"""
Implementation of math operations on Array objects.
"""


import math
from collections import namedtuple
import operator
import warnings

import llvmlite.ir
import numpy as np

from numba.core import types, cgutils
from numba.core.extending import overload, overload_method, register_jitable
from numba.np.numpy_support import (as_dtype, type_can_asarray, type_is_scalar,
                                    numpy_version, is_nonelike,
                                    check_is_integer, lt_floats, lt_complex)
from numba.core.imputils import (lower_builtin, impl_ret_borrowed,
                                 impl_ret_new_ref, impl_ret_untracked)
from numba.np.arrayobj import (make_array, load_item, store_item,
                               _empty_nd_impl)
from numba.np.linalg import ensure_blas

from numba.core.extending import intrinsic
from numba.core.errors import (RequireLiteralValue, TypingError,
                               NumbaValueError, NumbaNotImplementedError,
                               NumbaTypeError, NumbaDeprecationWarning)
from numba.cpython.unsafe.tuple import tuple_setitem


def _check_blas():
    # Checks if a BLAS is available so e.g. dot will work
    try:
        ensure_blas()
    except ImportError:
        return False
    return True


_HAVE_BLAS = _check_blas()


@intrinsic
def _create_tuple_result_shape(tyctx, shape_list, shape_tuple):
    """
    This routine converts shape list where the axis dimension has already
    been popped to a tuple for indexing of the same size.  The original shape
    tuple is also required because it contains a length field at compile time
    whereas the shape list does not.
    """

    # The new tuple's size is one less than the original tuple since axis
    # dimension removed.
    nd = len(shape_tuple) - 1
    # The return type of this intrinsic is an int tuple of length nd.
    tupty = types.UniTuple(types.intp, nd)
    # The function signature for this intrinsic.
    function_sig = tupty(shape_list, shape_tuple)

    def codegen(cgctx, builder, signature, args):
        lltupty = cgctx.get_value_type(tupty)
        # Create an empty int tuple.
        tup = cgutils.get_null_value(lltupty)

        # Get the shape list from the args and we don't need shape tuple.
        [in_shape, _] = args

        def array_indexer(a, i):
            return a[i]

        # loop to fill the tuple
        for i in range(nd):
            dataidx = cgctx.get_constant(types.intp, i)
            # compile and call array_indexer
            data = cgctx.compile_internal(builder, array_indexer,
                                          types.intp(shape_list, types.intp),
                                          [in_shape, dataidx])
            tup = builder.insert_value(tup, data, i)
        return tup

    return function_sig, codegen


@intrinsic
def _gen_index_tuple(tyctx, shape_tuple, value, axis):
    """
    Generates a tuple that can be used to index a specific slice from an
    array for sum with axis.  shape_tuple is the size of the dimensions of
    the input array.  'value' is the value to put in the indexing tuple
    in the axis dimension and 'axis' is that dimension.  For this to work,
    axis has to be a const.
    """
    if not isinstance(axis, types.Literal):
        raise RequireLiteralValue('axis argument must be a constant')
    # Get the value of the axis constant.
    axis_value = axis.literal_value
    # The length of the indexing tuple to be output.
    nd = len(shape_tuple)

    # If the axis value is impossible for the given size array then
    # just fake it like it was for axis 0.  This will stop compile errors
    # when it looks like it could be called from array_sum_axis but really
    # can't because that routine checks the axis mismatch and raise an
    # exception.
    if axis_value >= nd:
        axis_value = 0

    # Calculate the type of the indexing tuple.  All the non-axis
    # dimensions have slice2 type and the axis dimension has int type.
    before = axis_value
    after = nd - before - 1

    types_list = []
    types_list += [types.slice2_type] * before
    types_list += [types.intp]
    types_list += [types.slice2_type] * after

    # Creates the output type of the function.
    tupty = types.Tuple(types_list)
    # Defines the signature of the intrinsic.
    function_sig = tupty(shape_tuple, value, axis)

    def codegen(cgctx, builder, signature, args):
        lltupty = cgctx.get_value_type(tupty)
        # Create an empty indexing tuple.
        tup = cgutils.get_null_value(lltupty)

        # We only need value of the axis dimension here.
        # The rest are constants defined above.
        [_, value_arg, _] = args

        def create_full_slice():
            return slice(None, None)

        # loop to fill the tuple with slice(None,None) before
        # the axis dimension.

        # compile and call create_full_slice
        slice_data = cgctx.compile_internal(builder, create_full_slice,
                                            types.slice2_type(),
                                            [])
        for i in range(0, axis_value):
            tup = builder.insert_value(tup, slice_data, i)

        # Add the axis dimension 'value'.
        tup = builder.insert_value(tup, value_arg, axis_value)

        # loop to fill the tuple with slice(None,None) after
        # the axis dimension.
        for i in range(axis_value + 1, nd):
            tup = builder.insert_value(tup, slice_data, i)
        return tup

    return function_sig, codegen


#----------------------------------------------------------------------------
# Basic stats and aggregates

@lower_builtin(np.sum, types.Array)
@lower_builtin("array.sum", types.Array)
def array_sum(context, builder, sig, args):
    zero = sig.return_type(0)

    def array_sum_impl(arr):
        c = zero
        for v in np.nditer(arr):
            c += v.item()
        return c

    res = context.compile_internal(builder, array_sum_impl, sig, args,
                                   locals=dict(c=sig.return_type))
    return impl_ret_borrowed(context, builder, sig.return_type, res)


@register_jitable
def _array_sum_axis_nop(arr, v):
    return arr


def gen_sum_axis_impl(is_axis_const, const_axis_val, op, zero):
    def inner(arr, axis):
        """
        function that performs sums over one specific axis

        The third parameter to gen_index_tuple that generates the indexing
        tuples has to be a const so we can't just pass "axis" through since
        that isn't const.  We can check for specific values and have
        different instances that do take consts.  Supporting axis summation
        only up to the fourth dimension for now.

        typing/arraydecl.py:sum_expand defines the return type for sum with
        axis. It is one dimension less than the input array.
        """
        ndim = arr.ndim

        if not is_axis_const:
            # Catch where axis is negative or greater than 3.
            if axis < 0 or axis > 3:
                raise ValueError("Numba does not support sum with axis "
                                 "parameter outside the range 0 to 3.")

        # Catch the case where the user misspecifies the axis to be
        # more than the number of the array's dimensions.
        if axis >= ndim:
            raise ValueError("axis is out of bounds for array")

        # Convert the shape of the input array to a list.
        ashape = list(arr.shape)
        # Get the length of the axis dimension.
        axis_len = ashape[axis]
        # Remove the axis dimension from the list of dimensional lengths.
        ashape.pop(axis)
        # Convert this shape list back to a tuple using above intrinsic.
        ashape_without_axis = _create_tuple_result_shape(ashape, arr.shape)
        # Tuple needed here to create output array with correct size.
        result = np.full(ashape_without_axis, zero, type(zero))

        # Iterate through the axis dimension.
        for axis_index in range(axis_len):
            if is_axis_const:
                # constant specialized version works for any valid axis value
                index_tuple_generic = _gen_index_tuple(arr.shape, axis_index,
                                                       const_axis_val)
                result += arr[index_tuple_generic]
            else:
                # Generate a tuple used to index the input array.
                # The tuple is ":" in all dimensions except the axis
                # dimension where it is "axis_index".
                if axis == 0:
                    index_tuple1 = _gen_index_tuple(arr.shape, axis_index, 0)
                    result += arr[index_tuple1]
                elif axis == 1:
                    index_tuple2 = _gen_index_tuple(arr.shape, axis_index, 1)
                    result += arr[index_tuple2]
                elif axis == 2:
                    index_tuple3 = _gen_index_tuple(arr.shape, axis_index, 2)
                    result += arr[index_tuple3]
                elif axis == 3:
                    index_tuple4 = _gen_index_tuple(arr.shape, axis_index, 3)
                    result += arr[index_tuple4]
        return op(result, 0)
    return inner


@lower_builtin(np.sum, types.Array, types.intp, types.DTypeSpec)
@lower_builtin(np.sum, types.Array, types.IntegerLiteral, types.DTypeSpec)
@lower_builtin("array.sum", types.Array, types.intp, types.DTypeSpec)
@lower_builtin("array.sum", types.Array, types.IntegerLiteral, types.DTypeSpec)
def array_sum_axis_dtype(context, builder, sig, args):
    retty = sig.return_type
    zero = getattr(retty, 'dtype', retty)(0)
    # if the return is scalar in type then "take" the 0th element of the
    # 0d array accumulator as the return value
    if getattr(retty, 'ndim', None) is None:
        op = np.take
    else:
        op = _array_sum_axis_nop
    [ty_array, ty_axis, ty_dtype] = sig.args
    is_axis_const = False
    const_axis_val = 0
    if isinstance(ty_axis, types.Literal):
        # this special-cases for constant axis
        const_axis_val = ty_axis.literal_value
        # fix negative axis
        if const_axis_val < 0:
            const_axis_val = ty_array.ndim + const_axis_val
        if const_axis_val < 0 or const_axis_val > ty_array.ndim:
            raise ValueError("'axis' entry is out of bounds")

        ty_axis = context.typing_context.resolve_value_type(const_axis_val)
        axis_val = context.get_constant(ty_axis, const_axis_val)
        # rewrite arguments
        args = args[0], axis_val, args[2]
        # rewrite sig
        sig = sig.replace(args=[ty_array, ty_axis, ty_dtype])
        is_axis_const = True

    gen_impl = gen_sum_axis_impl(is_axis_const, const_axis_val, op, zero)
    compiled = register_jitable(gen_impl)

    def array_sum_impl_axis(arr, axis, dtype):
        return compiled(arr, axis)

    res = context.compile_internal(builder, array_sum_impl_axis, sig, args)
    return impl_ret_new_ref(context, builder, sig.return_type, res)


@lower_builtin(np.sum, types.Array,  types.DTypeSpec)
@lower_builtin("array.sum", types.Array, types.DTypeSpec)
def array_sum_dtype(context, builder, sig, args):
    zero = sig.return_type(0)

    def array_sum_impl(arr, dtype):
        c = zero
        for v in np.nditer(arr):
            c += v.item()
        return c

    res = context.compile_internal(builder, array_sum_impl, sig, args,
                                   locals=dict(c=sig.return_type))
    return impl_ret_borrowed(context, builder, sig.return_type, res)


@lower_builtin(np.sum, types.Array, types.intp)
@lower_builtin(np.sum, types.Array, types.IntegerLiteral)
@lower_builtin("array.sum", types.Array, types.intp)
@lower_builtin("array.sum", types.Array, types.IntegerLiteral)
def array_sum_axis(context, builder, sig, args):
    retty = sig.return_type
    zero = getattr(retty, 'dtype', retty)(0)
    # if the return is scalar in type then "take" the 0th element of the
    # 0d array accumulator as the return value
    if getattr(retty, 'ndim', None) is None:
        op = np.take
    else:
        op = _array_sum_axis_nop
    [ty_array, ty_axis] = sig.args
    is_axis_const = False
    const_axis_val = 0
    if isinstance(ty_axis, types.Literal):
        # this special-cases for constant axis
        const_axis_val = ty_axis.literal_value
        # fix negative axis
        if const_axis_val < 0:
            const_axis_val = ty_array.ndim + const_axis_val
        if const_axis_val < 0 or const_axis_val > ty_array.ndim:
            msg = f"'axis' entry ({const_axis_val}) is out of bounds"
            raise NumbaValueError(msg)

        ty_axis = context.typing_context.resolve_value_type(const_axis_val)
        axis_val = context.get_constant(ty_axis, const_axis_val)
        # rewrite arguments
        args = args[0], axis_val
        # rewrite sig
        sig = sig.replace(args=[ty_array, ty_axis])
        is_axis_const = True

    gen_impl = gen_sum_axis_impl(is_axis_const, const_axis_val, op, zero)
    compiled = register_jitable(gen_impl)

    def array_sum_impl_axis(arr, axis):
        return compiled(arr, axis)

    res = context.compile_internal(builder, array_sum_impl_axis, sig, args)
    return impl_ret_new_ref(context, builder, sig.return_type, res)


def get_accumulator(dtype, value):
    if dtype.type == np.timedelta64:
        acc_init = np.int64(value).view(dtype)
    else:
        acc_init = dtype.type(value)
    return acc_init


@overload(np.prod)
@overload_method(types.Array, "prod")
def array_prod(a):
    if isinstance(a, types.Array):
        dtype = as_dtype(a.dtype)

        acc_init = get_accumulator(dtype, 1)

        def array_prod_impl(a):
            c = acc_init
            for v in np.nditer(a):
                c *= v.item()
            return c

        return array_prod_impl


@overload(np.cumsum)
@overload_method(types.Array, "cumsum")
def array_cumsum(a):
    if isinstance(a, types.Array):
        is_integer = a.dtype in types.signed_domain
        is_bool = a.dtype == types.bool_
        if (is_integer and a.dtype.bitwidth < types.intp.bitwidth)\
                or is_bool:
            dtype = as_dtype(types.intp)
        else:
            dtype = as_dtype(a.dtype)

        acc_init = get_accumulator(dtype, 0)

        def array_cumsum_impl(a):
            out = np.empty(a.size, dtype)
            c = acc_init
            for idx, v in enumerate(a.flat):
                c += v
                out[idx] = c
            return out

        return array_cumsum_impl


@overload(np.cumprod)
@overload_method(types.Array, "cumprod")
def array_cumprod(a):
    if isinstance(a, types.Array):
        is_integer = a.dtype in types.signed_domain
        is_bool = a.dtype == types.bool_
        if (is_integer and a.dtype.bitwidth < types.intp.bitwidth)\
                or is_bool:
            dtype = as_dtype(types.intp)
        else:
            dtype = as_dtype(a.dtype)

        acc_init = get_accumulator(dtype, 1)

        def array_cumprod_impl(a):
            out = np.empty(a.size, dtype)
            c = acc_init
            for idx, v in enumerate(a.flat):
                c *= v
                out[idx] = c
            return out

        return array_cumprod_impl


@overload(np.mean)
@overload_method(types.Array, "mean")
def array_mean(a):
    if isinstance(a, types.Array):
        is_number = a.dtype in types.integer_domain | frozenset([types.bool_])
        if is_number:
            dtype = as_dtype(types.float64)
        else:
            dtype = as_dtype(a.dtype)

        acc_init = get_accumulator(dtype, 0)

        def array_mean_impl(a):
            # Can't use the naive `arr.sum() / arr.size`, as it would return
            # a wrong result on integer sum overflow.
            c = acc_init
            for v in np.nditer(a):
                c += v.item()
            return c / a.size

        return array_mean_impl


@overload(np.var)
@overload_method(types.Array, "var")
def array_var(a):
    if isinstance(a, types.Array):
        def array_var_impl(a):
            # Compute the mean
            m = a.mean()

            # Compute the sum of square diffs
            ssd = 0
            for v in np.nditer(a):
                val = (v.item() - m)
                ssd += np.real(val * np.conj(val))
            return ssd / a.size

        return array_var_impl


@overload(np.std)
@overload_method(types.Array, "std")
def array_std(a):
    if isinstance(a, types.Array):
        def array_std_impl(a):
            return a.var() ** 0.5

        return array_std_impl


@register_jitable
def min_comparator(a, min_val):
    return a < min_val


@register_jitable
def max_comparator(a, min_val):
    return a > min_val


@register_jitable
def return_false(a):
    return False


@overload(np.min)
@overload(np.amin)
@overload_method(types.Array, "min")
def npy_min(a):
    if not isinstance(a, types.Array):
        return

    if isinstance(a.dtype, (types.NPDatetime, types.NPTimedelta)):
        pre_return_func = np.isnat
        comparator = min_comparator
    elif isinstance(a.dtype, types.Complex):
        pre_return_func = return_false

        def comp_func(a, min_val):
            if a.real < min_val.real:
                return True
            elif a.real == min_val.real:
                if a.imag < min_val.imag:
                    return True
            return False

        comparator = register_jitable(comp_func)
    elif isinstance(a.dtype, types.Float):
        pre_return_func = np.isnan
        comparator = min_comparator
    else:
        pre_return_func = return_false
        comparator = min_comparator

    def impl_min(a):
        if a.size == 0:
            raise ValueError("zero-size array to reduction operation "
                             "minimum which has no identity")

        it = np.nditer(a)
        min_value = next(it).take(0)
        if pre_return_func(min_value):
            return min_value

        for view in it:
            v = view.item()
            if pre_return_func(v):
                return v
            if comparator(v, min_value):
                min_value = v
        return min_value

    return impl_min


@overload(np.max)
@overload(np.amax)
@overload_method(types.Array, "max")
def npy_max(a):
    if not isinstance(a, types.Array):
        return

    if isinstance(a.dtype, (types.NPDatetime, types.NPTimedelta)):
        pre_return_func = np.isnat
        comparator = max_comparator
    elif isinstance(a.dtype, types.Complex):
        pre_return_func = return_false

        def comp_func(a, max_val):
            if a.real > max_val.real:
                return True
            elif a.real == max_val.real:
                if a.imag > max_val.imag:
                    return True
            return False

        comparator = register_jitable(comp_func)
    elif isinstance(a.dtype, types.Float):
        pre_return_func = np.isnan
        comparator = max_comparator
    else:
        pre_return_func = return_false
        comparator = max_comparator

    def impl_max(a):
        if a.size == 0:
            raise ValueError("zero-size array to reduction operation "
                             "maximum which has no identity")

        it = np.nditer(a)
        max_value = next(it).take(0)
        if pre_return_func(max_value):
            return max_value

        for view in it:
            v = view.item()
            if pre_return_func(v):
                return v
            if comparator(v, max_value):
                max_value = v
        return max_value

    return impl_max


@register_jitable
def array_argmin_impl_datetime(arry):
    if arry.size == 0:
        raise ValueError("attempt to get argmin of an empty sequence")
    it = np.nditer(arry)
    min_value = next(it).take(0)
    min_idx = 0
    if np.isnat(min_value):
        return min_idx

    idx = 1
    for view in it:
        v = view.item()
        if np.isnat(v):
            return idx
        if v < min_value:
            min_value = v
            min_idx = idx
        idx += 1
    return min_idx


@register_jitable
def array_argmin_impl_float(arry):
    if arry.size == 0:
        raise ValueError("attempt to get argmin of an empty sequence")
    for v in arry.flat:
        min_value = v
        min_idx = 0
        break
    if np.isnan(min_value):
        return min_idx

    idx = 0
    for v in arry.flat:
        if np.isnan(v):
            return idx
        if v < min_value:
            min_value = v
            min_idx = idx
        idx += 1
    return min_idx


