import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_poi_fused_add_div_pow_reciprocal_sub_0(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr1 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp4 = tl.load(in_ptr2 + 0)
    tmp5 = tl.broadcast_to(tmp4, [XBLOCK])
    tmp8 = tl.load(in_ptr3 + 0)
    tmp9 = tl.broadcast_to(tmp8, [XBLOCK])
    tmp3 = tmp0 - tmp2
    tmp6 = tmp3 / tmp5
    tmp7 = tmp6 * tmp6
    tmp10 = libdevice.pow(tmp7, tmp9)
    tmp11 = 1.0
    tmp12 = tmp10 + tmp11
    tmp13 = tl.full([1], 1, tl.int32)
    tmp14 = tmp13 / tmp12
    tl.store(out_ptr0 + x0, tmp14, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (), ())
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (), ())
    assert_size_stride(primals_4, (), ())
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_div_pow_reciprocal_sub_0[grid(256)](primals_2,
            primals_1, primals_3, primals_4, buf0, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
    return buf0, primals_1, primals_2, primals_3, primals_4, buf0


def _mk_param(val):
    """Make a torch parameter from a scalar value"""
    if isinstance(val, torch.Tensor):
        val = val.item()
    return torch.nn.Parameter(torch.tensor(val, dtype=torch.float))


class BellMembFuncNew(torch.nn.Module):
    """
        Generalised Bell membership function; defined by three parameters:
            a, the half-width (at the crossover point)
            b, controls the slope at the crossover point (which is -b/2a)
            c, the center point
    """

    def __init__(self, a, b, c):
        super(BellMembFuncNew, self).__init__()
        self.register_parameter('a', _mk_param(a))
        self.register_parameter('b', _mk_param(b))
        self.register_parameter('c', _mk_param(c))
        self.b.register_hook(BellMembFuncNew.b_log_hook)

    @staticmethod
    def b_log_hook(grad):
        """
            Possibility of a log(0) in the grad for b, giving a nan.
            Fix this by replacing any nan in the grad with ~0.
        """
        grad[torch.isnan(grad)] = 1e-09
        return grad

    def pretty(self):
        return 'BellMembFunc {} {} {}'.format(self.a, self.b, self.c)

    def forward(self, input_0):
        primals_1 = self.a
        primals_3 = self.b
        primals_4 = self.c
        primals_2 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]


Measured latency_ms: 0.015956