import math
from functools import reduce

import numpy as np
import operator
import warnings

from llvmlite import ir
from llvmlite.llvmpy.core import Type, Constant
import llvmlite.llvmpy.core as lc

from numba.core.imputils import (lower_builtin, lower_getattr,
                                 lower_getattr_generic, lower_cast,
                                 lower_constant, iternext_impl,
                                 call_getiter, call_iternext, impl_ret_borrowed,
                                 impl_ret_untracked, numba_typeref_ctor)
from numba.core import typing, types, utils, cgutils
from numba.core.extending import overload, intrinsic
from numba.core.typeconv import Conversion
from numba.core.errors import (TypingError, LoweringError,
                               NumbaExperimentalFeatureWarning)
from numba.misc.special import literal_unroll
from numba.core.typing.asnumbatype import as_numba_type
from numba.core.errors import NumbaTypeError


@overload(operator.truth)
def ol_truth(val):
    if isinstance(val, types.Boolean):
        def impl(val):
            return val
        return impl


@lower_builtin(operator.is_not, types.Any, types.Any)
def generic_is_not(context, builder, sig, args):
    """
    Implement `x is not y` as `not (x is y)`.
    """
    is_impl = context.get_function(operator.is_, sig)
    return builder.not_(is_impl(builder, args))


@lower_builtin(operator.is_, types.Any, types.Any)
def generic_is(context, builder, sig, args):
    """
    Default implementation for `x is y`
    """
    lhs_type, rhs_type = sig.args
    # the lhs and rhs have the same type
    if lhs_type == rhs_type:
            # mutable types
            if lhs_type.mutable:
                msg = 'no default `is` implementation'
                raise LoweringError(msg)
            # immutable types
            else:
                # fallbacks to `==`
                try:
                    eq_impl = context.get_function(operator.eq, sig)
                except NotImplementedError:
                    # no `==` implemented for this type
                    return cgutils.false_bit
                else:
                    return eq_impl(builder, args)
    else:
        return cgutils.false_bit


@lower_builtin(operator.is_, types.Opaque, types.Opaque)
def opaque_is(context, builder, sig, args):
    """
    Implementation for `x is y` for Opaque types.
    """
    lhs_type, rhs_type = sig.args
    # the lhs and rhs have the same type
    if lhs_type == rhs_type:
        lhs_ptr = builder.ptrtoint(args[0], cgutils.intp_t)
        rhs_ptr = builder.ptrtoint(args[1], cgutils.intp_t)

        return builder.icmp_unsigned('==', lhs_ptr, rhs_ptr)
    else:
        return cgutils.false_bit


@lower_builtin(operator.is_, types.Boolean, types.Boolean)
def bool_is_impl(context, builder, sig, args):
    """
    Implementation for `x is y` for types derived from types.Boolean
    (e.g. BooleanLiteral), and cross-checks between literal and non-literal
    booleans, to satisfy Python's behavior preserving identity for bools.
    """
    arg1, arg2 = args
    arg1_type, arg2_type = sig.args
    _arg1 = context.cast(builder, arg1, arg1_type, types.boolean)
    _arg2 = context.cast(builder, arg2, arg2_type, types.boolean)
    eq_impl = context.get_function(
        operator.eq,
        typing.signature(types.boolean, types.boolean, types.boolean)
    )
    return eq_impl(builder, (_arg1, _arg2))


# keep types.IntegerLiteral, as otherwise there's ambiguity between this and int_eq_impl
@lower_builtin(operator.eq, types.Literal, types.Literal)
@lower_builtin(operator.eq, types.IntegerLiteral, types.IntegerLiteral)
def const_eq_impl(context, builder, sig, args):
    arg1, arg2 = sig.args
    val = 0
    if arg1.literal_value == arg2.literal_value:
        val = 1
    res = ir.Constant(ir.IntType(1), val)
    return impl_ret_untracked(context, builder, sig.return_type, res)


