# 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 logging
from unittest.mock import Mock

import torch

from pytorch_lightning import Callback, Trainer
from pytorch_lightning.callbacks import (
    EarlyStopping,
    GradientAccumulationScheduler,
    LearningRateMonitor,
    ModelCheckpoint,
    ProgressBar,
)
from pytorch_lightning.trainer.connectors.callback_connector import CallbackConnector
from tests.helpers import BoringModel


def test_checkpoint_callbacks_are_last(tmpdir):
    """Test that checkpoint callbacks always get moved to the end of the list, with preserved order."""
    checkpoint1 = ModelCheckpoint(tmpdir)
    checkpoint2 = ModelCheckpoint(tmpdir)
    early_stopping = EarlyStopping()
    lr_monitor = LearningRateMonitor()
    progress_bar = ProgressBar()

    # no model callbacks
    model = Mock()
    model.configure_callbacks.return_value = []
    trainer = Trainer(callbacks=[checkpoint1, progress_bar, lr_monitor, checkpoint2])
    trainer.model = model
    cb_connector = CallbackConnector(trainer)
    cb_connector._attach_model_callbacks()
    assert trainer.callbacks == [progress_bar, lr_monitor, checkpoint1, checkpoint2]

    # with model-specific callbacks that substitute ones in Trainer
    model = Mock()
    model.configure_callbacks.return_value = [checkpoint1, early_stopping, checkpoint2]
    trainer = Trainer(callbacks=[progress_bar, lr_monitor, ModelCheckpoint(tmpdir)])
    trainer.model = model
    cb_connector = CallbackConnector(trainer)
    cb_connector._attach_model_callbacks()
    assert trainer.callbacks == [progress_bar, lr_monitor, early_stopping, checkpoint1, checkpoint2]


class StatefulCallback0(Callback):
    def on_save_checkpoint(self, *args):
        return {"content0": 0}


class StatefulCallback1(Callback):
    def on_save_checkpoint(self, *args):
        return {"content1": 1}


def test_all_callback_states_saved_before_checkpoint_callback(tmpdir):
    """Test that all callback states get saved even if the ModelCheckpoint is not given as last."""

    callback0 = StatefulCallback0()
    callback1 = StatefulCallback1()
    checkpoint_callback = ModelCheckpoint(dirpath=tmpdir, filename="all_states")
    model = BoringModel()
    trainer = Trainer(
        default_root_dir=tmpdir, max_steps=1, limit_val_batches=1, callbacks=[callback0, checkpoint_callback, callback1]
    )
    trainer.fit(model)

    ckpt = torch.load(str(tmpdir / "all_states.ckpt"))
    state0 = ckpt["callbacks"]["StatefulCallback0"]
    state1 = ckpt["callbacks"]["StatefulCallback1"]
    assert "content0" in state0 and state0["content0"] == 0
    assert "content1" in state1 and state1["content1"] == 1
    assert "ModelCheckpoint" in ckpt["callbacks"]


def test_attach_model_callbacks():
    """Test that the callbacks defined in the model and through Trainer get merged correctly."""

    def assert_composition(trainer_callbacks, model_callbacks, expected):
        model = Mock()
        model.configure_callbacks.return_value = model_callbacks
        trainer = Trainer(checkpoint_callback=False, progress_bar_refresh_rate=0, callbacks=trainer_callbacks)
        trainer.model = model
        cb_connector = CallbackConnector(trainer)
        cb_connector._attach_model_callbacks()
        assert trainer.callbacks == expected

    early_stopping = EarlyStopping()
    progress_bar = ProgressBar()
    lr_monitor = LearningRateMonitor()
    grad_accumulation = GradientAccumulationScheduler({1: 1})

    # no callbacks
    assert_composition(trainer_callbacks=[], model_callbacks=[], expected=[])

    # callbacks of different types
    assert_composition(
        trainer_callbacks=[early_stopping], model_callbacks=[progress_bar], expected=[early_stopping, progress_bar]
    )

    # same callback type twice, different instance
    assert_composition(
        trainer_callbacks=[progress_bar, EarlyStopping()],
        model_callbacks=[early_stopping],
        expected=[progress_bar, early_stopping],
    )

    # multiple callbacks of the same type in trainer
    assert_composition(
        trainer_callbacks=[LearningRateMonitor(), EarlyStopping(), LearningRateMonitor(), EarlyStopping()],
        model_callbacks=[early_stopping, lr_monitor],
        expected=[early_stopping, lr_monitor],
    )

    # multiple callbacks of the same type, in both trainer and model
    assert_composition(
        trainer_callbacks=[
            LearningRateMonitor(),
            progress_bar,
            EarlyStopping(),
            LearningRateMonitor(),
            EarlyStopping(),
        ],
        model_callbacks=[early_stopping, lr_monitor, grad_accumulation, early_stopping],
        expected=[progress_bar, early_stopping, lr_monitor, grad_accumulation, early_stopping],
    )


def test_attach_model_callbacks_override_info(caplog):
    """Test that the logs contain the info about overriding callbacks returned by configure_callbacks."""
    model = Mock()
    model.configure_callbacks.return_value = [LearningRateMonitor(), EarlyStopping()]
    trainer = Trainer(checkpoint_callback=False, callbacks=[EarlyStopping(), LearningRateMonitor(), ProgressBar()])
    trainer.model = model
    cb_connector = CallbackConnector(trainer)
    with caplog.at_level(logging.INFO):
        cb_connector._attach_model_callbacks()

    assert "existing callbacks passed to Trainer: EarlyStopping, LearningRateMonitor" in caplog.text