# 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 argparse import ArgumentParser
from unittest import mock

import pytest
import torch
from torch.utils.data import DataLoader

import tests_pytorch.helpers.pipelines as tpipes
import tests_pytorch.helpers.utils as tutils
from pytorch_lightning import Trainer
from pytorch_lightning.accelerators import TPUAccelerator
from pytorch_lightning.callbacks import EarlyStopping
from pytorch_lightning.demos.boring_classes import BoringModel, RandomDataset
from pytorch_lightning.strategies import TPUSpawnStrategy
from pytorch_lightning.strategies.launchers.xla import _save_spawn
from pytorch_lightning.trainer.connectors.logger_connector.result import _Sync
from pytorch_lightning.utilities import _TPU_AVAILABLE
from pytorch_lightning.utilities.distributed import ReduceOp
from pytorch_lightning.utilities.exceptions import MisconfigurationException
from tests_pytorch.helpers.runif import RunIf

if _TPU_AVAILABLE:
    import torch_xla


class SerialLoaderBoringModel(BoringModel):
    def train_dataloader(self):
        return DataLoader(RandomDataset(32, 2000), batch_size=32)

    def val_dataloader(self):
        return DataLoader(RandomDataset(32, 2000), batch_size=32)


@RunIf(tpu=True, standalone=True)
def test_model_tpu_devices_1(tmpdir):
    """Make sure model trains on TPU."""
    tutils.reset_seed()
    trainer_options = dict(
        default_root_dir=tmpdir,
        enable_progress_bar=False,
        max_epochs=2,
        accelerator="tpu",
        devices=1,
        limit_train_batches=4,
        limit_val_batches=4,
    )

    model = BoringModel()
    tpipes.run_model_test(trainer_options, model, with_hpc=False)


@pytest.mark.parametrize("tpu_core", [1, 5])
@RunIf(tpu=True, standalone=True)
def test_model_tpu_index(tmpdir, tpu_core):
    """Make sure model trains on TPU."""
    tutils.reset_seed()
    trainer_options = dict(
        default_root_dir=tmpdir,
        enable_progress_bar=False,
        max_epochs=2,
        accelerator="tpu",
        devices=[tpu_core],
        limit_train_batches=4,
        limit_val_batches=4,
    )

    model = BoringModel()
    tpipes.run_model_test(trainer_options, model, with_hpc=False)
    assert torch_xla._XLAC._xla_get_default_device() == f"xla:{tpu_core}"


@RunIf(tpu=True)
def test_model_tpu_devices_8(tmpdir):
    """Make sure model trains on TPU."""
    tutils.reset_seed()
    trainer_options = dict(
        default_root_dir=tmpdir,
        enable_progress_bar=False,
        max_epochs=1,
        accelerator="tpu",
        devices=8,
        limit_train_batches=4,
        limit_val_batches=4,
    )

    # 8 cores needs a big dataset
    model = SerialLoaderBoringModel()
    tpipes.run_model_test(trainer_options, model, with_hpc=False, min_acc=0.05)


@RunIf(tpu=True, standalone=True)
def test_model_16bit_tpu_devices_1(tmpdir):
    """Make sure model trains on TPU."""
    tutils.reset_seed()
    trainer_options = dict(
        default_root_dir=tmpdir,
        precision=16,
        enable_progress_bar=False,
        max_epochs=2,
        accelerator="tpu",
        devices=1,
        limit_train_batches=8,
        limit_val_batches=2,
    )

    model = BoringModel()
    tpipes.run_model_test(trainer_options, model)


@pytest.mark.parametrize("tpu_core", [1, 5])
@RunIf(tpu=True, standalone=True)
def test_model_16bit_tpu_index(tmpdir, tpu_core):
    """Make sure model trains on TPU."""
    tutils.reset_seed()
    trainer_options = dict(
        default_root_dir=tmpdir,
        precision=16,
        enable_progress_bar=False,
        max_epochs=2,
        accelerator="tpu",
        devices=[tpu_core],
        limit_train_batches=4,
        limit_val_batches=2,
    )

    model = BoringModel()
    tpipes.run_model_test(trainer_options, model)
    assert torch_xla._XLAC._xla_get_default_device() == f"xla:{tpu_core}"


@RunIf(tpu=True)
def test_model_16bit_tpu_devices_8(tmpdir):
    """Make sure model trains on TPU."""
    tutils.reset_seed()
    trainer_options = dict(
        default_root_dir=tmpdir,
        precision=16,
        enable_progress_bar=False,
        max_epochs=1,
        accelerator="tpu",
        devices=8,
        limit_train_batches=4,
        limit_val_batches=4,
    )

    # 8 cores needs a big dataset
    model = SerialLoaderBoringModel()
    tpipes.run_model_test(trainer_options, model, with_hpc=False, min_acc=0.05)


@RunIf(tpu=True)
def test_model_tpu_early_stop(tmpdir):
    """Test if single TPU core training works."""

