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import pytest

import triton

import torch

import triton.language as tl

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

import test_common



@triton.jit

def asin_kernel(x_ptr, y_ptr, n_elements, BLOCK_SIZE: tl.constexpr):

    pid = tl.program_id(axis=0)

    block_start = pid * BLOCK_SIZE

    offsets = block_start + tl.arange(0, BLOCK_SIZE)

    

    mask = offsets < n_elements

    

    x = tl.load(x_ptr + offsets, mask=mask)

    

    y = libdevice.asin(x)

    

    tl.store(y_ptr + offsets, y, mask=mask)



@pytest.mark.parametrize('shape', [(12,16),])

@pytest.mark.parametrize('dtype', ['float32'])

def test_asin(shape, dtype):   

    n_elements = shape[0] * shape[1]

    

    x = test_common.generate_tensor(shape, dtype).npu()

    

    # Ensure to include some boundary cases

    x[0, 0] = 0.0

    x[0, 1] = 0.5

    x[0, 2] = -0.5

    x[0, 3] = 1.0

    x[0, 4] = -1.0

    x[0, 5] = 0.707  # sin(π/4)

    x[0, 6] = 0.866  # sin(π/3)

    

    # Add some out-of-range values

    x[0, 7] = 1.1

    x[0, 8] = -1.1

    

    y = torch.empty_like(x)

    

    BLOCK_SIZE = 192

    grid = lambda meta: (triton.cdiv(n_elements, meta['BLOCK_SIZE']),)

    

    asin_kernel[grid](x, y, n_elements, BLOCK_SIZE=BLOCK_SIZE)

    

    expected = torch.asin(x)

    

    # Check the accuracy for values within the effective range.

    valid_mask = (x >= -1) & (x <= 1)

    

    if torch.any(valid_mask):

        valid_y = y[valid_mask]

        valid_expected = expected[valid_mask]

        

        torch.testing.assert_close(valid_y, valid_expected, rtol=1e-3, atol=1e-3)

    

    # Check if values outside the range return NaN

    invalid_mask = (x < -1) | (x > 1)

    if torch.any(invalid_mask):

        invalid_y = y[invalid_mask]

        assert torch.all(torch.isnan(invalid_y)), "Invalid inputs should return NaN"

    

    print("✓ ASIN test PASSED!")