 'test': 'train', 'valid': "val"}


class VOC2012(Dataset[tuple["_ImageDataType", "npt.NDArray[Any]"]]):
    """
    Implementation of `VOC2012 <http://host.robots.ox.ac.uk/pascal/VOC/voc2012/>`_ 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/voc2012.
        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, 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 VOC2012 dataset.

    Examples:

        .. code-block:: pycon

            >>> # doctest: +TIMEOUT(120)
            >>> import itertools
            >>> import paddle
            >>> import paddle.vision.transforms as T
            >>> from paddle.vision.datasets import VOC2012


            >>> voc2012 = VOC2012()
            >>> print(len(voc2012))
            2913

            >>> for i in range(5):  # only show first 5 images
            ...     img, label = voc2012[i]
            ...     # do something with img and label
            ...     print(type(img), img.size)
            ...     # <class 'PIL.JpegImagePlugin.JpegImageFile'> (500, 281)
            ...     print(type(label), label.size)
            ...     # <class 'PIL.PngImagePlugin.PngImageFile'> (500, 281)


            >>> transform = T.Compose(
            ...     [
            ...         T.ToTensor(),
            ...         T.Normalize(
            ...             mean=[0.5, 0.5, 0.5],
            ...             std=[0.5, 0.5, 0.5],
            ...             to_rgb=True,
            ...         ),
            ...     ]
            ... )

            >>> voc2012_test = VOC2012(
            ...     mode="test",
            ...     transform=transform,  # apply transform to every image
            ...     backend="cv2",  # use OpenCV as image transform backend
            ... )
            >>> print(len(voc2012_test))
            1464

            >>> for img, label in itertools.islice(iter(voc2012_test), 5):  # only show first 5 images
            ...     # do something with img and label
            ...     assert isinstance(img, paddle.Tensor)
            ...     print(type(img), img.shape)
            ...     # <class 'paddle.Tensor'> [3, 281, 500]
            ...     print(type(label), label.shape)
            ...     # <class 'numpy.ndarray'> (281, 500)
    """

    data_file: str | None
    mode: _DatasetMode
    transform: _Transform[Any, Any] | None
    backend: _ImageBackend
    flag: str
    dtype: DTypeLike

    def __init__(
        self,
        data_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

        self.flag = MODE_FLAG_MAP[mode.lower()]

        self.data_file = data_file
        if self.data_file is None:
            assert download, (
                "data_file is not set and downloading automatically is disabled"
            )
            self.data_file = _check_exists_and_download(
                data_file, VOC_URL, VOC_MD5, CACHE_DIR, download
            )
        self.transform = transform

        # read dataset into memory
        self._load_anno()

        self.dtype = paddle.get_default_dtype()

    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

        set_file = SET_FILE.format(self.flag)
        sets = self.data_tar.extractfile(self.name2mem[set_file])

        self.data = []
        self.labels = []

        for line in sets:
            line = line.strip()
            data = DATA_FILE.format(line.decode('utf-8'))
            label = LABEL_FILE.format(line.decode('utf-8'))
            self.data.append(data)
            self.labels.append(label)

    def __getitem__(self, idx: int) -> tuple[_ImageDataType, npt.NDArray[Any]]:
        data_file = self.data[idx]
        label_file = self.labels[idx]

        data = self.data_tar.extractfile(self.name2mem[data_file]).read()
        label = self.data_tar.extractfile(self.name2mem[label_file]).read()
        data = Image.open(io.BytesIO(data))
        label = Image.open(io.BytesIO(label))

        if self.backend == 'cv2':
            data = np.array(data)
            label = np.array(label)

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

        if self.backend == 'cv2':
            return data.astype(self.dtype), label.astype(self.dtype)

        return data, label

    def __len__(self) -> int:
        return len(self.data)

    def __del__(self) -> None:
        if self.data_tar:
            self.data_tar.close()