# keep types.IntegerLiteral, as otherwise there's ambiguity between this and int_ne_impl
@lower_builtin(operator.ne, types.Literal, types.Literal)
@lower_builtin(operator.ne, types.IntegerLiteral, types.IntegerLiteral)
def const_ne_impl(context, builder, sig, args):
    arg1, arg2 = sig.args
    val = 0
    if arg1.literal_value != arg2.literal_value:
        val = 1
    res = ir.Constant(ir.IntType(1), val)
    return impl_ret_untracked(context, builder, sig.return_type, res)


def gen_non_eq(val):
    def none_equality(a, b):
        a_none = isinstance(a, types.NoneType)
        b_none = isinstance(b, types.NoneType)
        if a_none and b_none:
            def impl(a, b):
                return val
            return impl
        elif a_none ^ b_none:
            def impl(a, b):
                return not val
            return impl
    return none_equality

overload(operator.eq)(gen_non_eq(True))
overload(operator.ne)(gen_non_eq(False))

#-------------------------------------------------------------------------------

@lower_getattr_generic(types.DeferredType)
def deferred_getattr(context, builder, typ, value, attr):
    """
    Deferred.__getattr__ => redirect to the actual type.
    """
    inner_type = typ.get()
    val = context.cast(builder, value, typ, inner_type)
    imp = context.get_getattr(inner_type, attr)
    return imp(context, builder, inner_type, val, attr)

@lower_cast(types.Any, types.DeferredType)
@lower_cast(types.Optional, types.DeferredType)
@lower_cast(types.Boolean, types.DeferredType)
def any_to_deferred(context, builder, fromty, toty, val):
    actual = context.cast(builder, val, fromty, toty.get())
    model = context.data_model_manager[toty]
    return model.set(builder, model.make_uninitialized(), actual)

@lower_cast(types.DeferredType, types.Any)
@lower_cast(types.DeferredType, types.Boolean)
@lower_cast(types.DeferredType, types.Optional)
def deferred_to_any(context, builder, fromty, toty, val):
    model = context.data_model_manager[fromty]
    val = model.get(builder, val)
    return context.cast(builder, val, fromty.get(), toty)


#------------------------------------------------------------------------------

@lower_builtin(operator.getitem, types.CPointer, types.Integer)
def getitem_cpointer(context, builder, sig, args):
    base_ptr, idx = args
    elem_ptr = builder.gep(base_ptr, [idx])
    res = builder.load(elem_ptr)
    return impl_ret_borrowed(context, builder, sig.return_type, res)


@lower_builtin(operator.setitem, types.CPointer, types.Integer, types.Any)
def setitem_cpointer(context, builder, sig, args):
    base_ptr, idx, val = args
    elem_ptr = builder.gep(base_ptr, [idx])
    builder.store(val, elem_ptr)


#-------------------------------------------------------------------------------

def do_minmax(context, builder, argtys, args, cmpop):
    assert len(argtys) == len(args), (argtys, args)
    assert len(args) > 0

    def binary_minmax(accumulator, value):
        # This is careful to reproduce Python's algorithm, e.g.
        # max(1.5, nan, 2.5) should return 2.5 (not nan or 1.5)
        accty, acc = accumulator
        vty, v = value
        ty = context.typing_context.unify_types(accty, vty)
        assert ty is not None
        acc = context.cast(builder, acc, accty, ty)
        v = context.cast(builder, v, vty, ty)
        cmpsig = typing.signature(types.boolean, ty, ty)
        ge = context.get_function(cmpop, cmpsig)
        pred = ge(builder, (v, acc))
        res = builder.select(pred, v, acc)
        return ty, res

    typvals = zip(argtys, args)
    resty, resval = reduce(binary_minmax, typvals)
    return resval


@lower_builtin(max, types.BaseTuple)
def max_iterable(context, builder, sig, args):
    argtys = list(sig.args[0])
    args = cgutils.unpack_tuple(builder, args[0])
    return do_minmax(context, builder, argtys, args, operator.gt)

@lower_builtin(max, types.VarArg(types.Any))
def max_vararg(context, builder, sig, args):
    return do_minmax(context, builder, sig.args, args, operator.gt)

@lower_builtin(min, types.BaseTuple)
def min_iterable(context, builder, sig, args):
    argtys = list(sig.args[0])
    args = cgutils.unpack_tuple(builder, args[0])
    return do_minmax(context, builder, argtys, args, operator.lt)

@lower_builtin(min, types.VarArg(types.Any))
def min_vararg(context, builder, sig, args):
    return do_minmax(context, builder, sig.args, args, operator.lt)


def _round_intrinsic(tp):
    # round() rounds half to even
    return "llvm.rint.f%d" % (tp.bitwidth,)

