# 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
import os
from argparse import Namespace
from unittest import mock
from unittest.mock import Mock

import numpy as np
import pytest
import torch
from tests_fabric.test_fabric import BoringModel

from lightning_fabric.loggers import TensorBoardLogger
from lightning_fabric.loggers.tensorboard import _TENSORBOARD_AVAILABLE


def test_tensorboard_automatic_versioning(tmpdir):
    """Verify that automatic versioning works."""
    root_dir = tmpdir / "tb_versioning"
    root_dir.mkdir()
    (root_dir / "version_0").mkdir()
    (root_dir / "version_1").mkdir()

    logger = TensorBoardLogger(root_dir=tmpdir, name="tb_versioning")
    assert logger.version == 2


def test_tensorboard_manual_versioning(tmpdir):
    """Verify that manual versioning works."""
    root_dir = tmpdir / "tb_versioning"
    root_dir.mkdir()
    (root_dir / "version_0").mkdir()
    (root_dir / "version_1").mkdir()
    (root_dir / "version_2").mkdir()

    logger = TensorBoardLogger(root_dir=tmpdir, name="tb_versioning", version=1)
    assert logger.version == 1


def test_tensorboard_named_version(tmpdir):
    """Verify that manual versioning works for string versions, e.g. '2020-02-05-162402'."""
    name = "tb_versioning"
    (tmpdir / name).mkdir()
    expected_version = "2020-02-05-162402"

    logger = TensorBoardLogger(root_dir=tmpdir, name=name, version=expected_version)
    logger.log_hyperparams({"a": 1, "b": 2, 123: 3, 3.5: 4, 5j: 5})  # Force data to be written

    assert logger.version == expected_version
    assert os.listdir(tmpdir / name) == [expected_version]
    assert os.listdir(tmpdir / name / expected_version)


@pytest.mark.parametrize("name", ["", None])
def test_tensorboard_no_name(tmpdir, name):
    """Verify that None or empty name works."""
    logger = TensorBoardLogger(root_dir=tmpdir, name=name)
    logger.log_hyperparams({"a": 1, "b": 2, 123: 3, 3.5: 4, 5j: 5})  # Force data to be written
    assert os.path.normpath(logger.root_dir) == tmpdir  # use os.path.normpath to handle trailing /
    assert os.listdir(tmpdir / "version_0")


def test_tensorboard_log_sub_dir(tmpdir):
    # no sub_dir specified
    root_dir = tmpdir / "logs"
    logger = TensorBoardLogger(root_dir, name="name", version="version")
    assert logger.log_dir == os.path.join(root_dir, "name", "version")

    # sub_dir specified
    logger = TensorBoardLogger(root_dir, name="name", version="version", sub_dir="sub_dir")
    assert logger.log_dir == os.path.join(root_dir, "name", "version", "sub_dir")


def test_tensorboard_expand_home():
    """Test that the home dir (`~`) gets expanded properly."""
    root_dir = "~/tmp"
    explicit_root_dir = os.path.expanduser(root_dir)
    logger = TensorBoardLogger(root_dir, name="name", version="version", sub_dir="sub_dir")
    assert logger.root_dir == root_dir
    assert logger.log_dir == os.path.join(explicit_root_dir, "name", "version", "sub_dir")


@mock.patch.dict(os.environ, {"TEST_ENV_DIR": "some_directory"})
def test_tensorboard_expand_env_vars():
    """Test that the env vars in path names (`$`) get handled properly."""
    test_env_dir = os.environ["TEST_ENV_DIR"]
    root_dir = "$TEST_ENV_DIR/tmp"
    explicit_root_dir = f"{test_env_dir}/tmp"
    logger = TensorBoardLogger(root_dir, name="name", version="version", sub_dir="sub_dir")
    assert logger.log_dir == os.path.join(explicit_root_dir, "name", "version", "sub_dir")


