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import triton
import triton.language as tl
import triton.language.extra.cann.libdevice as libdevice
import torch
import pytest
import test_common
import os

PERF_TEST_ENABLE = os.getenv('PERF_TEST_ENABLE', 'False').lower() == 'true'

def torch_pointwise(x0):
    return x0.to(torch.float32)

@triton.jit
def triton_half2float(in_ptr0, 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 - 1) // XBLOCK_SUB
    for loop1 in range(loops1):
        x = offset + (loop1 * XBLOCK_SUB) + base1
        tmp0 = tl.load(in_ptr0 + x, None)
        tmp1 = libdevice.half2float(tmp0)
        tl.store(out_ptr0 + x, tmp1, None)

default_param_list = test_common.make_default_param_list(['float16'])
full_param_list = test_common.make_full_param_list(['float16'])

@pytest.mark.parametrize('param_list', default_param_list if not PERF_TEST_ENABLE else full_param_list)
def test_common_case(param_list):
    dtype, shape, ncore, xblock, xblock_sub = param_list
    x0 = test_common.generate_tensor(shape, dtype).npu()
    y_ref = torch_pointwise(x0)
    y_cal = torch.zeros(shape, dtype=torch.float32).npu()
    if PERF_TEST_ENABLE:
        test_common.run_with_profiler(
            lambda: triton_half2float[ncore, 1, 1](x0, y_cal, xblock, xblock_sub, force_simt_only=True),
            shape,
            'half2float'
        )
    else:
        triton_half2float[ncore, 1, 1](x0, y_cal, xblock, xblock_sub, force_simt_only=True)
    test_common.validate_cmp('float32', y_cal, y_ref)