from __future__ import division

from itertools import product, cycle
import sys

import numpy as np

from numba import unittest_support as unittest
from numba import jit, typeof, types
from numba.compiler import compile_isolated
from numba.errors import TypingError, LoweringError
from numba.numpy_support import (as_dtype, strict_ufunc_typing,
                                 version as numpy_version)
from .support import TestCase, CompilationCache, MemoryLeak, MemoryLeakMixin, tag


def np_around_array(arr, decimals, out):
    np.around(arr, decimals, out)

def np_around_binary(val, decimals):
    return np.around(val, decimals)

def np_around_unary(val):
    return np.around(val)

def np_round_array(arr, decimals, out):
    np.round(arr, decimals, out)

def np_round_binary(val, decimals):
    return np.round(val, decimals)

def np_round_unary(val):
    return np.round(val)

def _fixed_np_round(arr, decimals=0, out=None):
    """
    A slightly bugfixed version of np.round().
    """
    if out is not None and arr.dtype.kind == 'c':
        # workaround for https://github.com/numpy/numpy/issues/5779
        _fixed_np_round(arr.real, decimals, out.real)
        _fixed_np_round(arr.imag, decimals, out.imag)
        return out
    else:
        res = np.round(arr, decimals, out)
        if out is None:
            # workaround for https://github.com/numpy/numpy/issues/5780
            def fixup_signed_zero(arg, res):
                if res == 0.0 and arg < 0:
                    return -np.abs(res)
                else:
                    return res
            if isinstance(arr, (complex, np.complexfloating)):
                res = complex(fixup_signed_zero(arr.real, res.real),
                              fixup_signed_zero(arr.imag, res.imag))
            else:
                res = fixup_signed_zero(arr, res)
        return res


def array_T(arr):
    return arr.T

def array_transpose(arr):
    return arr.transpose()

def array_copy(arr):
    return arr.copy()

def np_copy(arr):
    return np.copy(arr)

def np_asfortranarray(arr):
    return np.asfortranarray(arr)

def np_ascontiguousarray(arr):
    return np.ascontiguousarray(arr)

def array_view(arr, newtype):
    return arr.view(newtype)

def array_take(arr, indices):
    return arr.take(indices)

def array_take_kws(arr, indices, axis):
    return arr.take(indices, axis=axis)

def array_fill(arr, val):
    return arr.fill(val)

# XXX Can't pass a dtype as a Dispatcher argument for now
def make_array_view(newtype):
    def array_view(arr):
        return arr.view(newtype)
    return array_view

def array_sliced_view(arr, ):
    return arr[0:4].view(np.float32)[0]

def make_array_astype(newtype):
    def array_astype(arr):
        return arr.astype(newtype)
    return array_astype


def np_frombuffer(b):
    """
    np.frombuffer() on a Python-allocated buffer.
    """
    return np.frombuffer(b)

def np_frombuffer_dtype(b):
    return np.frombuffer(b, dtype=np.complex64)

def np_frombuffer_allocated(shape):
    """
    np.frombuffer() on a Numba-allocated buffer.
    """
    arr = np.ones(shape, dtype=np.int32)
    return np.frombuffer(arr)

def np_frombuffer_allocated_dtype(shape):
    arr = np.ones(shape, dtype=np.int32)
    return np.frombuffer(arr, dtype=np.complex64)

def identity_usecase(a, b):
    return (a is b), (a is not b)

def array_nonzero(a):
    return a.nonzero()

def np_nonzero(a):
    return np.nonzero(a)

def np_where_1(c):
    return np.where(c)

def np_where_3(c, x, y):
    return np.where(c, x, y)

def array_item(a):
    return a.item()

def array_itemset(a, v):
    a.itemset(v)

def array_sum(a, *args):
    return a.sum(*args)

def array_sum_kws(a, axis):
    return a.sum(axis=axis)

