# Copyright The PyTorch Lightning team.
#
# 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.
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"""LightningDataModule for loading DataLoaders with ease."""
from argparse import ArgumentParser, Namespace
from typing import Any, List, Mapping, Optional, Sequence, Tuple, Union

from torch.utils.data import DataLoader, Dataset, IterableDataset

from pytorch_lightning.core.hooks import CheckpointHooks, DataHooks
from pytorch_lightning.core.mixins import HyperparametersMixin
from pytorch_lightning.utilities import rank_zero_deprecation
from pytorch_lightning.utilities.argparse import add_argparse_args, from_argparse_args, get_init_arguments_and_types


class LightningDataModule(CheckpointHooks, DataHooks, HyperparametersMixin):
    """A DataModule standardizes the training, val, test splits, data preparation and transforms. The main
    advantage is consistent data splits, data preparation and transforms across models.

    Example::

        class MyDataModule(LightningDataModule):
            def __init__(self):
                super().__init__()
            def prepare_data(self):
                # download, split, etc...
                # only called on 1 GPU/TPU in distributed
            def setup(self, stage):
                # make assignments here (val/train/test split)
                # called on every process in DDP
            def train_dataloader(self):
                train_split = Dataset(...)
                return DataLoader(train_split)
            def val_dataloader(self):
                val_split = Dataset(...)
                return DataLoader(val_split)
            def test_dataloader(self):
                test_split = Dataset(...)
                return DataLoader(test_split)
            def teardown(self):
                # clean up after fit or test
                # called on every process in DDP

    A DataModule implements 6 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 val dataloader(s).
    * **test_dataloader** the test dataloader(s).
    * **teardown** (things to do on every accelerator in distributed mode when finished)

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

    name: str = ...

    def __init__(self, train_transforms=None, val_transforms=None, test_transforms=None, dims=None):
        super().__init__()
        if train_transforms is not None:
            rank_zero_deprecation(
                "DataModule property `train_transforms` was deprecated in v1.5 and will be removed in v1.7."
            )
        if val_transforms is not None:
            rank_zero_deprecation(
                "DataModule property `val_transforms` was deprecated in v1.5 and will be removed in v1.7."
            )
        if test_transforms is not None:
            rank_zero_deprecation(
                "DataModule property `test_transforms` was deprecated in v1.5 and will be removed in v1.7."
            )
        if dims is not None:
            rank_zero_deprecation("DataModule property `dims` was deprecated in v1.5 and will be removed in v1.7.")
        self._train_transforms = train_transforms
        self._val_transforms = val_transforms
        self._test_transforms = test_transforms
        self._dims = dims if dims is not None else ()

        # Pointer to the trainer object
        self.trainer = None

    @property
    def train_transforms(self):
        """Optional transforms (or collection of transforms) you can apply to train dataset.

        .. deprecated:: v1.5     Will be removed in v1.7.0.
        """

        rank_zero_deprecation(
            "DataModule property `train_transforms` was deprecated in v1.5 and will be removed in v1.7."
        )
        return self._train_transforms

    @train_transforms.setter
    def train_transforms(self, t):
        rank_zero_deprecation(
            "DataModule property `train_transforms` was deprecated in v1.5 and will be removed in v1.7."
        )
        self._train_transforms = t

    @property
    def val_transforms(self):
        """Optional transforms (or collection of transforms) you can apply to validation dataset.

        .. deprecated:: v1.5     Will be removed in v1.7.0.
        """

        rank_zero_deprecation(
            "DataModule property `val_transforms` was deprecated in v1.5 and will be removed in v1.7."
        )
        return self._val_transforms

    @val_transforms.setter
    def val_transforms(self, t):
        rank_zero_deprecation(
            "DataModule property `val_transforms` was deprecated in v1.5 and will be removed in v1.7."
        )
        self._val_transforms = t

    @property
    def test_transforms(self):
        """Optional transforms (or collection of transforms) you can apply to test dataset.

        .. deprecated:: v1.5     Will be removed in v1.7.0.
        """

        rank_zero_deprecation(
            "DataModule property `test_transforms` was deprecated in v1.5 and will be removed in v1.7."
        )
        return self._test_transforms

    @test_transforms.setter
    def test_transforms(self, t):
        rank_zero_deprecation(
            "DataModule property `test_transforms` was deprecated in v1.5 and will be removed in v1.7."
        )
        self._test_transforms = t

    @property
    def dims(self):
        """A tuple describing the shape of your data. Extra functionality exposed in ``size``.

