# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.

"""EuroSAT dataset."""

import os
from typing import Callable, Dict, Optional, cast

import matplotlib.pyplot as plt
import numpy as np
from torch import Tensor

from .geo import VisionClassificationDataset
from .utils import check_integrity, download_url, extract_archive, rasterio_loader


class EuroSAT(VisionClassificationDataset):
    """EuroSAT dataset.

    The `EuroSAT <https://github.com/phelber/EuroSAT>`_ dataset is based on Sentinel-2
    satellite images covering 13 spectral bands and consists of 10 target classes with
    a total of 27,000 labeled and geo-referenced images.

    Dataset format:

    * rasters are 13-channel GeoTiffs
    * labels are values in the range [0,9]

    Dataset classes:

    * Industrial Buildings
    * Residential Buildings
    * Annual Crop
    * Permanent Crop
    * River
    * Sea and Lake
    * Herbaceous Vegetation
    * Highway
    * Pasture
    * Forest

    This dataset uses the train/val/test splits defined in the "In-domain representation
    learning for remote sensing" paper:

    * https://arxiv.org/abs/1911.06721

    If you use this dataset in your research, please cite the following papers:

    * https://ieeexplore.ieee.org/document/8736785
    * https://ieeexplore.ieee.org/document/8519248
    """

    url = "http://madm.dfki.de/files/sentinel/EuroSATallBands.zip"  # 2.0 GB download
    filename = "EuroSATallBands.zip"
    md5 = "5ac12b3b2557aa56e1826e981e8e200e"

    # For some reason the class directories are actually nested in this directory
    base_dir = os.path.join(
        "ds", "images", "remote_sensing", "otherDatasets", "sentinel_2", "tif"
    )

    splits = ["train", "val", "test"]
    split_urls = {
        "train": "https://storage.googleapis.com/remote_sensing_representations/eurosat-train.txt",  # noqa: E501
        "val": "https://storage.googleapis.com/remote_sensing_representations/eurosat-val.txt",  # noqa: E501
        "test": "https://storage.googleapis.com/remote_sensing_representations/eurosat-test.txt",  # noqa: E501
    }
    split_md5s = {
        "train": "908f142e73d6acdf3f482c5e80d851b1",
        "val": "95de90f2aa998f70a3b2416bfe0687b4",
        "test": "7ae5ab94471417b6e315763121e67c5f",
    }
    classes = [
        "Industrial Buildings",
        "Residential Buildings",
        "Annual Crop",
        "Permanent Crop",
        "River",
        "Sea and Lake",
        "Herbaceous Vegetation",
        "Highway",
        "Pasture",
        "Forest",
    ]

    def __init__(
        self,
        root: str = "data",
        split: str = "train",
        transforms: Optional[Callable[[Dict[str, Tensor]], Dict[str, Tensor]]] = None,
        download: bool = False,
        checksum: bool = False,
    ) -> None:
        """Initialize a new EuroSAT dataset instance.

        Args:
            root: root directory where dataset can be found
            split: one of "train", "val", or "test"
            transforms: a function/transform that takes input sample and its target as
                entry and returns a transformed version
            download: if True, download dataset and store it in the root directory
            checksum: if True, check the MD5 of the downloaded files (may be slow)

        Raises:
            RuntimeError: if ``download=False`` and data is not found, or checksums
                don't match
        """
        self.root = root
        self.transforms = transforms
        self.download = download
        self.checksum = checksum
        self._verify()

        valid_fns = set()
        with open(os.path.join(self.root, f"eurosat-{split}.txt"), "r") as f:
            for fn in f:
                valid_fns.add(fn.strip().replace(".jpg", ".tif"))
        is_in_split: Callable[[str], bool] = lambda x: os.path.basename(x) in valid_fns

        super().__init__(
            root=os.path.join(root, self.base_dir),
            transforms=transforms,
            loader=rasterio_loader,
            is_valid_file=is_in_split,
        )

    def _check_integrity(self) -> bool:
        """Check integrity of dataset.

        Returns:
            True if dataset files are found and/or MD5s match, else False
        """
        integrity: bool = check_integrity(
            os.path.join(self.root, self.filename), self.md5 if self.checksum else None
        )
        return integrity

    def _verify(self) -> None:
        """Verify the integrity of the dataset.

        Raises:
            RuntimeError: if ``download=False`` but dataset is missing or checksum fails
        """
        # Check if the files already exist
        filepath = os.path.join(self.root, self.base_dir)
        if os.path.exists(filepath):
            return

        # Check if zip file already exists (if so then extract)
        if self._check_integrity():
            self._extract()
            return

        # Check if the user requested to download the dataset
        if not self.download:
            raise RuntimeError(
                "Dataset not found in `root` directory and `download=False`, "
                "either specify a different `root` directory or use `download=True` "
                "to automaticaly download the dataset."
            )

        # Download and extract the dataset
        self._download()
        self._extract()

    def _download(self) -> None:
        """Download the dataset."""
        download_url(
            self.url,
            self.root,
            filename=self.filename,
            md5=self.md5 if self.checksum else None,
        )
        for split in self.splits:
            download_url(
                self.split_urls[split],
                self.root,
                filename=f"eurosat-{split}.txt",
                md5=self.split_md5s[split] if self.checksum else None,
            )

    def _extract(self) -> None:
        """Extract the dataset."""
        filepath = os.path.join(self.root, self.filename)
        extract_archive(filepath)

    def plot(
        self,
        sample: Dict[str, Tensor],
        show_titles: bool = True,
        suptitle: Optional[str] = None,
    ) -> plt.Figure:
        """Plot a sample from the dataset.

        Args:
            sample: a sample returned by :meth:`VisionClassificationDataset.__getitem__`
            show_titles: flag indicating whether to show titles above each panel
            suptitle: optional string to use as a suptitle

        Returns:
            a matplotlib Figure with the rendered sample

        .. versionadded:: 0.2
        """
        image = np.rollaxis(sample["image"][[3, 2, 1]].numpy(), 0, 3).copy()
        image = np.clip(image / 3000, 0, 1)

        label = cast(int, sample["label"].item())
        label_class = self.classes[label]

        showing_predictions = "prediction" in sample
        if showing_predictions:
            prediction = cast(int, sample["prediction"].item())
            prediction_class = self.classes[prediction]

        fig, ax = plt.subplots(figsize=(4, 4))
        ax.imshow(image)
        ax.axis("off")
        if show_titles:
            title = f"Label: {label_class}"
            if showing_predictions:
                title += f"\nPrediction: {prediction_class}"
            ax.set_title(title)

        if suptitle is not None:
            plt.suptitle(suptitle)
        return fig
