from torch.nn import Module
import torch
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


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)


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


def get_init_inputs():
    return [[], {'n_in': 4, 'n_out': 4}]

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
from torch.nn import Module
from torch.nn.parameter import Parameter
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)


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


class NACNew(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_0):
        primals_1 = self.W_hat
        primals_2 = self.M_hat
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]
