# Copyright The Lightning AI 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 contextlib import contextmanager
from typing import Any, cast, Generator, List, Literal, Tuple

import torch
import torch.nn as nn
from lightning_utilities.core.apply_func import apply_to_collection
from torch import Tensor
from torch.optim import Optimizer

import lightning.pytorch as pl
from lightning.fabric.plugins.precision.utils import _convert_fp_tensor
from lightning.pytorch.overrides.base import _LightningPrecisionModuleWrapperBase
from lightning.pytorch.plugins.precision.precision_plugin import PrecisionPlugin


class LightningDoublePrecisionModule(_LightningPrecisionModuleWrapperBase):
    """LightningModule wrapper which converts incoming floating point data in ``*_step`` and ``forward`` to double
    (``torch.float64``) precision.

    Args:
        pl_module: the model to wrap
    """

    @staticmethod
    def _move_float_tensors_to_double(collection: Any) -> Any:
        return apply_to_collection(collection, Tensor, function=_convert_fp_tensor, dst_type=torch.double)

    def training_step(self, *args: Any, **kwargs: Any) -> Any:
        return self.module.training_step(
            *LightningDoublePrecisionModule._move_float_tensors_to_double(args),
            **LightningDoublePrecisionModule._move_float_tensors_to_double(kwargs),
        )

    def validation_step(self, *args: Any, **kwargs: Any) -> Any:
        return self.module.validation_step(
            *LightningDoublePrecisionModule._move_float_tensors_to_double(args),
            **LightningDoublePrecisionModule._move_float_tensors_to_double(kwargs),
        )

    def test_step(self, *args: Any, **kwargs: Any) -> Any:
        return self.module.test_step(
            *LightningDoublePrecisionModule._move_float_tensors_to_double(args),
            **LightningDoublePrecisionModule._move_float_tensors_to_double(kwargs),
        )

    def predict_step(self, *args: Any, **kwargs: Any) -> Any:
        return self.module.predict_step(
            *LightningDoublePrecisionModule._move_float_tensors_to_double(args),
            **LightningDoublePrecisionModule._move_float_tensors_to_double(kwargs),
        )

    def forward(self, *args: Any, **kwargs: Any) -> Any:
        return self.module(
            *LightningDoublePrecisionModule._move_float_tensors_to_double(args),
            **LightningDoublePrecisionModule._move_float_tensors_to_double(kwargs),
        )


class DoublePrecisionPlugin(PrecisionPlugin):
    """Plugin for training with double (``torch.float64``) precision."""

    precision: Literal["64-true"] = "64-true"

    def connect(
        self, model: nn.Module, optimizers: List[Optimizer], lr_schedulers: List[Any]
    ) -> Tuple[nn.Module, List["Optimizer"], List[Any]]:
        """Converts the model to double precision and wraps it in a ``LightningDoublePrecisionModule`` to convert
        incoming floating point data to double (``torch.float64``) precision.

        Does not alter `optimizers` or `lr_schedulers`.
        """
        model = cast(pl.LightningModule, model.double())
        model = LightningDoublePrecisionModule(model)

        return super().connect(model, optimizers, lr_schedulers)

    @contextmanager
    def init_context(self) -> Generator[None, None, None]:
        """A context manager to change the default tensor type when initializing module parameters or tensors.

        See: :meth:`torch.set_default_dtype`
        """
        default_dtype = torch.get_default_dtype()
        torch.set_default_dtype(torch.float64)
        yield
        torch.set_default_dtype(default_dtype)

    @contextmanager
    def forward_context(self) -> Generator[None, None, None]:
        """A context manager to change the default tensor type.

        See: :meth:`torch.set_default_dtype`
        """
        default_dtype = torch.get_default_dtype()
        torch.set_default_dtype(torch.float64)
        yield
        torch.set_default_dtype(default_dtype)
