"""
Tests for the typeof() machinery.
"""
import array
from collections import namedtuple
import enum
import mmap
import typing as py_typing
import random
import string

import numpy as np

import unittest
from numba import jit, _dispatcher
from numba.core import types
from numba.core.errors import NumbaValueError, NumbaTypeError
from numba.misc.special import typeof
from numba.core.dispatcher import OmittedArg
from numba._dispatcher import compute_fingerprint

from numba.tests.support import TestCase, skip_unless_cffi, tag
from numba.tests.test_numpy_support import ValueTypingTestBase
from numba.tests.ctypes_usecases import *
from numba.tests.enum_usecases import *
from numba.np import numpy_support


recordtype = np.dtype([('a', np.float64),
                       ('b', np.int32),
                       ('c', np.complex64),
                       ('d', (np.str_, 5))])

recordtype2 = np.dtype([('e', np.int8),
                        ('f', np.float64)])

recordtype3 = np.dtype([('e', np.int8),
                        ('f', np.float64)], align=True)

Point = namedtuple('Point', ('x', 'y'))

Rect = namedtuple('Rect', ('width', 'height'))


class Custom(object):

    @property
    def _numba_type_(self):
        """
        Magic attribute expected by Numba to get the numba type that
        represents this object.
        """
        return types.UniTuple(types.boolean, 42)


class TestTypeof(ValueTypingTestBase, TestCase):
    """
    Test typeof() and, implicitly, typing.Context.get_argument_type().
    """

    def test_number_values(self):
        """
        Test special.typeof() with scalar number values.
        """
        self.check_number_values(typeof)
        # These values mirror Dispatcher semantics
        self.assertEqual(typeof(1), types.intp)
        self.assertEqual(typeof(-1), types.intp)
        self.assertEqual(typeof(2**40), types.int64)
        self.assertEqual(typeof(2**63), types.uint64)
        self.assertEqual(typeof(2**63 - 1), types.int64)
        self.assertEqual(typeof(-2**63), types.int64)

    def test_datetime_values(self):
        """
        Test special.typeof() with np.timedelta64 values.
        """
        self.check_datetime_values(typeof)

    def test_timedelta_values(self):
        """
        Test special.typeof() with np.timedelta64 values.
        """
        self.check_timedelta_values(typeof)

    def test_array_values(self):
        """
        Test special.typeof() with ndarray values.
        """
        def check(arr, ndim, layout, mutable, aligned):
            ty = typeof(arr)
            self.assertIsInstance(ty, types.Array)
            self.assertEqual(ty.ndim, ndim)
            self.assertEqual(ty.layout, layout)
            self.assertEqual(ty.mutable, mutable)
            self.assertEqual(ty.aligned, aligned)

        a1 = np.arange(10)
        check(a1, 1, 'C', True, True)
        a2 = np.arange(10).reshape(2, 5)
        check(a2, 2, 'C', True, True)
        check(a2.T, 2, 'F', True, True)
        a3 = (np.arange(60))[::2].reshape((2, 5, 3))
        check(a3, 3, 'A', True, True)
        a4 = np.arange(1).reshape(())
        check(a4, 0, 'C', True, True)
        a4.flags.writeable = False
        check(a4, 0, 'C', False, True)

        # Unsupported dtype
        a5 = a1.astype(a1.dtype.newbyteorder())
        with self.assertRaises(NumbaValueError) as raises:
            typeof(a5)
        self.assertIn("Unsupported array dtype: %s" % (a5.dtype,),
                      str(raises.exception))