def array_sum_const_multi(arr, axis):
    # use np.sum with different constant args multiple times to check
    # for internal compile cache to see if constant-specialization is
    # applied properly.
    a = np.sum(arr, axis=4)
    b = np.sum(arr, 3)
    # the last invocation uses runtime-variable
    c = np.sum(arr, axis)
    # as method
    d = arr.sum(axis=5)
    # negative const axis
    e = np.sum(arr, axis=-1)
    return a, b, c, d, e

def array_cumsum(a, *args):
    return a.cumsum(*args)

def array_cumsum_kws(a, axis):
    return a.cumsum(axis=axis)


class TestArrayMethods(MemoryLeakMixin, TestCase):
    """
    Test various array methods and array-related functions.
    """

    def setUp(self):
        super(TestArrayMethods, self).setUp()
        self.ccache = CompilationCache()

    def check_round_scalar(self, unary_pyfunc, binary_pyfunc):
        base_values = [-3.0, -2.5, -2.25, -1.5, 1.5, 2.25, 2.5, 2.75]
        complex_values = [x * (1 - 1j) for x in base_values]
        int_values = [int(x) for x in base_values]
        argtypes = (types.float64, types.float32, types.int32,
                    types.complex64, types.complex128)
        argvalues = [base_values, base_values, int_values,
                     complex_values, complex_values]

        pyfunc = binary_pyfunc
        for ty, values in zip(argtypes, argvalues):
            cres = compile_isolated(pyfunc, (ty, types.int32))
            cfunc = cres.entry_point
            for decimals in (1, 0, -1):
                for v in values:
                    if decimals > 0:
                        v *= 10
                    expected = _fixed_np_round(v, decimals)
                    got = cfunc(v, decimals)
                    self.assertPreciseEqual(got, expected)

        pyfunc = unary_pyfunc
        for ty, values in zip(argtypes, argvalues):
            cres = compile_isolated(pyfunc, (ty,))
            cfunc = cres.entry_point
            for v in values:
                expected = _fixed_np_round(v)
                got = cfunc(v)
                self.assertPreciseEqual(got, expected)

    def test_round_scalar(self):
        self.check_round_scalar(np_round_unary, np_round_binary)

    def test_around_scalar(self):
        self.check_round_scalar(np_around_unary, np_around_binary)

    def check_round_array(self, pyfunc):
        def check_round(cfunc, values, inty, outty, decimals):
            # Create input and output arrays of the right type
            arr = values.astype(as_dtype(inty))
            out = np.zeros_like(arr).astype(as_dtype(outty))
            pyout = out.copy()
            _fixed_np_round(arr, decimals, pyout)
            self.memory_leak_setup()
            cfunc(arr, decimals, out)
            self.memory_leak_teardown()
            np.testing.assert_allclose(out, pyout)
            # Output shape mismatch
            with self.assertRaises(ValueError) as raises:
                cfunc(arr, decimals, out[1:])
            self.assertEqual(str(raises.exception),
                             "invalid output shape")

        def check_types(argtypes, outtypes, values):
            for inty, outty in product(argtypes, outtypes):
                cres = compile_isolated(pyfunc,
                                        (types.Array(inty, 1, 'A'),
                                         types.int32,
                                         types.Array(outty, 1, 'A')))
                cfunc = cres.entry_point
                check_round(cres.entry_point, values, inty, outty, 0)
                check_round(cres.entry_point, values, inty, outty, 1)
                if not isinstance(outty, types.Integer):
                    check_round(cres.entry_point, values * 10, inty, outty, -1)
                else:
                    # Avoid Numpy bug when output is an int:
                    # https://github.com/numpy/numpy/issues/5777
                    pass

        values = np.array([-3.0, -2.5, -2.25, -1.5, 1.5, 2.25, 2.5, 2.75])

        if strict_ufunc_typing:
            argtypes = (types.float64, types.float32)
        else:
            argtypes = (types.float64, types.float32, types.int32)
        check_types(argtypes, argtypes, values)

        argtypes = (types.complex64, types.complex128)
        check_types(argtypes, argtypes, values * (1 - 1j))

