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# to you under the Apache License, Version 2.0 (the
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#
#   http://www.apache.org/licenses/LICENSE-2.0
#
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# specific language governing permissions and limitations
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# ruff: noqa: RUF012
"""Meta Schedule Postproc."""

from collections.abc import Callable
from typing import TYPE_CHECKING

# isort: off
from typing import Literal

# isort: on

from tvm_ffi import register_object

from tvm.runtime import Object
from tvm.s_tir.schedule import Schedule

from .. import _ffi_api

if TYPE_CHECKING:
    from ..tune_context import TuneContext


@register_object("s_tir.meta_schedule.Postproc")
class Postproc(Object):
    """Rules to apply a postprocessor to a schedule."""

    def _initialize_with_tune_context(self, context: "TuneContext") -> None:
        """Initialize the postprocessor with a tune context.

        Parameters
        ----------
        context : TuneContext
            The tuning context for initializing the postprocessor.
        """
        _ffi_api.PostprocInitializeWithTuneContext(  # type: ignore # pylint: disable=no-member
            self, context
        )

    def apply(self, sch: Schedule) -> bool:
        """Apply a postprocessor to the given schedule.

        Parameters
        ----------
        sch : tvm.s_tir.Schedule
            The schedule to be post processed.

        Returns
        -------
        result : bool
            Whether the postprocessor was successfully applied.
        """
        return _ffi_api.PostprocApply(self, sch)  # type: ignore # pylint: disable=no-member

    def clone(self) -> "Postproc":
        """Clone the postprocessor.

        Returns
        -------
        cloned_postproc : Postproc
            The cloned postprocessor.
        """
        return _ffi_api.PostprocClone(self)  # type: ignore # pylint: disable=no-member

    @staticmethod
    def create(kind: Literal["llvm", "cuda", "cuda-tensorcore", "hexagon"]) -> list["Postproc"]:
        """Create a list of default postprocessors.

        Parameters
        ----------
        kind : Literal["llvm", "cuda", "cuda-tensorcore", "hexagon"]
            The kind of the postprocessors.

        Returns
        -------
        postprocs : List[Mutator]
            The list of postprocessors.
        """
        funcs = {
            # pylint: disable=no-member
            "llvm": _ffi_api.PostprocDefaultLLVM,  # type: ignore
            "cuda": _ffi_api.PostprocDefaultCUDA,  # type: ignore
            "cuda-tensorcore": _ffi_api.PostprocDefaultCUDATensorCore,  # type: ignore
            "hexagon": _ffi_api.PostprocDefaultHexagon,  # type: ignore
            # pylint: enable=no-member
        }
        for k, v in funcs.items():
            if k == kind:
                return v()
        raise ValueError(f"Unsupported kind {kind} for postproc creation.")


create = Postproc.create  # pylint: disable=invalid-name


@register_object("s_tir.meta_schedule.PyPostproc")
class _PyPostproc(Postproc):
    """
    A TVM object post processor to support customization on the python side.
    This is NOT the user facing class for function overloading inheritance.

    See also: PyPostproc
    """

    def __init__(
        self,
        f_initialize_with_tune_context: Callable | None = None,
        f_apply: Callable | None = None,
        f_clone: Callable | None = None,
    ):
        """Constructor."""

        self.__init_handle_by_constructor__(
            _ffi_api.PostprocPyPostproc,  # type: ignore # pylint: disable=no-member
            f_initialize_with_tune_context,
            f_apply,
            f_clone,
        )


class PyPostproc:
    """
    An abstract post processor with customized methods on the python-side.
    This is the user facing class for function overloading inheritance.

    Note: @derived_object is required for proper usage of any inherited class.
    """

    _tvm_metadata = {
        "cls": _PyPostproc,
        "methods": ["_initialize_with_tune_context", "apply", "clone"],
    }

    def _initialize_with_tune_context(self, context: "TuneContext") -> None:
        """Initialize the postprocessor with a tune context.

        Parameters
        ----------
        context : TuneContext
            The tuning context for initializing the postprocessor.
        """
        raise NotImplementedError

    def apply(self, sch: Schedule) -> bool:
        """Apply a postprocessor to the given schedule.

        Parameters
        ----------
        sch : Schedule
            The schedule to be post processed.

        Returns
        -------
        result : bool
            Whether the postprocessor was successfully applied.
        """
        raise NotImplementedError

    def clone(self) -> Postproc:
        """Clone the postprocessor.

        Returns
        -------
        cloned_postproc : Postproc
            The cloned postprocessor.
        """
        raise NotImplementedError