        # Unsupported array type (masked array)
        with self.assertRaises(NumbaTypeError) as raises:
            masked_arr = np.ma.MaskedArray([1])
            typeof(masked_arr)
        self.assertIn(f"Unsupported array type: numpy.ma.MaskedArray",
                      str(raises.exception))

    def test_structured_arrays(self):
        def check(arr, dtype, ndim, layout, aligned):
            ty = typeof(arr)
            self.assertIsInstance(ty, types.Array)
            self.assertEqual(ty.dtype, dtype)
            self.assertEqual(ty.ndim, ndim)
            self.assertEqual(ty.layout, layout)
            self.assertEqual(ty.aligned, aligned)

        dtype = np.dtype([('m', np.int32), ('n', 'S5')])
        rec_ty = numpy_support.from_struct_dtype(dtype)

        arr = np.empty(4, dtype=dtype)
        check(arr, rec_ty, 1, "C", False)
        arr = np.recarray(4, dtype=dtype)
        check(arr, rec_ty, 1, "C", False)

        dtype = np.dtype([('m', np.int32), ('n', 'S5')], align=True)
        rec_ty = numpy_support.from_struct_dtype(dtype)

        arr = np.empty(4, dtype=dtype)
        check(arr, rec_ty, 1, "C", True)
        arr = np.recarray(4, dtype=dtype)
        check(arr, rec_ty, 1, "C", True)

    def test_buffers(self):
        b = b"xx"
        ty = typeof(b)
        self.assertEqual(ty, types.Bytes(types.uint8, 1, "C"))
        self.assertFalse(ty.mutable)
        ty = typeof(memoryview(b))
        self.assertEqual(ty, types.MemoryView(types.uint8, 1, "C",
                                                readonly=True))
        self.assertFalse(ty.mutable)
        ty = typeof(array.array('i', [0, 1, 2]))
        self.assertEqual(ty, types.PyArray(types.int32, 1, "C"))
        self.assertTrue(ty.mutable)

        b = bytearray(10)
        ty = typeof(b)
        self.assertEqual(ty, types.ByteArray(types.uint8, 1, "C"))
        self.assertTrue(ty.mutable)

    def test_none(self):
        ty = typeof(None)
        self.assertEqual(ty, types.none)

    def test_ellipsis(self):
        ty = typeof(Ellipsis)
        self.assertEqual(ty, types.ellipsis)

    def test_str(self):
        ty = typeof("abc")
        self.assertEqual(ty, types.string)

    def test_slices(self):
        for args in [(1,), (1, 2), (1, 2, 1), (1, 2, None)]:
            v = slice(*args)
            self.assertIs(typeof(v), types.slice2_type)
        for args in [(1, 2, 2), (1, 2, -1), (None, None, -2)]:
            v = slice(*args)
            self.assertIs(typeof(v), types.slice3_type)

    def test_tuples(self):
        v = (1, 2)
        self.assertEqual(typeof(v), types.UniTuple(types.intp, 2))
        v = (1, (2.0, 3))
        self.assertEqual(typeof(v),
                         types.Tuple((types.intp,
                                      types.Tuple((types.float64, types.intp))))
                         )

    def test_lists(self):
        v = [1.0] * 100
        self.assertEqual(typeof(v), types.List(types.float64, reflected=True))

        bad_v = [{1: 3}]
        with self.assertRaises(ValueError) as raises:
            typeof(bad_v)
        self.assertIn("Cannot type list element type", str(raises.exception))

    def test_sets(self):
        v = set([1.0, 2.0, 3.0])
        self.assertEqual(typeof(v), types.Set(types.float64, reflected=True))
        v = frozenset(v)
        with self.assertRaises(ValueError) as raises:
            typeof(v)
        self.assertIn("Cannot determine Numba type of", str(raises.exception))

    def test_namedtuple(self):
        v = Point(1, 2)
        tp_point = typeof(v)
        self.assertEqual(tp_point,
                         types.NamedUniTuple(types.intp, 2, Point))
        v = Point(1, 2.0)
        self.assertEqual(typeof(v),
                         types.NamedTuple([types.intp, types.float64], Point))
        w = Rect(3, 4)
        tp_rect = typeof(w)
        self.assertEqual(tp_rect,
                         types.NamedUniTuple(types.intp, 2, Rect))
        self.assertNotEqual(tp_rect, tp_point)
        self.assertNotEqual(tp_rect, types.UniTuple(tp_rect.dtype, tp_rect.count))