@register_jitable
def array_argmin_impl_generic(arry):
    if arry.size == 0:
        raise ValueError("attempt to get argmin of an empty sequence")
    for v in arry.flat:
        min_value = v
        min_idx = 0
        break
    else:
        raise RuntimeError('unreachable')

    idx = 0
    for v in arry.flat:
        if v < min_value:
            min_value = v
            min_idx = idx
        idx += 1
    return min_idx


@overload(np.argmin)
@overload_method(types.Array, "argmin")
def array_argmin(a, axis=None):
    if isinstance(a.dtype, (types.NPDatetime, types.NPTimedelta)):
        flatten_impl = array_argmin_impl_datetime
    elif isinstance(a.dtype, types.Float):
        flatten_impl = array_argmin_impl_float
    else:
        flatten_impl = array_argmin_impl_generic

    if is_nonelike(axis):
        def array_argmin_impl(a, axis=None):
            return flatten_impl(a)
    else:
        array_argmin_impl = build_argmax_or_argmin_with_axis_impl(
            a, axis, flatten_impl
        )
    return array_argmin_impl


@register_jitable
def array_argmax_impl_datetime(arry):
    if arry.size == 0:
        raise ValueError("attempt to get argmax of an empty sequence")
    it = np.nditer(arry)
    max_value = next(it).take(0)
    max_idx = 0
    if np.isnat(max_value):
        return max_idx

    idx = 1
    for view in it:
        v = view.item()
        if np.isnat(v):
            return idx
        if v > max_value:
            max_value = v
            max_idx = idx
        idx += 1
    return max_idx


@register_jitable
def array_argmax_impl_float(arry):
    if arry.size == 0:
        raise ValueError("attempt to get argmax of an empty sequence")
    for v in arry.flat:
        max_value = v
        max_idx = 0
        break
    if np.isnan(max_value):
        return max_idx

    idx = 0
    for v in arry.flat:
        if np.isnan(v):
            return idx
        if v > max_value:
            max_value = v
            max_idx = idx
        idx += 1
    return max_idx


@register_jitable
def array_argmax_impl_generic(arry):
    if arry.size == 0:
        raise ValueError("attempt to get argmax of an empty sequence")
    for v in arry.flat:
        max_value = v
        max_idx = 0
        break

    idx = 0
    for v in arry.flat:
        if v > max_value:
            max_value = v
            max_idx = idx
        idx += 1
    return max_idx


def build_argmax_or_argmin_with_axis_impl(a, axis, flatten_impl):
    """
    Given a function that implements the logic for handling a flattened
    array, return the implementation function.
    """
    check_is_integer(axis, "axis")
    retty = types.intp

    tuple_buffer = tuple(range(a.ndim))

    def impl(a, axis=None):
        if axis < 0:
            axis = a.ndim + axis

        if axis < 0 or axis >= a.ndim:
            raise ValueError("axis is out of bounds")

        # Short circuit 1-dimensional arrays:
        if a.ndim == 1:
            return flatten_impl(a)

        # Make chosen axis the last axis:
        tmp = tuple_buffer
        for i in range(axis, a.ndim - 1):
            tmp = tuple_setitem(tmp, i, i + 1)
        transpose_index = tuple_setitem(tmp, a.ndim - 1, axis)
        transposed_arr = a.transpose(transpose_index)

        # Flatten along that axis; since we've transposed, we can just get
        # batches off the overall flattened array.
        m = transposed_arr.shape[-1]
        raveled = transposed_arr.ravel()
        assert raveled.size == a.size
        assert transposed_arr.size % m == 0
        out = np.empty(transposed_arr.size // m, retty)
        for i in range(out.size):
            out[i] = flatten_impl(raveled[i * m:(i + 1) * m])

        # Reshape based on axis we didn't flatten over:
        return out.reshape(transposed_arr.shape[:-1])

    return impl


@overload(np.argmax)
@overload_method(types.Array, "argmax")
def array_argmax(a, axis=None):
    if isinstance(a.dtype, (types.NPDatetime, types.NPTimedelta)):
        flatten_impl = array_argmax_impl_datetime
    elif isinstance(a.dtype, types.Float):
        flatten_impl = array_argmax_impl_float
    else:
        flatten_impl = array_argmax_impl_generic

    if is_nonelike(axis):
        def array_argmax_impl(a, axis=None):
            return flatten_impl(a)
    else:
        array_argmax_impl = build_argmax_or_argmin_with_axis_impl(
            a, axis, flatten_impl
        )
    return array_argmax_impl


@overload(np.all)
@overload_method(types.Array, "all")
def np_all(a):

    # for scalar
    if isinstance(a, (types.Number, types.Boolean)):
        def scalar_all(a):
            return bool(a)
        return scalar_all

    # for array
    if isinstance(a, types.Array):
        def flat_all(a):
            for v in np.nditer(a):
                if not v.item():
                    return False
            return True
        return flat_all

    return None


@register_jitable
def _allclose_scalars(a_v, b_v, rtol=1e-05, atol=1e-08, equal_nan=False):
    a_v_isnan = np.isnan(a_v)
    b_v_isnan = np.isnan(b_v)

    # only one of the values is NaN and the
    # other is not.
    if ( (not a_v_isnan and b_v_isnan) or
            (a_v_isnan and not b_v_isnan) ):
        return False

    # either both of the values are NaN
    # or both are numbers
    if a_v_isnan and b_v_isnan:
        if not equal_nan:
            return False
    else:
        if np.isinf(a_v) or np.isinf(b_v):
            return a_v == b_v

        if np.abs(a_v - b_v) > atol + rtol * np.abs(b_v * 1.0):
            return False

    return True


@overload(np.allclose)
@overload_method(types.Array, "allclose")
def np_allclose(a, b, rtol=1e-05, atol=1e-08, equal_nan=False):

    if not type_can_asarray(a):
        raise TypingError('The first argument "a" must be array-like')

    if not type_can_asarray(b):
        raise TypingError('The second argument "b" must be array-like')

    if not isinstance(rtol, (float, types.Float)):
        raise TypingError('The third argument "rtol" must be a '
                          'floating point')

    if not isinstance(atol, (float, types.Float)):
        raise TypingError('The fourth argument "atol" must be a '
                          'floating point')

    if not isinstance(equal_nan, (bool, types.Boolean)):
        raise TypingError('The fifth argument "equal_nan" must be a '
                          'boolean')

    is_a_scalar = isinstance(a, types.Number)
    is_b_scalar = isinstance(b, types.Number)

    if is_a_scalar and is_b_scalar:
        def np_allclose_impl_scalar_scalar(a, b, rtol=1e-05, atol=1e-08,
                                           equal_nan=False):
            return _allclose_scalars(a, b, rtol=rtol, atol=atol,
                                     equal_nan=equal_nan)
        return np_allclose_impl_scalar_scalar
    elif is_a_scalar and not is_b_scalar:
        def np_allclose_impl_scalar_array(a, b, rtol=1e-05, atol=1e-08,
                                          equal_nan=False):
            b = np.asarray(b)
            for bv in np.nditer(b):
                if not _allclose_scalars(a, bv.item(), rtol=rtol, atol=atol,
                                         equal_nan=equal_nan):
                    return False
            return True
        return np_allclose_impl_scalar_array
    elif not is_a_scalar and is_b_scalar:
        def np_allclose_impl_array_scalar(a, b, rtol=1e-05, atol=1e-08,
                                          equal_nan=False):
            a = np.asarray(a)
            for av in np.nditer(a):
                if not _allclose_scalars(av.item(), b, rtol=rtol, atol=atol,
                                         equal_nan=equal_nan):
                    return False
            return True
        return np_allclose_impl_array_scalar
    elif not is_a_scalar and not is_b_scalar:
        def np_allclose_impl_array_array(a, b, rtol=1e-05, atol=1e-08,
                                         equal_nan=False):
            a = np.asarray(a)
            b = np.asarray(b)
            a_a, b_b = np.broadcast_arrays(a, b)

            for av, bv in np.nditer((a_a, b_b)):
                if not _allclose_scalars(av.item(), bv.item(), rtol=rtol,
                                         atol=atol, equal_nan=equal_nan):
                    return False

            return True

        return np_allclose_impl_array_array


@overload(np.any)
@overload_method(types.Array, "any")
def np_any(a):
    # for scalar
    if isinstance(a, (types.Number, types.Boolean)):
        def scalar_any(a):
            return bool(a)
        return scalar_any

    # for array
    if isinstance(a, types.Array):
        def flat_any(a):
            for v in np.nditer(a):
                if v.item():
                    return True
            return False
        return flat_any

    return None


@overload(np.average)
def np_average(a, axis=None, weights=None):
    if axis is not None and not isinstance(axis, types.NoneType):
        def np_average_impl(a, axis=None, weights=None):
            raise TypeError("Numba does not support average with axis.")
    else:
        if weights is None or isinstance(weights, types.NoneType):
            def np_average_impl(a, axis=None, weights=None):
                arr = np.asarray(a)
                return np.mean(arr)
        else:
            def np_average_impl(a, axis=None, weights=None):
                arr = np.asarray(a)
                weights = np.asarray(weights)

                if arr.shape != weights.shape:
                    if weights.ndim != 1:
                        raise TypeError(
                            "1D weights expected when shapes of "
                            "a and weights differ.")

                scl = np.sum(weights)
                if scl == 0.0:
                    raise ZeroDivisionError(
                        "Weights sum to zero, can't be normalized.")

                avg = np.sum(np.multiply(arr, weights)) / scl
                return avg

    return np_average_impl


def get_isnan(dtype):
    """
    A generic isnan() function
    """
    if isinstance(dtype, (types.Float, types.Complex)):
        return np.isnan
    else:
        @register_jitable
        def _trivial_isnan(x):
            return False
        return _trivial_isnan


@overload(np.iscomplex)
def np_iscomplex(x):
    if type_can_asarray(x):
        # NumPy uses asanyarray here!
        return lambda x: np.asarray(x).imag != 0
    return None


@overload(np.isreal)
def np_isreal(x):
    if type_can_asarray(x):
        # NumPy uses asanyarray here!
        return lambda x: np.asarray(x).imag == 0
    return None


@overload(np.iscomplexobj)
def iscomplexobj(x):
    # Implementation based on NumPy
    # https://github.com/numpy/numpy/blob/d9b1e32cb8ef90d6b4a47853241db2a28146a57d/numpy/lib/type_check.py#L282-L320
    dt = determine_dtype(x)
    if isinstance(x, types.Optional):
        dt = determine_dtype(x.type)
    iscmplx = np.issubdtype(dt, np.complexfloating)

    if isinstance(x, types.Optional):
        def impl(x):
            if x is None:
                return False
            return iscmplx
    else:
        def impl(x):
            return iscmplx
    return impl


@overload(np.isrealobj)
def isrealobj(x):
    # Return True if x is not a complex type.
    # Implementation based on NumPy
    # https://github.com/numpy/numpy/blob/ccfbcc1cd9a4035a467f2e982a565ab27de25b6b/numpy/lib/type_check.py#L290-L322
    def impl(x):
        return not np.iscomplexobj(x)
    return impl


@overload(np.isscalar)
def np_isscalar(element):
    res = type_is_scalar(element)

    def impl(element):
        return res
    return impl


def is_np_inf_impl(x, out, fn):

    # if/else branch should be unified after PR #5606 is merged
    if is_nonelike(out):
        def impl(x, out=None):
            return np.logical_and(np.isinf(x), fn(np.signbit(x)))
    else:
        def impl(x, out=None):
            return np.logical_and(np.isinf(x), fn(np.signbit(x)), out)

    return impl


@overload(np.isneginf)
def isneginf(x, out=None):
    fn = register_jitable(lambda x: x)
    return is_np_inf_impl(x, out, fn)


@overload(np.isposinf)
def isposinf(x, out=None):
    fn = register_jitable(lambda x: ~x)
    return is_np_inf_impl(x, out, fn)


@register_jitable
def less_than(a, b):
    return a < b


@register_jitable
def greater_than(a, b):
    return a > b


@register_jitable
def check_array(a):
    if a.size == 0:
        raise ValueError('zero-size array to reduction operation not possible')


def nan_min_max_factory(comparison_op, is_complex_dtype):
    if is_complex_dtype:
        def impl(a):
            arr = np.asarray(a)
            check_array(arr)
            it = np.nditer(arr)
            return_val = next(it).take(0)
            for view in it:
                v = view.item()
                if np.isnan(return_val.real) and not np.isnan(v.real):
                    return_val = v
                else:
                    if comparison_op(v.real, return_val.real):
                        return_val = v
                    elif v.real == return_val.real:
                        if comparison_op(v.imag, return_val.imag):
                            return_val = v
            return return_val
    else:
        def impl(a):
            arr = np.asarray(a)
            check_array(arr)
            it = np.nditer(arr)
            return_val = next(it).take(0)
            for view in it:
                v = view.item()
                if not np.isnan(v):
                    if not comparison_op(return_val, v):
                        return_val = v
            return return_val

    return impl


real_nanmin = register_jitable(
    nan_min_max_factory(less_than, is_complex_dtype=False)
)
real_nanmax = register_jitable(
    nan_min_max_factory(greater_than, is_complex_dtype=False)
)
complex_nanmin = register_jitable(
    nan_min_max_factory(less_than, is_complex_dtype=True)
)
complex_nanmax = register_jitable(
    nan_min_max_factory(greater_than, is_complex_dtype=True)
)


@register_jitable
def _isclose_item(x, y, rtol, atol, equal_nan):
    if np.isnan(x) and np.isnan(y):
        return equal_nan
    elif np.isinf(x) and np.isinf(y):
        return (x > 0) == (y > 0)
    elif np.isinf(x) or np.isinf(y):
        return False
    else:
        return abs(x - y) <= atol + rtol * abs(y)


@overload(np.isclose)
def isclose(a, b, rtol=1e-05, atol=1e-08, equal_nan=False):
    if not type_can_asarray(a):
        raise TypingError('The first argument "a" must be array-like')

    if not type_can_asarray(b):
        raise TypingError('The second argument "b" must be array-like')

    if not isinstance(rtol, (float, types.Float)):
        raise TypingError('The third argument "rtol" must be a '
                          'floating point')

    if not isinstance(atol, (float, types.Float)):
        raise TypingError('The fourth argument "atol" must be a '
                          'floating point')

    if not isinstance(equal_nan, (bool, types.Boolean)):
        raise TypingError('The fifth argument "equal_nan" must be a '
                          'boolean')

    if isinstance(a, types.Array) and isinstance(b, types.Number):
        def isclose_impl(a, b, rtol=1e-05, atol=1e-08, equal_nan=False):
            x = a.reshape(-1)
            y = b
            out = np.zeros(len(x), np.bool_)
            for i in range(len(out)):
                out[i] = _isclose_item(x[i], y, rtol, atol, equal_nan)
            return out.reshape(a.shape)

    elif isinstance(a, types.Number) and isinstance(b, types.Array):
        def isclose_impl(a, b, rtol=1e-05, atol=1e-08, equal_nan=False):
            x = a
            y = b.reshape(-1)
            out = np.zeros(len(y), np.bool_)
            for i in range(len(out)):
                out[i] = _isclose_item(x, y[i], rtol, atol, equal_nan)
            return out.reshape(b.shape)

    elif isinstance(a, types.Array) and isinstance(b, types.Array):
        def isclose_impl(a, b, rtol=1e-05, atol=1e-08, equal_nan=False):
            shape = np.broadcast_shapes(a.shape, b.shape)
            a_ = np.broadcast_to(a, shape)
            b_ = np.broadcast_to(b, shape)

            out = np.zeros(len(a_), dtype=np.bool_)
            for i, (av, bv) in enumerate(np.nditer((a_, b_))):
                out[i] = _isclose_item(av.item(), bv.item(), rtol, atol,
                                       equal_nan)
            return np.broadcast_to(out, shape)

    else:
        def isclose_impl(a, b, rtol=1e-05, atol=1e-08, equal_nan=False):
            return _isclose_item(a, b, rtol, atol, equal_nan)

    return isclose_impl


@overload(np.nanmin)
def np_nanmin(a):
    dt = determine_dtype(a)
    if np.issubdtype(dt, np.complexfloating):
        return complex_nanmin
    else:
        return real_nanmin


@overload(np.nanmax)
def np_nanmax(a):
    dt = determine_dtype(a)
    if np.issubdtype(dt, np.complexfloating):
        return complex_nanmax
    else:
        return real_nanmax


@overload(np.nanmean)
def np_nanmean(a):
    if not isinstance(a, types.Array):
        return
    isnan = get_isnan(a.dtype)

    def nanmean_impl(a):
        c = 0.0
        count = 0
        for view in np.nditer(a):
            v = view.item()
            if not isnan(v):
                c += v.item()
                count += 1
        # np.divide() doesn't raise ZeroDivisionError
        return np.divide(c, count)

    return nanmean_impl


@overload(np.nanvar)
def np_nanvar(a):
    if not isinstance(a, types.Array):
        return
    isnan = get_isnan(a.dtype)

    def nanvar_impl(a):
        # Compute the mean
        m = np.nanmean(a)