        .. deprecated:: v1.5     Will be removed in v1.7.0.
        """
        rank_zero_deprecation("DataModule property `dims` was deprecated in v1.5 and will be removed in v1.7.")
        return self._dims

    @dims.setter
    def dims(self, d):
        rank_zero_deprecation("DataModule property `dims` was deprecated in v1.5 and will be removed in v1.7.")
        self._dims = d

    def size(self, dim=None) -> Union[Tuple, List[Tuple]]:
        """Return the dimension of each input either as a tuple or list of tuples. You can index this just as you
        would with a torch tensor.

        .. deprecated:: v1.5     Will be removed in v1.7.0.
        """
        rank_zero_deprecation("DataModule property `size` was deprecated in v1.5 and will be removed in v1.7.")

        if dim is not None:
            return self.dims[dim]

        return self.dims

    @classmethod
    def add_argparse_args(cls, parent_parser: ArgumentParser, **kwargs) -> ArgumentParser:
        """Extends existing argparse by default `LightningDataModule` attributes."""
        return add_argparse_args(cls, parent_parser, **kwargs)

    @classmethod
    def from_argparse_args(cls, args: Union[Namespace, ArgumentParser], **kwargs):
        """Create an instance from CLI arguments.

        Args:
            args: The parser or namespace to take arguments from. Only known arguments will be
                parsed and passed to the :class:`~pytorch_lightning.core.datamodule.LightningDataModule`.
            **kwargs: Additional keyword arguments that may override ones in the parser or namespace.
                These must be valid DataModule arguments.

        Example::

            parser = ArgumentParser(add_help=False)
            parser = LightningDataModule.add_argparse_args(parser)
            module = LightningDataModule.from_argparse_args(args)
        """
        return from_argparse_args(cls, args, **kwargs)

    @classmethod
    def get_init_arguments_and_types(cls) -> List[Tuple[str, Tuple, Any]]:
        r"""Scans the DataModule signature and returns argument names, types and default values.

        Returns:
            List with tuples of 3 values:
            (argument name, set with argument types, argument default value).
        """
        return get_init_arguments_and_types(cls)

    @classmethod
    def from_datasets(
        cls,
        train_dataset: Optional[Union[Dataset, Sequence[Dataset], Mapping[str, Dataset]]] = None,
        val_dataset: Optional[Union[Dataset, Sequence[Dataset]]] = None,
        test_dataset: Optional[Union[Dataset, Sequence[Dataset]]] = None,
        batch_size: int = 1,
        num_workers: int = 0,
    ):
        r"""
        Create an instance from torch.utils.data.Dataset.

        Args:
            train_dataset: (optional) Dataset to be used for train_dataloader()
            val_dataset: (optional) Dataset or list of Dataset to be used for val_dataloader()
            test_dataset: (optional) Dataset or list of Dataset to be used for test_dataloader()
            batch_size: Batch size to use for each dataloader. Default is 1.
            num_workers: Number of subprocesses to use for data loading. 0 means that the
                data will be loaded in the main process. Number of CPUs available.

        """

        def dataloader(ds: Dataset, shuffle: bool = False) -> DataLoader:
            shuffle &= not isinstance(ds, IterableDataset)
            return DataLoader(ds, batch_size=batch_size, shuffle=shuffle, num_workers=num_workers, pin_memory=True)

        def train_dataloader():
            if isinstance(train_dataset, Mapping):
                return {key: dataloader(ds, shuffle=True) for key, ds in train_dataset.items()}
            if isinstance(train_dataset, Sequence):
                return [dataloader(ds, shuffle=True) for ds in train_dataset]
            return dataloader(train_dataset, shuffle=True)

        def val_dataloader():
            if isinstance(val_dataset, Sequence):
                return [dataloader(ds) for ds in val_dataset]
            return dataloader(val_dataset)

        def test_dataloader():
            if isinstance(test_dataset, Sequence):
                return [dataloader(ds) for ds in test_dataset]
            return dataloader(test_dataset)

        datamodule = cls()
        if train_dataset is not None:
            datamodule.train_dataloader = train_dataloader
        if val_dataset is not None:
            datamodule.val_dataloader = val_dataloader
        if test_dataset is not None:
            datamodule.test_dataloader = test_dataloader
        return datamodule