    def test_enum(self):
        tp_red = typeof(Color.red)
        self.assertEqual(tp_red, types.EnumMember(Color, types.intp))
        self.assertEqual(tp_red, typeof(Color.blue))
        tp_choc = typeof(Shake.chocolate)
        self.assertEqual(tp_choc, types.EnumMember(Shake, types.intp))
        self.assertEqual(tp_choc, typeof(Shake.mint))
        self.assertNotEqual(tp_choc, tp_red)
        tp_404 = typeof(RequestError.not_found)
        self.assertEqual(tp_404, types.IntEnumMember(RequestError, types.intp))
        self.assertEqual(tp_404, typeof(RequestError.internal_error))

        with self.assertRaises(ValueError) as raises:
            typeof(HeterogeneousEnum.red)
        self.assertEqual(str(raises.exception),
                         "Cannot type heterogeneous enum: got value types complex128, float64")

    def test_enum_class(self):
        tp_color = typeof(Color)
        self.assertEqual(tp_color, types.EnumClass(Color, types.intp))
        tp_shake = typeof(Shake)
        self.assertEqual(tp_shake, types.EnumClass(Shake, types.intp))
        self.assertNotEqual(tp_shake, tp_color)
        tp_shape = typeof(Shape)
        self.assertEqual(tp_shape, types.IntEnumClass(Shape, types.intp))
        tp_error = typeof(RequestError)
        self.assertEqual(tp_error,
                         types.IntEnumClass(RequestError, types.intp))
        self.assertNotEqual(tp_error, tp_shape)

        with self.assertRaises(ValueError) as raises:
            typeof(HeterogeneousEnum)
        self.assertEqual(str(raises.exception),
                         "Cannot type heterogeneous enum: got value types complex128, float64")

    def test_dtype(self):
        dtype = np.dtype('int64')
        self.assertEqual(typeof(dtype), types.DType(types.int64))

        dtype = np.dtype([('m', np.int32), ('n', 'S5')])
        rec_ty = numpy_support.from_struct_dtype(dtype)
        self.assertEqual(typeof(dtype), types.DType(rec_ty))

    def test_dtype_values(self):
        self.assertEqual(typeof(np.int64), types.NumberClass(types.int64))
        self.assertEqual(typeof(np.float64), types.NumberClass(types.float64))
        self.assertEqual(typeof(np.int32), types.NumberClass(types.int32))
        self.assertEqual(typeof(np.int8), types.NumberClass(types.int8))

    def test_ctypes(self):
        ty_cos = typeof(c_cos)
        ty_sin = typeof(c_sin)
        self.assertIsInstance(ty_cos, types.ExternalFunctionPointer)
        self.assertEqual(ty_cos.sig.args, (types.float64,))
        self.assertEqual(ty_cos.sig.return_type, types.float64)
        self.assertEqual(ty_cos, ty_sin)
        self.assertNotEqual(ty_cos.get_pointer(c_cos),
                            ty_sin.get_pointer(c_sin))

    @skip_unless_cffi
    def test_cffi(self):
        from numba.tests import cffi_usecases as mod
        mod.init()
        ty_cffi_cos = typeof(mod.cffi_cos)
        ty_cffi_sin = typeof(mod.cffi_sin)
        ty_cffi_boolean = typeof(mod.cffi_bool)
        self.assertIsInstance(ty_cffi_cos, types.ExternalFunctionPointer)
        self.assertEqual(ty_cffi_boolean.sig.return_type, types.boolean)
        self.assertEqual(ty_cffi_cos.sig.args, (types.float64,))
        self.assertEqual(ty_cffi_cos.sig.return_type, types.float64)
        self.assertEqual(ty_cffi_cos, ty_cffi_sin)
        ty_ctypes_cos = typeof(c_cos)
        self.assertNotEqual(ty_cffi_cos, ty_ctypes_cos)
        self.assertNotEqual(ty_cffi_cos.get_pointer(mod.cffi_cos),
                            ty_cffi_sin.get_pointer(mod.cffi_sin))
        self.assertEqual(ty_cffi_cos.get_pointer(mod.cffi_cos),
                         ty_ctypes_cos.get_pointer(c_cos))

