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# pylint: disable=invalid-name,unused-argument
"""Common pass infrastructure across IR variants."""
import types
import inspect
import functools

import tvm._ffi

import tvm.runtime
from tvm.runtime import ndarray as _nd

from . import _ffi_transform_api

@tvm._ffi.register_object("transform.PassInfo")
class PassInfo(tvm.runtime.Object):
    """The class contains the meta data required by a pass. It is the
    container of information needed by running an optimization or analysis.
    This class can be extended by adding new members when more meta data is
    needed.

    Parameters
    ----------
    opt_level : int
        The optimization level of this pass.

    name : str
        The pass name.

    required : List[str]
        The list of passes that are required by a certain pass.
    """

    def __init__(self, opt_level, name, required=None):
        self.__init_handle_by_constructor__(
            _ffi_transform_api.PassInfo, opt_level, name, required)


@tvm._ffi.register_object("transform.PassContext")
class PassContext(tvm.runtime.Object):
    """The basis where a Relay optimization/analysis runs on.
    Each pass context contains a number of auxiliary information that is used
    to help an optimization pass. Such information includes the error reporter
    to record the errors of during the optimization, etc.

    opt_level : Optional[int]
        The optimization level of this pass.

    fallback_device : Optional[Union[int, str, TVMContext]]
        The fallback device type. It is also used as the default device for
        operators that are not annotated during heterogeneous execution.

    required_pass : Optional[Union[List[str], Set[str], Tuple[str]]]
        The list of passes that are required by a certain pass.

    disabled_pass : Optional[Union[List[str], Set[str], Tuple[str]]]
        The list of passes that are disabled.
    """
    def __init__(self,
                 opt_level=2,
                 fallback_device=_nd.cpu(),
                 required_pass=None,
                 disabled_pass=None,
                 trace=None):
        if isinstance(fallback_device, str):
            fallback_device = _nd.context(fallback_device).device_type
        elif isinstance(fallback_device, tvm.runtime.TVMContext):
            fallback_device = fallback_device.device_type
        if not isinstance(fallback_device, int):
            raise TypeError("fallback_device is expected to be the type of " +
                            "int/str/TVMContext.")

        required = list(required_pass) if required_pass else []
        if not isinstance(required, (list, tuple)):
            raise TypeError("required_pass is expected to be the type of " +
                            "list/tuple/set.")

        disabled = list(disabled_pass) if disabled_pass else []
        if not isinstance(disabled, (list, tuple)):
            raise TypeError("disabled_pass is expected to be the type of " +
                            "list/tuple/set.")

        self.__init_handle_by_constructor__(_ffi_transform_api.PassContext, opt_level,
                                            fallback_device, required,
                                            disabled, trace)

    def __enter__(self):
        _ffi_transform_api.EnterPassContext(self)
        return self

    def __exit__(self, ptype, value, trace):
        _ffi_transform_api.ExitPassContext(self)

    @staticmethod
    def current():
        """Return the current pass context."""
        return _ffi_transform_api.GetCurrentPassContext()


@tvm._ffi.register_object("transform.Pass")
class Pass(tvm.runtime.Object):
    """The base class of all passes. All methods here are just simple wrappers
    that are implemented in the backend. They are defined for users to
    conveniently interact with the base class.
    """

    @property
    def info(self):
        """Get the pass meta."""
        return _ffi_transform_api.Info(self)

    def __call__(self, mod):
        """Execute the pass. Note that for sequential pass, the dependency among
        different passes will be resolved in the backend.

        Parameters
        ----------
        mod : tvm.IRModule
            The module that a certain optimization is performed on.

        Returns
        -------
        mod : tvm.IRModule
            The updated module after applying this pass.
        """
        return _ffi_transform_api.RunPass(self, mod)


@tvm._ffi.register_object("transform.ModulePass")
class ModulePass(Pass):
    """A pass that works on tvm.IRModule. Users don't need to interact with
    this class directly. Instead, a module pass should be created through
    `module_pass`, because the design of the `module_pass` API is flexible
    enough to handle the creation of a module pass in different manners. In
    addition, all members of a module pass can be accessed from the base class.
    The same rule applies to FunctionPass as well.
    """


@tvm._ffi.register_object("transform.Sequential")
class Sequential(Pass):
    """A pass that works on a sequence of pass objects. Multiple passes can be
    executed sequentially using this class.

    Some typical usage of the sequential pass are:
    1. Users provide a list of passes for optimization.
    2. Only an optimization level is provided so that the backend system has
    to glob all passes at this level and below to perform the optimizations.

    Note that users can also provide a series of passes that they don't want to
    apply when running a sequential pass. Pass dependency will be resolved in
    the backend as well.