@pytest.mark.parametrize("step_idx", [10, None])
def test_tensorboard_log_metrics(tmpdir, step_idx):
    logger = TensorBoardLogger(tmpdir)
    metrics = {"float": 0.3, "int": 1, "FloatTensor": torch.tensor(0.1), "IntTensor": torch.tensor(1)}
    logger.log_metrics(metrics, step_idx)


def test_tensorboard_log_hyperparams(tmpdir):
    logger = TensorBoardLogger(tmpdir)
    hparams = {
        "float": 0.3,
        "int": 1,
        "string": "abc",
        "bool": True,
        "dict": {"a": {"b": "c"}},
        "list": [1, 2, 3],
        "namespace": Namespace(foo=Namespace(bar="buzz")),
        "layer": torch.nn.BatchNorm1d,
        "tensor": torch.empty(2, 2, 2),
        "array": np.empty([2, 2, 2]),
    }
    logger.log_hyperparams(hparams)


def test_tensorboard_log_hparams_and_metrics(tmpdir):
    logger = TensorBoardLogger(tmpdir, default_hp_metric=False)
    hparams = {
        "float": 0.3,
        "int": 1,
        "string": "abc",
        "bool": True,
        "dict": {"a": {"b": "c"}},
        "list": [1, 2, 3],
        "namespace": Namespace(foo=Namespace(bar="buzz")),
        "layer": torch.nn.BatchNorm1d,
        "tensor": torch.empty(2, 2, 2),
        "array": np.empty([2, 2, 2]),
    }
    metrics = {"abc": torch.tensor([0.54])}
    logger.log_hyperparams(hparams, metrics)


@pytest.mark.parametrize("example_input_array", [None, torch.rand(2, 32)])
def test_tensorboard_log_graph(tmpdir, example_input_array):
    """test that log graph works with both model.example_input_array and if array is passed externally."""
    # TODO(fabric): Test both nn.Module and LightningModule
    # TODO(fabric): Assert _apply_batch_transfer_handler is calling the batch transfer hooks
    model = BoringModel()
    if example_input_array is not None:
        model.example_input_array = None

    logger = TensorBoardLogger(tmpdir, log_graph=True)
    logger.log_graph(model, example_input_array)


@pytest.mark.skipif(not _TENSORBOARD_AVAILABLE, reason=str(_TENSORBOARD_AVAILABLE))
def test_tensorboard_log_graph_warning_no_example_input_array(tmpdir):
    """test that log graph throws warning if model.example_input_array is None."""
    model = BoringModel()
    model.example_input_array = None
    logger = TensorBoardLogger(tmpdir, log_graph=True)
    with pytest.warns(
        UserWarning,
        match="Could not log computational graph to TensorBoard: The `model.example_input_array` .* was not given",
    ):
        logger.log_graph(model)

    model.example_input_array = dict(x=1, y=2)
    with pytest.warns(
        UserWarning, match="Could not log computational graph to TensorBoard: .* can't be traced by TensorBoard"
    ):
        logger.log_graph(model)


def test_tensorboard_finalize(monkeypatch, tmpdir):
    """Test that the SummaryWriter closes in finalize."""
    if _TENSORBOARD_AVAILABLE:
        import torch.utils.tensorboard as tb
    else:
        import tensorboardX as tb

    monkeypatch.setattr(tb, "SummaryWriter", Mock())
    logger = TensorBoardLogger(root_dir=tmpdir)
    assert logger._experiment is None
    logger.finalize("any")

    # no log calls, no experiment created -> nothing to flush
    logger.experiment.assert_not_called()

    logger = TensorBoardLogger(root_dir=tmpdir)
    logger.log_metrics({"flush_me": 11.1})  # trigger creation of an experiment
    logger.finalize("any")

    # finalize flushes to experiment directory
    logger.experiment.flush.assert_called()
    logger.experiment.close.assert_called()


@mock.patch("lightning_fabric.loggers.tensorboard.log")
def test_tensorboard_with_symlink(log, tmpdir):
    """Tests a specific failure case when tensorboard logger is used with empty name, symbolic link ``save_dir``,
    and relative paths."""
    os.chdir(tmpdir)  # need to use relative paths
    source = os.path.join(".", "lightning_logs")
    dest = os.path.join(".", "sym_lightning_logs")

    os.makedirs(source, exist_ok=True)
    os.symlink(source, dest)

    logger = TensorBoardLogger(root_dir=dest, name="")
    _ = logger.version

    log.warning.assert_not_called()


def test_tensorboard_missing_folder_warning(tmpdir, caplog):
    """Verify that the logger throws a warning for invalid directory."""

    name = "fake_dir"
    logger = TensorBoardLogger(root_dir=tmpdir, name=name)

    with caplog.at_level(logging.WARNING):
        assert logger.version == 0

    assert "Missing logger folder:" in caplog.text