from typing import Any, Dict, Generic, Optional, Sequence, TypeVar, Union

from pytorch_ie.core import Document
from pytorch_ie.core.taskmodule import (
    IterableTaskEncodingDataset,
    TaskEncoding,
    TaskEncodingDataset,
    TaskModule,
)
from pytorch_lightning import LightningDataModule
from torch.utils.data import DataLoader, Sampler
from typing_extensions import TypeAlias

from .components.sampler import ImbalancedDatasetSampler

DocumentType = TypeVar("DocumentType", bound=Document)
InputEncoding = TypeVar("InputEncoding")
TargetEncoding = TypeVar("TargetEncoding")
DatasetType: TypeAlias = Union[
    TaskEncodingDataset[TaskEncoding[DocumentType, InputEncoding, TargetEncoding]],
    IterableTaskEncodingDataset[TaskEncoding[DocumentType, InputEncoding, TargetEncoding]],
]


class PieDataModule(LightningDataModule, Generic[DocumentType, InputEncoding, TargetEncoding]):
    """A simple LightningDataModule for PIE document datasets.

    A DataModule implements 5 key methods:
        - prepare_data (things to do on 1 GPU/TPU, not on every GPU/TPU in distributed mode)
        - setup (things to do on every accelerator in distributed mode)
        - train_dataloader (the training dataloader)
        - val_dataloader (the validation dataloader(s))
        - test_dataloader (the test dataloader(s))

    This allows you to share a full dataset without explaining how to download,
    split, transform and process the data.

    Read the docs:
        https://pytorch-lightning.readthedocs.io/en/latest/extensions/datamodules.html
    """

    def __init__(
        self,
        taskmodule: TaskModule[DocumentType, InputEncoding, TargetEncoding, Any, Any, Any],
        dataset: Dict[str, Sequence[DocumentType]],
        data_config_path: Optional[str] = None,
        train_split: Optional[str] = "train",
        val_split: Optional[str] = "validation",
        test_split: Optional[str] = "test",
        show_progress_for_encode: bool = False,
        train_sampler: Optional[str] = None,
        **dataloader_kwargs,
    ):
        super().__init__()

        self.taskmodule = taskmodule
        self.config_path = data_config_path
        self.dataset = dataset
        self.train_split = train_split
        self.val_split = val_split
        self.test_split = test_split
        self.show_progress_for_encode = show_progress_for_encode
        self.train_sampler_name = train_sampler
        self.dataloader_kwargs = dataloader_kwargs

        self._data: Dict[str, DatasetType] = {}

    @property
    def num_train(self) -> int:
        if self.train_split is None:
            raise ValueError("no train_split assigned")
        data_train = self._data.get(self.train_split, None)
        if data_train is None:
            raise ValueError("can not get train size if setup() was not yet called")
        if isinstance(data_train, IterableTaskEncodingDataset):
            raise TypeError("IterableTaskEncodingDataset has no length")
        return len(data_train)

    def setup(self, stage: str):
        if stage == "fit":
            split_names = [self.train_split, self.val_split]
        elif stage == "validate":
            split_names = [self.val_split]
        elif stage == "test":
            split_names = [self.test_split]
        else:
            raise NotImplementedError(f"not implemented for stage={stage} ")

        for split in split_names:
            if split is None or split not in self.dataset:
                continue
            task_encoding_dataset = self.taskmodule.encode(
                self.dataset[split],
                encode_target=True,
                as_dataset=True,
                show_progress=self.show_progress_for_encode,
            )
            if not isinstance(
                task_encoding_dataset,
                (TaskEncodingDataset, IterableTaskEncodingDataset),
            ):
                raise TypeError(
                    f"taskmodule.encode did not return a (Iterable)TaskEncodingDataset, but: {type(task_encoding_dataset)}"
                )
            self._data[split] = task_encoding_dataset

    def data_split(self, split: Optional[str] = None) -> DatasetType:
        if split is None or split not in self._data:
            raise ValueError(f"data for split={split} not available")
        return self._data[split]

    def get_train_sampler(
        self,
        sampler_name: str,
        dataset: DatasetType,
    ) -> Sampler:
        if sampler_name == "imbalanced_dataset":
            # for now, this work only with targets that have a single entry
            return ImbalancedDatasetSampler(
                dataset, callback_get_label=lambda ds: [x.targets[0] for x in ds]
            )
        else:
            raise ValueError(f"unknown sampler name: {sampler_name}")

    def train_dataloader(self):
        ds = self.data_split(self.train_split)
        if self.train_sampler_name is not None:
            sampler = self.get_train_sampler(sampler_name=self.train_sampler_name, dataset=ds)
        else:
            sampler = None
        return DataLoader(
            dataset=ds,
            sampler=sampler,
            collate_fn=self.taskmodule.collate,
            # don't shuffle streamed datasets or if we use a sampler
            shuffle=not (isinstance(ds, IterableTaskEncodingDataset) or sampler is not None),
            **self.dataloader_kwargs,
        )

    def val_dataloader(self):
        return DataLoader(
            dataset=self.data_split(self.val_split),
            collate_fn=self.taskmodule.collate,
            shuffle=False,
            **self.dataloader_kwargs,
        )

    def test_dataloader(self):
        return DataLoader(
            dataset=self.data_split(self.test_split),
            collate_fn=self.taskmodule.collate,
            shuffle=False,
            **self.dataloader_kwargs,
        )