# 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 typing import Any, Callable

from torch.optim import Optimizer

import pytorch_lightning as pl
from pytorch_lightning.plugins.precision.precision_plugin import PrecisionPlugin
from pytorch_lightning.utilities import _XLA_AVAILABLE
from pytorch_lightning.utilities.exceptions import MisconfigurationException

if _XLA_AVAILABLE:
    import torch_xla.core.xla_model as xm


class TPUPrecisionPlugin(PrecisionPlugin):
    def pre_optimizer_step(
        self,
        model: "pl.LightningModule",
        optimizer: Optimizer,
        optimizer_idx: int,
        lambda_closure: Callable[[], Any],
        **kwargs: Any,
    ) -> bool:
        super().pre_optimizer_step(model, optimizer, optimizer_idx, lambda_closure, **kwargs)
        closure_result = xm.optimizer_step(optimizer, optimizer_args={"closure": lambda_closure, **kwargs})
        skipped_backward = closure_result is None
        # in manual optimization, the closure does not return a value
        if model.automatic_optimization and skipped_backward:
            # we lack coverage here so disable this - something to explore if there's demand
            raise MisconfigurationException(
                "Skipping backward by returning `None` from your `training_step` is not implemented for TPUs."
                " Please, open an issue in `https://github.com/PyTorchLightning/pytorch-lightning/issues`"
                " requesting this feature."
            )
        return False