#                       Data Parallel Control (dpctl)
#
#  Copyright 2020-2024 Intel Corporation
#
#  Licensed under the Apache License, Version 2.0 (the "License");
#  you may not use this file except in compliance with the License.
#  You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
#  Unless required by applicable law or agreed to in writing, software
#  distributed under the License is distributed on an "AS IS" BASIS,
#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#  See the License for the specific language governing permissions and
#  limitations under the License.

import ctypes

import numpy as np
import pytest

import dpctl
import dpctl.tensor as dpt
from dpctl.tensor._type_utils import _can_cast
from dpctl.tests.helper import get_queue_or_skip, skip_if_dtype_not_supported

from .utils import (
    _all_dtypes,
    _compare_dtypes,
    _complex_fp_dtypes,
    _real_fp_dtypes,
    _usm_types,
)


@pytest.mark.parametrize("op1_dtype", _all_dtypes)
@pytest.mark.parametrize("op2_dtype", _all_dtypes)
def test_divide_dtype_matrix(op1_dtype, op2_dtype):
    q = get_queue_or_skip()
    skip_if_dtype_not_supported(op1_dtype, q)
    skip_if_dtype_not_supported(op2_dtype, q)

    sz = 127
    ar1 = dpt.ones(sz, dtype=op1_dtype)
    ar2 = dpt.ones_like(ar1, dtype=op2_dtype)

    r = dpt.divide(ar1, ar2)
    assert isinstance(r, dpt.usm_ndarray)
    expected = np.divide(
        np.ones(1, dtype=op1_dtype), np.ones(1, dtype=op2_dtype)
    )
    assert _compare_dtypes(r.dtype, expected.dtype, sycl_queue=q)
    assert r.shape == ar1.shape
    assert (dpt.asnumpy(r) == expected.astype(r.dtype)).all()
    assert r.sycl_queue == ar1.sycl_queue

    ar3 = dpt.ones(sz, dtype=op1_dtype)
    ar4 = dpt.ones(2 * sz, dtype=op2_dtype)

    r = dpt.divide(ar3[::-1], ar4[::2])
    assert isinstance(r, dpt.usm_ndarray)
    expected = np.divide(
        np.ones(1, dtype=op1_dtype), np.ones(1, dtype=op2_dtype)
    )
    assert _compare_dtypes(r.dtype, expected.dtype, sycl_queue=q)
    assert r.shape == ar3.shape
    assert (dpt.asnumpy(r) == expected.astype(r.dtype)).all()


@pytest.mark.parametrize("op1_usm_type", _usm_types)
@pytest.mark.parametrize("op2_usm_type", _usm_types)
def test_divide_usm_type_matrix(op1_usm_type, op2_usm_type):
    get_queue_or_skip()

    sz = 128
    ar1 = dpt.ones(sz, dtype="i4", usm_type=op1_usm_type)
    ar2 = dpt.ones_like(ar1, dtype="i4", usm_type=op2_usm_type)

    r = dpt.divide(ar1, ar2)
    assert isinstance(r, dpt.usm_ndarray)
    expected_usm_type = dpctl.utils.get_coerced_usm_type(
        (op1_usm_type, op2_usm_type)
    )
    assert r.usm_type == expected_usm_type


def test_divide_order():
    get_queue_or_skip()

    ar1 = dpt.ones((20, 20), dtype="i4", order="C")
    ar2 = dpt.ones((20, 20), dtype="i4", order="C")
    r1 = dpt.divide(ar1, ar2, order="C")
    assert r1.flags.c_contiguous
    r2 = dpt.divide(ar1, ar2, order="F")
    assert r2.flags.f_contiguous
    r3 = dpt.divide(ar1, ar2, order="A")
    assert r3.flags.c_contiguous
    r4 = dpt.divide(ar1, ar2, order="K")
    assert r4.flags.c_contiguous

    ar1 = dpt.ones((20, 20), dtype="i4", order="F")
    ar2 = dpt.ones((20, 20), dtype="i4", order="F")
    r1 = dpt.divide(ar1, ar2, order="C")
    assert r1.flags.c_contiguous
    r2 = dpt.divide(ar1, ar2, order="F")
    assert r2.flags.f_contiguous
    r3 = dpt.divide(ar1, ar2, order="A")
    assert r3.flags.f_contiguous
    r4 = dpt.divide(ar1, ar2, order="K")
    assert r4.flags.f_contiguous

    ar1 = dpt.ones((40, 40), dtype="i4", order="C")[:20, ::-2]
    ar2 = dpt.ones((40, 40), dtype="i4", order="C")[:20, ::-2]
    r4 = dpt.divide(ar1, ar2, order="K")
    assert r4.strides == (20, -1)

    ar1 = dpt.ones((40, 40), dtype="i4", order="C")[:20, ::-2].mT
    ar2 = dpt.ones((40, 40), dtype="i4", order="C")[:20, ::-2].mT
    r4 = dpt.divide(ar1, ar2, order="K")
    assert r4.strides == (-1, 20)


def test_divide_broadcasting():
    get_queue_or_skip()

    m = dpt.ones((100, 5), dtype="i4")
    v = dpt.arange(1, 6, dtype="i4")

    r = dpt.divide(m, v)

    expected = np.divide(
        np.ones((100, 5), dtype="i4"), np.arange(1, 6, dtype="i4")
    )
    assert (dpt.asnumpy(r) == expected.astype(r.dtype)).all()

    r2 = dpt.divide(v, m)
    expected2 = np.divide(
        np.arange(1, 6, dtype="i4"), np.ones((100, 5), dtype="i4")
    )
    assert (dpt.asnumpy(r2) == expected2.astype(r2.dtype)).all()


