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# SPDX-License-Identifier: Apache-2.0
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"""Sparsity mode descriptor."""

from torch import nn

from modelopt.torch.opt.config import ModeloptBaseConfig
from modelopt.torch.opt.conversion import ApplyModeError
from modelopt.torch.opt.dynamic import DynamicSpace
from modelopt.torch.opt.mode import (
    ConvertEntrypoint,
    ConvertReturnType,
    MetadataDict,
    ModeDescriptor,
    RestoreEntrypoint,
    UpdateEntrypoint,
    _ModeRegistryCls,
)
from modelopt.torch.opt.searcher import BaseSearcher
from modelopt.torch.utils import compare_dict, unwrap_model

from .config import ExportSparseConfig, SparseGPTConfig, SparseMagnitudeConfig
from .magnitude import MagnitudeSearcher
from .module import SpDMRegistry
from .sparsegpt import SparseGPTSearcher

SparsityModeRegistry = _ModeRegistryCls("sparsity")


def convert_sparse_model(model: nn.Module, config: ModeloptBaseConfig) -> ConvertReturnType:
    """Function for converting a model to a sparsity meta-model."""
    # we use the search space utility here with a custom registry to convert the model
    dynamic_space = DynamicSpace(model)
    dynamic_space.convert_to_dynamic(config.model_dump(), SpDMRegistry)

    return dynamic_space.model, {"subnet_config": DynamicSpace(model).config()}


def restore_sparse_model(
    model: nn.Module, config: ModeloptBaseConfig, metadata: MetadataDict
) -> nn.Module:
    """Function for restoring a previously convert model to a sparsity meta-model."""
    model, _ = convert_sparse_model(model, config)

    if "subnet_config" in metadata:
        DynamicSpace(model).select(metadata["subnet_config"])

    return model


def update_sparse_metadata(
    model: nn.Module, config: ModeloptBaseConfig, metadata: MetadataDict
) -> None:
    """Update subnet config to current subnet config of model."""
    metadata["subnet_config"] = DynamicSpace(model).config()


def export_sparse(model: nn.Module, config: ExportSparseConfig) -> ConvertReturnType:
    """Export a sparse model to a regular model."""
    # sanity check to avoid DP/DDP here in the entrypoint
    model = unwrap_model(model, raise_error=True)

    # store config from model if we can find it for a future convert/restore process
    metadata = {"subnet_config": DynamicSpace(model).config()}

    # export model in-place
    model = DynamicSpace(model).export(SpDMRegistry)

    return model, metadata


def restore_export_sparse(
    model: nn.Module, config: ExportSparseConfig, metadata: MetadataDict
) -> nn.Module:
    """Restore & export a sparse model to a regular model."""
    # select activated/deactivated sparse modules
    DynamicSpace(model).select(metadata["subnet_config"])

    # run export
    model, metadata_new = export_sparse(model, config)

    # double check metadata
    unmatched_keys = compare_dict(metadata, metadata_new)
    if unmatched_keys:
        raise ApplyModeError(f"Unmatched metadata={unmatched_keys}!")

    return model


@SparsityModeRegistry.register_mode
class SparseMagnitudeModeDescriptor(ModeDescriptor):
    """Class to define and describe magnitude-based sparsification."""

    @property
    def name(self) -> str:
        """Returns the name of the mode."""
        return "sparse_magnitude"

    @property
    def config_class(self) -> type[ModeloptBaseConfig]:
        """Specifies the config class for the mode."""
        return SparseMagnitudeConfig

    @property
    def next_modes(self) -> set[str] | None:
        """Specifies the next modes for the mode."""
        return {"export_sparse", "kd_loss", "quantize", "sparse_attention"}

    @property
    def export_mode(self) -> str | None:
        """The mode that corresponds to the export mode of this mode."""
        return "export_sparse"

    @property
    def search_algorithm(self) -> type[BaseSearcher]:
        """Specifies the search algorithm for the mode."""
        return MagnitudeSearcher

    @property
    def convert(self) -> ConvertEntrypoint:
        """The mode's entrypoint for converting a model."""
        return convert_sparse_model

    @property
    def restore(self) -> RestoreEntrypoint:
        """The mode's entrypoint for restoring a model."""
        return restore_sparse_model

    @property
    def update_for_save(self) -> UpdateEntrypoint:
        """The mode's entrypoint for updating the models metadata."""
        return update_sparse_metadata

    @property
    def update_for_new_mode(self) -> UpdateEntrypoint:
        """The mode's entrypoint for updating the models metadata."""
        return update_sparse_metadata


@SparsityModeRegistry.register_mode
class SparseGPTModeDescriptor(SparseMagnitudeModeDescriptor):
    """Class to define and describe sparsification based on SparseGPT."""

    @property
    def name(self) -> str:
        """Returns the name of the mode."""
        return "sparsegpt"

    @property
    def config_class(self) -> type[ModeloptBaseConfig]:
        """Specifies the config class for the mode."""
        return SparseGPTConfig

    @property
    def search_algorithm(self) -> type[BaseSearcher]:
        """Specifies the search algorithm for the mode."""
        return SparseGPTSearcher


@SparsityModeRegistry.register_mode
class ExportSparseModeDescriptor(ModeDescriptor):
    """Class to describe the ``"export_sparse"`` mode.

    The properties of this mode can be inspected via the source code.
    """

    @property
    def name(self) -> str:
        """Returns the value (str representation) of the mode."""
        return "export_sparse"

    @property
    def config_class(self) -> type[ModeloptBaseConfig]:
        """Specifies the config class for the mode."""
        return ExportSparseConfig

    @property
    def is_export_mode(self) -> bool:
        """Specifies if this mode is an export mode."""
        return True

    @property
    def convert(self) -> ConvertEntrypoint:
        """The mode's entrypoint for converting a model."""
        return export_sparse

    @property
    def restore(self) -> RestoreEntrypoint:
        """The mode's entrypoint for restoring a model."""
        return restore_export_sparse