# Owner(s): ["module: dynamo"]

import functools
import operator
import pickle
import types
from itertools import permutations
from typing import Any
from unittest import skipIf as skipif

import pytest
from pytest import raises as assert_raises

from torch.testing._internal.common_utils import (
    instantiate_parametrized_tests,
    parametrize,
    run_tests,
    subtest,
    TEST_WITH_TORCHDYNAMO,
    TestCase,
    xfailIfTorchDynamo,
    xpassIfTorchDynamo_np,
)


skip = functools.partial(skipif, True)

if TEST_WITH_TORCHDYNAMO:
    import numpy as np
    from numpy.testing import assert_, assert_equal
else:
    import torch._numpy as np
    from torch._numpy.testing import assert_, assert_equal

import numpy


def assert_dtype_equal(a, b):
    assert_equal(a, b)
    assert_equal(
        hash(a), hash(b), "two equivalent types do not hash to the same value !"
    )


def assert_dtype_not_equal(a, b):
    assert_(a != b)
    assert_(hash(a) != hash(b), "two different types hash to the same value !")


@instantiate_parametrized_tests
class TestBuiltin(TestCase):
    @parametrize("t", [int, float, complex, np.int32])
    def test_run(self, t):
        """Only test hash runs at all."""
        dt = np.dtype(t)
        hash(dt)

    def test_equivalent_dtype_hashing(self):
        # Make sure equivalent dtypes with different type num hash equal
        intp = np.dtype(np.intp)
        if intp.itemsize == 4:
            left = intp
            right = np.dtype(np.int32)
        else:
            left = intp
            right = np.dtype(np.int64)
        assert_(left == right)
        assert_(hash(left) == hash(right))

    @xfailIfTorchDynamo  # TypeError -> InternalTorchDynamoError
    def test_invalid_types(self):
        # Make sure invalid type strings raise an error

        assert_raises(TypeError, np.dtype, "O3")
        assert_raises(TypeError, np.dtype, "O5")
        assert_raises(TypeError, np.dtype, "O7")
        assert_raises(TypeError, np.dtype, "b3")
        assert_raises(TypeError, np.dtype, "h4")
        assert_raises(TypeError, np.dtype, "I5")
        assert_raises(TypeError, np.dtype, "e3")
        assert_raises(TypeError, np.dtype, "f5")

        if np.dtype("l").itemsize == 8:
            assert_raises(TypeError, np.dtype, "l4")
            assert_raises(TypeError, np.dtype, "L4")
        else:
            assert_raises(TypeError, np.dtype, "l8")
            assert_raises(TypeError, np.dtype, "L8")

    # XXX: what is 'q'? on my 64-bit ubuntu maching it's int64, same as 'l'
    #       if np.dtype('q').itemsize == 8:
    #           assert_raises(TypeError, np.dtype, 'q4')
    #           assert_raises(TypeError, np.dtype, 'Q4')
    #       else:
    #           assert_raises(TypeError, np.dtype, 'q8')
    #           assert_raises(TypeError, np.dtype, 'Q8')

    def test_richcompare_invalid_dtype_equality(self):
        # Make sure objects that cannot be converted to valid
        # dtypes results in False/True when compared to valid dtypes.
        # Here 7 cannot be converted to dtype. No exceptions should be raised

        assert not np.dtype(np.int32) == 7, "dtype richcompare failed for =="
        assert np.dtype(np.int32) != 7, "dtype richcompare failed for !="

    @parametrize("operation", [operator.le, operator.lt, operator.ge, operator.gt])
    def test_richcompare_invalid_dtype_comparison(self, operation):
        # Make sure TypeError is raised for comparison operators
        # for invalid dtypes. Here 7 is an invalid dtype.

        with pytest.raises(TypeError):
            operation(np.dtype(np.int32), 7)

    @skipif(
        numpy.__version__ < "1.24",
        reason="older numpies emit DeprecatioWarnings instead",
    )
    @parametrize(
        "dtype",
        [
            "Bool",
            "Bytes0",
            "Complex32",
            "Complex64",
            "Datetime64",
            "Float16",
            "Float32",
            "Float64",
            "Int8",
            "Int16",
            "Int32",
            "Int64",
            "Object0",
            "Str0",
            "Timedelta64",
            "UInt8",
            "UInt16",
            "Uint32",
            "UInt32",
            "Uint64",
            "UInt64",
            "Void0",
            "Float128",
            "Complex128",
        ],
    )
    def test_numeric_style_types_are_invalid(self, dtype):
        with assert_raises(TypeError):
            np.dtype(dtype)