        # Compute the sum of square diffs
        ssd = 0.0
        count = 0
        for view in np.nditer(a):
            v = view.item()
            if not isnan(v):
                val = (v.item() - m)
                ssd += np.real(val * np.conj(val))
                count += 1
        # np.divide() doesn't raise ZeroDivisionError
        return np.divide(ssd, count)

    return nanvar_impl


@overload(np.nanstd)
def np_nanstd(a):
    if not isinstance(a, types.Array):
        return

    def nanstd_impl(a):
        return np.nanvar(a) ** 0.5

    return nanstd_impl


@overload(np.nansum)
def np_nansum(a):
    if not isinstance(a, types.Array):
        return
    if isinstance(a.dtype, types.Integer):
        retty = types.intp
    else:
        retty = a.dtype
    zero = retty(0)
    isnan = get_isnan(a.dtype)

    def nansum_impl(a):
        c = zero
        for view in np.nditer(a):
            v = view.item()
            if not isnan(v):
                c += v
        return c

    return nansum_impl


@overload(np.nanprod)
def np_nanprod(a):
    if not isinstance(a, types.Array):
        return
    if isinstance(a.dtype, types.Integer):
        retty = types.intp
    else:
        retty = a.dtype
    one = retty(1)
    isnan = get_isnan(a.dtype)

    def nanprod_impl(a):
        c = one
        for view in np.nditer(a):
            v = view.item()
            if not isnan(v):
                c *= v
        return c

    return nanprod_impl


@overload(np.nancumprod)
def np_nancumprod(a):
    if not isinstance(a, types.Array):
        return

    if isinstance(a.dtype, (types.Boolean, types.Integer)):
        # dtype cannot possibly contain NaN
        return lambda a: np.cumprod(a)
    else:
        retty = a.dtype
        is_nan = get_isnan(retty)
        one = retty(1)

        def nancumprod_impl(a):
            out = np.empty(a.size, retty)
            c = one
            for idx, v in enumerate(a.flat):
                if ~is_nan(v):
                    c *= v
                out[idx] = c
            return out

        return nancumprod_impl


@overload(np.nancumsum)
def np_nancumsum(a):
    if not isinstance(a, types.Array):
        return

    if isinstance(a.dtype, (types.Boolean, types.Integer)):
        # dtype cannot possibly contain NaN
        return lambda a: np.cumsum(a)
    else:
        retty = a.dtype
        is_nan = get_isnan(retty)
        zero = retty(0)

        def nancumsum_impl(a):
            out = np.empty(a.size, retty)
            c = zero
            for idx, v in enumerate(a.flat):
                if ~is_nan(v):
                    c += v
                out[idx] = c
            return out

        return nancumsum_impl


@register_jitable
def prepare_ptp_input(a):
    arr = _asarray(a)
    if len(arr) == 0:
        raise ValueError('zero-size array reduction not possible')
    else:
        return arr


def _compute_current_val_impl_gen(op, current_val, val):
    if isinstance(current_val, types.Complex):
        # The sort order for complex numbers is lexicographic. If both the
        # real and imaginary parts are non-nan then the order is determined
        # by the real parts except when they are equal, in which case the
        # order is determined by the imaginary parts.
        # https://github.com/numpy/numpy/blob/577a86e/numpy/core/fromnumeric.py#L874-L877    # noqa: E501
        def impl(current_val, val):
            if op(val.real, current_val.real):
                return val
            elif (val.real == current_val.real
                    and op(val.imag, current_val.imag)):
                return val
            return current_val
    else:
        def impl(current_val, val):
            return val if op(val, current_val) else current_val
    return impl


def _compute_a_max(current_val, val):
    pass


def _compute_a_min(current_val, val):
    pass


@overload(_compute_a_max)
def _compute_a_max_impl(current_val, val):
    return _compute_current_val_impl_gen(operator.gt, current_val, val)


@overload(_compute_a_min)
def _compute_a_min_impl(current_val, val):
    return _compute_current_val_impl_gen(operator.lt, current_val, val)


def _early_return(val):
    pass


@overload(_early_return)
def _early_return_impl(val):
    UNUSED = 0
    if isinstance(val, types.Complex):
        def impl(val):
            if np.isnan(val.real):
                if np.isnan(val.imag):
                    return True, np.nan + np.nan * 1j
                else:
                    return True, np.nan + 0j
            else:
                return False, UNUSED
    elif isinstance(val, types.Float):
        def impl(val):
            if np.isnan(val):
                return True, np.nan
            else:
                return False, UNUSED
    else:
        def impl(val):
            return False, UNUSED
    return impl


@overload(np.ptp)
def np_ptp(a):

    if hasattr(a, 'dtype'):
        if isinstance(a.dtype, types.Boolean):
            raise TypingError("Boolean dtype is unsupported (as per NumPy)")
            # Numpy raises a TypeError

    def np_ptp_impl(a):
        arr = prepare_ptp_input(a)

        a_flat = arr.flat
        a_min = a_flat[0]
        a_max = a_flat[0]

        for i in range(arr.size):
            val = a_flat[i]
            take_branch, retval = _early_return(val)
            if take_branch:
                return retval
            a_max = _compute_a_max(a_max, val)
            a_min = _compute_a_min(a_min, val)

        return a_max - a_min

    return np_ptp_impl


if numpy_version < (2, 0):
    overload_method(types.Array, 'ptp')(np_ptp)

#----------------------------------------------------------------------------
# Median and partitioning


@register_jitable
def nan_aware_less_than(a, b):
    if np.isnan(a):
        return False
    else:
        if np.isnan(b):
            return True
        else:
            return a < b


def _partition_factory(pivotimpl, argpartition=False):
    def _partition(A, low, high, I=None):
        mid = (low + high) >> 1
        # NOTE: the pattern of swaps below for the pivot choice and the
        # partitioning gives good results (i.e. regular O(n log n))
        # on sorted, reverse-sorted, and uniform arrays.  Subtle changes
        # risk breaking this property.

        # Use median of three {low, middle, high} as the pivot
        if pivotimpl(A[mid], A[low]):
            A[low], A[mid] = A[mid], A[low]
            if argpartition:
                I[low], I[mid] = I[mid], I[low]
        if pivotimpl(A[high], A[mid]):
            A[high], A[mid] = A[mid], A[high]
            if argpartition:
                I[high], I[mid] = I[mid], I[high]
        if pivotimpl(A[mid], A[low]):
            A[low], A[mid] = A[mid], A[low]
            if argpartition:
                I[low], I[mid] = I[mid], I[low]
        pivot = A[mid]

        A[high], A[mid] = A[mid], A[high]
        if argpartition:
            I[high], I[mid] = I[mid], I[high]
        i = low
        j = high - 1
        while True:
            while i < high and pivotimpl(A[i], pivot):
                i += 1
            while j >= low and pivotimpl(pivot, A[j]):
                j -= 1
            if i >= j:
                break
            A[i], A[j] = A[j], A[i]
            if argpartition:
                I[i], I[j] = I[j], I[i]
            i += 1
            j -= 1
        # Put the pivot back in its final place (all items before `i`
        # are smaller than the pivot, all items at/after `i` are larger)
        A[i], A[high] = A[high], A[i]
        if argpartition:
            I[i], I[high] = I[high], I[i]
        return i
    return _partition


_partition = register_jitable(_partition_factory(less_than))
_partition_w_nan = register_jitable(_partition_factory(nan_aware_less_than))
_argpartition_w_nan = register_jitable(_partition_factory(
    nan_aware_less_than,
    argpartition=True)
)


def _select_factory(partitionimpl):
    def _select(arry, k, low, high, idx=None):
        """
        Select the k'th smallest element in array[low:high + 1].
        """
        i = partitionimpl(arry, low, high, idx)
        while i != k:
            if i < k:
                low = i + 1
                i = partitionimpl(arry, low, high, idx)
            else:
                high = i - 1
                i = partitionimpl(arry, low, high, idx)
        return arry[k]
    return _select


_select = register_jitable(_select_factory(_partition))
_select_w_nan = register_jitable(_select_factory(_partition_w_nan))
_arg_select_w_nan = register_jitable(_select_factory(_argpartition_w_nan))


@register_jitable
def _select_two(arry, k, low, high):
    """
    Select the k'th and k+1'th smallest elements in array[low:high + 1].

    This is significantly faster than doing two independent selections
    for k and k+1.
    """
    while True:
        assert high > low  # by construction
        i = _partition(arry, low, high)
        if i < k:
            low = i + 1
        elif i > k + 1:
            high = i - 1
        elif i == k:
            _select(arry, k + 1, i + 1, high)
            break
        else:  # i == k + 1
            _select(arry, k, low, i - 1)
            break

    return arry[k], arry[k + 1]


@register_jitable
def _median_inner(temp_arry, n):
    """
    The main logic of the median() call.  *temp_arry* must be disposable,
    as this function will mutate it.
    """
    low = 0
    high = n - 1
    half = n >> 1
    if n & 1 == 0:
        a, b = _select_two(temp_arry, half - 1, low, high)
        return (a + b) / 2
    else:
        return _select(temp_arry, half, low, high)


@overload(np.median)
def np_median(a):
    if not isinstance(a, types.Array):
        return

    is_datetime = as_dtype(a.dtype).char in 'mM'

    def median_impl(a):
        # np.median() works on the flattened array, and we need a temporary
        # workspace anyway
        temp_arry = a.flatten()
        n = temp_arry.shape[0]
        if not is_datetime and n == 0:
            return np.nan
        return _median_inner(temp_arry, n)
    return median_impl


@register_jitable
def _collect_percentiles_inner(a, q):
    #TODO: This needs rewriting to be closer to NumPy, particularly the nan/inf
    # handling which is generally subject to algorithmic changes.
    n = len(a)

    if n == 1:
        # single element array; output same for all percentiles
        out = np.full(len(q), a[0], dtype=np.float64)
    else:
        out = np.empty(len(q), dtype=np.float64)
        for i in range(len(q)):
            percentile = q[i]

            # bypass pivoting where requested percentile is 100
            if percentile == 100:
                val = np.max(a)
                # heuristics to handle infinite values a la NumPy
                if ~np.all(np.isfinite(a)):
                    if ~np.isfinite(val):
                        val = np.nan

            # bypass pivoting where requested percentile is 0
            elif percentile == 0:
                val = np.min(a)
                # convoluted heuristics to handle infinite values a la NumPy
                if ~np.all(np.isfinite(a)):
                    num_pos_inf = np.sum(a == np.inf)
                    num_neg_inf = np.sum(a == -np.inf)
                    num_finite = n - (num_neg_inf + num_pos_inf)
                    if num_finite == 0:
                        val = np.nan
                    if num_pos_inf == 1 and n == 2:
                        val = np.nan
                    if num_neg_inf > 1:
                        val = np.nan
                    if num_finite == 1:
                        if num_pos_inf > 1:
                            if num_neg_inf != 1:
                                val = np.nan

            else:
                # linear interp between closest ranks
                rank = 1 + (n - 1) * np.true_divide(percentile, 100.0)
                f = math.floor(rank)
                m = rank - f
                lower, upper = _select_two(a, k=int(f - 1), low=0, high=(n - 1))
                val = lower * (1 - m) + upper * m
            out[i] = val

    return out


@register_jitable
def _can_collect_percentiles(a, nan_mask, skip_nan):
    if skip_nan:
        a = a[~nan_mask]
        if len(a) == 0:
            return False  # told to skip nan, but no elements remain
    else:
        if np.any(nan_mask):
            return False  # told *not* to skip nan, but nan encountered

    if len(a) == 1:  # single element array
        val = a[0]
        return np.isfinite(val)  # can collect percentiles if element is finite
    else:
        return True


@register_jitable
def check_valid(q, q_upper_bound):
    valid = True

    # avoid expensive reductions where possible
    if q.ndim == 1 and q.size < 10:
        for i in range(q.size):
            if q[i] < 0.0 or q[i] > q_upper_bound or np.isnan(q[i]):
                valid = False
                break
    else:
        if np.any(np.isnan(q)) or np.any(q < 0.0) or np.any(q > q_upper_bound):
            valid = False

    return valid


@register_jitable
def percentile_is_valid(q):
    if not check_valid(q, q_upper_bound=100.0):
        raise ValueError('Percentiles must be in the range [0, 100]')


@register_jitable
def quantile_is_valid(q):
    if not check_valid(q, q_upper_bound=1.0):
        raise ValueError('Quantiles must be in the range [0, 1]')


@register_jitable
def _collect_percentiles(a, q, check_q, factor, skip_nan):
    q = np.asarray(q, dtype=np.float64).flatten()
    check_q(q)
    q = q * factor

    temp_arry = np.asarray(a, dtype=np.float64).flatten()
    nan_mask = np.isnan(temp_arry)

    if _can_collect_percentiles(temp_arry, nan_mask, skip_nan):
        temp_arry = temp_arry[~nan_mask]
        out = _collect_percentiles_inner(temp_arry, q)
    else:
        out = np.full(len(q), np.nan)

    return out


def _percentile_quantile_inner(a, q, skip_nan, factor, check_q):
    """
    The underlying algorithm to find percentiles and quantiles
    is the same, hence we converge onto the same code paths
    in this inner function implementation
    """
    dt = determine_dtype(a)
    if np.issubdtype(dt, np.complexfloating):
        raise TypingError('Not supported for complex dtype')
        # this could be supported, but would require a
        # lexicographic comparison

    def np_percentile_q_scalar_impl(a, q):
        return _collect_percentiles(a, q, check_q, factor, skip_nan)[0]

    def np_percentile_impl(a, q):
        return _collect_percentiles(a, q, check_q, factor, skip_nan)

    if isinstance(q, (types.Number, types.Boolean)):
        return np_percentile_q_scalar_impl
    elif isinstance(q, types.Array) and q.ndim == 0:
        return np_percentile_q_scalar_impl
    else:
        return np_percentile_impl


@overload(np.percentile)
def np_percentile(a, q):
    return _percentile_quantile_inner(
        a, q, skip_nan=False, factor=1.0, check_q=percentile_is_valid
    )


@overload(np.nanpercentile)
def np_nanpercentile(a, q):
    return _percentile_quantile_inner(
        a, q, skip_nan=True, factor=1.0, check_q=percentile_is_valid
    )


@overload(np.quantile)
def np_quantile(a, q):
    return _percentile_quantile_inner(
        a, q, skip_nan=False, factor=100.0, check_q=quantile_is_valid
    )


@overload(np.nanquantile)
def np_nanquantile(a, q):
    return _percentile_quantile_inner(
        a, q, skip_nan=True, factor=100.0, check_q=quantile_is_valid
    )


@overload(np.nanmedian)
def np_nanmedian(a):
    if not isinstance(a, types.Array):
        return
    isnan = get_isnan(a.dtype)

    def nanmedian_impl(a):
        # Create a temporary workspace with only non-NaN values
        temp_arry = np.empty(a.size, a.dtype)
        n = 0
        for view in np.nditer(a):
            v = view.item()
            if not isnan(v):
                temp_arry[n] = v
                n += 1

        # all NaNs
        if n == 0:
            return np.nan

        return _median_inner(temp_arry, n)

    return nanmedian_impl


@register_jitable
def np_partition_impl_inner(a, kth_array):

    # allocate and fill empty array rather than copy a and mutate in place
    # as the latter approach fails to preserve strides
    out = np.empty_like(a)

    idx = np.ndindex(a.shape[:-1])  # Numpy default partition axis is -1
    for s in idx:
        arry = a[s].copy()
        low = 0
        high = len(arry) - 1

        for kth in kth_array:
            _select_w_nan(arry, kth, low, high)
            low = kth  # narrow span of subsequent partition

        out[s] = arry
    return out


@register_jitable
def np_argpartition_impl_inner(a, kth_array):

    # allocate and fill empty array rather than copy a and mutate in place
    # as the latter approach fails to preserve strides
    out = np.empty_like(a, dtype=np.intp)

    idx = np.ndindex(a.shape[:-1])  # Numpy default partition axis is -1
    for s in idx:
        arry = a[s].copy()
        idx_arry = np.arange(len(arry))
        low = 0
        high = len(arry) - 1

        for kth in kth_array:
            _arg_select_w_nan(arry, kth, low, high, idx_arry)
            low = kth  # narrow span of subsequent partition

        out[s] = idx_arry
    return out


@register_jitable
def valid_kths(a, kth):
    """
    Returns a sorted, unique array of kth values which serve
    as indexers for partitioning the input array, a.

    If the absolute value of any of the provided values
    is greater than a.shape[-1] an exception is raised since
    we are partitioning along the last axis (per Numpy default
    behaviour).

    Values less than 0 are transformed to equivalent positive
    index values.
    """
    # cast boolean to int, where relevant
    kth_array = _asarray(kth).astype(np.int64)

    if kth_array.ndim != 1:
        raise ValueError('kth must be scalar or 1-D')
        # numpy raises ValueError: object too deep for desired array

    if np.any(np.abs(kth_array) >= a.shape[-1]):
        raise ValueError("kth out of bounds")

    out = np.empty_like(kth_array)

    for index, val in np.ndenumerate(kth_array):
        if val < 0:
            out[index] = val + a.shape[-1]  # equivalent positive index
        else:
            out[index] = val

    return np.unique(out)


@overload(np.partition)
def np_partition(a, kth):

    if not isinstance(a, (types.Array, types.Sequence, types.Tuple)):
        raise NumbaTypeError('The first argument must be an array-like')

    if isinstance(a, types.Array) and a.ndim == 0:
        msg = 'The first argument must be at least 1-D (found 0-D)'
        raise NumbaTypeError(msg)

    kthdt = getattr(kth, 'dtype', kth)
    if numpy_version >= (2, 3):
        kth_types = types.Integer
    else:
        kth_types = (types.Boolean, types.Integer)
    if not isinstance(kthdt, kth_types):
        # bool gets cast to int subsequently
        raise NumbaTypeError('Partition index must be integer')

    def np_partition_impl(a, kth):
        a_tmp = _asarray(a)
        if a_tmp.size == 0:
            return a_tmp.copy()
        else:
            kth_array = valid_kths(a_tmp, kth)
            return np_partition_impl_inner(a_tmp, kth_array)

    return np_partition_impl


@overload(np.argpartition)
def np_argpartition(a, kth):

    if not isinstance(a, (types.Array, types.Sequence, types.Tuple)):
        raise NumbaTypeError('The first argument must be an array-like')

    if isinstance(a, types.Array) and a.ndim == 0:
        msg = 'The first argument must be at least 1-D (found 0-D)'
        raise NumbaTypeError(msg)

    kthdt = getattr(kth, 'dtype', kth)
    if numpy_version >= (2, 3):
        kth_types = types.Integer
    else:
        kth_types = (types.Boolean, types.Integer)
    if not isinstance(kthdt, kth_types):
        # bool gets cast to int subsequently
        raise NumbaTypeError('Partition index must be integer')

    def np_argpartition_impl(a, kth):
        a_tmp = _asarray(a)
        if a_tmp.size == 0:
            return a_tmp.copy().astype('intp')
        else:
            kth_array = valid_kths(a_tmp, kth)
            return np_argpartition_impl_inner(a_tmp, kth_array)

    return np_argpartition_impl


#----------------------------------------------------------------------------
# Building matrices

@register_jitable
def _tri_impl(N, M, k):
    shape = max(0, N), max(0, M)  # numpy floors each dimension at 0
    out = np.empty(shape, dtype=np.float64)  # numpy default dtype

    for i in range(shape[0]):
        m_max = min(max(0, i + k + 1), shape[1])
        out[i, :m_max] = 1
        out[i, m_max:] = 0

    return out


@overload(np.tri)
def np_tri(N, M=None, k=0):

    # we require k to be integer, unlike numpy
    check_is_integer(k, 'k')

    def tri_impl(N, M=None, k=0):
        if M is None:
            M = N
        return _tri_impl(N, M, k)

    return tri_impl


@register_jitable
def _make_square(m):
    """
    Takes a 1d array and tiles it to form a square matrix
    - i.e. a facsimile of np.tile(m, (len(m), 1))
    """
    assert m.ndim == 1

    len_m = len(m)
    out = np.empty((len_m, len_m), dtype=m.dtype)

    for i in range(len_m):
        out[i] = m

    return out


@register_jitable
def np_tril_impl_2d(m, k=0):
    mask = np.tri(m.shape[-2], M=m.shape[-1], k=k).astype(np.uint)
    return np.where(mask, m, np.zeros_like(m, dtype=m.dtype))


@overload(np.tril)
def my_tril(m, k=0):