    class CustomBoringModel(BoringModel):
        def validation_step(self, *args, **kwargs):
            out = super().validation_step(*args, **kwargs)
            self.log("val_loss", out["x"])
            return out

    tutils.reset_seed()
    model = CustomBoringModel()
    trainer = Trainer(
        callbacks=[EarlyStopping(monitor="val_loss")],
        default_root_dir=tmpdir,
        enable_progress_bar=False,
        max_epochs=2,
        limit_train_batches=2,
        limit_val_batches=2,
        accelerator="tpu",
        devices=8,
    )
    trainer.fit(model)
    trainer.test(dataloaders=DataLoader(RandomDataset(32, 2000), batch_size=32))


@RunIf(tpu=True, standalone=True)
def test_tpu_grad_norm(tmpdir):
    """Test if grad_norm works on TPU."""
    tutils.reset_seed()
    trainer_options = dict(
        default_root_dir=tmpdir,
        enable_progress_bar=False,
        max_epochs=4,
        accelerator="tpu",
        devices=1,
        limit_train_batches=0.4,
        limit_val_batches=0.4,
        gradient_clip_val=0.5,
    )

    model = BoringModel()
    tpipes.run_model_test(trainer_options, model, with_hpc=False)


@RunIf(tpu=True, standalone=True)
def test_tpu_clip_grad_by_value(tmpdir):
    """Test if clip_gradients by value works on TPU."""
    tutils.reset_seed()
    trainer_options = dict(
        default_root_dir=tmpdir,
        enable_progress_bar=False,
        max_epochs=4,
        accelerator="tpu",
        devices=1,
        limit_train_batches=10,
        limit_val_batches=10,
        gradient_clip_val=0.5,
        gradient_clip_algorithm="value",
    )

    model = BoringModel()
    tpipes.run_model_test(trainer_options, model, with_hpc=False)


@RunIf(tpu=True)
def test_dataloaders_passed_to_fit(tmpdir):
    """Test if dataloaders passed to trainer works on TPU."""
    tutils.reset_seed()
    model = BoringModel()

    trainer = Trainer(default_root_dir=tmpdir, max_epochs=1, accelerator="tpu", devices=8)
    trainer.fit(model, train_dataloaders=model.train_dataloader(), val_dataloaders=model.val_dataloader())


@RunIf(tpu=True)
@pytest.mark.parametrize("tpu_cores", [[1, 8], "9, ", [9], [0], 2, 10])
def test_tpu_misconfiguration(tpu_cores):
    with pytest.raises(MisconfigurationException, match="`tpu_cores` can only be"):
        Trainer(accelerator="tpu", devices=tpu_cores)


@pytest.mark.skipif(_TPU_AVAILABLE, reason="test requires missing TPU")
def test_exception_when_no_tpu_found():
    """Test if exception is thrown when xla devices are not available."""

    with pytest.raises(MisconfigurationException, match="TPUAccelerator can not run on your system"):
        Trainer(accelerator="tpu", devices=8)


@pytest.mark.parametrize("tpu_cores", [1, 8, [1]])
@RunIf(tpu=True, standalone=True)
def test_accelerator_set_when_using_tpu(tpu_cores):
    """Test if the accelerator is set to `tpu` when tpu_cores is not None."""
    assert isinstance(Trainer(accelerator="tpu", devices=tpu_cores).accelerator, TPUAccelerator)


@RunIf(tpu=True)
def test_broadcast_on_tpu():
    """Checks if an object from the main process is broadcasted to other processes correctly."""

    def test_broadcast(rank):
        trainer = Trainer(accelerator="tpu", devices=8)
        assert isinstance(trainer.accelerator, TPUAccelerator)
        assert isinstance(trainer.strategy, TPUSpawnStrategy)
        trainer.strategy._launched = True
        obj = ("ver_0.5", "logger_name", rank)
        result = trainer.strategy.broadcast(obj)
        assert result == ("ver_0.5", "logger_name", 0)

    _save_spawn(test_broadcast, nprocs=8, start_method="fork")


@pytest.mark.parametrize(
    ["cli_args", "expected"],
    [("--tpu_cores=8", {"tpu_cores": 8}), ("--tpu_cores=1,", {"tpu_cores": "1,"})],
)
@RunIf(tpu=True, standalone=True)
def test_tpu_cores_with_argparse(cli_args, expected):
    """Test passing tpu_cores in command line."""
    cli_args = cli_args.split(" ") if cli_args else []
    with mock.patch("argparse._sys.argv", ["any.py"] + cli_args):
        parser = ArgumentParser(add_help=False)
        parser = Trainer.add_argparse_args(parent_parser=parser)
        args = Trainer.parse_argparser(parser)

    for k, v in expected.items():
        assert getattr(args, k) == v
    with pytest.deprecated_call(match=r"is deprecated in v1.7 and will be removed in v2.0."):
        assert Trainer.from_argparse_args(args)


@RunIf(tpu=True)
def test_tpu_reduce():
    """Test tpu spawn reduce operation."""

