# 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 copy
from typing import Callable, Union

import pytest
import torch
from torchmetrics.functional import mean_relative_error

from pytorch_lightning import seed_everything, Trainer
from pytorch_lightning.callbacks import QuantizationAwareTraining
from pytorch_lightning.utilities.exceptions import MisconfigurationException
from pytorch_lightning.utilities.memory import get_model_size_mb
from tests.helpers.datamodules import RegressDataModule
from tests.helpers.runif import RunIf
from tests.helpers.simple_models import RegressionModel


@pytest.mark.parametrize("observe", ["average", "histogram"])
@pytest.mark.parametrize("fuse", [True, False])
@pytest.mark.parametrize("convert", [True, False])
@RunIf(quantization=True)
def test_quantization(tmpdir, observe: str, fuse: bool, convert: bool):
    """Parity test for quant model"""
    seed_everything(42)
    dm = RegressDataModule()
    trainer_args = dict(default_root_dir=tmpdir, max_epochs=7, gpus=int(torch.cuda.is_available()))
    model = RegressionModel()
    qmodel = copy.deepcopy(model)

    trainer = Trainer(**trainer_args)
    trainer.fit(model, datamodule=dm)
    org_size = get_model_size_mb(model)
    org_score = torch.mean(torch.tensor([mean_relative_error(model(x), y) for x, y in dm.test_dataloader()]))

    fusing_layers = [(f"layer_{i}", f"layer_{i}a") for i in range(3)] if fuse else None
    qcb = QuantizationAwareTraining(observer_type=observe, modules_to_fuse=fusing_layers, quantize_on_fit_end=convert)
    trainer = Trainer(callbacks=[qcb], **trainer_args)
    trainer.fit(qmodel, datamodule=dm)

    quant_calls = qcb._forward_calls
    assert quant_calls == qcb._forward_calls
    quant_score = torch.mean(torch.tensor([mean_relative_error(qmodel(x), y) for x, y in dm.test_dataloader()]))
    # test that the test score is almost the same as with pure training
    assert torch.allclose(org_score, quant_score, atol=0.45)
    model_path = trainer.checkpoint_callback.best_model_path

    trainer_args.update(dict(max_epochs=1, checkpoint_callback=False))
    if not convert:
        trainer = Trainer(callbacks=[QuantizationAwareTraining()], **trainer_args)
        trainer.fit(qmodel, datamodule=dm)
        qmodel.eval()
        torch.quantization.convert(qmodel, inplace=True)

    quant_size = get_model_size_mb(qmodel)
    # test that the trained model is smaller then initial
    size_ratio = quant_size / org_size
    assert size_ratio < 0.65

    # todo: make it work also with strict loading
    qmodel2 = RegressionModel.load_from_checkpoint(model_path, strict=False)
    quant2_score = torch.mean(torch.tensor([mean_relative_error(qmodel2(x), y) for x, y in dm.test_dataloader()]))
    assert torch.allclose(org_score, quant2_score, atol=0.45)


@RunIf(quantization=True)
def test_quantize_torchscript(tmpdir):
    """Test converting to torchscipt"""
    dm = RegressDataModule()
    qmodel = RegressionModel()
    qcb = QuantizationAwareTraining(input_compatible=False)
    trainer = Trainer(callbacks=[qcb], default_root_dir=tmpdir, max_epochs=1)
    trainer.fit(qmodel, datamodule=dm)

    batch = iter(dm.test_dataloader()).next()
    qmodel(qmodel.quant(batch[0]))

    tsmodel = qmodel.to_torchscript()
    tsmodel(tsmodel.quant(batch[0]))


@RunIf(quantization=True)
def test_quantization_exceptions(tmpdir):
    """Test wrong fuse layers"""
    with pytest.raises(MisconfigurationException, match="Unsupported qconfig"):
        QuantizationAwareTraining(qconfig=["abc"])

    with pytest.raises(MisconfigurationException, match="Unsupported observer type"):
        QuantizationAwareTraining(observer_type="abc")

    with pytest.raises(MisconfigurationException, match="Unsupported `collect_quantization`"):
        QuantizationAwareTraining(collect_quantization="abc")

    with pytest.raises(MisconfigurationException, match="Unsupported `collect_quantization`"):
        QuantizationAwareTraining(collect_quantization=1.2)

    fusing_layers = [(f"layers.mlp_{i}", f"layers.NONE-mlp_{i}a") for i in range(3)]
    qcb = QuantizationAwareTraining(modules_to_fuse=fusing_layers)
    trainer = Trainer(callbacks=[qcb], default_root_dir=tmpdir, max_epochs=1)
    with pytest.raises(MisconfigurationException, match="one or more of them is not your model attributes"):
        trainer.fit(RegressionModel(), datamodule=RegressDataModule())


def custom_trigger_never(trainer):
    return False


def custom_trigger_even(trainer):
    return trainer.current_epoch % 2 == 0


def custom_trigger_last(trainer):
    return trainer.current_epoch == (trainer.max_epochs - 1)


@pytest.mark.parametrize(
    "trigger_fn,expected_count",
    [(None, 9), (3, 3), (custom_trigger_never, 0), (custom_trigger_even, 5), (custom_trigger_last, 2)],
)
@RunIf(quantization=True)
def test_quantization_triggers(tmpdir, trigger_fn: Union[None, int, Callable], expected_count: int):
    """Test  how many times the quant is called"""
    dm = RegressDataModule()
    qmodel = RegressionModel()
    qcb = QuantizationAwareTraining(collect_quantization=trigger_fn)
    trainer = Trainer(
        callbacks=[qcb], default_root_dir=tmpdir, limit_train_batches=1, limit_val_batches=1, max_epochs=4
    )
    trainer.fit(qmodel, datamodule=dm)

    assert qcb._forward_calls == expected_count

@@ -695,30 +695,49 @@
 rt mean_
-relative_error
+absolute_percentage_error as mape
 %0A%0Afrom p
@@ -1818,34 +1818,19 @@
 ensor(%5Bm
-ean_relative_error
+ape
 (model(x
@@ -2300,34 +2300,19 @@
 ensor(%5Bm
-ean_relative_error
+ape
 (qmodel(
@@ -3192,26 +3192,11 @@
 r(%5Bm
-ean_relative_error
+ape
 (qmo

fix deprecated TM mape (#8830)