# 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.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Any, Dict, Optional

import torch
from sklearn.datasets import make_classification, make_regression
from sklearn.model_selection import train_test_split
from torch.utils.data import DataLoader, random_split
from torch.utils.data.distributed import DistributedSampler

from pytorch_lightning.core.datamodule import LightningDataModule
from tests.helpers.datasets import MNIST, SklearnDataset, TrialMNIST


class TrialMNISTDataModule(LightningDataModule):

    def __init__(self, data_dir: str = "./"):
        super().__init__()
        self.data_dir = data_dir
        self.non_picklable = None
        self.checkpoint_state: Optional[str] = None

    def prepare_data(self):
        TrialMNIST(self.data_dir, train=True, download=True)
        TrialMNIST(self.data_dir, train=False, download=True)

    def setup(self, stage: Optional[str] = None):

        if stage == "fit" or stage is None:
            mnist_full = TrialMNIST(root=self.data_dir, train=True, num_samples=64, download=True)
            self.mnist_train, self.mnist_val = random_split(mnist_full, [128, 64])
            self.dims = self.mnist_train[0][0].shape

        if stage == "test" or stage is None:
            self.mnist_test = TrialMNIST(root=self.data_dir, train=False, num_samples=64, download=True)
            self.dims = getattr(self, "dims", self.mnist_test[0][0].shape)

        self.non_picklable = lambda x: x**2

    def train_dataloader(self):
        return DataLoader(self.mnist_train, batch_size=32)

    def val_dataloader(self):
        return DataLoader(self.mnist_val, batch_size=32)

    def test_dataloader(self):
        return DataLoader(self.mnist_test, batch_size=32)

    def on_save_checkpoint(self, checkpoint: Dict[str, Any]) -> None:
        checkpoint[self.__class__.__name__] = self.__class__.__name__

    def on_load_checkpoint(self, checkpoint: Dict[str, Any]) -> None:
        self.checkpoint_state = checkpoint.get(self.__class__.__name__)


class MNISTDataModule(LightningDataModule):

    def __init__(self, data_dir: str = "./", batch_size: int = 32, dist_sampler: bool = False) -> None:
        super().__init__()

        self.dist_sampler = dist_sampler
        self.data_dir = data_dir
        self.batch_size = batch_size

        # self.dims is returned when you call dm.size()
        # Setting default dims here because we know them.
        # Could optionally be assigned dynamically in dm.setup()
        self.dims = (1, 28, 28)

    def prepare_data(self):
        # download only
        MNIST(self.data_dir, train=True, download=True, normalize=(0.1307, 0.3081))
        MNIST(self.data_dir, train=False, download=True, normalize=(0.1307, 0.3081))

    def setup(self, stage: Optional[str] = None):

        # Assign train/val datasets for use in dataloaders
        # TODO: need to split using random_split once updated to torch >= 1.6
        if stage == "fit" or stage is None:
            self.mnist_train = MNIST(self.data_dir, train=True, normalize=(0.1307, 0.3081))

        # Assign test dataset for use in dataloader(s)
        if stage == "test" or stage is None:
            self.mnist_test = MNIST(self.data_dir, train=False, normalize=(0.1307, 0.3081))

    def train_dataloader(self):
        dist_sampler = None
        if self.dist_sampler:
            dist_sampler = DistributedSampler(self.mnist_train, shuffle=False)

        return DataLoader(
            self.mnist_train,
            batch_size=self.batch_size,
            sampler=dist_sampler,
            shuffle=False,
        )

    def test_dataloader(self):
        return DataLoader(self.mnist_test, batch_size=self.batch_size, shuffle=False)


class SklearnDataModule(LightningDataModule):

    def __init__(self, sklearn_dataset, x_type, y_type, batch_size: int = 10):
        super().__init__()
        self.batch_size = batch_size
        self._x, self._y = sklearn_dataset
        self._split_data()
        self._x_type = x_type
        self._y_type = y_type

    def _split_data(self):
        self.x_train, self.x_test, self.y_train, self.y_test = \
            train_test_split(self._x, self._y, test_size=0.20, random_state=42)
        self.x_train, self.x_valid, self.y_train, self.y_valid = \
            train_test_split(self.x_train, self.y_train, test_size=0.40, random_state=42)

    def train_dataloader(self):
        return DataLoader(
            SklearnDataset(self.x_train, self.y_train, self._x_type, self._y_type), batch_size=self.batch_size
        )

    def val_dataloader(self):
        return DataLoader(
            SklearnDataset(self.x_valid, self.y_valid, self._x_type, self._y_type), batch_size=self.batch_size
        )

    def test_dataloader(self):
        return DataLoader(
            SklearnDataset(self.x_test, self.y_test, self._x_type, self._y_type), batch_size=self.batch_size
        )

    @property
    def sample(self):
        return torch.tensor([self._x[0]], dtype=self._x_type)


class ClassifDataModule(SklearnDataModule):

    def __init__(self, num_features=32, length=800, num_classes=3, batch_size=10):
        data = make_classification(
            n_samples=length, n_features=num_features, n_classes=num_classes, n_clusters_per_class=1, random_state=42
        )
        super().__init__(data, x_type=torch.float32, y_type=torch.long, batch_size=batch_size)


class RegressDataModule(SklearnDataModule):

    def __init__(self, num_features=16, length=800, batch_size=10):
        x, y = make_regression(n_samples=length, n_features=num_features, random_state=42)
        y = [[v] for v in y]
        super().__init__((x, y), x_type=torch.float32, y_type=torch.float32, batch_size=batch_size)