# 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 AbstractContextManager, nullcontext
from typing import Any, Literal, Optional, Union

from torch import Tensor
from torch.nn import Module
from torch.optim import Optimizer

from lightning_fabric.utilities.types import _PARAMETERS, Optimizable

_PRECISION_INPUT_INT = Literal[64, 32, 16]
_PRECISION_INPUT_STR_ALIAS_CONVERSION = {"64": "64-true", "32": "32-true", "16": "16-mixed", "bf16": "bf16-mixed"}
_PRECISION_INPUT_STR_ALIAS = Literal["64", "32", "16", "bf16"]
_PRECISION_INPUT_STR = Literal[
    "transformer-engine",
    "transformer-engine-float16",
    "16-true",
    "16-mixed",
    "bf16-true",
    "bf16-mixed",
    "32-true",
    "64-true",
]
_PRECISION_INPUT = Union[_PRECISION_INPUT_INT, _PRECISION_INPUT_STR, _PRECISION_INPUT_STR_ALIAS]


class Precision:
    """Base class for all plugins handling the precision-specific parts of the training.

    The class attribute precision must be overwritten in child classes. The default value reflects fp32 training.

    """

    precision: _PRECISION_INPUT_STR = "32-true"

    def convert_module(self, module: Module) -> Module:
        """Convert the module parameters to the precision type this plugin handles.

        This is optional and depends on the precision limitations during optimization.

        """
        return module

    def tensor_init_context(self) -> AbstractContextManager:
        """Controls how tensors get created (device, dtype)."""
        return nullcontext()

    def module_init_context(self) -> AbstractContextManager:
        """Instantiate module parameters or tensors in the precision type this plugin handles.

        This is optional and depends on the precision limitations during optimization.

        """
        return nullcontext()

    def forward_context(self) -> AbstractContextManager:
        """A contextmanager for managing model forward/training_step/evaluation_step/predict_step."""
        return nullcontext()

    def convert_input(self, data: Any) -> Any:
        """Convert model inputs (forward) to the floating point precision type of this plugin.

        This is a no-op in the base precision plugin, since we assume the data already has the desired type (default is
        torch.float32).

        """
        return data

    def convert_output(self, data: Any) -> Any:
        """Convert outputs to the floating point precision type expected after model's forward.

        This is a no-op in the base precision plugin, since we assume the data already has the desired type (default is
        torch.float32).

        """
        return data

    def pre_backward(self, tensor: Tensor, module: Optional[Module]) -> Any:
        """Runs before precision plugin executes backward.

        Args:
            tensor: The tensor that will be used for backpropagation
            module: The module that was involved in producing the tensor and whose parameters need the gradients

        """

    def backward(self, tensor: Tensor, model: Optional[Module], *args: Any, **kwargs: Any) -> None:
        """Performs the actual backpropagation.

        Args:
            tensor: The tensor that will be used for backpropagation
            model: The module that was involved in producing the tensor and whose parameters need the gradients

        """
        tensor.backward(*args, **kwargs)

    def post_backward(self, tensor: Tensor, module: Optional[Module]) -> Any:
        """Runs after precision plugin executes backward.

        Args:
            tensor: The tensor that will be used for backpropagation
            module: The module that was involved in producing the tensor and whose parameters need the gradients

        """

    def optimizer_step(
        self,
        optimizer: Optimizable,
        **kwargs: Any,
    ) -> Any:
        """Hook to run the optimizer step."""
        return optimizer.step(**kwargs)

    def main_params(self, optimizer: Optimizer) -> _PARAMETERS:
        """The main params of the model.

        Returns the plain model params here. Maybe different in other precision plugins.

        """
        for group in optimizer.param_groups:
            yield from group["params"]

    def unscale_gradients(self, optimizer: Optimizer) -> None:
        return

    def state_dict(self) -> dict[str, Any]:
        """Called when saving a checkpoint, implement to generate precision plugin state_dict.

        Returns:
            A dictionary containing precision plugin state.

        """
        return {}

    def load_state_dict(self, state_dict: dict[str, Any]) -> None:
        """Called when loading a checkpoint, implement to reload precision plugin state given precision plugin
        state_dict.

        Args:
            state_dict: the precision plugin state returned by ``state_dict``.

        """
        pass

    def teardown(self) -> None:
        """This method is called to teardown the training process.

        It is the right place to release memory and free other resources.

        """