    # we require k to be integer, unlike numpy
    check_is_integer(k, 'k')

    def np_tril_impl_1d(m, k=0):
        m_2d = _make_square(m)
        return np_tril_impl_2d(m_2d, k)

    def np_tril_impl_multi(m, k=0):
        mask = np.tri(m.shape[-2], M=m.shape[-1], k=k).astype(np.uint)
        idx = np.ndindex(m.shape[:-2])
        z = np.empty_like(m)
        zero_opt = np.zeros_like(mask, dtype=m.dtype)
        for sel in idx:
            z[sel] = np.where(mask, m[sel], zero_opt)
        return z

    if m.ndim == 1:
        return np_tril_impl_1d
    elif m.ndim == 2:
        return np_tril_impl_2d
    else:
        return np_tril_impl_multi


@overload(np.tril_indices)
def np_tril_indices(n, k=0, m=None):

    # we require integer arguments, unlike numpy
    check_is_integer(n, 'n')
    check_is_integer(k, 'k')
    if not is_nonelike(m):
        check_is_integer(m, 'm')

    def np_tril_indices_impl(n, k=0, m=None):
        return np.nonzero(np.tri(n, m, k=k))
    return np_tril_indices_impl


@overload(np.tril_indices_from)
def np_tril_indices_from(arr, k=0):

    # we require k to be integer, unlike numpy
    check_is_integer(k, 'k')

    if arr.ndim != 2:
        raise TypingError("input array must be 2-d")

    def np_tril_indices_from_impl(arr, k=0):
        return np.tril_indices(arr.shape[0], k=k, m=arr.shape[1])
    return np_tril_indices_from_impl


@register_jitable
def np_triu_impl_2d(m, k=0):
    mask = np.tri(m.shape[-2], M=m.shape[-1], k=k - 1).astype(np.uint)
    return np.where(mask, np.zeros_like(m, dtype=m.dtype), m)


@overload(np.triu)
def my_triu(m, k=0):
    # we require k to be integer, unlike numpy
    check_is_integer(k, 'k')

    def np_triu_impl_1d(m, k=0):
        m_2d = _make_square(m)
        return np_triu_impl_2d(m_2d, k)

    def np_triu_impl_multi(m, k=0):
        mask = np.tri(m.shape[-2], M=m.shape[-1], k=k - 1).astype(np.uint)
        idx = np.ndindex(m.shape[:-2])
        z = np.empty_like(m)
        zero_opt = np.zeros_like(mask, dtype=m.dtype)
        for sel in idx:
            z[sel] = np.where(mask, zero_opt, m[sel])
        return z

    if m.ndim == 1:
        return np_triu_impl_1d
    elif m.ndim == 2:
        return np_triu_impl_2d
    else:
        return np_triu_impl_multi


@overload(np.triu_indices)
def np_triu_indices(n, k=0, m=None):

    # we require integer arguments, unlike numpy
    check_is_integer(n, 'n')
    check_is_integer(k, 'k')
    if not is_nonelike(m):
        check_is_integer(m, 'm')

    def np_triu_indices_impl(n, k=0, m=None):
        return np.nonzero(1 - np.tri(n, m, k=k - 1))
    return np_triu_indices_impl


@overload(np.triu_indices_from)
def np_triu_indices_from(arr, k=0):

    # we require k to be integer, unlike numpy
    check_is_integer(k, 'k')

    if arr.ndim != 2:
        raise TypingError("input array must be 2-d")

    def np_triu_indices_from_impl(arr, k=0):
        return np.triu_indices(arr.shape[0], k=k, m=arr.shape[1])
    return np_triu_indices_from_impl


def _prepare_array(arr):
    pass


@overload(_prepare_array)
def _prepare_array_impl(arr):
    if arr in (None, types.none):
        return lambda arr: np.array(())
    else:
        return lambda arr: _asarray(arr).ravel()


def _dtype_of_compound(inobj):
    obj = inobj
    while True:
        if isinstance(obj, (types.Number, types.Boolean)):
            return as_dtype(obj)
        l = getattr(obj, '__len__', None)
        if l is not None and l() == 0: # empty tuple or similar
            return np.float64
        dt = getattr(obj, 'dtype', None)
        if dt is None:
            raise NumbaTypeError("type has no dtype attr")
        if isinstance(obj, types.Sequence):
            obj = obj.dtype
        else:
            return as_dtype(dt)


@overload(np.ediff1d)
def np_ediff1d(ary, to_end=None, to_begin=None):

    if isinstance(ary, types.Array):
        if isinstance(ary.dtype, types.Boolean):
            raise NumbaTypeError("Boolean dtype is unsupported (as per NumPy)")
            # Numpy tries to do this: return ary[1:] - ary[:-1] which
            # results in a TypeError exception being raised

    # Check that to_end and to_begin are compatible with ary
    ary_dt = _dtype_of_compound(ary)
    to_begin_dt = None
    if not (is_nonelike(to_begin)):
        to_begin_dt = _dtype_of_compound(to_begin)
    to_end_dt = None
    if not (is_nonelike(to_end)):
        to_end_dt = _dtype_of_compound(to_end)

    if to_begin_dt is not None and not np.can_cast(to_begin_dt, ary_dt):
        msg = "dtype of to_begin must be compatible with input ary"
        raise NumbaTypeError(msg)

    if to_end_dt is not None and not np.can_cast(to_end_dt, ary_dt):
        msg = "dtype of to_end must be compatible with input ary"
        raise NumbaTypeError(msg)

    def np_ediff1d_impl(ary, to_end=None, to_begin=None):
        # transform each input into an equivalent 1d array
        start = _prepare_array(to_begin)
        mid = _prepare_array(ary)
        end = _prepare_array(to_end)

        out_dtype = mid.dtype
        # output array dtype determined by ary dtype, per NumPy
        # (for the most part); an exception to the rule is a zero length
        # array-like, where NumPy falls back to np.float64; this behaviour
        # is *not* replicated

        if len(mid) > 0:
            out = np.empty((len(start) + len(mid) + len(end) - 1),
                           dtype=out_dtype)
            start_idx = len(start)
            mid_idx = len(start) + len(mid) - 1
            out[:start_idx] = start
            out[start_idx:mid_idx] = np.diff(mid)
            out[mid_idx:] = end
        else:
            out = np.empty((len(start) + len(end)), dtype=out_dtype)
            start_idx = len(start)
            out[:start_idx] = start
            out[start_idx:] = end
        return out

    return np_ediff1d_impl


def _select_element(arr):
    pass


@overload(_select_element)
def _select_element_impl(arr):
    zerod = getattr(arr, 'ndim', None) == 0
    if zerod:
        def impl(arr):
            x = np.array((1,), dtype=arr.dtype)
            x[:] = arr
            return x[0]
        return impl
    else:
        def impl(arr):
            return arr
        return impl


def _get_d(dx, x):
    pass


@overload(_get_d)
def get_d_impl(x, dx):
    if is_nonelike(x):
        def impl(x, dx):
            return np.asarray(dx)
    else:
        def impl(x, dx):
            return np.diff(np.asarray(x))
    return impl


def _trapz_impl(y, x=None, dx=1.0):
    """Shared implementation for both np.trapz and np.trapezoid."""
    if isinstance(y, (types.Number, types.Boolean)):
        raise TypingError('y cannot be a scalar')
    elif isinstance(y, types.Array) and y.ndim == 0:
        raise TypingError('y cannot be 0D')
        # NumPy raises IndexError: list assignment index out of range

    # inspired by:
    # https://github.com/numpy/numpy/blob/7ee52003/numpy/lib/function_base.py#L4040-L4065    # noqa: E501
    def impl(y, x=None, dx=1.0):
        yarr = np.asarray(y)
        d = _get_d(x, dx)
        y_ave = (yarr[..., slice(1, None)] + yarr[..., slice(None, -1)]) / 2.0
        ret = np.sum(d * y_ave, -1)
        processed = _select_element(ret)
        return processed

    return impl


# np.trapz exists in NumPy < 2.4 (deprecated in 2.0, removed in 2.4)
if numpy_version < (2, 4):
    @overload(np.trapz)
    def np_trapz(y, x=None, dx=1.0):
        return _trapz_impl(y, x, dx)


# np.trapezoid introduced in NumPy 2.0 as replacement for np.trapz
if numpy_version >= (2, 0):
    @overload(np.trapezoid)
    def np_trapezoid(y, x=None, dx=1.0):
        return _trapz_impl(y, x, dx)


@register_jitable
def _np_vander(x, N, increasing, out):
    """
    Generate an N-column Vandermonde matrix from a supplied 1-dimensional
    array, x. Store results in an output matrix, out, which is assumed to
    be of the required dtype.

    Values are accumulated using np.multiply to match the floating point
    precision behaviour of numpy.vander.
    """
    m, n = out.shape
    assert m == len(x)
    assert n == N

    if increasing:
        for i in range(N):
            if i == 0:
                out[:, i] = 1
            else:
                out[:, i] = np.multiply(x, out[:, (i - 1)])
    else:
        for i in range(N - 1, -1, -1):
            if i == N - 1:
                out[:, i] = 1
            else:
                out[:, i] = np.multiply(x, out[:, (i + 1)])


@register_jitable
def _check_vander_params(x, N):
    if x.ndim > 1:
        raise ValueError('x must be a one-dimensional array or sequence.')
    if N < 0:
        raise ValueError('Negative dimensions are not allowed')


@overload(np.vander)
def np_vander(x, N=None, increasing=False):
    if N not in (None, types.none):
        if not isinstance(N, types.Integer):
            raise TypingError('Second argument N must be None or an integer')

    def np_vander_impl(x, N=None, increasing=False):
        if N is None:
            N = len(x)

        _check_vander_params(x, N)

        # allocate output matrix using dtype determined in closure
        out = np.empty((len(x), int(N)), dtype=dtype)

        _np_vander(x, N, increasing, out)
        return out

    def np_vander_seq_impl(x, N=None, increasing=False):
        if N is None:
            N = len(x)

        x_arr = np.array(x)
        _check_vander_params(x_arr, N)

        # allocate output matrix using dtype inferred when x_arr was created
        out = np.empty((len(x), int(N)), dtype=x_arr.dtype)

        _np_vander(x_arr, N, increasing, out)
        return out

    if isinstance(x, types.Array):
        x_dt = as_dtype(x.dtype)
        # replicate numpy behaviour w.r.t.type promotion
        dtype = np.promote_types(x_dt, int)
        return np_vander_impl
    elif isinstance(x, (types.Tuple, types.Sequence)):
        return np_vander_seq_impl


@overload(np.roll)
def np_roll(a, shift):
    if not isinstance(shift, (types.Integer, types.Boolean)):
        raise TypingError('shift must be an integer')

    def np_roll_impl(a, shift):
        arr = np.asarray(a)
        out = np.empty(arr.shape, dtype=arr.dtype)
        # empty_like might result in different contiguity vs NumPy

        arr_flat = arr.flat
        for i in range(arr.size):
            idx = (i + shift) % arr.size
            out.flat[idx] = arr_flat[i]

        return out

    if isinstance(a, (types.Number, types.Boolean)):
        return lambda a, shift: np.asarray(a)
    else:
        return np_roll_impl


#----------------------------------------------------------------------------
# Mathematical functions

LIKELY_IN_CACHE_SIZE = 8


@register_jitable
def binary_search_with_guess(key, arr, length, guess):
    # NOTE: Do not refactor... see note in np_interp function impl below
    # this is a facsimile of binary_search_with_guess prior to 1.15:
    # https://github.com/numpy/numpy/blob/maintenance/1.15.x/numpy/core/src/multiarray/compiled_base.c    # noqa: E501
    # Permanent reference:
    # https://github.com/numpy/numpy/blob/3430d78c01a3b9a19adad75f1acb5ae18286da73/numpy/core/src/multiarray/compiled_base.c#L447    # noqa: E501
    imin = 0
    imax = length

    # Handle keys outside of the arr range first
    if key > arr[length - 1]:
        return length
    elif key < arr[0]:
        return -1

    # If len <= 4 use linear search.
    # From above we know key >= arr[0] when we start.
    if length <= 4:
        i = 1
        while i < length and key >= arr[i]:
            i += 1
        return i - 1

    if guess > length - 3:
        guess = length - 3

    if guess < 1:
        guess = 1

    # check most likely values: guess - 1, guess, guess + 1
    if key < arr[guess]:
        if key < arr[guess - 1]:
            imax = guess - 1

            # last attempt to restrict search to items in cache
            if guess > LIKELY_IN_CACHE_SIZE and \
                    key >= arr[guess - LIKELY_IN_CACHE_SIZE]:
                imin = guess - LIKELY_IN_CACHE_SIZE
        else:
            # key >= arr[guess - 1]
            return guess - 1
    else:
        # key >= arr[guess]
        if key < arr[guess + 1]:
            return guess
        else:
            # key >= arr[guess + 1]
            if key < arr[guess + 2]:
                return guess + 1
            else:
                # key >= arr[guess + 2]
                imin = guess + 2
                # last attempt to restrict search to items in cache
                if (guess < (length - LIKELY_IN_CACHE_SIZE - 1)) and \
                        (key < arr[guess + LIKELY_IN_CACHE_SIZE]):
                    imax = guess + LIKELY_IN_CACHE_SIZE

    # finally, find index by bisection
    while imin < imax:
        imid = imin + ((imax - imin) >> 1)
        if key >= arr[imid]:
            imin = imid + 1
        else:
            imax = imid

    return imin - 1


@register_jitable
def np_interp_impl_complex_inner(x, xp, fp, dtype):
    # NOTE: Do not refactor... see note in np_interp function impl below
    # this is a facsimile of arr_interp_complex post 1.16 with added
    # branching to support np1.17 style NaN handling.
    # https://github.com/numpy/numpy/blob/maintenance/1.16.x/numpy/core/src/multiarray/compiled_base.c    # noqa: E501
    # Permanent reference:
    # https://github.com/numpy/numpy/blob/971e2e89d08deeae0139d3011d15646fdac13c92/numpy/core/src/multiarray/compiled_base.c#L628    # noqa: E501
    dz = np.asarray(x)
    dx = np.asarray(xp)
    dy = np.asarray(fp)

    if len(dx) == 0:
        raise ValueError('array of sample points is empty')

    if len(dx) != len(dy):
        raise ValueError('fp and xp are not of the same size.')

    if dx.size == 1:
        return np.full(dz.shape, fill_value=dy[0], dtype=dtype)

    dres = np.empty(dz.shape, dtype=dtype)

    lenx = dz.size
    lenxp = len(dx)
    lval = dy[0]
    rval = dy[lenxp - 1]

    if lenxp == 1:
        xp_val = dx[0]
        fp_val = dy[0]

        for i in range(lenx):
            x_val = dz.flat[i]
            if x_val < xp_val:
                dres.flat[i] = lval
            elif x_val > xp_val:
                dres.flat[i] = rval
            else:
                dres.flat[i] = fp_val

    else:
        j = 0

        # only pre-calculate slopes if there are relatively few of them.
        if lenxp <= lenx:
            slopes = np.empty((lenxp - 1), dtype=dtype)
        else:
            slopes = np.empty(0, dtype=dtype)

        if slopes.size:
            for i in range(lenxp - 1):
                inv_dx = 1 / (dx[i + 1] - dx[i])
                real = (dy[i + 1].real - dy[i].real) * inv_dx
                imag = (dy[i + 1].imag - dy[i].imag) * inv_dx
                slopes[i] = real + 1j * imag

        for i in range(lenx):
            x_val = dz.flat[i]

            if np.isnan(x_val):
                real = x_val
                imag = 0.0
                dres.flat[i] = real + 1j * imag
                continue

            j = binary_search_with_guess(x_val, dx, lenxp, j)

            if j == -1:
                dres.flat[i] = lval
            elif j == lenxp:
                dres.flat[i] = rval
            elif j == lenxp - 1:
                dres.flat[i] = dy[j]
            elif dx[j] == x_val:
                # Avoid potential non-finite interpolation
                dres.flat[i] = dy[j]
            else:
                if slopes.size:
                    slope = slopes[j]
                else:
                    inv_dx = 1 / (dx[j + 1] - dx[j])
                    real = (dy[j + 1].real - dy[j].real) * inv_dx
                    imag = (dy[j + 1].imag - dy[j].imag) * inv_dx
                    slope = real + 1j * imag

                # NumPy 1.17 handles NaN correctly - this is a copy of
                # innermost part of arr_interp_complex post 1.17:
                # https://github.com/numpy/numpy/blob/maintenance/1.17.x/numpy/core/src/multiarray/compiled_base.c    # noqa: E501
                # Permanent reference:
                # https://github.com/numpy/numpy/blob/91fbe4dde246559fa5b085ebf4bc268e2b89eea8/numpy/core/src/multiarray/compiled_base.c#L798-L812    # noqa: E501

                # If we get NaN in one direction, try the other
                real = slope.real * (x_val - dx[j]) + dy[j].real
                if np.isnan(real):
                    real = slope.real * (x_val - dx[j + 1]) + dy[j + 1].real
                    if np.isnan(real) and dy[j].real == dy[j + 1].real:
                        real = dy[j].real

                imag = slope.imag * (x_val - dx[j]) + dy[j].imag
                if np.isnan(imag):
                    imag = slope.imag * (x_val - dx[j + 1]) + dy[j + 1].imag
                    if np.isnan(imag) and dy[j].imag == dy[j + 1].imag:
                        imag = dy[j].imag

                dres.flat[i] = real + 1j * imag

    return dres


@register_jitable
def np_interp_impl_inner(x, xp, fp, dtype):
    # NOTE: Do not refactor... see note in np_interp function impl below
    # this is a facsimile of arr_interp post 1.16:
    # https://github.com/numpy/numpy/blob/maintenance/1.16.x/numpy/core/src/multiarray/compiled_base.c    # noqa: E501
    # Permanent reference:
    # https://github.com/numpy/numpy/blob/971e2e89d08deeae0139d3011d15646fdac13c92/numpy/core/src/multiarray/compiled_base.c#L473     # noqa: E501
    dz = np.asarray(x, dtype=np.float64)
    dx = np.asarray(xp, dtype=np.float64)
    dy = np.asarray(fp, dtype=np.float64)

    if len(dx) == 0:
        raise ValueError('array of sample points is empty')

    if len(dx) != len(dy):
        raise ValueError('fp and xp are not of the same size.')

