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import pytest

import triton
import triton.language as tl
import triton.language.extra.ascend.libdevice as libdevice
import test_common

import torch
import torch_npu


def torch_hypot(x0, x1):
    return torch.hypot(x0, x1)


@triton.jit
def triton_hypot(in_ptr0, in_ptr1, out_ptr0, XBLOCK : tl.constexpr, XBLOCK_SUB : tl.constexpr):
    offset = tl.program_id(0) * XBLOCK
    base1 = tl.arange(0, XBLOCK_SUB)
    loops1: tl.constexpr = XBLOCK // XBLOCK_SUB
    for loop1 in range(loops1):
        x0 = offset + (loop1 * XBLOCK_SUB) + base1
        tmp0 = tl.load(in_ptr0 + (x0), None)
        tmp1 = tl.load(in_ptr1 + (x0), None)
        tmp2 = libdevice.hypot(tmp0, tmp1)
        tl.store(out_ptr0 + (x0), tmp2, None)


@pytest.mark.parametrize('param_list',
                            [
                                ['float32', (2, 4096, 8), 2, 32768, 1024],
                                ['float16', (2, 4096, 8), 2, 32768, 1024],
                                ['bfloat16', (2, 4096, 8), 2, 32768, 1024],
                            ])
def test_hypot(param_list):
    dtype, shape, ncore, xblock, xblock_sub = param_list
    x0 = test_common.generate_tensor(shape, dtype).npu()
    x1 = test_common.generate_tensor(shape, dtype).npu()
    y_ref = torch_hypot(x0, x1)
    y_cal = torch.zeros(shape, dtype = eval('torch.' + dtype)).npu()
    triton_hypot[ncore, 1, 1](x0, x1, y_cal, xblock, xblock_sub)
    test_common.validate_cmp(dtype, y_cal, y_ref)