    def test_custom(self):
        ty = typeof(Custom())
        self.assertEqual(ty, types.UniTuple(types.boolean, 42))

    def test_omitted_args(self):
        ty0 = typeof(OmittedArg(0.0))
        ty1 = typeof(OmittedArg(1))
        ty2 = typeof(OmittedArg(1.0))
        ty3 = typeof(OmittedArg(1.0))
        self.assertEqual(ty0, types.Omitted(0.0))
        self.assertEqual(ty1, types.Omitted(1))
        self.assertEqual(ty2, types.Omitted(1.0))
        self.assertEqual(len({ty0, ty1, ty2}), 3)
        self.assertEqual(ty3, ty2)

    def test_np_random(self):
        rng = np.random.default_rng()
        ty_rng = typeof(rng)
        ty_bitgen = typeof(rng.bit_generator)

        self.assertEqual(ty_rng, types.npy_rng)
        self.assertEqual(ty_bitgen, types.npy_bitgen)


class DistinctChecker(object):

    def __init__(self):
        self._distinct = set()

    def add(self, obj):
        if obj in self._distinct:
            raise AssertionError("%r already in %r" % (obj, self._distinct))
        self._distinct.add(obj)


class TestFingerprint(TestCase):
    """
    Tests for _dispatcher.compute_fingerprint()

    Each fingerprint must denote values of only one Numba type (this is
    the condition for correctness), but values of a Numba type may be
    denoted by several distinct fingerprints (it only makes the cache
    less efficient).
    """

    def test_floats(self):
        s = compute_fingerprint(1.0)
        self.assertEqual(compute_fingerprint(2.0), s)
        s = compute_fingerprint(np.float32())
        self.assertEqual(compute_fingerprint(np.float32(2.0)), s)
        self.assertNotEqual(compute_fingerprint(np.float64()), s)

    def test_ints(self):
        s = compute_fingerprint(1)
        for v in (-1, 2**60):
            self.assertEqual(compute_fingerprint(v), s)
        # Different int widths resolve to different fingerprints
        distinct = set()
        for tp in ('int8', 'int16', 'int32', 'int64',
                   'uint8', 'uint16', 'uint32', 'uint64'):
            tp = getattr(np, tp)
            distinct.add(compute_fingerprint(tp()))
        self.assertEqual(len(distinct), 8, distinct)

    def test_bool(self):
        s = compute_fingerprint(True)
        self.assertEqual(compute_fingerprint(False), s)
        self.assertNotEqual(compute_fingerprint(1), s)

    def test_complex(self):
        s = compute_fingerprint(1j)
        self.assertEqual(s, compute_fingerprint(1+0j))
        s = compute_fingerprint(np.complex64())
        self.assertEqual(compute_fingerprint(np.complex64(2.0)), s)
        self.assertNotEqual(compute_fingerprint(np.complex128()), s)

    def test_none(self):
        compute_fingerprint(None)

    def test_enums(self):
        # Enums should fail fingerprinting, even IntEnums
        with self.assertRaises(NotImplementedError):
            compute_fingerprint(Color.red)
        with self.assertRaises(NotImplementedError):
            compute_fingerprint(RequestError.not_found)