    if dx.size == 1:
        return np.full(dz.shape, fill_value=dy[0], dtype=dtype)

    dres = np.empty(dz.shape, dtype=dtype)

    lenx = dz.size
    lenxp = len(dx)
    lval = dy[0]
    rval = dy[lenxp - 1]

    if lenxp == 1:
        xp_val = dx[0]
        fp_val = dy[0]

        for i in range(lenx):
            x_val = dz.flat[i]
            if x_val < xp_val:
                dres.flat[i] = lval
            elif x_val > xp_val:
                dres.flat[i] = rval
            else:
                dres.flat[i] = fp_val

    else:
        j = 0

        # only pre-calculate slopes if there are relatively few of them.
        if lenxp <= lenx:
            slopes = (dy[1:] - dy[:-1]) / (dx[1:] - dx[:-1])
        else:
            slopes = np.empty(0, dtype=dtype)

        for i in range(lenx):
            x_val = dz.flat[i]

            if np.isnan(x_val):
                dres.flat[i] = x_val
                continue

            j = binary_search_with_guess(x_val, dx, lenxp, j)

            if j == -1:
                dres.flat[i] = lval
            elif j == lenxp:
                dres.flat[i] = rval
            elif j == lenxp - 1:
                dres.flat[i] = dy[j]
            elif dx[j] == x_val:
                # Avoid potential non-finite interpolation
                dres.flat[i] = dy[j]
            else:
                if slopes.size:
                    slope = slopes[j]
                else:
                    slope = (dy[j + 1] - dy[j]) / (dx[j + 1] - dx[j])

                dres.flat[i] = slope * (x_val - dx[j]) + dy[j]

                # NOTE: this is in np1.17
                # https://github.com/numpy/numpy/blob/maintenance/1.17.x/numpy/core/src/multiarray/compiled_base.c    # noqa: E501
                # Permanent reference:
                # https://github.com/numpy/numpy/blob/91fbe4dde246559fa5b085ebf4bc268e2b89eea8/numpy/core/src/multiarray/compiled_base.c#L610-L616    # noqa: E501
                #
                # If we get nan in one direction, try the other
                if np.isnan(dres.flat[i]):
                    dres.flat[i] = slope * (x_val - dx[j + 1]) + dy[j + 1]    # noqa: E501
                    if np.isnan(dres.flat[i]) and dy[j] == dy[j + 1]:
                        dres.flat[i] = dy[j]

    return dres


@overload(np.interp)
def np_interp(x, xp, fp):
    # Replicating basic interp is relatively simple, but matching the behaviour
    # of NumPy for edge cases is really quite hard. After a couple of attempts
    # to avoid translation of the C source it was deemed necessary.

    if hasattr(xp, 'ndim') and xp.ndim > 1:
        raise TypingError('xp must be 1D')
    if hasattr(fp, 'ndim') and fp.ndim > 1:
        raise TypingError('fp must be 1D')

    complex_dtype_msg = (
        "Cannot cast array data from complex dtype to float64 dtype"
    )

    xp_dt = determine_dtype(xp)
    if np.issubdtype(xp_dt, np.complexfloating):
        raise TypingError(complex_dtype_msg)

    fp_dt = determine_dtype(fp)
    dtype = np.result_type(fp_dt, np.float64)

    if np.issubdtype(dtype, np.complexfloating):
        inner = np_interp_impl_complex_inner
    else:
        inner = np_interp_impl_inner

    def np_interp_impl(x, xp, fp):
        return inner(x, xp, fp, dtype)

    def np_interp_scalar_impl(x, xp, fp):
        return inner(x, xp, fp, dtype).flat[0]

    if isinstance(x, types.Number):
        if isinstance(x, types.Complex):
            raise TypingError(complex_dtype_msg)
        return np_interp_scalar_impl

    return np_interp_impl


#----------------------------------------------------------------------------
# Statistics

@register_jitable
def row_wise_average(a):
    assert a.ndim == 2

    m, n = a.shape
    out = np.empty((m, 1), dtype=a.dtype)

    for i in range(m):
        out[i, 0] = np.sum(a[i, :]) / n

    return out


@register_jitable
def np_cov_impl_inner(X, bias, ddof):

    # determine degrees of freedom
    if ddof is None:
        if bias:
            ddof = 0
        else:
            ddof = 1

    # determine the normalization factor
    fact = X.shape[1] - ddof

    # numpy warns if less than 0 and floors at 0
    fact = max(fact, 0.0)

    # de-mean
    X -= row_wise_average(X)

    # calculate result - requires blas
    c = np.dot(X, np.conj(X.T))
    c *= np.true_divide(1, fact)
    return c


def _prepare_cov_input_inner():
    pass


@overload(_prepare_cov_input_inner)
def _prepare_cov_input_impl(m, y, rowvar, dtype):
    if y in (None, types.none):
        def _prepare_cov_input_inner(m, y, rowvar, dtype):
            m_arr = np.atleast_2d(_asarray(m))

            if not rowvar:
                m_arr = m_arr.T

            return m_arr
    else:
        def _prepare_cov_input_inner(m, y, rowvar, dtype):
            m_arr = np.atleast_2d(_asarray(m))
            y_arr = np.atleast_2d(_asarray(y))

            # transpose if asked to and not a (1, n) vector - this looks
            # wrong as you might end up transposing one and not the other,
            # but it's what numpy does
            if not rowvar:
                if m_arr.shape[0] != 1:
                    m_arr = m_arr.T
                if y_arr.shape[0] != 1:
                    y_arr = y_arr.T

            m_rows, m_cols = m_arr.shape
            y_rows, y_cols = y_arr.shape

            if m_cols != y_cols:
                raise ValueError("m and y have incompatible dimensions")

            # allocate and fill output array
            out = np.empty((m_rows + y_rows, m_cols), dtype=dtype)
            out[:m_rows, :] = m_arr
            out[-y_rows:, :] = y_arr

            return out

    return _prepare_cov_input_inner


@register_jitable
def _handle_m_dim_change(m):
    if m.ndim == 2 and m.shape[0] == 1:
        msg = ("2D array containing a single row is unsupported due to "
               "ambiguity in type inference. To use numpy.cov in this case "
               "simply pass the row as a 1D array, i.e. m[0].")
        raise RuntimeError(msg)


_handle_m_dim_nop = register_jitable(lambda x: x)


def determine_dtype(array_like):
    array_like_dt = np.float64
    if isinstance(array_like, types.Array):
        array_like_dt = as_dtype(array_like.dtype)
    elif isinstance(array_like, (types.Number, types.Boolean)):
        array_like_dt = as_dtype(array_like)
    elif isinstance(array_like, (types.UniTuple, types.Tuple)):
        coltypes = set()
        for val in array_like:
            if hasattr(val, 'count'):
                [coltypes.add(v) for v in val]
            else:
                coltypes.add(val)
        if len(coltypes) > 1:
            array_like_dt = np.promote_types(*[as_dtype(ty) for ty in coltypes])
        elif len(coltypes) == 1:
            array_like_dt = as_dtype(coltypes.pop())

    return array_like_dt


def check_dimensions(array_like, name):
    if isinstance(array_like, types.Array):
        if array_like.ndim > 2:
            raise NumbaTypeError("{0} has more than 2 dimensions".format(name))
    elif isinstance(array_like, types.Sequence):
        if isinstance(array_like.key[0], types.Sequence):
            if isinstance(array_like.key[0].key[0], types.Sequence):
                msg = "{0} has more than 2 dimensions".format(name)
                raise NumbaTypeError(msg)


@register_jitable
def _handle_ddof(ddof):
    if not np.isfinite(ddof):
        raise ValueError('Cannot convert non-finite ddof to integer')
    if ddof - int(ddof) != 0:
        raise ValueError('ddof must be integral value')


_handle_ddof_nop = register_jitable(lambda x: x)


@register_jitable
def _prepare_cov_input(m, y, rowvar, dtype, ddof, _DDOF_HANDLER,
                       _M_DIM_HANDLER):
    _M_DIM_HANDLER(m)
    _DDOF_HANDLER(ddof)
    return _prepare_cov_input_inner(m, y, rowvar, dtype)


def scalar_result_expected(mandatory_input, optional_input):
    opt_is_none = optional_input in (None, types.none)

    if isinstance(mandatory_input, types.Array) and mandatory_input.ndim == 1:
        return opt_is_none

    if isinstance(mandatory_input, types.BaseTuple):
        if all(isinstance(x, (types.Number, types.Boolean))
               for x in mandatory_input.types):
            return opt_is_none
        else:
            if (len(mandatory_input.types) == 1 and
                    isinstance(mandatory_input.types[0], types.BaseTuple)):
                return opt_is_none

    if isinstance(mandatory_input, (types.Number, types.Boolean)):
        return opt_is_none

    if isinstance(mandatory_input, types.Sequence):
        if (not isinstance(mandatory_input.key[0], types.Sequence) and
                opt_is_none):
            return True

    return False


@register_jitable
def _clip_corr(x):
    return np.where(np.fabs(x) > 1, np.sign(x), x)


@register_jitable
def _clip_complex(x):
    real = _clip_corr(x.real)
    imag = _clip_corr(x.imag)
    return real + 1j * imag


@overload(np.cov)
def np_cov(m, y=None, rowvar=True, bias=False, ddof=None):

    # reject problem if m and / or y are more than 2D
    check_dimensions(m, 'm')
    check_dimensions(y, 'y')

    # reject problem if ddof invalid (either upfront if type is
    # obviously invalid, or later if value found to be non-integral)
    if ddof in (None, types.none):
        _DDOF_HANDLER = _handle_ddof_nop
    else:
        if isinstance(ddof, (types.Integer, types.Boolean)):
            _DDOF_HANDLER = _handle_ddof_nop
        elif isinstance(ddof, types.Float):
            _DDOF_HANDLER = _handle_ddof
        else:
            raise TypingError('ddof must be a real numerical scalar type')

    # special case for 2D array input with 1 row of data - select
    # handler function which we'll call later when we have access
    # to the shape of the input array
    _M_DIM_HANDLER = _handle_m_dim_nop
    if isinstance(m, types.Array):
        _M_DIM_HANDLER = _handle_m_dim_change

    # infer result dtype
    m_dt = determine_dtype(m)
    y_dt = determine_dtype(y)
    dtype = np.result_type(m_dt, y_dt, np.float64)

    def np_cov_impl(m, y=None, rowvar=True, bias=False, ddof=None):
        X = _prepare_cov_input(m, y, rowvar, dtype, ddof, _DDOF_HANDLER,
                               _M_DIM_HANDLER).astype(dtype)

        if np.any(np.array(X.shape) == 0):
            return np.full((X.shape[0], X.shape[0]), fill_value=np.nan,
                           dtype=dtype)
        else:
            return np_cov_impl_inner(X, bias, ddof)

    def np_cov_impl_single_variable(m, y=None, rowvar=True, bias=False,
                                    ddof=None):
        X = _prepare_cov_input(m, y, rowvar, ddof, dtype, _DDOF_HANDLER,
                               _M_DIM_HANDLER).astype(dtype)

        if np.any(np.array(X.shape) == 0):
            variance = np.nan
        else:
            variance = np_cov_impl_inner(X, bias, ddof).flat[0]

        return np.array(variance)

    if scalar_result_expected(m, y):
        return np_cov_impl_single_variable
    else:
        return np_cov_impl


@overload(np.corrcoef)
def np_corrcoef(x, y=None, rowvar=True):

    x_dt = determine_dtype(x)
    y_dt = determine_dtype(y)
    dtype = np.result_type(x_dt, y_dt, np.float64)

    if dtype == np.complex128:
        clip_fn = _clip_complex
    else:
        clip_fn = _clip_corr

    def np_corrcoef_impl(x, y=None, rowvar=True):
        c = np.cov(x, y, rowvar)
        d = np.diag(c)
        stddev = np.sqrt(d.real)

        for i in range(c.shape[0]):
            c[i, :] /= stddev
            c[:, i] /= stddev

        return clip_fn(c)

    def np_corrcoef_impl_single_variable(x, y=None, rowvar=True):
        c = np.cov(x, y, rowvar)
        return c / c

    if scalar_result_expected(x, y):
        return np_corrcoef_impl_single_variable
    else:
        return np_corrcoef_impl


#----------------------------------------------------------------------------
# Element-wise computations


@overload(np.argwhere)
def np_argwhere(a):
    # needs to be much more array-like for the array impl to work, Numba bug
    # in one of the underlying function calls?

    use_scalar = isinstance(a, (types.Number, types.Boolean))
    if type_can_asarray(a) and not use_scalar:
        def impl(a):
            arr = np.asarray(a)
            if arr.shape == ():
                return np.zeros((0, 1), dtype=types.intp)
            return np.transpose(np.vstack(np.nonzero(arr)))
    else:
        falseish = (0, 0)
        trueish = (1, 0)

        def impl(a):
            if a is not None and bool(a):
                return np.zeros(trueish, dtype=types.intp)
            else:
                return np.zeros(falseish, dtype=types.intp)

    return impl


@overload(np.flatnonzero)
def np_flatnonzero(a):

    if type_can_asarray(a):
        def impl(a):
            arr = np.asarray(a)
            return np.nonzero(np.ravel(arr))[0]
    else:
        def impl(a):
            if a is not None and bool(a):
                data = [0]
            else:
                data = [x for x in range(0)]
            return np.array(data, dtype=types.intp)

    return impl


@register_jitable
def _fill_diagonal_params(a, wrap):
    if a.ndim == 2:
        m = a.shape[0]
        n = a.shape[1]
        step = 1 + n
        if wrap:
            end = n * m
        else:
            end = n * min(m, n)
    else:
        shape = np.array(a.shape)

        if not np.all(np.diff(shape) == 0):
            raise ValueError("All dimensions of input must be of equal length")

        step = 1 + (np.cumprod(shape[:-1])).sum()
        end = shape.prod()

    return end, step


@register_jitable
def _fill_diagonal_scalar(a, val, wrap):
    end, step = _fill_diagonal_params(a, wrap)

    for i in range(0, end, step):
        a.flat[i] = val


@register_jitable
def _fill_diagonal(a, val, wrap):
    end, step = _fill_diagonal_params(a, wrap)
    ctr = 0
    v_len = len(val)

    for i in range(0, end, step):
        a.flat[i] = val[ctr]
        ctr += 1
        ctr = ctr % v_len


@register_jitable
def _check_val_int(a, val):
    iinfo = np.iinfo(a.dtype)
    v_min = iinfo.min
    v_max = iinfo.max

    # check finite values are within bounds
    if np.any(~np.isfinite(val)) or np.any(val < v_min) or np.any(val > v_max):
        raise ValueError('Unable to safely conform val to a.dtype')


@register_jitable
def _check_val_float(a, val):
    finfo = np.finfo(a.dtype)
    v_min = finfo.min
    v_max = finfo.max

    # check finite values are within bounds
    finite_vals = val[np.isfinite(val)]
    if np.any(finite_vals < v_min) or np.any(finite_vals > v_max):
        raise ValueError('Unable to safely conform val to a.dtype')


# no check performed, needed for pathway where no check is required
_check_nop = register_jitable(lambda x, y: x)


def _asarray(x):
    pass


@overload(_asarray)
def _asarray_impl(x):
    if isinstance(x, types.Array):
        return lambda x: x
    elif isinstance(x, (types.Sequence, types.Tuple)):
        return lambda x: np.array(x)
    elif isinstance(x, (types.Number, types.Boolean)):
        ty = as_dtype(x)
        return lambda x: np.array([x], dtype=ty)


@overload(np.fill_diagonal)
def np_fill_diagonal(a, val, wrap=False):

    if a.ndim > 1:
        # the following can be simplified after #3088; until then, employ
        # a basic mechanism for catching cases where val is of a type/value
        # which cannot safely be cast to a.dtype
        if isinstance(a.dtype, types.Integer):
            checker = _check_val_int
        elif isinstance(a.dtype, types.Float):
            checker = _check_val_float
        else:
            checker = _check_nop

        def scalar_impl(a, val, wrap=False):
            tmpval = _asarray(val).flatten()
            checker(a, tmpval)
            _fill_diagonal_scalar(a, val, wrap)

        def non_scalar_impl(a, val, wrap=False):
            tmpval = _asarray(val).flatten()
            checker(a, tmpval)
            _fill_diagonal(a, tmpval, wrap)

        if isinstance(val, (types.Float, types.Integer, types.Boolean)):
            return scalar_impl
        elif isinstance(val, (types.Tuple, types.Sequence, types.Array)):
            return non_scalar_impl
    else:
        msg = "The first argument must be at least 2-D (found %s-D)" % a.ndim
        raise TypingError(msg)


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


@intrinsic
def _np_round_float(typingctx, val):
    sig = val(val)

    def codegen(context, builder, sig, args):
        [val] = args
        tp = sig.args[0]
        llty = context.get_value_type(tp)
        module = builder.module
        fnty = llvmlite.ir.FunctionType(llty, [llty])
        fn = cgutils.get_or_insert_function(module, fnty,
                                            _np_round_intrinsic(tp))
        res = builder.call(fn, (val,))
        return impl_ret_untracked(context, builder, sig.return_type, res)

    return sig, codegen


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

    # NOTE: this is CPython's algorithm, but perhaps this is overkill
    # when emulating Numpy's behaviour.
    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 (_np_round_float(y) / pow2) / pow1

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


@overload(np.around)
@overload(np.round)
def impl_np_round(a, decimals=0, out=None):
    if not type_can_asarray(a):
        raise TypingError('The argument "a" must be array-like')

    if not (isinstance(out, types.Array) or is_nonelike(out)):
        msg = 'The argument "out" must be an array if it is provided'
        raise TypingError(msg)

    if isinstance(a, (types.Float, types.Integer, types.Complex)):
        if is_nonelike(out):
            if isinstance(a, types.Float):
                def impl(a, decimals=0, out=None):
                    if decimals == 0:
                        return _np_round_float(a)
                    else:
                        return round_ndigits(a, decimals)
                return impl
            elif isinstance(a, types.Integer):
                def impl(a, decimals=0, out=None):
                    if decimals == 0:
                        return a
                    else:
                        return int(round_ndigits(a, decimals))
                return impl
            elif isinstance(a, types.Complex):
                def impl(a, decimals=0, out=None):
                    if decimals == 0:
                        real = _np_round_float(a.real)
                        imag = _np_round_float(a.imag)
                    else:
                        real = round_ndigits(a.real, decimals)
                        imag = round_ndigits(a.imag, decimals)
                    return complex(real, imag)
                return impl
        else:
            def impl(a, decimals=0, out=None):
                out[0] = np.round(a, decimals)
                return out
            return impl
    elif isinstance(a, types.Array):
        if is_nonelike(out):
            def impl(a, decimals=0, out=None):
                out = np.empty_like(a)
                return np.round(a, decimals, out)
            return impl
        else:
            def impl(a, decimals=0, out=None):
                if a.shape != out.shape:
                    raise ValueError("invalid output shape")
                for index, val in np.ndenumerate(a):
                    out[index] = np.round(val, decimals)
                return out
            return impl


if numpy_version < (2, 0):
    overload(np.round_)(impl_np_round)


@overload(np.sinc)
def impl_np_sinc(x):
    if isinstance(x, types.Number):
        def impl(x):
            if x == 0.e0: # to match np impl
                x = 1e-20
            x *= np.pi # np sinc is the normalised variant
            return np.sin(x) / x
        return impl
    elif isinstance(x, types.Array):
        def impl(x):
            out = np.zeros_like(x)
            for index, val in np.ndenumerate(x):
                out[index] = np.sinc(val)
            return out
        return impl
    else:
        raise NumbaTypeError('Argument "x" must be a Number or array-like.')