        # Exceptions leak references
        self.disable_leak_check()

    def test_round_array(self):
        self.check_round_array(np_round_array)

    def test_around_array(self):
        self.check_round_array(np_around_array)

    def test_array_view(self):

        def run(arr, dtype):
            pyfunc = make_array_view(dtype)
            cres = self.ccache.compile(pyfunc, (typeof(arr),))
            return cres.entry_point(arr)
        def check(arr, dtype):
            expected = arr.view(dtype)
            self.memory_leak_setup()
            got = run(arr, dtype)
            self.assertPreciseEqual(got, expected)
            del got
            self.memory_leak_teardown()
        def check_err(arr, dtype):
            with self.assertRaises(ValueError) as raises:
                run(arr, dtype)
            self.assertEqual(str(raises.exception),
                             "new type not compatible with array")

        dt1 = np.dtype([('a', np.int8), ('b', np.int8)])
        dt2 = np.dtype([('u', np.int16), ('v', np.int8)])
        dt3 = np.dtype([('x', np.int16), ('y', np.int16)])

        # C-contiguous
        arr = np.arange(24, dtype=np.int8)
        check(arr, np.dtype('int16'))
        check(arr, np.int16)
        check(arr, np.int8)
        check(arr, np.float32)
        check(arr, np.complex64)
        check(arr, dt1)
        check(arr, dt2)
        check_err(arr, np.complex128)

        # Last dimension must have a compatible size
        arr = arr.reshape((3, 8))
        check(arr, np.int8)
        check(arr, np.float32)
        check(arr, np.complex64)
        check(arr, dt1)
        check_err(arr, dt2)
        check_err(arr, np.complex128)

        # F-contiguous
        arr = np.arange(24, dtype=np.int8).reshape((3, 8)).T
        check(arr, np.int8)
        check(arr, np.float32)
        check(arr, np.complex64)
        check(arr, dt1)
        check_err(arr, dt2)
        check_err(arr, np.complex128)

        # Non-contiguous: only a type with the same itemsize can be used
        arr = np.arange(16, dtype=np.int32)[::2]
        check(arr, np.uint32)
        check(arr, np.float32)
        check(arr, dt3)
        check_err(arr, np.int8)
        check_err(arr, np.int16)
        check_err(arr, np.int64)
        check_err(arr, dt1)
        check_err(arr, dt2)

        # Zero-dim array: only a type with the same itemsize can be used
        arr = np.array([42], dtype=np.int32).reshape(())
        check(arr, np.uint32)
        check(arr, np.float32)
        check(arr, dt3)
        check_err(arr, np.int8)
        check_err(arr, np.int16)
        check_err(arr, np.int64)
        check_err(arr, dt1)
        check_err(arr, dt2)

        # Exceptions leak references
        self.disable_leak_check()

    def test_array_sliced_view(self):
        """
        Test .view() on A layout array but has contiguous innermost dimension.
        """
        pyfunc = array_sliced_view
        cres = self.ccache.compile(pyfunc, (types.uint8[:],))
        cfunc = cres.entry_point

        orig = np.array([1.5, 2], dtype=np.float32)
        byteary = orig.view(np.uint8)

        expect = pyfunc(byteary)
        got = cfunc(byteary)

        self.assertEqual(expect, got)

    def test_array_astype(self):

        def run(arr, dtype):
            pyfunc = make_array_astype(dtype)
            cres = self.ccache.compile(pyfunc, (typeof(arr),))
            return cres.entry_point(arr)
        def check(arr, dtype):
            expected = arr.astype(dtype).copy(order='A')
            got = run(arr, dtype)
            self.assertPreciseEqual(got, expected)

        # C-contiguous
        arr = np.arange(24, dtype=np.int8)
        check(arr, np.dtype('int16'))
        check(arr, np.int32)
        check(arr, np.float32)
        check(arr, np.complex128)

