# 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 unittest.mock import patch

import numpy as np
import pytest

from pytorch_lightning import Trainer
from pytorch_lightning.demos.boring_classes import BoringModel


class ModelWithManualGradTracker(BoringModel):
    def __init__(self, norm_type, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.stored_grad_norms, self.norm_type = [], float(norm_type)

    # validation spoils logger's metrics with `val_loss` records
    validation_step = None
    val_dataloader = None

    def training_step(self, batch, batch_idx, optimizer_idx=None):
        # just return a loss, no log or progress bar meta
        output = self(batch)
        loss = self.loss(batch, output)
        return {"loss": loss}

    def on_after_backward(self):
        out, norms = {}, []
        prefix = f"grad_{self.norm_type}_norm_"
        for name, p in self.named_parameters():
            if p.grad is None:
                continue

            # `np.linalg.norm` implementation likely uses fp64 intermediates
            flat = p.grad.data.cpu().numpy().ravel()
            norm = np.linalg.norm(flat, self.norm_type)
            norms.append(norm)

            out[prefix + name] = round(norm, 4)

        # handle total norm
        norm = np.linalg.norm(norms, self.norm_type)
        out[prefix + "total"] = round(norm, 4)
        self.stored_grad_norms.append(out)


@pytest.mark.parametrize("norm_type", [1.0, 1.25, 2, 3, 5, 10, "inf"])
def test_grad_tracking(tmpdir, norm_type, rtol=5e-3):
    # rtol=5e-3 respects the 3 decimals rounding in `.grad_norms` and above
    class TestModel(ModelWithManualGradTracker):
        logged_metrics = []

        def on_train_batch_end(self, *_) -> None:
            # copy so they don't get reduced
            self.logged_metrics.append(self.trainer.logged_metrics.copy())

    model = TestModel(norm_type)

    trainer = Trainer(
        default_root_dir=tmpdir,
        max_epochs=3,
        track_grad_norm=norm_type,
        log_every_n_steps=1,  # request grad_norms every batch
    )
    trainer.fit(model)
    assert trainer.state.finished, f"Training failed with {trainer.state}"

    assert len(model.logged_metrics) == len(model.stored_grad_norms)
    # compare the logged metrics against tracked norms on `.backward`
    for mod, log in zip(model.stored_grad_norms, model.logged_metrics):
        for k in mod.keys() & log.keys():
            assert np.allclose(mod[k], log[k], rtol=rtol), k


@pytest.mark.parametrize("log_every_n_steps", [1, 2, 3])
def test_grad_tracking_interval(tmpdir, log_every_n_steps):
    """Test that gradient norms get tracked in the right interval and that everytime the same keys get logged."""
    trainer = Trainer(default_root_dir=tmpdir, track_grad_norm=2, log_every_n_steps=log_every_n_steps, max_steps=10)

    with patch.object(trainer.logger, "log_metrics") as mocked:
        model = BoringModel()
        trainer.fit(model)
        expected = trainer.global_step // log_every_n_steps
        grad_norm_dicts = []
        for _, kwargs in mocked.call_args_list:
            metrics = kwargs.get("metrics", {})
            grad_norm_dict = {k: v for k, v in metrics.items() if k.startswith("grad_")}
            if grad_norm_dict:
                grad_norm_dicts.append(grad_norm_dict)

        # logging on n steps + 1 epochs
        assert len(grad_norm_dicts) == expected + 1
        # check all metrics derived from steps have the same keys
        assert all(grad_norm_dicts[0].keys() == g.keys() for g in grad_norm_dicts[1:-1])
        epoch_end_keys = [k.replace("step", "epoch") for k in grad_norm_dicts[0]]
        assert epoch_end_keys == list(grad_norm_dicts[-1])