# 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.
import os
from pathlib import Path
from typing import Any, Dict, Optional
from unittest.mock import MagicMock, Mock

import torch

from lightning_lite.plugins import CheckpointIO, TorchCheckpointIO
from lightning_lite.utilities.types import _PATH
from pytorch_lightning import Trainer
from pytorch_lightning.callbacks import ModelCheckpoint
from pytorch_lightning.demos.boring_classes import BoringModel
from pytorch_lightning.plugins.io.async_plugin import AsyncCheckpointIO
from pytorch_lightning.strategies import SingleDeviceStrategy


class CustomCheckpointIO(CheckpointIO):
    def save_checkpoint(self, checkpoint: Dict[str, Any], path: _PATH, storage_options: Optional[Any] = None) -> None:
        torch.save(checkpoint, path)

    def load_checkpoint(self, path: _PATH, storage_options: Optional[Any] = None) -> Dict[str, Any]:
        return torch.load(path)

    def remove_checkpoint(self, path: _PATH) -> None:
        os.remove(path)


def test_checkpoint_plugin_called(tmpdir):
    """Ensure that the custom checkpoint IO plugin and torch checkpoint IO plugin is called when saving/loading."""
    checkpoint_plugin = CustomCheckpointIO()
    checkpoint_plugin = MagicMock(wraps=checkpoint_plugin, spec=CustomCheckpointIO)

    ck = ModelCheckpoint(dirpath=tmpdir, save_last=True)

    model = BoringModel()
    trainer = Trainer(
        default_root_dir=tmpdir,
        strategy=SingleDeviceStrategy("cpu", checkpoint_io=checkpoint_plugin),
        callbacks=ck,
        max_epochs=2,
        limit_train_batches=1,
        limit_val_batches=0,
        limit_test_batches=1,
    )
    trainer.fit(model)

    ckpt_files = {fn.name for fn in Path(tmpdir).glob("*.ckpt")}
    assert ckpt_files == {"epoch=1-step=2.ckpt", "last.ckpt"}
    assert trainer.checkpoint_callback.best_model_path == tmpdir / "epoch=1-step=2.ckpt"
    assert trainer.checkpoint_callback.last_model_path == tmpdir / "last.ckpt"
    assert checkpoint_plugin.save_checkpoint.call_count == 4
    assert checkpoint_plugin.remove_checkpoint.call_count == 1

    trainer.test(model, ckpt_path=ck.last_model_path)
    checkpoint_plugin.load_checkpoint.assert_called_with(tmpdir / "last.ckpt")

    checkpoint_plugin.reset_mock()
    ck = ModelCheckpoint(dirpath=tmpdir, save_last=True)

    model = BoringModel()
    trainer = Trainer(
        default_root_dir=tmpdir,
        strategy=SingleDeviceStrategy("cpu"),
        plugins=[checkpoint_plugin],
        callbacks=ck,
        max_epochs=2,
        limit_train_batches=1,
        limit_val_batches=0,
        limit_test_batches=1,
    )
    trainer.fit(model)

    ckpt_files = {fn.name for fn in Path(tmpdir).glob("*.ckpt")}
    assert ckpt_files == {"epoch=1-step=2.ckpt", "last.ckpt", "epoch=1-step=2-v1.ckpt", "last-v1.ckpt"}
    assert trainer.checkpoint_callback.best_model_path == tmpdir / "epoch=1-step=2-v1.ckpt"
    assert trainer.checkpoint_callback.last_model_path == tmpdir / "last-v1.ckpt"
    assert checkpoint_plugin.save_checkpoint.call_count == 4
    assert checkpoint_plugin.remove_checkpoint.call_count == 1

    trainer.test(model, ckpt_path=ck.last_model_path)
    checkpoint_plugin.load_checkpoint.assert_called_once()
    checkpoint_plugin.load_checkpoint.assert_called_with(tmpdir / "last-v1.ckpt")


def test_async_checkpoint_plugin(tmpdir):
    """Ensure that the custom checkpoint IO plugin and torch checkpoint IO plugin is called when async saving and
    loading."""

    checkpoint_plugin = AsyncCheckpointIO()

    checkpoint_plugin.save_checkpoint = Mock(wraps=checkpoint_plugin.save_checkpoint)
    checkpoint_plugin.remove_checkpoint = Mock(wraps=checkpoint_plugin.remove_checkpoint)

    class CustomBoringModel(BoringModel):
        def on_fit_start(self):
            base_ckpt_io = self.trainer.strategy.checkpoint_io.checkpoint_io
            base_ckpt_io.save_checkpoint = Mock(wraps=base_ckpt_io.save_checkpoint)
            base_ckpt_io.remove_checkpoint = Mock(wraps=base_ckpt_io.remove_checkpoint)

    ck = ModelCheckpoint(dirpath=tmpdir, save_top_k=2, monitor="step", mode="max")

    model = CustomBoringModel()
    trainer = Trainer(
        default_root_dir=tmpdir,
        plugins=[checkpoint_plugin],
        callbacks=ck,
        max_epochs=3,
        limit_train_batches=1,
        limit_val_batches=0,
        enable_progress_bar=False,
        enable_model_summary=False,
    )
    trainer.fit(model)

    assert checkpoint_plugin.save_checkpoint.call_count == 3
    assert checkpoint_plugin.remove_checkpoint.call_count == 1

    base_ckpt_io = trainer.strategy.checkpoint_io.checkpoint_io
    assert base_ckpt_io.save_checkpoint.call_count == 3
    assert base_ckpt_io.remove_checkpoint.call_count == 1


def test_multi_wrapped_checkpoint_io_initialization():
    base_ckpt_io = TorchCheckpointIO()
    wrap_ckpt = AsyncCheckpointIO(base_ckpt_io)
    ckpt_io = AsyncCheckpointIO(wrap_ckpt)
    assert ckpt_io.checkpoint_io is wrap_ckpt
    assert ckpt_io.checkpoint_io.checkpoint_io is base_ckpt_io
    assert ckpt_io._base_checkpoint_io_configured is True
    assert ckpt_io.checkpoint_io._base_checkpoint_io_configured is True

    wrap_ckpt = AsyncCheckpointIO()
    ckpt_io = AsyncCheckpointIO(wrap_ckpt)
    trainer = Trainer(accelerator="cpu", plugins=[ckpt_io])
    trainer.strategy.checkpoint_io
    assert ckpt_io.checkpoint_io is wrap_ckpt
    assert isinstance(ckpt_io.checkpoint_io.checkpoint_io, TorchCheckpointIO)
    assert ckpt_io._base_checkpoint_io_configured is True
    assert ckpt_io.checkpoint_io._base_checkpoint_io_configured is True