@lower_builtin(round, types.Float)
def round_impl_unary(context, builder, sig, args):
    fltty = sig.args[0]
    llty = context.get_value_type(fltty)
    module = builder.module
    fnty = Type.function(llty, [llty])
    fn = cgutils.get_or_insert_function(module, fnty, _round_intrinsic(fltty))
    res = builder.call(fn, args)
    # unary round() returns an int
    res = builder.fptosi(res, context.get_value_type(sig.return_type))
    return impl_ret_untracked(context, builder, sig.return_type, res)

@lower_builtin(round, types.Float, types.Integer)
def round_impl_binary(context, builder, sig, args):
    fltty = sig.args[0]
    # Allow calling the intrinsic from the Python implementation below.
    # This avoids the conversion to an int in Python 3's unary round().
    _round = types.ExternalFunction(
        _round_intrinsic(fltty), typing.signature(fltty, fltty))

    def round_ndigits(x, ndigits):
        if math.isinf(x) or math.isnan(x):
            return x

        if ndigits >= 0:
            if ndigits > 22:
                # pow1 and pow2 are each safe from overflow, but
                # pow1*pow2 ~= pow(10.0, ndigits) might overflow.
                pow1 = 10.0 ** (ndigits - 22)
                pow2 = 1e22
            else:
                pow1 = 10.0 ** ndigits
                pow2 = 1.0
            y = (x * pow1) * pow2
            if math.isinf(y):
                return x
            return (_round(y) / pow2) / pow1

        else:
            pow1 = 10.0 ** (-ndigits)
            y = x / pow1
            return _round(y) * pow1

    res = context.compile_internal(builder, round_ndigits, sig, args)
    return impl_ret_untracked(context, builder, sig.return_type, res)


#-------------------------------------------------------------------------------
# Numeric constructors

@lower_builtin(int, types.Any)
@lower_builtin(float, types.Any)
def int_impl(context, builder, sig, args):
    [ty] = sig.args
    [val] = args
    res = context.cast(builder, val, ty, sig.return_type)
    return impl_ret_untracked(context, builder, sig.return_type, res)


@lower_builtin(complex, types.VarArg(types.Any))
def complex_impl(context, builder, sig, args):
    complex_type = sig.return_type
    float_type = complex_type.underlying_float
    if len(sig.args) == 1:
        [argty] = sig.args
        [arg] = args
        if isinstance(argty, types.Complex):
            # Cast Complex* to Complex*
            res = context.cast(builder, arg, argty, complex_type)
            return impl_ret_untracked(context, builder, sig.return_type, res)
        else:
            real = context.cast(builder, arg, argty, float_type)
            imag = context.get_constant(float_type, 0)

    elif len(sig.args) == 2:
        [realty, imagty] = sig.args
        [real, imag] = args
        real = context.cast(builder, real, realty, float_type)
        imag = context.cast(builder, imag, imagty, float_type)

    cmplx = context.make_complex(builder, complex_type)
    cmplx.real = real
    cmplx.imag = imag
    res = cmplx._getvalue()
    return impl_ret_untracked(context, builder, sig.return_type, res)


@lower_builtin(types.NumberClass, types.Any)
def number_constructor(context, builder, sig, args):
    """
    Call a number class, e.g. np.int32(...)
    """
    if isinstance(sig.return_type, types.Array):
        # Array constructor
        dt = sig.return_type.dtype
        def foo(*arg_hack):
            return np.array(arg_hack, dtype=dt)
        res = context.compile_internal(builder, foo, sig, args)
        return impl_ret_untracked(context, builder, sig.return_type, res)
    else:
        # Scalar constructor
        [val] = args
        [valty] = sig.args
        return context.cast(builder, val, valty, sig.return_type)


#-------------------------------------------------------------------------------
# Constants

@lower_constant(types.Dummy)
def constant_dummy(context, builder, ty, pyval):
    # This handles None, etc.
    return context.get_dummy_value()

@lower_constant(types.ExternalFunctionPointer)
def constant_function_pointer(context, builder, ty, pyval):
    ptrty = context.get_function_pointer_type(ty)
    ptrval = context.add_dynamic_addr(builder, ty.get_pointer(pyval),
                                      info=str(pyval))
    return builder.bitcast(ptrval, ptrty)