    def test_records(self):
        d1 = np.dtype([('m', np.int32), ('n', np.int64)])
        d2 = np.dtype([('m', np.int32), ('n', np.int16)])
        v1 = np.empty(1, dtype=d1)[0]
        v2 = np.empty(1, dtype=d2)[0]
        self.assertNotEqual(compute_fingerprint(v1),
                            compute_fingerprint(v2))

    def test_datetime(self):
        a = np.datetime64(1, 'Y')
        b = np.datetime64(2, 'Y')
        c = np.datetime64(2, 's')
        d = np.timedelta64(2, 's')
        self.assertEqual(compute_fingerprint(a),
                         compute_fingerprint(b))
        distinct = set(compute_fingerprint(x) for x in (a, c, d))
        self.assertEqual(len(distinct), 3, distinct)

    def test_arrays(self):
        distinct = DistinctChecker()

        # 1D
        arr = np.empty(4, dtype=np.float64)
        s = compute_fingerprint(arr)
        distinct.add(s)
        self.assertEqual(compute_fingerprint(arr[:1]), s)
        # Non-contiguous
        distinct.add(compute_fingerprint(arr[::2]))
        # Other type
        distinct.add(compute_fingerprint(arr.astype(np.complex64)))
        # Readonly
        arr.setflags(write=False)
        distinct.add(compute_fingerprint(arr))

        # 2D
        arr = np.empty((4, 4), dtype=np.float64)
        distinct.add(compute_fingerprint(arr))
        # F-contiguous
        distinct.add(compute_fingerprint(arr.T))
        # Non-contiguous
        distinct.add(compute_fingerprint(arr[::2]))

        # 0D
        arr = np.empty((), dtype=np.float64)
        distinct.add(compute_fingerprint(arr))

        # Structured arrays
        arr = np.empty(5, dtype=recordtype)
        s = compute_fingerprint(arr)
        distinct.add(s)
        self.assertEqual(compute_fingerprint(arr[:1]), s)
        arr = np.empty(5, dtype=recordtype2)
        distinct.add(compute_fingerprint(arr))
        arr = np.empty(5, dtype=recordtype3)
        distinct.add(compute_fingerprint(arr))

        # np.recarray() is peculiar: it creates a new dtype instance in
        # its constructor; check that the fingerprint remains efficient
        a = np.recarray(1, dtype=recordtype)
        b = np.recarray(1, dtype=recordtype)
        self.assertEqual(compute_fingerprint(a),
                         compute_fingerprint(b))

    def test_buffers(self):
        distinct = DistinctChecker()

        s = compute_fingerprint(b'')
        self.assertEqual(compute_fingerprint(b'xx'), s)
        distinct.add(s)
        distinct.add(compute_fingerprint(bytearray()))
        distinct.add(compute_fingerprint(memoryview(b'')))
        m_uint8_1d = compute_fingerprint(memoryview(bytearray()))
        distinct.add(m_uint8_1d)

        arr = array.array('B', [42])
        distinct.add(compute_fingerprint(arr))
        self.assertEqual(compute_fingerprint(memoryview(arr)), m_uint8_1d)
        for array_code in 'bi':
            arr = array.array(array_code, [0, 1, 2])
            distinct.add(compute_fingerprint(arr))
            distinct.add(compute_fingerprint(memoryview(arr)))

        arr = np.empty(16, dtype=np.uint8)
        distinct.add(compute_fingerprint(arr))
        self.assertEqual(compute_fingerprint(memoryview(arr)), m_uint8_1d)
        arr = arr.reshape((4, 4))
        distinct.add(compute_fingerprint(arr))
        distinct.add(compute_fingerprint(memoryview(arr)))
        arr = arr.T
        distinct.add(compute_fingerprint(arr))
        distinct.add(compute_fingerprint(memoryview(arr)))
        arr = arr[::2]
        distinct.add(compute_fingerprint(arr))
        distinct.add(compute_fingerprint(memoryview(arr)))

        m = mmap.mmap(-1, 16384)
        distinct.add(compute_fingerprint(m))
        self.assertEqual(compute_fingerprint(memoryview(m)), m_uint8_1d)

    def test_dtype(self):
        distinct = DistinctChecker()

        s = compute_fingerprint(np.dtype('int64'))
        self.assertEqual(compute_fingerprint(np.dtype('int64')), s)
        distinct.add(s)

        for descr in ('int32', 'm8[s]', 'm8[W]', 'M8[s]'):
            distinct.add(np.dtype(descr))

        distinct.add(recordtype)
        distinct.add(recordtype2)