@skip(reason="dtype attributes not yet implemented")
class TestDtypeAttributeDeletion(TestCase):
    def test_dtype_non_writable_attributes_deletion(self):
        dt = np.dtype(np.double)
        attr = [
            "subdtype",
            "descr",
            "str",
            "name",
            "base",
            "shape",
            "isbuiltin",
            "isnative",
            "isalignedstruct",
            "fields",
            "metadata",
            "hasobject",
        ]

        for s in attr:
            assert_raises(AttributeError, delattr, dt, s)

    def test_dtype_writable_attributes_deletion(self):
        dt = np.dtype(np.double)
        attr = ["names"]
        for s in attr:
            assert_raises(AttributeError, delattr, dt, s)


@instantiate_parametrized_tests
class TestPickling(TestCase):
    def check_pickling(self, dtype):
        for proto in range(pickle.HIGHEST_PROTOCOL + 1):
            buf = pickle.dumps(dtype, proto)
            # The dtype pickling itself pickles `np.dtype` if it is pickled
            # as a singleton `dtype` should be stored in the buffer:
            assert b"_DType_reconstruct" not in buf
            assert b"dtype" in buf
            pickled = pickle.loads(buf)
            assert_equal(pickled, dtype)

            # XXX: out dtypes do not have .descr
            #         assert_equal(pickled.descr, dtype.descr)
            #         if dtype.metadata is not None:
            #             assert_equal(pickled.metadata, dtype.metadata)
            # Check the reconstructed dtype is functional

            x = np.zeros(3, dtype=dtype)
            y = np.zeros(3, dtype=pickled)
            assert_equal(x, y)
            assert_equal(x[0], y[0])

    @parametrize("t", [int, float, complex, np.int32, bool])
    def test_builtin(self, t):
        self.check_pickling(np.dtype(t))

    @parametrize(
        "DType",
        [
            subtest(type(np.dtype(t)), name=f"{np.dtype(t).name}_{i}")
            for i, t in enumerate(np.typecodes["All"])
        ]
        + [np.dtype],
    )
    def test_pickle_types(self, DType):
        # Check that DTypes (the classes/types) roundtrip when pickling
        for proto in range(pickle.HIGHEST_PROTOCOL + 1):
            roundtrip_DType = pickle.loads(pickle.dumps(DType, proto))
            assert roundtrip_DType is DType


@skip(reason="XXX: value-based promotions, we don't have.")
@instantiate_parametrized_tests
class TestPromotion(TestCase):
    """Test cases related to more complex DType promotions.  Further promotion
    tests are defined in `test_numeric.py`
    """

    @parametrize(
        "other, expected, expected_weak",
        [
            (2**16 - 1, np.complex64, None),
            (2**32 - 1, np.complex128, np.complex64),
            subtest((np.float16(2), np.complex64, None), name="float16_complex64_None"),
            subtest((np.float32(2), np.complex64, None), name="float32_complex64_None"),
            # repeat for complex scalars:
            subtest(
                (np.complex64(2), np.complex64, None), name="complex64_complex64_None"
            ),
        ],
    )
    def test_complex_other_value_based(
        self, weak_promotion, other, expected, expected_weak
    ):
        if weak_promotion and expected_weak is not None:
            expected = expected_weak

        # This would change if we modify the value based promotion
        min_complex = np.dtype(np.complex64)

        res = np.result_type(other, min_complex)
        assert res == expected
        # Check the same for a simple ufunc call that uses the same logic:
        res = np.minimum(other, np.ones(3, dtype=min_complex)).dtype
        assert res == expected