@lower_constant(types.Optional)
def constant_optional(context, builder, ty, pyval):
    if pyval is None:
        return context.make_optional_none(builder, ty.type)
    else:
        return context.make_optional_value(builder, ty.type, pyval)


# -----------------------------------------------------------------------------

@lower_builtin(type, types.Any)
def type_impl(context, builder, sig, args):
    """
    One-argument type() builtin.
    """
    return context.get_dummy_value()


@lower_builtin(iter, types.IterableType)
def iter_impl(context, builder, sig, args):
    ty, = sig.args
    val, = args
    iterval = call_getiter(context, builder, ty, val)
    return iterval


@lower_builtin(next, types.IteratorType)
def next_impl(context, builder, sig, args):
    iterty, = sig.args
    iterval, = args

    res = call_iternext(context, builder, iterty, iterval)

    with builder.if_then(builder.not_(res.is_valid()), likely=False):
        context.call_conv.return_user_exc(builder, StopIteration, ())

    return res.yielded_value()


# -----------------------------------------------------------------------------

@lower_builtin("not in", types.Any, types.Any)
def not_in(context, builder, sig, args):
    def in_impl(a, b):
        return operator.contains(b, a)

    res = context.compile_internal(builder, in_impl, sig, args)
    return builder.not_(res)


# -----------------------------------------------------------------------------

@lower_builtin(len, types.ConstSized)
def constsized_len(context, builder, sig, args):
    [ty] = sig.args
    retty = sig.return_type
    res = context.get_constant(retty, len(ty.types))
    return impl_ret_untracked(context, builder, sig.return_type, res)


@lower_builtin(bool, types.Sized)
def sized_bool(context, builder, sig, args):
    [ty] = sig.args
    if len(ty):
        return cgutils.true_bit
    else:
        return cgutils.false_bit

@lower_builtin(tuple)
def lower_empty_tuple(context, builder, sig, args):
    retty = sig.return_type
    res = context.get_constant_undef(retty)
    return impl_ret_untracked(context, builder, sig.return_type, res)

@lower_builtin(tuple, types.BaseTuple)
def lower_tuple(context, builder, sig, args):
    val, = args
    return impl_ret_untracked(context, builder, sig.return_type, val)

@overload(bool)
def bool_sequence(x):
    valid_types = (
        types.CharSeq,
        types.UnicodeCharSeq,
        types.DictType,
        types.ListType,
        types.UnicodeType,
        types.Set,
    )

    if isinstance(x, valid_types):
        def bool_impl(x):
            return len(x) > 0
        return bool_impl

@overload(bool, inline='always')
def bool_none(x):
    if isinstance(x, types.NoneType) or x is None:
        return lambda x: False

# -----------------------------------------------------------------------------

def get_type_max_value(typ):
    if isinstance(typ, types.Float):
        return np.inf
    if isinstance(typ, types.Integer):
        return typ.maxval
    raise NotImplementedError("Unsupported type")

def get_type_min_value(typ):
    if isinstance(typ, types.Float):
        return -np.inf
    if isinstance(typ, types.Integer):
        return typ.minval
    raise NotImplementedError("Unsupported type")

@lower_builtin(get_type_min_value, types.NumberClass)
@lower_builtin(get_type_min_value, types.DType)
def lower_get_type_min_value(context, builder, sig, args):
    typ = sig.args[0].dtype
    bw = typ.bitwidth

    if isinstance(typ, types.Integer):
        lty = ir.IntType(bw)
        val = typ.minval
        res = ir.Constant(lty, val)
    elif isinstance(typ, types.Float):
        if bw == 32:
            lty = ir.FloatType()
        elif bw == 64:
            lty = ir.DoubleType()
        else:
            raise NotImplementedError("llvmlite only supports 32 and 64 bit floats")
        npty = getattr(np, 'float{}'.format(bw))
        res = ir.Constant(lty, -np.inf)
    return impl_ret_untracked(context, builder, lty, res)

@lower_builtin(get_type_max_value, types.NumberClass)
@lower_builtin(get_type_max_value, types.DType)
def lower_get_type_max_value(context, builder, sig, args):
    typ = sig.args[0].dtype
    bw = typ.bitwidth

    if isinstance(typ, types.Integer):
        lty = ir.IntType(bw)
        val = typ.maxval
        res = ir.Constant(lty, val)
    elif isinstance(typ, types.Float):
        if bw == 32:
            lty = ir.FloatType()
        elif bw == 64:
            lty = ir.DoubleType()
        else:
            raise NotImplementedError("llvmlite only supports 32 and 64 bit floats")
        npty = getattr(np, 'float{}'.format(bw))
        res = ir.Constant(lty, np.inf)
    return impl_ret_untracked(context, builder, lty, res)

