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import torch
import torch_npu

import triton
import triton.language as tl

import pytest

@triton.jit
def triton_kernel(x_ptr, y_ptr, output_ptr, n_elements, BLOCK_SIZE: tl.constexpr, SLICE_OFFSET: tl.constexpr, SLICE_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 = tl.load(y_ptr + offsets, mask=mask)
    x_sub = tl.extract_slice(x, [block_start+SLICE_OFFSET], [SLICE_SIZE], [1])
    y_sub = tl.extract_slice(y, [block_start+SLICE_OFFSET], [SLICE_SIZE], [1])
    output_sub = x_sub + y_sub
    output = tl.load(output_ptr + offsets, mask=mask)
    output = tl.insert_slice(output, output_sub, [block_start+SLICE_OFFSET], [SLICE_SIZE], [1])
    tl.store(output_ptr + offsets, output, mask=mask)

def triton_func(x: torch.Tensor, y: torch.Tensor, slice_offset: int, slice_size: int):
    output = torch.empty_like(x)
    n_elements = output.numel()
    grid = lambda meta: (triton.cdiv(n_elements, meta['BLOCK_SIZE']), )
    triton_kernel[grid](x, y, output, n_elements, BLOCK_SIZE=1024, SLICE_OFFSET=0, SLICE_SIZE=32)
    return output

def test_extract_slice():
    size = 1024
    slice_offset = 0
    slice_size = 32
    x = torch.rand(size, device='npu')
    y = torch.rand(size, device='npu')
    torch_ref = x + y
    triton_cal = triton_func(x, y, slice_offset, slice_size)
    torch.testing.assert_close(triton_cal[slice_offset:slice_offset+slice_size],
                               torch_ref[slice_offset:slice_offset+slice_size])