        # F-contiguous
        arr = np.arange(24, dtype=np.int8).reshape((3, 8)).T
        check(arr, np.float32)

        # Non-contiguous
        arr = np.arange(16, dtype=np.int32)[::2]
        check(arr, np.uint64)

        # Invalid conversion
        dt = np.dtype([('x', np.int8)])
        with self.assertTypingError() as raises:
            check(arr, dt)
        self.assertIn('cannot convert from int32 to Record',
                      str(raises.exception))

    def check_np_frombuffer(self, pyfunc):
        def run(buf):
            cres = self.ccache.compile(pyfunc, (typeof(buf),))
            return cres.entry_point(buf)
        def check(buf):
            old_refcnt = sys.getrefcount(buf)
            expected = pyfunc(buf)
            self.memory_leak_setup()
            got = run(buf)
            self.assertPreciseEqual(got, expected)
            del expected
            self.assertEqual(sys.getrefcount(buf), old_refcnt + 1)
            del got
            self.assertEqual(sys.getrefcount(buf), old_refcnt)
            self.memory_leak_teardown()

        b = bytearray(range(16))
        check(b)
        if sys.version_info >= (3,):
            check(bytes(b))
            check(memoryview(b))
        check(np.arange(12))
        b = np.arange(12).reshape((3, 4))
        check(b)

        # Exceptions leak references
        self.disable_leak_check()

        with self.assertRaises(ValueError) as raises:
            run(bytearray(b"xxx"))
        self.assertEqual("buffer size must be a multiple of element size",
                         str(raises.exception))

    def test_np_frombuffer(self):
        self.check_np_frombuffer(np_frombuffer)

    def test_np_frombuffer_dtype(self):
        self.check_np_frombuffer(np_frombuffer_dtype)

    def check_layout_dependent_func(self, pyfunc, fac=np.arange,
                                    check_sameness=True):
        def is_same(a, b):
            return a.ctypes.data == b.ctypes.data
        def check_arr(arr):
            cres = compile_isolated(pyfunc, (typeof(arr),))
            expected = pyfunc(arr)
            got = cres.entry_point(arr)
            self.assertPreciseEqual(expected, got)
            if check_sameness:
                self.assertEqual(is_same(expected, arr), is_same(got, arr))
        arr = fac(24)
        check_arr(arr)
        check_arr(arr.reshape((3, 8)))
        check_arr(arr.reshape((3, 8)).T)
        check_arr(arr.reshape((3, 8))[::2])
        check_arr(arr.reshape((2, 3, 4)))
        check_arr(arr.reshape((2, 3, 4)).T)
        check_arr(arr.reshape((2, 3, 4))[::2])
        arr = np.array([0]).reshape(())
        check_arr(arr)

    def test_array_transpose(self):
        self.check_layout_dependent_func(array_transpose)

    @tag('important')
    def test_array_T(self):
        self.check_layout_dependent_func(array_T)

    @tag('important')
    def test_array_copy(self):
        self.check_layout_dependent_func(array_copy)

    def test_np_copy(self):
        self.check_layout_dependent_func(np_copy)

    def test_np_asfortranarray(self):
        self.check_layout_dependent_func(np_asfortranarray,
                                         check_sameness=numpy_version >= (1, 8))

    def test_np_ascontiguousarray(self):
        self.check_layout_dependent_func(np_ascontiguousarray,
                                         check_sameness=numpy_version > (1, 11))

    def check_np_frombuffer_allocated(self, pyfunc):
        def run(shape):
            cres = self.ccache.compile(pyfunc, (typeof(shape),))
            return cres.entry_point(shape)
        def check(shape):
            expected = pyfunc(shape)
            got = run(shape)
            self.assertPreciseEqual(got, expected)

        check((16,))
        check((4, 4))
        check((1, 0, 1))

    def test_np_frombuffer_allocated(self):
        self.check_np_frombuffer_allocated(np_frombuffer_allocated)

    def test_np_frombuffer_allocated(self):
        self.check_np_frombuffer_allocated(np_frombuffer_allocated_dtype)

    def check_nonzero(self, pyfunc):
        def fac(N):
            np.random.seed(42)
            arr = np.random.random(N)
            arr[arr < 0.3] = 0.0
            arr[arr > 0.7] = float('nan')
            return arr

