# 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 pytest



import asc

import asc.runtime.config as config

import asc.lib.runtime as rt



try:

    import torch

except ModuleNotFoundError:

    pytest.skip("torch is not installed", allow_module_level=True)





@asc.jit

def duplicate_kernel(scalar, z: asc.GlobalAddress, block_length: asc.ConstExpr[int],

                buffer_num: asc.ConstExpr[int], tile_length: asc.ConstExpr[int], tile_num: asc.ConstExpr[int]):

    offset = asc.get_block_idx() * block_length

    z_gm = asc.GlobalTensor()

    z_gm.set_global_buffer(z + offset)

    pipe = asc.TPipe()

    out_queue_z = asc.TQue(asc.TPosition.VECOUT, buffer_num)

    pipe.init_buffer(que=out_queue_z, num=buffer_num, len=tile_length * z.dtype.sizeof())

    for i in range(tile_num):

        compute(scalar, z_gm, out_queue_z, tile_length)

        copy_out(i, z_gm, out_queue_z, tile_length)





@asc.jit

def compute(scalar, z_gm: asc.GlobalTensor, out_queue_z: asc.TQue, tile_length: asc.ConstExpr[int]):

    z_local = out_queue_z.alloc_tensor(z_gm.dtype)

    asc.duplicate(z_local, scalar, count=tile_length)

    out_queue_z.enque(z_local)





@asc.jit

def copy_out(i: int, z_gm: asc.GlobalTensor, out_queue_z: asc.TQue, tile_length: asc.ConstExpr[int]):

    z_local = out_queue_z.deque(z_gm.dtype)

    asc.data_copy(z_gm[i * tile_length:], z_local, count=tile_length)

    out_queue_z.free_tensor(z_local)





def duplicate_launch(scalar, size) -> torch.Tensor:

    z = torch.zeros(size)

    total_length = z.numel()

    use_core_num = 16

    block_length = (total_length + use_core_num - 1) // use_core_num

    tile_length = 512

    tile_num = (block_length + tile_length - 1) // tile_length

    buffer_num = 1

    duplicate_kernel[use_core_num, rt.current_stream()](scalar, z, block_length, buffer_num, tile_length, tile_num)

    return z





param_list = [

    [torch.float32, (1000,)],

    [torch.float32, (1,)],

    [torch.float16, (2048,)],

    [torch.int32, (8192,)],

    [torch.int16, (8192,)],

]





BACKENDS = [

    # config.Backend.Model,

    config.Backend.NPU,

]





@pytest.mark.parametrize("dtype, size", param_list)

@pytest.mark.parametrize("backend", BACKENDS)

def test_duplicate(dtype, size, backend: config.Backend):

    config.set_platform(backend)

    device = "npu" if config.Backend(backend) == config.Backend.NPU else "cpu"

    if dtype in {torch.float16, torch.float32}:

        scalar = torch.randn((), dtype=dtype, device=device).item()

    else:

        scalar = torch.randint(1, 99, (), dtype=dtype, device=device).item()

    z = duplicate_launch(scalar, size)

    assert torch.allclose(z, torch.full(size, scalar, dtype=z.dtype))