import torch


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 GaussMembFunc(torch.nn.Module):
    """
        Gaussian membership functions, defined by two parameters:
            mu, the mean (center)
            sigma, the standard deviation.
    """

    def __init__(self, mu, sigma):
        super(GaussMembFunc, self).__init__()
        self.register_parameter('mu', _mk_param(mu))
        self.register_parameter('sigma', _mk_param(sigma))

    def forward(self, x):
        val = torch.exp(-torch.pow(x - self.mu, 2) / (2 * self.sigma ** 2))
        return val

    def pretty(self):
        return 'GaussMembFunc {} {}'.format(self.mu, self.sigma)


def get_inputs():
    return [torch.rand([4, 4, 4, 4])]


def get_init_inputs():
    return [[], {'mu': 4, 'sigma': 4}]

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 math as tl_math
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_div_exp_mul_neg_pow_sub_0(in_ptr0, in_ptr1, in_ptr2,
    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])
    tmp6 = tl.load(in_ptr2 + 0)
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK])
    tmp3 = tmp0 - tmp2
    tmp4 = tmp3 * tmp3
    tmp5 = -tmp4
    tmp8 = tmp7 * tmp7
    tmp9 = 2.0
    tmp10 = tmp8 * tmp9
    tmp11 = tmp5 / tmp10
    tmp12 = tl_math.exp(tmp11)
    tl.store(out_ptr0 + x0, tmp12, xmask)


def call(args):
    primals_1, primals_2, primals_3 = 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, (), ())
    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_div_exp_mul_neg_pow_sub_0[grid(256)](primals_2,
            primals_1, primals_3, buf0, 256, XBLOCK=256, num_warps=4,
            num_stages=1)
    return buf0, primals_1, primals_2, primals_3, 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 GaussMembFuncNew(torch.nn.Module):
    """
        Gaussian membership functions, defined by two parameters:
            mu, the mean (center)
            sigma, the standard deviation.
    """

    def __init__(self, mu, sigma):
        super(GaussMembFuncNew, self).__init__()
        self.register_parameter('mu', _mk_param(mu))
        self.register_parameter('sigma', _mk_param(sigma))

    def pretty(self):
        return 'GaussMembFunc {} {}'.format(self.mu, self.sigma)

    def forward(self, input_0):
        primals_1 = self.mu
        primals_3 = self.sigma
        primals_2 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]