# -----------------------------------------------------------------------------

from numba.core.typing.builtins import IndexValue, IndexValueType
from numba.extending import overload, register_jitable

@lower_builtin(IndexValue, types.intp, types.Type)
@lower_builtin(IndexValue, types.uintp, types.Type)
def impl_index_value(context, builder, sig, args):
    typ = sig.return_type
    index, value = args
    index_value = cgutils.create_struct_proxy(typ)(context, builder)
    index_value.index = index
    index_value.value = value
    return index_value._getvalue()


@overload(min)
def indval_min(indval1, indval2):
    if isinstance(indval1, IndexValueType) and \
       isinstance(indval2, IndexValueType):
        def min_impl(indval1, indval2):
            if indval1.value > indval2.value:
                return indval2
            return indval1
        return min_impl


@overload(max)
def indval_max(indval1, indval2):
    if isinstance(indval1, IndexValueType) and \
       isinstance(indval2, IndexValueType):
        def max_impl(indval1, indval2):
            if indval2.value > indval1.value:
                return indval2
            return indval1
        return max_impl


greater_than = register_jitable(lambda a, b: a > b)
less_than = register_jitable(lambda a, b: a < b)


@register_jitable
def min_max_impl(iterable, op):
    if isinstance(iterable, types.IterableType):
        def impl(iterable):
            it = iter(iterable)
            return_val = next(it)
            for val in it:
                if op(val, return_val):
                    return_val = val
            return return_val
        return impl


@overload(min)
def iterable_min(iterable):
    return min_max_impl(iterable, less_than)


@overload(max)
def iterable_max(iterable):
    return min_max_impl(iterable, greater_than)


@lower_builtin(types.TypeRef, types.VarArg(types.Any))
def redirect_type_ctor(context, builder, sig, args):
    """Redirect constructor implementation to `numba_typeref_ctor(cls, *args)`,
    which should be overloaded by type implementator.

    For example:

        d = Dict()

    `d` will be typed as `TypeRef[DictType]()`.  Thus, it will call into this
    implementation.  We need to redirect the lowering to a function
    named ``numba_typeref_ctor``.
    """
    cls = sig.return_type

    def call_ctor(cls, *args):
        return numba_typeref_ctor(cls, *args)

    # Pack arguments into a tuple for `*args`
    ctor_args = types.Tuple.from_types(sig.args)
    # Make signature T(TypeRef[T], *args) where T is cls
    sig = typing.signature(cls, types.TypeRef(cls), ctor_args)
    if len(ctor_args) > 0:
        args = (context.get_dummy_value(),   # Type object has no runtime repr.
                context.make_tuple(builder, ctor_args, args))
    else:
        args = (context.get_dummy_value(),   # Type object has no runtime repr.
                context.make_tuple(builder, ctor_args, ()))

    return context.compile_internal(builder, call_ctor, sig, args)


@overload(sum)
def ol_sum(iterable, start=0):
    # Cpython explicitly rejects strings, bytes and bytearrays
    # https://github.com/python/cpython/blob/3.9/Python/bltinmodule.c#L2310-L2329 # noqa: E501
    error = None
    if isinstance(start, types.UnicodeType):
        error = ('strings', '')
    elif isinstance(start, types.Bytes):
        error = ('bytes', 'b')
    elif isinstance(start, types.ByteArray):
        error = ('bytearray', 'b')

    if error is not None:
        msg = "sum() can't sum {} [use {}''.join(seq) instead]".format(*error)
        raise TypingError(msg)

    # if the container is homogeneous then it's relatively easy to handle.
    if isinstance(iterable, (types.containers._HomogeneousTuple, types.List,
                             types.ListType, types.Array, types.RangeType)):
        iterator = iter
    elif isinstance(iterable, (types.containers._HeterogeneousTuple)):
        # if container is heterogeneous then literal unroll and hope for the
        # best.
        iterator = literal_unroll
    else:
        return None

    def impl(iterable, start=0):
        acc = start
        for x in iterator(iterable):
            # This most likely widens the type, this is expected Numba behaviour
            acc = acc + x
        return acc
    return impl