        def check_arr(arr):
            cres = compile_isolated(pyfunc, (typeof(arr),))
            expected = pyfunc(arr)
            # NOTE: Numpy 1.9 returns readonly arrays for multidimensional
            # arrays.  Workaround this by copying the results.
            expected = [a.copy() for a in expected]
            self.assertPreciseEqual(cres.entry_point(arr), expected)

        arr = np.int16([1, 0, -1, 0])
        check_arr(arr)
        arr = np.bool_([1, 0, 1])
        check_arr(arr)

        arr = fac(24)
        check_arr(arr)
        check_arr(arr.reshape((3, 8)))
        check_arr(arr.reshape((3, 8)).T)
        check_arr(arr.reshape((3, 8))[::2])
        check_arr(arr.reshape((2, 3, 4)))
        check_arr(arr.reshape((2, 3, 4)).T)
        check_arr(arr.reshape((2, 3, 4))[::2])
        for v in (0.0, 1.5, float('nan')):
            arr = np.array([v]).reshape(())
            check_arr(arr)

    def test_array_nonzero(self):
        self.check_nonzero(array_nonzero)

    def test_np_nonzero(self):
        self.check_nonzero(np_nonzero)

    def test_np_where_1(self):
        self.check_nonzero(np_where_1)

    def test_np_where_3(self):
        pyfunc = np_where_3
        def fac(N):
            np.random.seed(42)
            arr = np.random.random(N)
            arr[arr < 0.3] = 0.0
            arr[arr > 0.7] = float('nan')
            return arr

        def check_arr(arr):
            x = np.zeros_like(arr, dtype=np.float64)
            y = np.copy(x)
            x.fill(4)
            y.fill(9)
            cres = compile_isolated(pyfunc, (typeof(arr), typeof(x), typeof(y)))
            expected = pyfunc(arr, x, y)
            got = cres.entry_point(arr, x, y)
            # Contiguity of result varies accross Numpy versions, only
            # check contents.
            self.assertEqual(got.dtype, expected.dtype)
            np.testing.assert_array_equal(got, expected)

        def check_scal(scal):
            x = 4
            y = 5
            cres = compile_isolated(pyfunc, (typeof(scal), typeof(x), typeof(y)))
            expected = pyfunc(scal, x, y)
            got = cres.entry_point(scal, x, y)
            self.assertPreciseEqual(got, expected)

        arr = np.int16([1, 0, -1, 0])
        check_arr(arr)
        arr = np.bool_([1, 0, 1])
        check_arr(arr)

        arr = fac(24)
        check_arr(arr)
        check_arr(arr.reshape((3, 8)))
        check_arr(arr.reshape((3, 8)).T)
        check_arr(arr.reshape((3, 8))[::2])
        check_arr(arr.reshape((2, 3, 4)))
        check_arr(arr.reshape((2, 3, 4)).T)
        check_arr(arr.reshape((2, 3, 4))[::2])
        for v in (0.0, 1.5, float('nan')):
            arr = np.array([v]).reshape(())
            check_arr(arr)

        for x in (0, 1, True, False, 2.5, 0j):
            check_scal(x)

    def test_item(self):
        pyfunc = array_item
        cfunc = jit(nopython=True)(pyfunc)

        def check_ok(arg):
            expected = pyfunc(arg)
            got = cfunc(arg)
            self.assertPreciseEqual(got, expected)

        def check_err(arg):
            with self.assertRaises(ValueError) as raises:
                cfunc(arg)
            self.assertIn("item(): can only convert an array of size 1 to a Python scalar",
                          str(raises.exception))

        # Exceptions leak references
        self.disable_leak_check()

        # Test on different kinds of scalars and 1-item arrays
        check_ok(np.float32([1.5]))
        check_ok(np.complex128([[1.5j]]))
        check_ok(np.array(1.5))
        check_ok(np.bool_(True))
        check_ok(np.float32(1.5))

        check_err(np.array([1, 2]))
        check_err(np.array([]))

    def test_itemset(self):
        pyfunc = array_itemset
        cfunc = jit(nopython=True)(pyfunc)

        def check_ok(a, v):
            expected = a.copy()
            got = a.copy()
            pyfunc(expected, v)
            cfunc(got, v)
            self.assertPreciseEqual(got, expected)

        def check_err(a):
            with self.assertRaises(ValueError) as raises:
                cfunc(a, 42)
            self.assertIn("itemset(): can only write to an array of size 1",
                          str(raises.exception))

