#!/usr/bin/env python3

# -*- coding: utf-8 -*-

# ----------------------------------------------------------------------------

# Copyright (c) 2025 Huawei Technologies Co., Ltd.

# This program is free software, you can redistribute it and/or modify it under the terms and conditions of

# CANN Open Software License Agreement Version 2.0 (the "License").

# Please refer to the License for details. You may not use this file except in compliance with the License.

# THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED,

# INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE.

# See LICENSE in the root of the software repository for the full text of the License.

# ----------------------------------------------------------------------------



import torch

import torch_npu

import ascend_ops

import pytest





def test_add_interface_exist():

    """

    Test that the 'ascend_ops.add' operator is present in torch.ops.

    This existence test asserts that the custom operator registered under the

    'ascend_ops' namespace is discoverable from Python via torch.ops.ascend_ops.add.

    It does not exercise operator functionality — only that the Python binding

    and registration are available.

    Rationale:

    The presence of this test guards against a common failure mode where an

    operator is implemented and registered in C++/ATen but is not exposed to

    the Python torch.ops namespace due to mismatches between the PyTorch

    operator schema and the C++ registration signature (argument names, types,

    or overloads). Such schema/signature inconsistencies can cause the

    operator to be hidden or not exported to Python, breaking consumers that

    expect to call torch.ops.ascend_ops.add. This test will fail loudly if the

    binding is missing, prompting investigation into schema/registration issues.

    """

    # This test specifically protects against discrepancies between the

    # PyTorch operator schema and the C++ signature/registration that can

    # prevent the operator from being visible in torch.ops.ascend_ops.

    print(torch.ops.ascend_ops.add)

    assert hasattr(torch.ops.ascend_ops, "add"), "The 'add' operator is not registered in the 'torch.ops.ascend_ops' namespace."





SHAPES = [

    (1,),

    (3,),

    (10,),

    (100,),

    (1024,),

    (10000,),

    (10, 10),

    (32, 32),

    (100, 100),

    (10, 100),

    (100, 10),

    (256, 512),

    (5, 10, 15),

    (16, 32, 64),

    (32, 64, 128),

    (1, 3, 32, 32),

    (4, 3, 64, 64),

    (8, 3, 128, 128),

    (1000, 1000),

]



DTYPES = [

    torch.float32,

    torch.float16,

    torch.int32,

]





@pytest.mark.skipif(not torch.npu.is_available(), reason="NPU device not found")

@pytest.mark.parametrize("shape", SHAPES)

@pytest.mark.parametrize("dtype", DTYPES)

def test_add_operator(shape, dtype):

    """

    Test the functionality of the add operator, using concise but comprehensive combinations of shapes and data types.



    Parameters:

        shape: Tensor shape

        dtype: Data type

    """

    if dtype in [torch.int32]:

        a = torch.randint(-100, 100, shape, dtype=dtype)

        b = torch.randint(-100, 100, shape, dtype=dtype)

    else:

        a = torch.randn(*shape, dtype=dtype)

        b = torch.randn(*shape, dtype=dtype)



    expected = a + b

    a_npu = a.npu()

    b_npu = b.npu()

    result_npu = torch.ops.ascend_ops.add(a_npu, b_npu)

    result = result_npu.cpu()



    if dtype in [torch.int32]:

        assert torch.equal(result, expected), \

            f"Add failed for shape {shape}, dtype {dtype}. " \

            f"Expected {expected}, but got {result}"

    else:

        assert torch.allclose(result, expected, rtol=1e-4, atol=1e-4), \

            f"Add failed for shape {shape}, dtype {dtype}. " \

            f"Max diff: {torch.max(torch.abs(result - expected)):.6f}"



    print(f"✓ Test passed: shape={shape}, dtype={dtype}")