@overload(np.angle)
def ov_np_angle(z, deg=False):
    deg_mult = float(180 / np.pi)

    # non-complex scalar values are accepted as well
    if isinstance(z, types.Number):
        def impl(z, deg=False):
            if deg:
                return np.arctan2(z.imag, z.real) * deg_mult
            else:
                return np.arctan2(z.imag, z.real)
        return impl
    elif isinstance(z, types.Array):
        dtype = z.dtype

        if isinstance(dtype, types.Complex):
            ret_dtype = dtype.underlying_float
        elif isinstance(dtype, types.Float):
            ret_dtype = dtype
        else:
            return

        def impl(z, deg=False):
            out = np.zeros_like(z, dtype=ret_dtype)
            for index, val in np.ndenumerate(z):
                out[index] = np.angle(val, deg)
            return out
        return impl
    else:
        raise NumbaTypeError('Argument "z" must be a complex '
                             f'or Array[complex]. Got {z}')


@lower_builtin(np.nonzero, types.Array)
@lower_builtin("array.nonzero", types.Array)
def array_nonzero(context, builder, sig, args):
    aryty = sig.args[0]
    # Return type is a N-tuple of 1D C-contiguous arrays
    retty = sig.return_type
    outaryty = retty.dtype
    nouts = retty.count

    ary = make_array(aryty)(context, builder, args[0])
    shape = cgutils.unpack_tuple(builder, ary.shape)
    strides = cgutils.unpack_tuple(builder, ary.strides)
    data = ary.data
    layout = aryty.layout

    # First count the number of non-zero elements
    zero = context.get_constant(types.intp, 0)
    one = context.get_constant(types.intp, 1)
    count = cgutils.alloca_once_value(builder, zero)
    with cgutils.loop_nest(builder, shape, zero.type) as indices:
        ptr = cgutils.get_item_pointer2(context, builder, data, shape, strides,
                                        layout, indices)
        val = load_item(context, builder, aryty, ptr)
        nz = context.is_true(builder, aryty.dtype, val)
        with builder.if_then(nz):
            builder.store(builder.add(builder.load(count), one), count)

    # Then allocate output arrays of the right size
    out_shape = (builder.load(count),)
    outs = [_empty_nd_impl(context, builder, outaryty, out_shape)._getvalue()
            for i in range(nouts)]
    outarys = [make_array(outaryty)(context, builder, out) for out in outs]
    out_datas = [out.data for out in outarys]

    # And fill them up
    index = cgutils.alloca_once_value(builder, zero)
    with cgutils.loop_nest(builder, shape, zero.type) as indices:
        ptr = cgutils.get_item_pointer2(context, builder, data, shape, strides,
                                        layout, indices)
        val = load_item(context, builder, aryty, ptr)
        nz = context.is_true(builder, aryty.dtype, val)
        with builder.if_then(nz):
            # Store element indices in output arrays
            if not indices:
                # For a 0-d array, store 0 in the unique output array
                indices = (zero,)
            cur = builder.load(index)
            for i in range(nouts):
                ptr = cgutils.get_item_pointer2(context, builder, out_datas[i],
                                                out_shape, (),
                                                'C', [cur])
                store_item(context, builder, outaryty, indices[i], ptr)
            builder.store(builder.add(cur, one), index)

    tup = context.make_tuple(builder, sig.return_type, outs)
    return impl_ret_new_ref(context, builder, sig.return_type, tup)


def _where_zero_size_array_impl(dtype):
    def impl(condition, x, y):
        x_ = np.asarray(x).astype(dtype)
        y_ = np.asarray(y).astype(dtype)
        return x_ if condition else y_
    return impl


@register_jitable
def _where_generic_inner_impl(cond, x, y, res):
    for idx, c in np.ndenumerate(cond):
        res[idx] = x[idx] if c else y[idx]
    return res


@register_jitable
def _where_fast_inner_impl(cond, x, y, res):
    cf = cond.flat
    xf = x.flat
    yf = y.flat
    rf = res.flat
    for i in range(cond.size):
        rf[i] = xf[i] if cf[i] else yf[i]
    return res


def _where_generic_impl(dtype, layout):
    use_faster_impl = layout in [{'C'}, {'F'}]

    def impl(condition, x, y):
        cond1, x1, y1 = np.asarray(condition), np.asarray(x), np.asarray(y)
        shape = np.broadcast_shapes(cond1.shape, x1.shape, y1.shape)
        cond_ = np.broadcast_to(cond1, shape)
        x_ = np.broadcast_to(x1, shape)
        y_ = np.broadcast_to(y1, shape)

        if layout == 'F':
            res = np.empty(shape[::-1], dtype=dtype).T
        else:
            res = np.empty(shape, dtype=dtype)

        if use_faster_impl:
            return _where_fast_inner_impl(cond_, x_, y_, res)
        else:
            return _where_generic_inner_impl(cond_, x_, y_, res)

    return impl


@overload(np.where)
def ov_np_where(condition):
    if not type_can_asarray(condition):
        msg = 'The argument "condition" must be array-like'
        raise NumbaTypeError(msg)

    def where_cond_none_none(condition):
        return np.asarray(condition).nonzero()
    return where_cond_none_none


@overload(np.where)
def ov_np_where_x_y(condition, x, y):
    if not type_can_asarray(condition):
        msg = 'The argument "condition" must be array-like'
        raise NumbaTypeError(msg)

    # corner case: None is a valid value for np.where:
    # >>> np.where([0, 1], None, 2)
    # array([None, 2])
    #
    # >>> np.where([0, 1], 2, None)
    # array([2, None])
    #
    # >>> np.where([0, 1], None, None)
    # array([None, None])
    if is_nonelike(x) or is_nonelike(y):
        # skip it for now as np.asarray(None) is not supported
        raise NumbaTypeError('Argument "x" or "y" cannot be None')

    for arg, name in zip((x, y), ('x', 'y')):
        if not type_can_asarray(arg):
            msg = 'The argument "{}" must be array-like if provided'
            raise NumbaTypeError(msg.format(name))

    cond_arr = isinstance(condition, types.Array)
    x_arr = isinstance(x, types.Array)
    y_arr = isinstance(y, types.Array)

    if cond_arr:
        x_dt = determine_dtype(x)
        y_dt = determine_dtype(y)
        dtype = np.promote_types(x_dt, y_dt)

        # corner case - 0 dim values
        def check_0_dim(arg):
            return isinstance(arg, types.Number) or (
                isinstance(arg, types.Array) and arg.ndim == 0)
        special_0_case = all([check_0_dim(a) for a in (condition, x, y)])
        if special_0_case:
            return _where_zero_size_array_impl(dtype)

        layout = condition.layout
        if x_arr and y_arr:
            if x.layout == y.layout == condition.layout:
                layout = x.layout
            else:
                layout = 'A'
        return _where_generic_impl(dtype, layout)
    else:
        def impl(condition, x, y):
            return np.where(np.asarray(condition), np.asarray(x), np.asarray(y))
        return impl


@overload(np.real)
def np_real(val):
    def np_real_impl(val):
        return val.real

    return np_real_impl


@overload(np.imag)
def np_imag(val):
    def np_imag_impl(val):
        return val.imag

    return np_imag_impl


#----------------------------------------------------------------------------
# Misc functions

@overload(operator.contains)
def np_contains(arr, key):
    if not isinstance(arr, types.Array):
        return

    def np_contains_impl(arr, key):
        for x in np.nditer(arr):
            if x == key:
                return True
        return False

    return np_contains_impl


@overload(np.count_nonzero)
def np_count_nonzero(a, axis=None):
    if not type_can_asarray(a):
        raise TypingError("The argument to np.count_nonzero must be array-like")

    if is_nonelike(axis):
        def impl(a, axis=None):
            arr2 = np.ravel(a)
            return np.sum(arr2 != 0)
        return impl
    else:
        def impl(a, axis=None):
            arr2 = a.astype(np.bool_)
            return np.sum(arr2, axis=axis)
        return impl


np_delete_handler_isslice = register_jitable(lambda x : x)
np_delete_handler_isarray = register_jitable(lambda x : np.asarray(x))


@overload(np.delete)
def np_delete(arr, obj):
    # Implementation based on numpy
    # https://github.com/numpy/numpy/blob/af66e487a57bfd4850f4306e3b85d1dac3c70412/numpy/lib/function_base.py#L4065-L4267    # noqa: E501

    if not isinstance(arr, (types.Array, types.Sequence)):
        raise TypingError("arr must be either an Array or a Sequence")

    if isinstance(obj, (types.Array, types.Sequence, types.SliceType)):
        if isinstance(obj, (types.SliceType)):
            handler = np_delete_handler_isslice
        else:
            if not isinstance(obj.dtype, types.Integer):
                raise TypingError('obj should be of Integer dtype')
            handler = np_delete_handler_isarray

        def np_delete_impl(arr, obj):
            arr = np.ravel(np.asarray(arr))
            N = arr.size

            keep = np.ones(N, dtype=np.bool_)
            obj = handler(obj)
            keep[obj] = False
            return arr[keep]
        return np_delete_impl

    else: # scalar value
        if not isinstance(obj, types.Integer):
            raise TypingError('obj should be of Integer dtype')

        def np_delete_scalar_impl(arr, obj):
            arr = np.ravel(np.asarray(arr))
            N = arr.size
            pos = obj

            if (pos < -N or pos >= N):
                raise IndexError('obj must be less than the len(arr)')
                # NumPy raises IndexError: index 'i' is out of
                # bounds for axis 'x' with size 'n'

            if (pos < 0):
                pos += N

            return np.concatenate((arr[:pos], arr[pos + 1:]))
        return np_delete_scalar_impl


@overload(np.diff)
def np_diff_impl(a, n=1):
    if not isinstance(a, types.Array) or a.ndim == 0:
        return

    def diff_impl(a, n=1):
        if n == 0:
            return a.copy()
        if n < 0:
            raise ValueError("diff(): order must be non-negative")
        size = a.shape[-1]
        out_shape = a.shape[:-1] + (max(size - n, 0),)
        out = np.empty(out_shape, a.dtype)
        if out.size == 0:
            return out

        # np.diff() works on each last dimension subarray independently.
        # To make things easier, normalize input and output into 2d arrays
        a2 = a.reshape((-1, size))
        out2 = out.reshape((-1, out.shape[-1]))
        # A scratchpad for subarrays
        work = np.empty(size, a.dtype)

        for major in range(a2.shape[0]):
            # First iteration: diff a2 into work
            for i in range(size - 1):
                work[i] = a2[major, i + 1] - a2[major, i]
            # Other iterations: diff work into itself
            for niter in range(1, n):
                for i in range(size - niter - 1):
                    work[i] = work[i + 1] - work[i]
            # Copy final diff into out2
            out2[major] = work[:size - n]

        return out

    return diff_impl


@overload(np.array_equal)
def np_array_equal(a1, a2):

    if not (type_can_asarray(a1) and type_can_asarray(a2)):
        raise TypingError('Both arguments to "array_equals" must be array-like')

    accepted = (types.Boolean, types.Number)
    if isinstance(a1, accepted) and isinstance(a2, accepted):
        # special case
        def impl(a1, a2):
            return a1 == a2
    else:
        def impl(a1, a2):
            a = np.asarray(a1)
            b = np.asarray(a2)
            if a.shape == b.shape:
                return np.all(a == b)
            return False

    return impl


@overload(np.intersect1d)
def jit_np_intersect1d(ar1, ar2, assume_unique=False):
    # Not implemented to support return_indices
    # https://github.com/numpy/numpy/blob/v1.19.0/numpy/lib
    # /arraysetops.py#L347-L441
    if not (type_can_asarray(ar1) or type_can_asarray(ar2)):
        raise TypingError('intersect1d: first two args must be array-like')
    if not isinstance(assume_unique, (types.Boolean, bool)):
        raise TypingError('intersect1d: '
                          'argument "assume_unique" must be boolean')

    def np_intersects1d_impl(ar1, ar2, assume_unique=False):
        ar1 = np.asarray(ar1)
        ar2 = np.asarray(ar2)

        if not assume_unique:
            ar1 = np.unique(ar1)
            ar2 = np.unique(ar2)
        else:
            ar1 = ar1.ravel()
            ar2 = ar2.ravel()

        aux = np.concatenate((ar1, ar2))
        aux.sort()
        mask = aux[1:] == aux[:-1]
        int1d = aux[:-1][mask]
        return int1d
    return np_intersects1d_impl


def validate_1d_array_like(func_name, seq):
    if isinstance(seq, types.Array):
        if seq.ndim != 1:
            raise NumbaTypeError("{0}(): input should have dimension 1"
                                 .format(func_name))
    elif not isinstance(seq, types.Sequence):
        raise NumbaTypeError("{0}(): input should be an array or sequence"
                             .format(func_name))


@overload(np.bincount)
def np_bincount(a, weights=None, minlength=0):
    validate_1d_array_like("bincount", a)

    if not isinstance(a.dtype, types.Integer):
        return

    check_is_integer(minlength, 'minlength')

    if weights not in (None, types.none):
        validate_1d_array_like("bincount", weights)
        # weights is promoted to double in C impl
        # https://github.com/numpy/numpy/blob/maintenance/1.16.x/numpy/core/src/multiarray/compiled_base.c#L93-L95    # noqa: E501
        out_dtype = np.float64

        @register_jitable
        def validate_inputs(a, weights, minlength):
            if len(a) != len(weights):
                raise ValueError("bincount(): weights and list don't have "
                                 "the same length")

        @register_jitable
        def count_item(out, idx, val, weights):
            out[val] += weights[idx]

    else:
        out_dtype = types.intp

        @register_jitable
        def validate_inputs(a, weights, minlength):
            pass

        @register_jitable
        def count_item(out, idx, val, weights):
            out[val] += 1

    def bincount_impl(a, weights=None, minlength=0):
        validate_inputs(a, weights, minlength)
        if minlength < 0:
            raise ValueError("'minlength' must not be negative")

        n = len(a)
        a_max = a[0] if n > 0 else -1
        for i in range(1, n):
            if a[i] < 0:
                raise ValueError("bincount(): first argument must be "
                                 "non-negative")
            a_max = max(a_max, a[i])

        out_length = max(a_max + 1, minlength)
        out = np.zeros(out_length, out_dtype)
        for i in range(n):
            count_item(out, i, a[i], weights)
        return out

    return bincount_impl


less_than_float = register_jitable(lt_floats)
less_than_complex = register_jitable(lt_complex)


@register_jitable
def less_than_or_equal_complex(a, b):
    if np.isnan(a.real):
        if np.isnan(b.real):
            if np.isnan(a.imag):
                return np.isnan(b.imag)
            else:
                if np.isnan(b.imag):
                    return True
                else:
                    return a.imag <= b.imag
        else:
            return False

    else:
        if np.isnan(b.real):
            return True
        else:
            if np.isnan(a.imag):
                if np.isnan(b.imag):
                    return a.real <= b.real
                else:
                    return False
            else:
                if np.isnan(b.imag):
                    return True
                else:
                    if a.real < b.real:
                        return True
                    elif a.real == b.real:
                        return a.imag <= b.imag
                    return False


@register_jitable
def _less_than_or_equal(a, b):
    if isinstance(a, complex) or isinstance(b, complex):
        return less_than_or_equal_complex(a, b)

    elif isinstance(b, (float, types.float32, types.float64)):
        if np.isnan(b):
            return True

    return a <= b


@register_jitable
def _less_than(a, b):
    if isinstance(a, complex) or isinstance(b, complex):
        return less_than_complex(a, b)

    elif isinstance(b, (float, types.float32, types.float64)):
        return less_than_float(a, b)

    return a < b


@register_jitable
def _less_then_datetime64(a, b):
    # Original numpy code is at:
    # https://github.com/numpy/numpy/blob/3dad50936a8dc534a81a545365f69ee9ab162ffe/numpy/_core/src/npysort/npysort_common.h#L334-L346
    if np.isnat(a):
        return 0

    if np.isnat(b):
        return 1

    return a < b


@register_jitable
def _less_then_or_equal_datetime64(a, b):
    return not _less_then_datetime64(b, a)


def _searchsorted(cmp):
    # a facsimile of:
    # https://github.com/numpy/numpy/blob/4f84d719657eb455a35fcdf9e75b83eb1f97024a/numpy/core/src/npysort/binsearch.cpp#L61  # noqa: E501

    def impl(a, key_val, min_idx, max_idx):
        while min_idx < max_idx:
            # to avoid overflow
            mid_idx = min_idx + ((max_idx - min_idx) >> 1)
            mid_val = a[mid_idx]
            if cmp(mid_val, key_val):
                min_idx = mid_idx + 1
            else:
                max_idx = mid_idx
        return min_idx, max_idx

    return impl


VALID_SEARCHSORTED_SIDES = frozenset({'left', 'right'})


def make_searchsorted_implementation(np_dtype, side):
    assert side in VALID_SEARCHSORTED_SIDES

    if np_dtype.char in 'mM':
        # is datetime
        lt = _less_then_datetime64
        le = _less_then_or_equal_datetime64
    else:
        lt = _less_than
        le = _less_than_or_equal

    if side == 'left':
        _impl = _searchsorted(lt)
        _cmp = lt
    else:
        if np.issubdtype(np_dtype, np.inexact) and numpy_version < (1, 23):
            # change in behaviour for inexact types
            # introduced by:
            # https://github.com/numpy/numpy/pull/21867
            _impl = _searchsorted(le)
            _cmp = lt
        else:
            _impl = _searchsorted(le)
            _cmp = le

    return register_jitable(_impl), register_jitable(_cmp)


@overload(np.searchsorted)
def searchsorted(a, v, side='left'):
    side_val = getattr(side, 'literal_value', side)

    if side_val not in VALID_SEARCHSORTED_SIDES:
        # could change this so that side doesn't need to be
        # a compile-time constant
        raise NumbaValueError(f"Invalid value given for 'side': {side_val}")

    if isinstance(v, (types.Array, types.Sequence)):
        v_dt = as_dtype(v.dtype)
    else:
        v_dt = as_dtype(v)

    np_dt = np.promote_types(as_dtype(a.dtype), v_dt)
    _impl, _cmp = make_searchsorted_implementation(np_dt, side_val)

    if isinstance(v, types.Array):
        def impl(a, v, side='left'):
            out = np.empty(v.size, dtype=np.intp)
            last_key_val = v.flat[0]
            min_idx = 0
            max_idx = len(a)

            for i in range(v.size):
                key_val = v.flat[i]

                if _cmp(last_key_val, key_val):
                    max_idx = len(a)
                else:
                    min_idx = 0
                    if max_idx < len(a):
                        max_idx += 1
                    else:
                        max_idx = len(a)

                last_key_val = key_val
                min_idx, max_idx = _impl(a, key_val, min_idx, max_idx)
                out[i] = min_idx

            return out.reshape(v.shape)
    elif isinstance(v, types.Sequence):
        def impl(a, v, side='left'):
            v = np.asarray(v)
            return np.searchsorted(a, v, side=side)
    else:  # presumably `v` is scalar
        def impl(a, v, side='left'):
            r, _ = _impl(a, v, 0, len(a))
            return r
    return impl