    Parameters
    ----------
    passes : Optional[List[Pass]]
        A sequence of passes candidate for optimization.

    opt_level : Optional[int]
        The optimization level of this sequential pass.

    name : Optional[str]
        The name of the sequential pass.

    required : Optional[List[str]]
        The list of passes that the sequential pass is dependent on.
    """
    def __init__(self,
                 passes=None,
                 opt_level=2,
                 name="sequential",
                 required=None):
        passes = passes if passes else []
        if not isinstance(passes, (list, tuple)):
            raise TypeError("passes must be a list of Pass objects.")

        required = required if required else []
        if not isinstance(required, (list, tuple)):
            raise TypeError("Required is expected to be the type of list/tuple.")

        self.__init_handle_by_constructor__(_ffi_transform_api.Sequential,
                                            passes, opt_level, name, required)


def _wrap_class_module_pass(pass_cls, pass_info):
    """Wrap a python class as function pass"""
    class PyModulePass(ModulePass):
        """Internal wrapper class to create a class instance."""
        def __init__(self, *args, **kwargs):
            # initialize handle in cass pass_cls creation failed.fg
            self.handle = None
            inst = pass_cls(*args, **kwargs)
            # it is important not to capture self to
            # avoid a cyclic dependency
            def _pass_func(mod, ctx):
                return inst.transform_module(mod, ctx)
            self.__init_handle_by_constructor__(
                _ffi_transform_api.MakeModulePass, _pass_func, pass_info)
            self._inst = inst

        def __getattr__(self, name):
            # fall back to instance attribute if there is not any
            return self._inst.__getattribute__(name)

    functools.update_wrapper(PyModulePass.__init__, pass_cls.__init__)
    PyModulePass.__name__ = pass_cls.__name__
    PyModulePass.__doc__ = pass_cls.__doc__
    PyModulePass.__module__ = pass_cls.__module__
    return PyModulePass


def module_pass(pass_func=None, opt_level=None, name=None, required=None):
    """Decorate a module pass.

    This function returns a callback when pass_func is provided.
    Otherwise, it serves a decorator function.

    pass_func can also be a class type with a method transform_module.
    This function will create a decorated ModulePass using transform_module
    as the pass function.

    Parameters
    ----------
    pass_func : Optional[Callable[(Module, PassContext) ->Module]]
        The transformation function or class.

    opt_level : int
        The optimization level of this module pass.

    name : Optional[str]
        The name of the module pass. The name could be empty. In this case, the
        name of the optimization function will be used as the pass name.

    required : Optional[List[str]]
        The list of passes that the module pass is dependent on.

    Returns
    -------
    create_module_pass : Union[Callable, ModulePass]
        A decorator will be returned if pass_func is not provided,
        otherwise return the decorated result.
        The returned decorator has two behaviors depending on the input:
        A new ModulePass will be returned when we decorate a pass function.
        A new ModulePass class will be returned when we decorate a class type.

    Examples
    --------
    The following code block decorates a module pass class.

    .. code-block:: python

        @relay.transform.module_pass
        class CustomPipeline:
            def __init__(self, enable_fold):
                self.enable_fold = enable_fold
                self.cse = relay.transform.EliminateCommonSubexpr()
                self.const_fold = relay.transform.FoldConstant()

            def transform_module(self, mod, ctx):
                mod = self.cse(mod, ctx)
                if self.enable_fold:
                    mod = self.const_fold(mod, ctx)
                return mod

        # create an instance of customized pipeline
        pipeline = CustomPipeline(enable_fold=False)
        assert isinstance(pipeline, transform.ModulePass)
        # run the pipeline.
        output_module = pipeline(input_module)

    The following code creates a module pass by decorating
    a user defined transform function.

    .. code-block:: python

        @relay.transform.module_pass(opt_level=2)
        def transform(mod, ctx):
            tp = relay.TensorType((10,), "float32")
            x = relay.var("x", tp)
            gv = relay.GlobalVar("var")
            func = relay.Function([x], relay.abs(x))
            new_mod = tvm.IRModule({gv: func})
            new_mod.update(mod)
            return new_mod

        module_pass = transform
        assert isinstance(module_pass, transform.ModulePass)
        assert module_pass.info.opt_level == 2

        # Given a module m, the optimization could be invoked as the follwoing:
        updated_mod = module_pass(m)
        # Now a function abs should be added to the module m.
    """
    if opt_level is None:
        raise ValueError("Please provide opt_level for the module pass.")

    required = required if required else []
    if not isinstance(required, (list, tuple)):
        raise TypeError("Required is expected to be the type of " +
                        "list/tuple.")

    def create_module_pass(pass_arg):
        """Internal function that creates a module pass"""
        fname = name if name else pass_arg.__name__
        info = PassInfo(opt_level, fname, required)
        if inspect.isclass(pass_arg):
            return _wrap_class_module_pass(pass_arg, info)
        if not isinstance(pass_arg, (types.FunctionType, types.LambdaType)):
            raise TypeError("pass_func must be a callable for Module pass")
        return _ffi_transform_api.MakeModulePass(pass_arg, info)

    if pass_func:
        return create_module_pass(pass_func)
    return create_module_pass


def PrintIR(header="", show_meta_data=False):
    """A special trace pass that prints the header and IR.

    Parameters
    ----------
    header : str
        The header to be displayed along with the dump.

    show_meta_data : bool
        A boolean flag to indicate if meta data should be printed.

    Returns
    --------
    The pass
    """
    return _ffi_transform_api.PrintIR(header, show_meta_data)
