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

"""SEN12MS datamodule."""

from typing import Any, Dict

import torch
from sklearn.model_selection import GroupShuffleSplit
from torch import Tensor
from torch.utils.data import Subset

from ..datasets import SEN12MS
from .geo import NonGeoDataModule


class SEN12MSDataModule(NonGeoDataModule):
    """LightningDataModule implementation for the SEN12MS dataset.

    Implements 80/20 geographic train/val splits and uses the test split from the
    classification dataset definitions.

    Uses the Simplified IGBP scheme defined in the 2020 Data Fusion Competition. See
    https://arxiv.org/abs/2002.08254.
    """

    #: Mapping from the IGBP class definitions to the DFC2020, taken from the dataloader
    #: here: https://github.com/lukasliebel/dfc2020_baseline.
    DFC2020_CLASS_MAPPING = torch.tensor(
        [0, 1, 1, 1, 1, 1, 2, 2, 3, 3, 4, 5, 6, 7, 6, 8, 9, 10]
    )

    std = torch.tensor(
        [-25, -25, 1e4, 1e4, 1e4, 1e4, 1e4, 1e4, 1e4, 1e4, 1e4, 1e4, 1e4, 1e4, 1e4]
    )

    def __init__(
        self,
        batch_size: int = 64,
        num_workers: int = 0,
        band_set: str = "all",
        **kwargs: Any,
    ) -> None:
        """Initialize a new SEN12MSDataModule instance.

        Args:
            batch_size: Size of each mini-batch.
            num_workers: Number of workers for parallel data loading.
            band_set: Subset of S1/S2 bands to use. Options are: "all",
                "s1", "s2-all", and "s2-reduced" where the "s2-reduced" set includes:
                B2, B3, B4, B8, B11, and B12.
            **kwargs: Additional keyword arguments passed to
                :class:`~torchgeo.datasets.SEN12MS`.
        """
        kwargs["bands"] = SEN12MS.BAND_SETS[band_set]

        if band_set == "s1":
            self.std = self.std[:2]
        elif band_set == "s2-all":
            self.std = self.std[2:]
        elif band_set == "s2-reduced":
            self.std = self.std[torch.tensor([3, 4, 5, 9, 12, 13])]

        super().__init__(SEN12MS, batch_size, num_workers, **kwargs)

    def setup(self, stage: str) -> None:
        """Set up datasets.

        Args:
            stage: Either 'fit', 'validate', 'test', or 'predict'.
        """
        if stage in ["fit", "validate"]:
            season_to_int = {"winter": 0, "spring": 1000, "summer": 2000, "fall": 3000}

            self.dataset = SEN12MS(split="train", **self.kwargs)

            # A patch is a filename like:
            #     "ROIs{num}_{season}_s2_{scene_id}_p{patch_id}.tif"
            # This patch will belong to the scene that is uniquely identified by its
            # (season, scene_id) tuple. Because the largest scene_id is 149, we can
            # simply give each season a large number and representing a unique_scene_id
            # as (season_id + scene_id).
            scenes = []
            for scene_fn in self.dataset.ids:
                parts = scene_fn.split("_")
                season_id = season_to_int[parts[1]]
                scene_id = int(parts[3])
                scenes.append(season_id + scene_id)

            train_indices, val_indices = next(
                GroupShuffleSplit(test_size=0.2, n_splits=2, random_state=0).split(
                    scenes, groups=scenes
                )
            )

            self.train_dataset = Subset(self.dataset, train_indices)
            self.val_dataset = Subset(self.dataset, val_indices)
        if stage in ["test"]:
            self.test_dataset = SEN12MS(split="test", **self.kwargs)

    def on_after_batch_transfer(
        self, batch: Dict[str, Tensor], dataloader_idx: int
    ) -> Dict[str, Tensor]:
        """Apply batch augmentations to the batch after it is transferred to the device.

        Args:
            batch: A batch of data that needs to be altered or augmented.
            dataloader_idx: The index of the dataloader to which the batch belongs.

        Returns:
            A batch of data.
        """
        batch["mask"] = torch.take(self.DFC2020_CLASS_MAPPING, batch["mask"])

        return super().on_after_batch_transfer(batch, dataloader_idx)