@pytest.mark.parametrize("arr_dt", _all_dtypes)
def test_divide_python_scalar(arr_dt):
    q = get_queue_or_skip()
    skip_if_dtype_not_supported(arr_dt, q)

    X = dpt.ones((10, 10), dtype=arr_dt, sycl_queue=q)
    py_ones = (
        bool(1),
        int(1),
        float(1),
        complex(1),
        np.float32(1),
        ctypes.c_int(1),
    )
    for sc in py_ones:
        R = dpt.divide(X, sc)
        assert isinstance(R, dpt.usm_ndarray)
        R = dpt.divide(sc, X)
        assert isinstance(R, dpt.usm_ndarray)


class MockArray:
    def __init__(self, arr):
        self.data_ = arr

    @property
    def __sycl_usm_array_interface__(self):
        return self.data_.__sycl_usm_array_interface__


def test_divide_mock_array():
    get_queue_or_skip()
    a = dpt.arange(10)
    b = dpt.ones(10)
    c = MockArray(b)
    r = dpt.divide(a, c)
    assert isinstance(r, dpt.usm_ndarray)


def test_divide_canary_mock_array():
    get_queue_or_skip()
    a = dpt.arange(10)

    class Canary:
        def __init__(self):
            pass

        @property
        def __sycl_usm_array_interface__(self):
            return None

    c = Canary()
    with pytest.raises(ValueError):
        dpt.divide(a, c)


@pytest.mark.parametrize("dtype", _real_fp_dtypes + _complex_fp_dtypes)
def test_divide_inplace_python_scalar(dtype):
    q = get_queue_or_skip()
    skip_if_dtype_not_supported(dtype, q)
    X = dpt.zeros((10, 10), dtype=dtype, sycl_queue=q)
    dt_kind = X.dtype.kind
    if dt_kind == "f":
        X /= float(1)
    elif dt_kind == "c":
        X /= complex(1)


@pytest.mark.parametrize("op1_dtype", _all_dtypes)
@pytest.mark.parametrize("op2_dtype", _all_dtypes)
def test_divide_inplace_dtype_matrix(op1_dtype, op2_dtype):
    q = get_queue_or_skip()
    skip_if_dtype_not_supported(op1_dtype, q)
    skip_if_dtype_not_supported(op2_dtype, q)

    sz = 127
    ar1 = dpt.ones(sz, dtype=op1_dtype, sycl_queue=q)
    ar2 = dpt.ones_like(ar1, dtype=op2_dtype, sycl_queue=q)

    dev = q.sycl_device
    _fp16 = dev.has_aspect_fp16
    _fp64 = dev.has_aspect_fp64
    # out array only valid if it is inexact
    if (
        _can_cast(ar2.dtype, ar1.dtype, _fp16, _fp64)
        and dpt.dtype(op1_dtype).kind in "fc"
    ):
        ar1 /= ar2
        assert dpt.all(ar1 == 1)

        ar3 = dpt.ones(sz, dtype=op1_dtype, sycl_queue=q)[::-1]
        ar4 = dpt.ones(2 * sz, dtype=op2_dtype, sycl_queue=q)[::2]
        ar3 /= ar4
        assert dpt.all(ar3 == 1)
    else:
        with pytest.raises(ValueError):
            ar1 /= ar2
            dpt.divide(ar1, ar2, out=ar1)

    # out is second arg
    ar1 = dpt.ones(sz, dtype=op1_dtype, sycl_queue=q)
    ar2 = dpt.ones_like(ar1, dtype=op2_dtype, sycl_queue=q)
    if (
        _can_cast(ar1.dtype, ar2.dtype, _fp16, _fp64)
        and dpt.dtype(op2_dtype).kind in "fc"
    ):
        dpt.divide(ar1, ar2, out=ar2)
        assert dpt.all(ar2 == 1)

        ar3 = dpt.ones(sz, dtype=op1_dtype, sycl_queue=q)[::-1]
        ar4 = dpt.ones(2 * sz, dtype=op2_dtype, sycl_queue=q)[::2]
        dpt.divide(ar3, ar4, out=ar4)
        dpt.all(ar4 == 1)
    else:
        with pytest.raises(ValueError):
            dpt.divide(ar1, ar2, out=ar2)