@overload(np.digitize)
def np_digitize(x, bins, right=False):

    if isinstance(x, types.Array) and x.dtype in types.complex_domain:
        raise TypingError('x may not be complex')

    @register_jitable
    def _monotonicity(bins):

        # all bin edges hold the same value
        if len(bins) == 0:
            return 1

        # Skip repeated values at the beginning of the array
        last_value = bins[0]
        i = 1
        while i < len(bins) and bins[i] == last_value:
            i += 1

        # all bin edges hold the same value
        if i == len(bins):
            return 1

        next_value = bins[i]

        if last_value < next_value:
            # Possibly monotonic increasing
            for i in range(i + 1, len(bins)):
                last_value = next_value
                next_value = bins[i]
                if last_value > next_value:
                    return 0
            return 1

        else:
            # last > next, possibly monotonic decreasing
            for i in range(i + 1, len(bins)):
                last_value = next_value
                next_value = bins[i]
                if last_value < next_value:
                    return 0
            return -1

    def digitize_impl(x, bins, right=False):

        mono = _monotonicity(bins)

        if mono == 0:
            raise ValueError(
                "bins must be monotonically increasing or decreasing"
            )

        # this is backwards because the arguments below are swapped
        if right:
            if mono == -1:
                # reverse the bins, and invert the results
                return len(bins) - np.searchsorted(bins[::-1], x, side='left')
            else:
                return np.searchsorted(bins, x, side='left')
        else:
            if mono == -1:
                # reverse the bins, and invert the results
                return len(bins) - np.searchsorted(bins[::-1], x, side='right')
            else:
                return np.searchsorted(bins, x, side='right')

    return digitize_impl


_range = range


@overload(np.histogram)
def np_histogram(a, bins=10, range=None):
    if isinstance(bins, (int, types.Integer)):
        # With a uniform distribution of bins, use a fast algorithm
        # independent of the number of bins

        if range in (None, types.none):
            inf = float('inf')

            def histogram_impl(a, bins=10, range=None):
                bin_min = inf
                bin_max = -inf
                for view in np.nditer(a):
                    v = view.item()
                    if bin_min > v:
                        bin_min = v
                    if bin_max < v:
                        bin_max = v
                return np.histogram(a, bins, (bin_min, bin_max))

        else:
            def histogram_impl(a, bins=10, range=None):
                if bins <= 0:
                    raise ValueError("histogram(): `bins` should be a "
                                     "positive integer")
                bin_min, bin_max = range
                if not bin_min <= bin_max:
                    raise ValueError("histogram(): max must be larger than "
                                     "min in range parameter")

                hist = np.zeros(bins, np.intp)
                if bin_max > bin_min:
                    bin_ratio = bins / (bin_max - bin_min)
                    for view in np.nditer(a):
                        v = view.item()
                        b = math.floor((v - bin_min) * bin_ratio)
                        if 0 <= b < bins:
                            hist[int(b)] += 1
                        elif v == bin_max:
                            hist[bins - 1] += 1

                bins_array = np.linspace(bin_min, bin_max, bins + 1)
                return hist, bins_array

    else:
        # With a custom bins array, use a bisection search

        def histogram_impl(a, bins=10, range=None):
            nbins = len(bins) - 1
            for i in _range(nbins):
                # Note this also catches NaNs
                if not bins[i] <= bins[i + 1]:
                    raise ValueError("histogram(): bins must increase "
                                     "monotonically")

            bin_min = bins[0]
            bin_max = bins[nbins]
            hist = np.zeros(nbins, np.intp)

            if nbins > 0:
                for view in np.nditer(a):
                    v = view.item()
                    if not bin_min <= v <= bin_max:
                        # Value is out of bounds, ignore (also catches NaNs)
                        continue
                    # Bisect in bins[:-1]
                    lo = 0
                    hi = nbins - 1
                    while lo < hi:
                        # Note the `+ 1` is necessary to avoid an infinite
                        # loop where mid = lo => lo = mid
                        mid = (lo + hi + 1) >> 1
                        if v < bins[mid]:
                            hi = mid - 1
                        else:
                            lo = mid
                    hist[lo] += 1

            return hist, bins

    return histogram_impl


# Create np.finfo, np.iinfo and np.MachAr
# machar
_mach_ar_supported = ('ibeta', 'it', 'machep', 'eps', 'negep', 'epsneg',
                      'iexp', 'minexp', 'xmin', 'maxexp', 'xmax', 'irnd',
                      'ngrd', 'epsilon', 'tiny', 'huge', 'precision',
                      'resolution',)
MachAr = namedtuple('MachAr', _mach_ar_supported)

# Do not support MachAr field
# finfo
_finfo_supported = ('eps', 'epsneg', 'iexp', 'machep', 'max', 'maxexp', 'min',
                    'minexp', 'negep', 'nexp', 'nmant', 'precision',
                    'resolution', 'tiny', 'bits',)


finfo = namedtuple('finfo', _finfo_supported)

# iinfo
_iinfo_supported = ('min', 'max', 'bits',)

iinfo = namedtuple('iinfo', _iinfo_supported)


# This module is imported under the compiler lock which should deal with the
# lack of thread safety in the warning filter.
def _gen_np_machar():
    # NumPy 1.24 removed np.MachAr
    if numpy_version >= (1, 24):
        return

    w = None
    with warnings.catch_warnings(record=True) as w:
        msg = r'`np.MachAr` is deprecated \(NumPy 1.22\)'
        warnings.filterwarnings("always", message=msg,
                                category=DeprecationWarning,
                                module=r'.*numba.*arraymath')
        np_MachAr = np.MachAr

    @overload(np_MachAr)
    def MachAr_impl():
        f = np_MachAr()
        _mach_ar_data = tuple([getattr(f, x) for x in _mach_ar_supported])

        if w:
            wmsg = w[0]
            warnings.warn_explicit(wmsg.message.args[0],
                                   NumbaDeprecationWarning,
                                   wmsg.filename,
                                   wmsg.lineno)

        def impl():
            return MachAr(*_mach_ar_data)
        return impl


_gen_np_machar()


def generate_xinfo_body(arg, np_func, container, attr):
    nbty = getattr(arg, 'dtype', arg)
    np_dtype = as_dtype(nbty)
    try:
        f = np_func(np_dtype)
    except ValueError: # This exception instance comes from NumPy
        # The np function might not support the dtype
        return None
    data = tuple([getattr(f, x) for x in attr])

    @register_jitable
    def impl(arg):
        return container(*data)
    return impl


@overload(np.finfo)
def ol_np_finfo(dtype):
    fn = generate_xinfo_body(dtype, np.finfo, finfo, _finfo_supported)

    def impl(dtype):
        return fn(dtype)
    return impl


@overload(np.iinfo)
def ol_np_iinfo(int_type):
    fn = generate_xinfo_body(int_type, np.iinfo, iinfo, _iinfo_supported)

    def impl(int_type):
        return fn(int_type)
    return impl


def _get_inner_prod(dta, dtb):
    # gets an inner product implementation, if both types are float then
    # BLAS is used else a local function

    @register_jitable
    def _innerprod(a, b):
        acc = 0
        for i in range(len(a)):
            acc = acc + a[i] * b[i]
        return acc

    # no BLAS... use local function regardless
    if not _HAVE_BLAS:
        return _innerprod

    flty = types.real_domain | types.complex_domain
    floats = dta in flty and dtb in flty
    if not floats:
        return _innerprod
    else:
        a_dt = as_dtype(dta)
        b_dt = as_dtype(dtb)
        dt = np.promote_types(a_dt, b_dt)

        @register_jitable
        def _dot_wrap(a, b):
            return np.dot(a.astype(dt), b.astype(dt))
        return _dot_wrap


def _assert_1d(a, func_name):
    if isinstance(a, types.Array):
        if not a.ndim <= 1:
            raise TypingError("%s() only supported on 1D arrays " % func_name)


def _np_correlate_core(ap1, ap2, mode, direction):
    pass


@overload(_np_correlate_core)
def _np_correlate_core_impl(ap1, ap2, mode, direction):
    a_dt = as_dtype(ap1.dtype)
    b_dt = as_dtype(ap2.dtype)
    dt = np.promote_types(a_dt, b_dt)
    innerprod = _get_inner_prod(ap1.dtype, ap2.dtype)

    def impl(ap1, ap2, mode, direction):
        # Implementation loosely based on `_pyarray_correlate` from
        # https://github.com/numpy/numpy/blob/3bce2be74f228684ca2895ad02b63953f37e2a9d/numpy/core/src/multiarray/multiarraymodule.c#L1191    # noqa: E501
        # For "mode":
        # Convolve uses 'full' by default.
        # Correlate uses 'valid' by default.
        # For "direction", +1 to write the return values out in order 0->N
        # -1 to write them out N->0.

        n1 = len(ap1)
        n2 = len(ap2)

        if n1 < n2:
            # This should never occur when called by np.convolve because
            # _np_correlate.impl swaps arguments based on length.
            # The same applies for np.correlate.
            raise ValueError("'len(ap1)' must greater than 'len(ap2)'")

        length = n1
        n = n2
        if mode == "valid":
            length = length - n + 1
            n_left = 0
            n_right = 0
        elif mode == "full":
            n_right = n - 1
            n_left = n - 1
            length = length + n - 1
        elif mode == "same":
            n_left = n // 2
            n_right = n - n_left - 1
        else:
            raise ValueError(
                "Invalid 'mode', "
                "valid are 'full', 'same', 'valid'"
            )

        ret = np.zeros(length, dt)

        if direction == 1:
            idx = 0
            inc = 1
        elif direction == -1:
            idx = length - 1
            inc = -1
        else:
            raise ValueError("Invalid direction")

        for i in range(n_left):
            k = i + n - n_left
            ret[idx] = innerprod(ap1[:k], ap2[-k:])
            idx = idx + inc

        for i in range(n1 - n2 + 1):
            ret[idx] = innerprod(ap1[i : i + n2], ap2)
            idx = idx + inc

        for i in range(n_right):
            k = n - i - 1
            ret[idx] = innerprod(ap1[-k:], ap2[:k])
            idx = idx + inc

        return ret

    return impl


@overload(np.correlate)
def _np_correlate(a, v, mode="valid"):
    _assert_1d(a, 'np.correlate')
    _assert_1d(v, 'np.correlate')

    @register_jitable
    def op_conj(x):
        return np.conj(x)

    @register_jitable
    def op_nop(x):
        return x

    if a.dtype in types.complex_domain:
        if v.dtype in types.complex_domain:
            a_op = op_nop
            b_op = op_conj
        else:
            a_op = op_nop
            b_op = op_nop
    else:
        if v.dtype in types.complex_domain:
            a_op = op_nop
            b_op = op_conj
        else:
            a_op = op_conj
            b_op = op_nop

    def impl(a, v, mode="valid"):
        la = len(a)
        lv = len(v)

        if la == 0:
            raise ValueError("'a' cannot be empty")
        if lv == 0:
            raise ValueError("'v' cannot be empty")

        if la < lv:
            return _np_correlate_core(b_op(v), a_op(a), mode, -1)
        else:
            return _np_correlate_core(a_op(a), b_op(v), mode, 1)

    return impl


@overload(np.convolve)
def np_convolve(a, v, mode="full"):
    _assert_1d(a, 'np.convolve')
    _assert_1d(v, 'np.convolve')

    def impl(a, v, mode="full"):
        la = len(a)
        lv = len(v)

        if la == 0:
            raise ValueError("'a' cannot be empty")
        if lv == 0:
            raise ValueError("'v' cannot be empty")

        if la < lv:
            return _np_correlate_core(v, a[::-1], mode, 1)
        else:
            return _np_correlate_core(a, v[::-1], mode, 1)

    return impl


@overload(np.asarray)
def np_asarray(a, dtype=None):

    # developer note... keep this function (type_can_asarray) in sync with the
    # accepted types implementations below!
    if not type_can_asarray(a):
        return None

    if isinstance(a, types.Array):
        if is_nonelike(dtype) or a.dtype == dtype.dtype:
            def impl(a, dtype=None):
                return a
        else:
            def impl(a, dtype=None):
                return a.astype(dtype)
    elif isinstance(a, (types.Sequence, types.Tuple)):
        # Nested lists cannot be unpacked, therefore only single lists are
        # permitted and these conform to Sequence and can be unpacked along on
        # the same path as Tuple.
        if is_nonelike(dtype):
            def impl(a, dtype=None):
                return np.array(a)
        else:
            def impl(a, dtype=None):
                return np.array(a, dtype)
    elif isinstance(a, (types.Number, types.Boolean)):
        dt_conv = a if is_nonelike(dtype) else dtype
        ty = as_dtype(dt_conv)

        def impl(a, dtype=None):
            return np.array(a, ty)
    elif isinstance(a, types.containers.ListType):
        if not isinstance(a.dtype, (types.Number, types.Boolean)):
            raise TypingError(
                "asarray support for List is limited "
                "to Boolean and Number types")

        target_dtype = a.dtype if is_nonelike(dtype) else dtype

        def impl(a, dtype=None):
            l = len(a)
            ret = np.empty(l, dtype=target_dtype)
            for i, v in enumerate(a):
                ret[i] = v
            return ret
    elif isinstance(a, types.StringLiteral):
        arr = np.asarray(a.literal_value)

        def impl(a, dtype=None):
            return arr.copy()
    else:
        impl = None

    return impl


if numpy_version < (2, 0):
    @overload(np.asfarray)
    def np_asfarray(a, dtype=np.float64):
        # convert numba dtype types into NumPy dtype
        if isinstance(dtype, types.Type):
            dtype = as_dtype(dtype)
        if not np.issubdtype(dtype, np.inexact):
            dx = types.float64
        else:
            dx = dtype

        def impl(a, dtype=np.float64):
            return np.asarray(a, dx)
        return impl


@overload(np.extract)
def np_extract(condition, arr):

    def np_extract_impl(condition, arr):
        cond = np.asarray(condition).flatten()
        a = np.asarray(arr)

        if a.size == 0:
            raise ValueError('Cannot extract from an empty array')

        # the following looks odd but replicates NumPy...
        # https://github.com/numpy/numpy/issues/12859
        if np.any(cond[a.size:]) and cond.size > a.size:
            msg = 'condition shape inconsistent with arr shape'
            raise ValueError(msg)
            # NumPy raises IndexError: index 'm' is out of
            # bounds for size 'n'

        max_len = min(a.size, cond.size)
        out = [a.flat[idx] for idx in range(max_len) if cond[idx]]

        return np.array(out)

    return np_extract_impl


@overload(np.select)
def np_select(condlist, choicelist, default=0):

    def np_select_arr_impl(condlist, choicelist, default=0):
        if len(condlist) != len(choicelist):
            raise ValueError('list of cases must be same length as list '
                             'of conditions')
        out = default * np.ones(choicelist[0].shape, choicelist[0].dtype)
        # should use reversed+zip, but reversed is not available
        for i in range(len(condlist) - 1, -1, -1):
            cond = condlist[i]
            choice = choicelist[i]
            out = np.where(cond, choice, out)
        return out

    # first we check the types of the input parameters
    if not isinstance(condlist, (types.List, types.UniTuple)):
        raise NumbaTypeError('condlist must be a List or a Tuple')
    if not isinstance(choicelist, (types.List, types.UniTuple)):
        raise NumbaTypeError('choicelist must be a List or a Tuple')
    if not isinstance(default, (int, types.Number, types.Boolean)):
        raise NumbaTypeError('default must be a scalar (number or boolean)')
    # the types of the parameters have been checked, now we test the types
    # of the content of the parameters
    # implementation note: if in the future numba's np.where accepts tuples
    # as elements of condlist, then the check below should be extended to
    # accept tuples
    if not isinstance(condlist[0], types.Array):
        raise NumbaTypeError('items of condlist must be arrays')
    if not isinstance(choicelist[0], types.Array):
        raise NumbaTypeError('items of choicelist must be arrays')
    # the types of the parameters and their contents have been checked,
    # now we test the dtypes of the content of parameters
    if isinstance(condlist[0], types.Array):
        if not isinstance(condlist[0].dtype, types.Boolean):
            raise NumbaTypeError('condlist arrays must contain booleans')
    if isinstance(condlist[0], types.UniTuple):
        if not (isinstance(condlist[0], types.UniTuple)
                and isinstance(condlist[0][0], types.Boolean)):
            raise NumbaTypeError('condlist tuples must only contain booleans')
    # the input types are correct, now we perform checks on the dimensions
    if (isinstance(condlist[0], types.Array) and
            condlist[0].ndim != choicelist[0].ndim):
        raise NumbaTypeError('condlist and choicelist elements must have the '
                             'same number of dimensions')
    if isinstance(condlist[0], types.Array) and condlist[0].ndim < 1:
        raise NumbaTypeError('condlist arrays must be of at least dimension 1')

    return np_select_arr_impl


@overload(np.union1d)
def np_union1d(ar1, ar2):
    if not type_can_asarray(ar1) or not type_can_asarray(ar2):
        raise TypingError("The arguments to np.union1d must be array-like")
    if (('unichr' in ar1.dtype.name or 'unichr' in ar2.dtype.name) and
       ar1.dtype.name != ar2.dtype.name):
        raise TypingError("For Unicode arrays, arrays must have same dtype")

    def union_impl(ar1, ar2):
        a = np.ravel(np.asarray(ar1))
        b = np.ravel(np.asarray(ar2))
        return np.unique(np.concatenate((a, b)))

    return union_impl


@overload(np.asarray_chkfinite)
def np_asarray_chkfinite(a, dtype=None):

    msg = "The argument to np.asarray_chkfinite must be array-like"
    if not isinstance(a, (types.Array, types.Sequence, types.Tuple)):
        raise TypingError(msg)

    if is_nonelike(dtype):
        dt = a.dtype
    else:
        try:
            dt = as_dtype(dtype)
        except NumbaNotImplementedError:
            raise TypingError('dtype must be a valid Numpy dtype')

    def impl(a, dtype=None):
        a = np.asarray(a, dtype=dt)
        for i in np.nditer(a):
            if not np.isfinite(i):
                raise ValueError("array must not contain infs or NaNs")
        return a

    return impl


@overload(np.unwrap)
def numpy_unwrap(p, discont=None, axis=-1, period=6.283185307179586):
    if not isinstance(axis, (int, types.Integer)):
        msg = 'The argument "axis" must be an integer'
        raise TypingError(msg)

    if not type_can_asarray(p):
        msg = 'The argument "p" must be array-like'
        raise TypingError(msg)

    if (not isinstance(discont, (types.Integer, types.Float))
            and not cgutils.is_nonelike(discont)):
        msg = 'The argument "discont" must be a scalar'
        raise TypingError(msg)

    if not isinstance(period, (float, types.Number)):
        msg = 'The argument "period" must be a scalar'
        raise TypingError(msg)

    slice1 = (slice(1, None, None),)
    if isinstance(period, types.Number):
        dtype = np.result_type(as_dtype(p.dtype), as_dtype(period))
    else:
        dtype = np.result_type(as_dtype(p.dtype), np.float64)

    integer_input = np.issubdtype(dtype, np.integer)

    def impl(p, discont=None, axis=-1, period=6.283185307179586):
        if axis != -1:
            msg = 'Value for argument "axis" is not supported'
            raise ValueError(msg)
        # Flatten to a 2D array, keeping axis -1
        p_init = np.asarray(p).astype(dtype)
        init_shape = p_init.shape
        last_axis = init_shape[-1]
        p_new = p_init.reshape((p_init.size // last_axis, last_axis))
        # Manipulate discont and period
        if discont is None:
            discont = period / 2
        if integer_input:
            interval_high, rem = divmod(period, 2)
            boundary_ambiguous = rem == 0
        else:
            interval_high = period / 2
            boundary_ambiguous = True
        interval_low = -interval_high

