import torch
from torch._inductor.select_algorithm import extern_kernels
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, math as tl_math
from torch.nn import Module
from torch.nn.parameter import Parameter
from torch.nn import functional
from torch.nn import init
from torch.nn.modules import Module
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_mul_sigmoid_tanh_0(in_ptr0, in_ptr1, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp2 = tl.load(in_ptr1 + x0, xmask)
    tmp1 = libdevice.tanh(tmp0)
    tmp3 = tl.sigmoid(tmp2)
    tmp4 = tmp1 * tmp3
    tl.store(out_ptr0 + x0, tmp4, xmask)


@triton.jit
def triton_poi_fused_abs_add_log_1(in_ptr0, 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_math.abs(tmp0)
    tmp2 = 1e-06
    tmp3 = tmp1 + tmp2
    tmp4 = tl_math.log(tmp3)
    tl.store(out_ptr0 + x0, tmp4, xmask)


@triton.jit
def triton_poi_fused_add_exp_mul_rsub_sigmoid_2(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
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr1 + x2, xmask)
    tmp6 = tl.load(in_ptr2 + x2, xmask)
    tmp1 = tl.sigmoid(tmp0)
    tmp3 = tmp1 * tmp2
    tmp4 = 1.0
    tmp5 = tmp4 - tmp1
    tmp7 = tl_math.exp(tmp6)
    tmp8 = tmp5 * tmp7
    tmp9 = tmp3 + tmp8
    tl.store(out_ptr0 + x2, tmp9, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (1, 4), (4, 1))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_2, (64, 4), (4, 1), 0),
            reinterpret_tensor(primals_1, (4, 1), (1, 4), 0), out=buf0)
        del primals_1
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_mul_sigmoid_tanh_0[grid(16)](primals_3, primals_4,
            buf1, 16, XBLOCK=16, num_warps=1, num_stages=1)
        buf2 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_2, (64, 4), (4, 1), 0),
            reinterpret_tensor(buf1, (4, 4), (1, 4), 0), out=buf2)
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_abs_add_log_1[grid(256)](primals_2, buf3, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        buf4 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf3, (64, 4), (4, 1), 0),
            reinterpret_tensor(buf1, (4, 4), (1, 4), 0), out=buf4)
        del buf1
        buf5 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_add_exp_mul_rsub_sigmoid_2[grid(256)](buf0, buf2,
            buf4, buf5, 256, XBLOCK=128, num_warps=4, num_stages=1)
    return buf5, primals_3, primals_4, reinterpret_tensor(primals_2, (64, 4
        ), (4, 1), 0), buf0, buf2, reinterpret_tensor(buf3, (64, 4), (4, 1), 0
        ), buf4


class NAC(Module):

    def __init__(self, n_in, n_out):
        super().__init__()
        self.W_hat = Parameter(torch.Tensor(n_out, n_in))
        self.M_hat = Parameter(torch.Tensor(n_out, n_in))
        self.reset_parameters()

    def reset_parameters(self):
        init.kaiming_uniform_(self.W_hat)
        init.kaiming_uniform_(self.M_hat)

    def forward(self, input):
        weights = torch.tanh(self.W_hat) * torch.sigmoid(self.M_hat)
        return functional.linear(input, weights)


class NALUNew(Module):

    def __init__(self, n_in, n_out):
        super().__init__()
        self.NAC = NAC(n_in, n_out)
        self.G = Parameter(torch.Tensor(1, n_in))
        self.eps = 1e-06
        self.reset_parameters()

    def reset_parameters(self):
        init.kaiming_uniform_(self.G)

    def forward(self, input_0):
        primals_1 = self.G
        primals_3 = self.NAC.W_hat
        primals_4 = self.NAC.M_hat
        primals_2 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]


Measured latency_ms: 0.024737