        # Exceptions leak references
        self.disable_leak_check()

        # Test on different kinds of 1-item arrays
        check_ok(np.float32([1.5]), 42)
        check_ok(np.complex128([[1.5j]]), 42)
        check_ok(np.array(1.5), 42)

        check_err(np.array([1, 2]))
        check_err(np.array([]))

    def test_sum(self):
        pyfunc = array_sum
        cfunc = jit(nopython=True)(pyfunc)
        # OK
        a = np.ones((7, 6, 5, 4, 3))
        self.assertPreciseEqual(pyfunc(a), cfunc(a))
        # OK
        self.assertPreciseEqual(pyfunc(a, 0), cfunc(a, 0))

    def test_sum_kws(self):
        pyfunc = array_sum_kws
        cfunc = jit(nopython=True)(pyfunc)
        # OK
        a = np.ones((7, 6, 5, 4, 3))
        self.assertPreciseEqual(pyfunc(a, axis=1), cfunc(a, axis=1))
        # OK
        self.assertPreciseEqual(pyfunc(a, axis=2), cfunc(a, axis=2))

    def test_sum_const(self):
        pyfunc = array_sum_const_multi
        cfunc = jit(nopython=True)(pyfunc)

        arr = np.ones((3, 4, 5, 6, 7, 8))
        axis = 1
        self.assertPreciseEqual(pyfunc(arr, axis), cfunc(arr, axis))
        axis = 2
        self.assertPreciseEqual(pyfunc(arr, axis), cfunc(arr, axis))

    def test_sum_exceptions(self):
        # Exceptions leak references
        self.disable_leak_check()
        pyfunc = array_sum
        cfunc = jit(nopython=True)(pyfunc)

        a = np.ones((7, 6, 5, 4, 3))
        b = np.ones((4, 3))
        # BAD: axis > dimensions
        with self.assertRaises(ValueError):
            cfunc(b, 2)
        # BAD: negative axis
        with self.assertRaises(ValueError):
            cfunc(a, -1)
        # BAD: axis greater than 3
        with self.assertRaises(ValueError):
            cfunc(a, 4)

    def test_sum_const_negative(self):
        # Exceptions leak references
        self.disable_leak_check()

        @jit(nopython=True)
        def foo(arr):
            return arr.sum(axis=-3)

        # ndim == 4, axis == -3, OK
        a = np.ones((1, 2, 3, 4))
        self.assertPreciseEqual(foo(a), foo.py_func(a))
        # ndim == 3, axis == -3, OK
        a = np.ones((1, 2, 3))
        self.assertPreciseEqual(foo(a), foo.py_func(a))
        # ndim == 2, axis == -3, BAD
        a = np.ones((1, 2))
        with self.assertRaises(LoweringError) as raises:
            foo(a)
        errmsg = "'axis' entry is out of bounds"
        self.assertIn(errmsg, str(raises.exception))
        with self.assertRaises(ValueError) as raises:
            foo.py_func(a)
        # Numpy 1.13 has a different error message than prior numpy
        # Just check for the "out of bounds" phrase in it.
        self.assertIn("out of bounds", str(raises.exception))

    def test_cumsum(self):
        pyfunc = array_cumsum
        cfunc = jit(nopython=True)(pyfunc)
        # OK
        a = np.ones((2, 3))
        self.assertPreciseEqual(pyfunc(a), cfunc(a))
        # BAD: with axis
        with self.assertRaises(TypingError):
            cfunc(a, 1)
        # BAD: with kw axis
        pyfunc = array_cumsum_kws
        cfunc = jit(nopython=True)(pyfunc)
        with self.assertRaises(TypingError):
            cfunc(a, axis=1)


    def test_take(self):
        pyfunc = array_take
        cfunc = jit(nopython=True)(pyfunc)

        def check(arr, ind):
            expected = pyfunc(arr, ind)
            got = cfunc(arr, ind)
            self.assertPreciseEqual(expected, got)
            if hasattr(expected, 'order'):
                self.assertEqual(expected.order == got.order)