        # np.recarray() is peculiar: it creates a new dtype instance in
        # its constructor; check that the fingerprint remains efficient
        a = np.recarray(1, dtype=recordtype)
        b = np.recarray(1, dtype=recordtype)
        self.assertEqual(compute_fingerprint(a.dtype),
                         compute_fingerprint(b.dtype))

    def test_tuples(self):
        distinct = DistinctChecker()

        s = compute_fingerprint((1,))
        self.assertEqual(compute_fingerprint((2,)), s)
        distinct.add(s)

        distinct.add(compute_fingerprint(()))
        distinct.add(compute_fingerprint((1, 2, 3)))
        distinct.add(compute_fingerprint((1j, 2, 3)))
        distinct.add(compute_fingerprint((1, (), np.empty(5))))
        distinct.add(compute_fingerprint((1, (), np.empty((5, 1)))))

    def test_lists(self):
        distinct = DistinctChecker()

        s = compute_fingerprint([1])
        self.assertEqual(compute_fingerprint([2, 3]), s)
        distinct.add(s)

        distinct.add(compute_fingerprint([1j]))
        distinct.add(compute_fingerprint([4.5, 6.7]))
        distinct.add(compute_fingerprint([(1,)]))

        with self.assertRaises(ValueError):
            compute_fingerprint([])

    def test_sets(self):
        distinct = DistinctChecker()

        s = compute_fingerprint(set([1]))
        self.assertEqual(compute_fingerprint(set([2, 3])), s)
        distinct.add(s)

        distinct.add(compute_fingerprint([1]))
        distinct.add(compute_fingerprint(set([1j])))
        distinct.add(compute_fingerprint(set([4.5, 6.7])))
        distinct.add(compute_fingerprint(set([(1,)])))

        with self.assertRaises(ValueError):
            compute_fingerprint(set())
        with self.assertRaises(NotImplementedError):
            compute_fingerprint(frozenset([2, 3]))

    def test_omitted_args(self):
        distinct = DistinctChecker()

        v0 = OmittedArg(0.0)
        v1 = OmittedArg(1.0)
        v2 = OmittedArg(1)

        s = compute_fingerprint(v0)
        self.assertEqual(compute_fingerprint(v1), s)
        distinct.add(s)
        distinct.add(compute_fingerprint(v2))
        distinct.add(compute_fingerprint(0.0))
        distinct.add(compute_fingerprint(1))

    def test_complicated_type(self):
        # Generating a large fingerprint
        t = None
        for i in range(1000):
            t = (t,)
        s = compute_fingerprint(t)


class TestTypeOfMemCpy(TestCase):

    def test_memcpy_typeof_buffer(self):
        # https://github.com/numba/numba/issues/9097
        # bug is fixed if the code below compiles
        random.seed(0)
        chars = string.ascii_letters
        n = 256
        field = "".join([chars[random.randint(0, len(chars) - 1)] for x in range(n)])
        for i in range(1, n):
            lfield = field[:i]
            nt_ty = namedtuple("tuplename", lfield)
            nt = nt_ty(1)
            fp = _dispatcher.compute_fingerprint(nt)
            nt_name = nt.__class__.__name__
            expected = f"{nt_name}({lfield}i)"
            self.assertEqual(
                expected,
                fp.decode(),
                f"iteration {i} failed, {expected} != {fp.decode()}"
            )


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