    def test_reduce(rank):
        trainer = Trainer(accelerator="tpu", devices=8)
        trainer.strategy._launched = True

        with pytest.raises(ValueError, match="TPUSpawnStrategy only supports"):
            trainer.strategy.reduce(1, reduce_op="undefined")

        with pytest.raises(ValueError, match="TPUSpawnStrategy only supports"):
            trainer.strategy.reduce(1, reduce_op=ReduceOp.MAX)

        # it is faster to loop over here than to parameterize the test
        for reduce_op in ("mean", "AVG", "sum", ReduceOp.SUM):
            result = trainer.strategy.reduce(1, reduce_op=reduce_op)
            if isinstance(reduce_op, str) and reduce_op.lower() in ("mean", "avg"):
                assert result.item() == 1
            else:
                assert result.item() == 8

    _save_spawn(test_reduce, nprocs=8, start_method="fork")


@RunIf(tpu=True, standalone=True)
@pytest.mark.parametrize("clip_val", [10])
@mock.patch("torch.nn.utils.clip_grad_norm_")
def test_tpu_precision_16_clip_gradients(mock_clip_grad_norm, clip_val, tmpdir):
    """Ensure that clip gradients is only called if the value is greater than 0.

    TODO: Fix (test fails with parametrize)
    """
    tutils.reset_seed()
    trainer_options = dict(
        default_root_dir=tmpdir,
        enable_progress_bar=False,
        max_epochs=1,
        accelerator="tpu",
        devices=1,
        precision=16,
        limit_train_batches=4,
        limit_val_batches=4,
        gradient_clip_val=clip_val,
    )
    model = BoringModel()
    tpipes.run_model_test(trainer_options, model, with_hpc=False)

    if clip_val > 0:
        mock_clip_grad_norm.assert_called()
    else:
        mock_clip_grad_norm.assert_not_called()


@RunIf(tpu=True)
def test_if_test_works_with_checkpoint_false(tmpdir):
    """Ensure that model trains properly when `enable_checkpointing` is set to False."""

    # Train a model on TPU
    model = BoringModel()
    trainer = Trainer(
        max_epochs=1,
        accelerator="tpu",
        devices=8,
        default_root_dir=tmpdir,
        fast_dev_run=True,
        enable_checkpointing=False,
    )
    trainer.fit(model)
    assert trainer.state.finished, f"Training failed with {trainer.state}"


@RunIf(tpu=True)
def test_tpu_sync_dist():
    """Test tpu spawn sync dist operation."""

    def test_sync_dist(rank):
        trainer = Trainer(accelerator="tpu", devices=8)
        trainer.strategy._launched = True

        sync = _Sync(trainer.strategy.reduce, _should=True, _op=torch.distributed.ReduceOp.SUM)
        value = torch.tensor([1.0])
        value = sync(value)
        assert value.item() == 8

    _save_spawn(test_sync_dist, nprocs=8, start_method="fork")


@RunIf(tpu=True)
def test_tpu_debug_mode(tmpdir):
    """Test if debug mode works on TPU."""

    class DebugModel(BoringModel):
        def on_train_start(self):
            assert os.environ.get("PT_XLA_DEBUG") == str(1), "PT_XLA_DEBUG was not set in environment variables"

        def teardown(self, stage):
            assert "PT_XLA_DEBUG" not in os.environ

    tutils.reset_seed()
    trainer_options = dict(
        default_root_dir=tmpdir,
        enable_progress_bar=False,
        max_epochs=4,
        accelerator="tpu",
        devices=8,
        limit_train_batches=0.4,
        limit_val_batches=0.4,
        strategy=TPUSpawnStrategy(debug=True),
    )

    model = DebugModel()
    tpipes.run_model_test(trainer_options, model, with_hpc=False)


@RunIf(tpu=True)
def test_tpu_host_world_size(tmpdir):
    """Test Host World size env setup on TPU."""

    class DebugModel(BoringModel):
        def on_train_start(self):
            assert os.environ.get("XRT_HOST_WORLD_SIZE") == str(1)

    tutils.reset_seed()
    trainer_options = dict(
        default_root_dir=tmpdir,
        enable_progress_bar=False,
        max_epochs=4,
        accelerator="tpu",
        devices=8,
        limit_train_batches=0.4,
        limit_val_batches=0.4,
    )

    model = DebugModel()
    assert "XRT_HOST_WORLD_SIZE" not in os.environ
    tpipes.run_model_test(trainer_options, model, with_hpc=False)
    assert "XRT_HOST_WORLD_SIZE" not in os.environ


@RunIf(tpu=True)
def test_device_type_when_tpu_strategy_passed(tmpdir):
    trainer = Trainer(default_root_dir=tmpdir, strategy=TPUSpawnStrategy(), accelerator="tpu", devices=8)
    assert isinstance(trainer.strategy, TPUSpawnStrategy)
    assert isinstance(trainer.accelerator, TPUAccelerator)