        # need to check:
        # 1. scalar index
        # 2. 1d array index
        # 3. nd array index, >2d and F order
        # 4. reflected list
        # 5. tuples

        test_indices = []
        test_indices.append(1)
        test_indices.append(5)
        test_indices.append(11)
        test_indices.append(-2)
        test_indices.append(np.array([1, 5, 1, 11, 3]))
        test_indices.append(np.array([[1, 5, 1], [11, 3, 0]], order='F'))
        test_indices.append(np.array([[[1, 5, 1], [11, 3, 0]]]))
        test_indices.append(np.array([[[[1, 5]], [[11, 0]],[[1, 2]]]]))
        test_indices.append([1, 5, 1, 11, 3])
        test_indices.append((1, 5, 1))
        test_indices.append(((1, 5, 1), (11, 3, 2)))
        test_indices.append((((1,), (5,), (1,)), ((11,), (3,), (2,))))

        layouts = cycle(['C', 'F', 'A'])

        for dt in [np.float64, np.int64, np.complex128]:
            A = np.arange(12, dtype=dt).reshape((4, 3), order=next(layouts))
            for ind in test_indices:
                check(A, ind)

        #check illegal access raises
        A = np.arange(12, dtype=dt).reshape((4, 3), order=next(layouts))
        szA = A.size
        illegal_indices = [szA, -szA - 1, np.array(szA), np.array(-szA - 1),
                           [szA], [-szA - 1]]
        for x in illegal_indices:
            with self.assertRaises(IndexError):
                cfunc(A, x) # oob raises

        # check float indexing raises
        with self.assertRaises(TypingError):
            cfunc(A, [1.7])

        # check unsupported arg raises
        with self.assertRaises(TypingError):
            take_kws = jit(nopython=True)(array_take_kws)
            take_kws(A, 1, 1)

        # check kwarg unsupported raises
        with self.assertRaises(TypingError):
            take_kws = jit(nopython=True)(array_take_kws)
            take_kws(A, 1, axis=1)

        #exceptions leak refs
        self.disable_leak_check()


    def test_fill(self):
        pyfunc = array_fill
        cfunc = jit(nopython=True)(pyfunc)
        def check(arr, val):
            expected = np.copy(arr)
            erv = pyfunc(expected, val)
            self.assertTrue(erv is None)
            got = np.copy(arr)
            grv = cfunc(got, val)
            self.assertTrue(grv is None)
            # check mutation is the same
            self.assertPreciseEqual(expected, got)

        # scalar
        A = np.arange(1)
        for x in [np.float64, np.bool_]:
            check(A, x(10))

        # 2d
        A = np.arange(12).reshape(3, 4)
        for x in [np.float64, np.bool_]:
            check(A, x(10))

        # 4d
        A = np.arange(48, dtype=np.complex64).reshape(2, 3, 4, 2)
        for x in [np.float64, np.complex128, np.bool_]:
            check(A, x(10))

class TestArrayComparisons(TestCase):

    def test_identity(self):
        def check(a, b, expected):
            cres = compile_isolated(pyfunc, (typeof(a), typeof(b)))
            self.assertPreciseEqual(cres.entry_point(a, b),
                                    (expected, not expected))

        pyfunc = identity_usecase

        arr = np.zeros(10, dtype=np.int32).reshape((2, 5))
        check(arr, arr, True)
        check(arr, arr[:], True)
        check(arr, arr.copy(), False)
        check(arr, arr.view('uint32'), False)
        check(arr, arr.T, False)
        check(arr, arr[:-1], False)

    # Other comparison operators ('==', etc.) are tested in test_ufuncs


if __name__ == '__main__':
    unittest.main()