        # Work on each row separately
        for i in range(p_init.size // last_axis):
            row = p_new[i]
            dd = np.diff(row)
            ddmod = np.mod(dd - interval_low, period) + interval_low
            if boundary_ambiguous:
                ddmod = np.where((ddmod == interval_low) & (dd > 0),
                                 interval_high, ddmod)
            ph_correct = ddmod - dd

            ph_correct = np.where(np.array([abs(x) for x in dd]) < discont, 0,
                                  ph_correct)
            ph_ravel = np.where(np.array([abs(x) for x in dd]) < discont, 0,
                                ph_correct)
            ph_correct = np.reshape(ph_ravel, ph_correct.shape)
            up = np.copy(row)
            up[slice1] = row[slice1] + ph_correct.cumsum()
            p_new[i] = up

        return p_new.reshape(init_shape)

    return impl

#----------------------------------------------------------------------------
# Windowing functions
#   - translated from the numpy implementations found in:
#   https://github.com/numpy/numpy/blob/v1.16.1/numpy/lib/function_base.py#L2543-L3233    # noqa: E501
#   at commit: f1c4c758e1c24881560dd8ab1e64ae750
#   - and also, for NumPy >= 1.20, translated from implementations in
#   https://github.com/numpy/numpy/blob/156cd054e007b05d4ac4829e10a369d19dd2b0b1/numpy/lib/function_base.py#L2655-L3065  # noqa: E501


@register_jitable
def np_bartlett_impl(M):
    n = np.arange(1. - M, M, 2)
    return np.where(np.less_equal(n, 0), 1 + n / (M - 1), 1 - n / (M - 1))


@register_jitable
def np_blackman_impl(M):
    n = np.arange(1. - M, M, 2)
    return (0.42 + 0.5 * np.cos(np.pi * n / (M - 1)) +
            0.08 * np.cos(2.0 * np.pi * n / (M - 1)))


@register_jitable
def np_hamming_impl(M):
    n = np.arange(1 - M, M, 2)
    return 0.54 + 0.46 * np.cos(np.pi * n / (M - 1))


@register_jitable
def np_hanning_impl(M):
    n = np.arange(1 - M, M, 2)
    return 0.5 + 0.5 * np.cos(np.pi * n / (M - 1))


def window_generator(func):
    def window_overload(M):
        if not isinstance(M, types.Integer):
            raise TypingError('M must be an integer')

        def window_impl(M):

            if M < 1:
                return np.array((), dtype=np.float64)
            if M == 1:
                return np.ones(1, dtype=np.float64)
            return func(M)

        return window_impl
    return window_overload


overload(np.bartlett)(window_generator(np_bartlett_impl))
overload(np.blackman)(window_generator(np_blackman_impl))
overload(np.hamming)(window_generator(np_hamming_impl))
overload(np.hanning)(window_generator(np_hanning_impl))


_i0A = np.array([
    -4.41534164647933937950E-18,
    3.33079451882223809783E-17,
    -2.43127984654795469359E-16,
    1.71539128555513303061E-15,
    -1.16853328779934516808E-14,
    7.67618549860493561688E-14,
    -4.85644678311192946090E-13,
    2.95505266312963983461E-12,
    -1.72682629144155570723E-11,
    9.67580903537323691224E-11,
    -5.18979560163526290666E-10,
    2.65982372468238665035E-9,
    -1.30002500998624804212E-8,
    6.04699502254191894932E-8,
    -2.67079385394061173391E-7,
    1.11738753912010371815E-6,
    -4.41673835845875056359E-6,
    1.64484480707288970893E-5,
    -5.75419501008210370398E-5,
    1.88502885095841655729E-4,
    -5.76375574538582365885E-4,
    1.63947561694133579842E-3,
    -4.32430999505057594430E-3,
    1.05464603945949983183E-2,
    -2.37374148058994688156E-2,
    4.93052842396707084878E-2,
    -9.49010970480476444210E-2,
    1.71620901522208775349E-1,
    -3.04682672343198398683E-1,
    6.76795274409476084995E-1,
])

_i0B = np.array([
    -7.23318048787475395456E-18,
    -4.83050448594418207126E-18,
    4.46562142029675999901E-17,
    3.46122286769746109310E-17,
    -2.82762398051658348494E-16,
    -3.42548561967721913462E-16,
    1.77256013305652638360E-15,
    3.81168066935262242075E-15,
    -9.55484669882830764870E-15,
    -4.15056934728722208663E-14,
    1.54008621752140982691E-14,
    3.85277838274214270114E-13,
    7.18012445138366623367E-13,
    -1.79417853150680611778E-12,
    -1.32158118404477131188E-11,
    -3.14991652796324136454E-11,
    1.18891471078464383424E-11,
    4.94060238822496958910E-10,
    3.39623202570838634515E-9,
    2.26666899049817806459E-8,
    2.04891858946906374183E-7,
    2.89137052083475648297E-6,
    6.88975834691682398426E-5,
    3.36911647825569408990E-3,
    8.04490411014108831608E-1,
])


@register_jitable
def _chbevl(x, vals):
    b0 = vals[0]
    b1 = 0.0

    for i in range(1, len(vals)):
        b2 = b1
        b1 = b0
        b0 = x * b1 - b2 + vals[i]

    return 0.5 * (b0 - b2)


@register_jitable
def _i0(x):
    if x < 0:
        x = -x
    if x <= 8.0:
        y = (0.5 * x) - 2.0
        return np.exp(x) * _chbevl(y, _i0A)

    return np.exp(x) * _chbevl(32.0 / x - 2.0, _i0B) / np.sqrt(x)


@register_jitable
def _i0n(n, alpha, beta):
    y = np.empty_like(n, dtype=np.float64)
    t = _i0(np.float64(beta))
    for i in range(len(y)):
        y[i] = _i0(beta * np.sqrt(1 - ((n[i] - alpha) / alpha)**2.0)) / t

    return y


@overload(np.kaiser)
def np_kaiser(M, beta):
    if not isinstance(M, types.Integer):
        raise TypingError('M must be an integer')

    if not isinstance(beta, (types.Integer, types.Float)):
        raise TypingError('beta must be an integer or float')

    def np_kaiser_impl(M, beta):
        if M < 1:
            return np.array((), dtype=np.float64)
        if M == 1:
            return np.ones(1, dtype=np.float64)

        n = np.arange(0, M)
        alpha = (M - 1) / 2.0

        return _i0n(n, alpha, beta)

    return np_kaiser_impl


@register_jitable
def _cross_operation(a, b, out):

    def _cross_preprocessing(x):
        x0 = x[..., 0]
        x1 = x[..., 1]
        if x.shape[-1] == 3:
            x2 = x[..., 2]
        else:
            x2 = np.multiply(x.dtype.type(0), x0)
        return x0, x1, x2

    a0, a1, a2 = _cross_preprocessing(a)
    b0, b1, b2 = _cross_preprocessing(b)

    cp0 = np.multiply(a1, b2) - np.multiply(a2, b1)
    cp1 = np.multiply(a2, b0) - np.multiply(a0, b2)
    cp2 = np.multiply(a0, b1) - np.multiply(a1, b0)

    out[..., 0] = cp0
    out[..., 1] = cp1
    out[..., 2] = cp2


def _cross(a, b):
    pass


@overload(_cross)
def _cross_impl(a, b):
    dtype = np.promote_types(as_dtype(a.dtype), as_dtype(b.dtype))
    if a.ndim == 1 and b.ndim == 1:
        def impl(a, b):
            cp = np.empty((3,), dtype)
            _cross_operation(a, b, cp)
            return cp
    else:
        def impl(a, b):
            shape = np.add(a[..., 0], b[..., 0]).shape
            cp = np.empty(shape + (3,), dtype)
            _cross_operation(a, b, cp)
            return cp
    return impl


@overload(np.cross)
def np_cross(a, b):
    if not type_can_asarray(a) or not type_can_asarray(b):
        raise TypingError("Inputs must be array-like.")

    def impl(a, b):
        a_ = np.asarray(a)
        b_ = np.asarray(b)
        if a_.shape[-1] not in (2, 3) or b_.shape[-1] not in (2, 3):
            raise ValueError((
                "Incompatible dimensions for cross product\n"
                "(dimension must be 2 or 3)"
            ))

        if a_.shape[-1] == 3 or b_.shape[-1] == 3:
            return _cross(a_, b_)
        else:
            raise ValueError((
                "Dimensions for both inputs is 2.\n"
                "Please replace your numpy.cross(a, b) call with "
                "a call to `cross2d(a, b)` from `numba.np.extensions`."
            ))
    return impl


@register_jitable
def _cross2d_operation(a, b):

    def _cross_preprocessing(x):
        x0 = x[..., 0]
        x1 = x[..., 1]
        return x0, x1

    a0, a1 = _cross_preprocessing(a)
    b0, b1 = _cross_preprocessing(b)

    cp = np.multiply(a0, b1) - np.multiply(a1, b0)
    # If ndim of a and b is 1, cp is a scalar.
    # In this case np.cross returns a 0-D array, containing the scalar.
    # np.asarray is used to reconcile this case, without introducing
    # overhead in the case where cp is an actual N-D array.
    # (recall that np.asarray does not copy existing arrays)
    return np.asarray(cp)


def cross2d(a, b):
    pass


@overload(cross2d)
def cross2d_impl(a, b):
    if not type_can_asarray(a) or not type_can_asarray(b):
        raise TypingError("Inputs must be array-like.")

    def impl(a, b):
        a_ = np.asarray(a)
        b_ = np.asarray(b)
        if a_.shape[-1] != 2 or b_.shape[-1] != 2:
            raise ValueError((
                "Incompatible dimensions for 2D cross product\n"
                "(dimension must be 2 for both inputs)"
            ))
        return _cross2d_operation(a_, b_)

    return impl


@overload(np.trim_zeros)
def np_trim_zeros(filt, trim='fb'):
    if not isinstance(filt, types.Array):
        raise NumbaTypeError('The first argument must be an array')

    if filt.ndim > 1:
        raise NumbaTypeError('array must be 1D')

    if not isinstance(trim, (str, types.UnicodeType)):
        raise NumbaTypeError('The second argument must be a string')

    trim_escapes = numpy_version >= (2, 2)

    def impl(filt, trim='fb'):
        a_ = np.asarray(filt)
        first = 0
        trim = trim.lower()
        if 'f' in trim:
            for i in a_:
                if i == 0 or (trim_escapes and i == ''):
                    first = first + 1
                else:
                    break
        last = len(filt)
        if 'b' in trim:
            for i in a_[::-1]:
                if i == 0 or (trim_escapes and i == ''):
                    last = last - 1
                else:
                    break
        return a_[first:last]

    return impl


@overload(np.setxor1d)
def jit_np_setxor1d(ar1, ar2, assume_unique=False):
    if not (type_can_asarray(ar1) or type_can_asarray(ar2)):
        raise TypingError('setxor1d: first two args must be array-like')
    if not (isinstance(assume_unique, (types.Boolean, bool))):
        raise TypingError('setxor1d: Argument "assume_unique" must be boolean')

    # https://github.com/numpy/numpy/blob/03b62604eead0f7d279a5a4c094743eb29647368/numpy/lib/arraysetops.py#L477 # noqa: E501
    def np_setxor1d_impl(ar1, ar2, assume_unique=False):
        a = np.asarray(ar1)
        b = np.asarray(ar2)

        if not assume_unique:
            a = np.unique(a)
            b = np.unique(b)
        else:
            a = a.ravel()
            b = b.ravel()

        # Implementation very similar to np_intersect1d_impl:
        # We want union minus the intersect
        aux = np.concatenate((a, b))
        aux.sort()

        flag = np.empty(aux.shape[0] + 1, dtype=np.bool_)
        flag[0] = True
        flag[-1] = True
        flag[1:-1] = aux[1:] != aux[:-1]
        return aux[flag[1:] & flag[:-1]]

    return np_setxor1d_impl


# Internal helper for in1d functionality, used by isin and setdiff1d
# This is needed because np.in1d was removed in NumPy 2.4
@register_jitable
def _in1d_impl(ar1, ar2, assume_unique=False, invert=False):
    # https://github.com/numpy/numpy/blob/03b62604eead0f7d279a5a4c094743eb29647368/numpy/lib/arraysetops.py#L525 # noqa: E501

    # Ravel both arrays, behavior for the first array could be different
    ar1 = np.asarray(ar1).ravel()
    ar2 = np.asarray(ar2).ravel()

    # This code is run when it would make the code significantly faster
    # Sorting is also not guaranteed to work on objects but numba does
    # not support object arrays.
    if len(ar2) < 10 * len(ar1) ** 0.145:
        if invert:
            mask = np.ones(len(ar1), dtype=np.bool_)
            for a in ar2:
                mask &= (ar1 != a)
        else:
            mask = np.zeros(len(ar1), dtype=np.bool_)
            for a in ar2:
                mask |= (ar1 == a)
        return mask

    # Otherwise use sorting
    if not assume_unique:
        # Equivalent to ar1, inv_idx = np.unique(ar1, return_inverse=True)
        # https://github.com/numpy/numpy/blob/03b62604eead0f7d279a5a4c094743eb29647368/numpy/lib/arraysetops.py#L358C8-L358C8 # noqa: E501
        order1 = np.argsort(ar1)
        aux = ar1[order1]
        mask = np.empty(aux.shape, dtype=np.bool_)
        mask[:1] = True
        mask[1:] = aux[1:] != aux[:-1]
        ar1 = aux[mask]
        imask = np.cumsum(mask) - 1
        inv_idx = np.empty(mask.shape, dtype=np.intp)
        inv_idx[order1] = imask
        ar2 = np.unique(ar2)

    ar = np.concatenate((ar1, ar2))
    # We need this to be a stable sort, so always use 'mergesort'
    # here. The values from the first array should always come before
    # the values from the second array.
    order = ar.argsort(kind='mergesort')
    sar = ar[order]
    flag = np.empty(sar.size, np.bool_)
    if invert:
        flag[:-1] = (sar[1:] != sar[:-1])
    else:
        flag[:-1] = (sar[1:] == sar[:-1])
    flag[-1:] = invert
    ret = np.empty(ar.shape, dtype=np.bool_)
    ret[order] = flag

    # return ret[:len(ar1)]
    if assume_unique:
        return ret[:len(ar1)]
    else:
        return ret[inv_idx]


@overload(np.setdiff1d)
def jit_np_setdiff1d(ar1, ar2, assume_unique=False):
    if not (type_can_asarray(ar1) or type_can_asarray(ar2)):
        raise TypingError('setdiff1d: first two args must be array-like')
    if not (isinstance(assume_unique, (types.Boolean, bool))):
        raise TypingError('setdiff1d: Argument "assume_unique" must be boolean')

    # https://github.com/numpy/numpy/blob/03b62604eead0f7d279a5a4c094743eb29647368/numpy/lib/arraysetops.py#L940 # noqa: E501
    def np_setdiff1d_impl(ar1, ar2, assume_unique=False):
        ar1 = np.asarray(ar1)
        ar2 = np.asarray(ar2)
        if assume_unique:
            ar1 = ar1.ravel()
            ar2 = ar2.ravel()
        else:
            ar1 = np.unique(ar1)
            ar2 = np.unique(ar2)
        return ar1[_in1d_impl(ar1, ar2, assume_unique=True, invert=True)]

    return np_setdiff1d_impl


def jit_np_in1d(ar1, ar2, assume_unique=False, invert=False):
    if not (type_can_asarray(ar1) or type_can_asarray(ar2)):
        raise TypingError('in1d: first two args must be array-like')
    if not isinstance(assume_unique, (types.Boolean, bool)):
        raise TypingError('in1d: Argument "assume_unique" must be boolean')
    if not isinstance(invert, (types.Boolean, bool)):
        raise TypingError('in1d: Argument "invert" must be boolean')

    def np_in1d_impl(ar1, ar2, assume_unique=False, invert=False):
        return _in1d_impl(ar1, ar2, assume_unique, invert)

    return np_in1d_impl


# np.in1d was removed in NumPy 2.4
if numpy_version < (2, 4):
    overload(np.in1d)(jit_np_in1d)


@overload(np.isin)
def jit_np_isin(element, test_elements, assume_unique=False, invert=False):
    if not (type_can_asarray(element) or type_can_asarray(test_elements)):
        raise TypingError('isin: first two args must be array-like')
    if not (isinstance(assume_unique, (types.Boolean, bool))):
        raise TypingError('isin: Argument "assume_unique" must be boolean')
    if not (isinstance(invert, (types.Boolean, bool))):
        raise TypingError('isin: Argument "invert" must be boolean')

    # https://github.com/numpy/numpy/blob/03b62604eead0f7d279a5a4c094743eb29647368/numpy/lib/arraysetops.py#L889 # noqa: E501
    def np_isin_impl(element, test_elements, assume_unique=False, invert=False):

        element = np.asarray(element)
        return _in1d_impl(element, test_elements, assume_unique=assume_unique,
                          invert=invert).reshape(element.shape)

    return np_isin_impl
