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

"""Vaihingen datamodule."""

from typing import Any, Dict, Optional

import pytorch_lightning as pl
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose

from ..datasets import Vaihingen2D
from .utils import dataset_split


class Vaihingen2DDataModule(pl.LightningDataModule):
    """LightningDataModule implementation for the Vaihingen2D dataset.

    Uses the train/test splits from the dataset.

    .. versionadded:: 0.2
    """

    def __init__(
        self,
        root_dir: str,
        batch_size: int = 64,
        num_workers: int = 0,
        val_split_pct: float = 0.2,
        **kwargs: Any,
    ) -> None:
        """Initialize a LightningDataModule for Vaihingen2D based DataLoaders.

        Args:
            root_dir: The ``root`` argument to pass to the Vaihingen Dataset classes
            batch_size: The batch size to use in all created DataLoaders
            num_workers: The number of workers to use in all created DataLoaders
            val_split_pct: What percentage of the dataset to use as a validation set
        """
        super().__init__()
        self.root_dir = root_dir
        self.batch_size = batch_size
        self.num_workers = num_workers
        self.val_split_pct = val_split_pct

    def preprocess(self, sample: Dict[str, Any]) -> Dict[str, Any]:
        """Transform a single sample from the Dataset.

        Args:
            sample: input image dictionary

        Returns:
            preprocessed sample
        """
        sample["image"] = sample["image"].float()
        sample["image"] /= 255.0
        return sample

    def setup(self, stage: Optional[str] = None) -> None:
        """Initialize the main ``Dataset`` objects.

        This method is called once per GPU per run.

        Args:
            stage: stage to set up
        """
        transforms = Compose([self.preprocess])

        dataset = Vaihingen2D(self.root_dir, "train", transforms=transforms)

        self.train_dataset: Dataset[Any]
        self.val_dataset: Dataset[Any]

        if self.val_split_pct > 0.0:
            self.train_dataset, self.val_dataset, _ = dataset_split(
                dataset, val_pct=self.val_split_pct, test_pct=0.0
            )
        else:
            self.train_dataset = dataset
            self.val_dataset = dataset

        self.test_dataset = Vaihingen2D(self.root_dir, "test", transforms=transforms)

    def train_dataloader(self) -> DataLoader[Any]:
        """Return a DataLoader for training.

        Returns:
            training data loader
        """
        return DataLoader(
            self.train_dataset,
            batch_size=self.batch_size,
            num_workers=self.num_workers,
            shuffle=True,
        )

    def val_dataloader(self) -> DataLoader[Any]:
        """Return a DataLoader for validation.

        Returns:
            validation data loader
        """
        return DataLoader(
            self.val_dataset,
            batch_size=self.batch_size,
            num_workers=self.num_workers,
            shuffle=False,
        )

    def test_dataloader(self) -> DataLoader[Any]:
        """Return a DataLoader for testing.

        Returns:
            testing data loader
        """
        return DataLoader(
            self.test_dataset,
            batch_size=self.batch_size,
            num_workers=self.num_workers,
            shuffle=False,
        )