# ------------------------------------------------------------------------------
# map, filter, reduce


@overload(map)
def ol_map(func, iterable, *args):
    def impl(func, iterable, *args):
        for x in zip(iterable, *args):
            yield func(*x)
    return impl


@overload(filter)
def ol_filter(func, iterable):
    if (func is None) or isinstance(func, types.NoneType):
        def impl(func, iterable):
            for x in iterable:
                if x:
                    yield x
    else:
        def impl(func, iterable):
            for x in iterable:
                if func(x):
                    yield x
    return impl


@overload(isinstance)
def ol_isinstance(var, typs):

    def true_impl(var, typs):
        return True

    def false_impl(var, typs):
        return False

    var_ty = as_numba_type(var)

    if isinstance(var_ty, types.Optional):
        msg = f'isinstance cannot handle optional types. Found: "{var_ty}"'
        raise NumbaTypeError(msg)

    # NOTE: The current implementation of `isinstance` restricts the type of the
    # instance variable to types that are well known and in common use. The
    # danger of unrestricted tyoe comparison is that a "default" of `False` is
    # required and this means that if there is a bug in the logic of the
    # comparison tree `isinstance` returns False! It's therefore safer to just
    # reject the compilation as untypable!
    supported_var_ty = (types.Number, types.Bytes, types.RangeType,
                        types.DictType, types.LiteralStrKeyDict, types.List,
                        types.ListType, types.Tuple, types.UniTuple, types.Set,
                        types.Function, types.ClassType, types.UnicodeType,
                        types.ClassInstanceType, types.NoneType, types.Array)
    if not isinstance(var_ty, supported_var_ty):
        msg = f'isinstance() does not support variables of type "{var_ty}".'
        raise NumbaTypeError(msg)

    # Warn about the experimental nature of this feature.
    msg = "Use of isinstance() detected. This is an experimental feature."
    warnings.warn(msg, category=NumbaExperimentalFeatureWarning)

    t_typs = typs

    # Check the types that the var can be an instance of, it'll be a scalar,
    # a unituple or a tuple.
    if isinstance(t_typs, types.UniTuple):
        # corner case - all types in isinstance are the same
        t_typs = (t_typs.key[0])

    if not isinstance(t_typs, types.Tuple):
        t_typs = (t_typs, )

    for typ in t_typs:

        if isinstance(typ, types.Function):
            key = typ.key[0]  # functions like int(..), float(..), str(..)
        elif isinstance(typ, types.ClassType):
            key = typ  # jitclasses
        else:
            key = typ.key

        # corner cases for bytes, range, ...
        # avoid registering those types on `as_numba_type`
        types_not_registered = {
            bytes: types.Bytes,
            range: types.RangeType,
            dict: (types.DictType, types.LiteralStrKeyDict),
            list: types.List,
            tuple: types.BaseTuple,
            set: types.Set,
        }
        if key in types_not_registered:
            if isinstance(var_ty, types_not_registered[key]):
                return true_impl
            continue

        if isinstance(typ, types.TypeRef):
            # Use of Numba type classes is in general not supported as they do
            # not work when the jit is disabled.
            if key not in (types.ListType, types.DictType):
                msg = ("Numba type classes (except numba.typed.* container "
                       "types) are not supported.")
                raise NumbaTypeError(msg)
            # Case for TypeRef (i.e. isinstance(var, typed.List))
            #      var_ty == ListType[int64] (instance)
            #         typ == types.ListType  (class)
            return true_impl if type(var_ty) is key else false_impl
        else:
            numba_typ = as_numba_type(key)
            if var_ty == numba_typ:
                return true_impl
            elif isinstance(numba_typ, types.ClassType) and \
                    isinstance(var_ty, types.ClassInstanceType) and \
                    var_ty.key == numba_typ.instance_type.key:
                # check for jitclasses
                return true_impl
            elif isinstance(numba_typ, types.Container) and \
                    numba_typ.key[0] == types.undefined:
                # check for containers (list, tuple, set, ...)
                if isinstance(var_ty, numba_typ.__class__) or \
                    (isinstance(var_ty, types.BaseTuple) and \
                        isinstance(numba_typ, types.BaseTuple)):
                    return true_impl

    return false_impl