    @parametrize(
        "other, expected",
        [
            (np.bool_, np.complex128),
            (np.int64, np.complex128),
            (np.float16, np.complex64),
            (np.float32, np.complex64),
            (np.float64, np.complex128),
            (np.complex64, np.complex64),
            (np.complex128, np.complex128),
        ],
    )
    def test_complex_scalar_value_based(self, other, expected):
        # This would change if we modify the value based promotion
        complex_scalar = 1j

        res = np.result_type(other, complex_scalar)
        assert res == expected
        # Check the same for a simple ufunc call that uses the same logic:
        res = np.minimum(np.ones(3, dtype=other), complex_scalar).dtype
        assert res == expected

    @parametrize("val", [2, 2**32, 2**63, 2**64, 2 * 100])
    def test_python_integer_promotion(self, val):
        # If we only path scalars (mainly python ones!), the result must take
        # into account that the integer may be considered int32, int64, uint64,
        # or object depending on the input value.  So test those paths!
        expected_dtype = np.result_type(np.array(val).dtype, np.array(0).dtype)
        assert np.result_type(val, 0) == expected_dtype
        # For completeness sake, also check with a NumPy scalar as second arg:
        assert np.result_type(val, np.int8(0)) == expected_dtype

    @parametrize(
        "dtypes, expected",
        [
            # These promotions are not associative/commutative:
            ([np.int16, np.float16], np.float32),
            ([np.int8, np.float16], np.float32),
            ([np.uint8, np.int16, np.float16], np.float32),
            # The following promotions are not ambiguous, but cover code
            # paths of abstract promotion (no particular logic being tested)
            ([1, 1, np.float64], np.float64),
            ([1, 1.0, np.complex128], np.complex128),
            ([1, 1j, np.float64], np.complex128),
            ([1.0, 1.0, np.int64], np.float64),
            ([1.0, 1j, np.float64], np.complex128),
            ([1j, 1j, np.float64], np.complex128),
            ([1, True, np.bool_], np.int_),
        ],
    )
    def test_permutations_do_not_influence_result(self, dtypes, expected):
        # Tests that most permutations do not influence the result.  In the
        # above some uint and int combintations promote to a larger integer
        # type, which would then promote to a larger than necessary float.
        for perm in permutations(dtypes):
            assert np.result_type(*perm) == expected


class TestMisc(TestCase):
    def test_dtypes_are_true(self):
        # test for gh-6294
        assert bool(np.dtype("f8"))
        assert bool(np.dtype("i8"))

    @xpassIfTorchDynamo_np  # (reason="No keyword arg for dtype ctor.")
    def test_keyword_argument(self):
        # test for https://github.com/numpy/numpy/pull/16574#issuecomment-642660971
        assert np.dtype(dtype=np.float64) == np.dtype(np.float64)


class TestFromDTypeAttribute(TestCase):
    def test_simple(self):
        class dt:
            dtype = np.dtype("f8")

        assert np.dtype(dt) == np.float64
        assert np.dtype(dt()) == np.float64

    @skip(
        reason="We simply require the .name attribute, so this "
        "fails with an AttributeError."
    )
    def test_recursion(self):
        class dt:
            pass

        dt.dtype = dt
        with pytest.raises(RecursionError):
            np.dtype(dt)

        dt_instance = dt()
        dt_instance.dtype = dt
        with pytest.raises(RecursionError):
            np.dtype(dt_instance)


@skip(reason="Parameteric dtypes, our stuff is simpler.")
@instantiate_parametrized_tests
class TestClassGetItem(TestCase):
    def test_dtype(self) -> None:
        alias = np.dtype[Any]
        assert isinstance(alias, types.GenericAlias)
        assert alias.__origin__ is np.dtype

    @parametrize("code", np.typecodes["All"])
    def test_dtype_subclass(self, code: str) -> None:
        cls = type(np.dtype(code))
        alias = cls[Any]
        assert isinstance(alias, types.GenericAlias)
        assert alias.__origin__ is cls

    @parametrize("arg_len", range(4))
    def test_subscript_tuple(self, arg_len: int) -> None:
        arg_tup = (Any,) * arg_len
        if arg_len == 1:
            assert np.dtype[arg_tup]
        else:
            with pytest.raises(TypeError):
                np.dtype[arg_tup]

    def test_subscript_scalar(self) -> None:
        assert np.dtype[Any]


if __name__ == "__main__":
    run_tests()
