import torch
import torch.optim
import torch.jit
import torch.nn as nn


class SimpleMLP(nn.Module):

    def __init__(self, num_in_features: 'int', num_out_features: 'int',
        neurons_per_layer: 'int'):
        super(SimpleMLP, self).__init__()
        self.act = nn.ELU()
        self.l_in = nn.Linear(in_features=num_in_features, out_features=
            neurons_per_layer)
        self.l1 = nn.Linear(in_features=neurons_per_layer, out_features=
            neurons_per_layer)
        self.l2 = nn.Linear(in_features=neurons_per_layer, out_features=
            neurons_per_layer)
        self.l3 = nn.Linear(in_features=neurons_per_layer, out_features=
            neurons_per_layer)
        self.l4 = nn.Linear(in_features=neurons_per_layer, out_features=
            neurons_per_layer)
        self.l5 = nn.Linear(in_features=neurons_per_layer, out_features=
            neurons_per_layer)
        self.l6 = nn.Linear(in_features=neurons_per_layer, out_features=
            neurons_per_layer)
        self.l_out = nn.Linear(in_features=neurons_per_layer, out_features=
            num_out_features)
        torch.nn.init.xavier_normal_(self.l_in.weight)
        torch.nn.init.zeros_(self.l_in.bias)
        torch.nn.init.xavier_normal_(self.l1.weight)
        torch.nn.init.zeros_(self.l1.bias)
        torch.nn.init.xavier_normal_(self.l2.weight)
        torch.nn.init.zeros_(self.l2.bias)
        torch.nn.init.xavier_normal_(self.l3.weight)
        torch.nn.init.zeros_(self.l3.bias)
        torch.nn.init.xavier_normal_(self.l4.weight)
        torch.nn.init.zeros_(self.l4.bias)
        torch.nn.init.xavier_normal_(self.l5.weight)
        torch.nn.init.zeros_(self.l5.bias)
        torch.nn.init.xavier_normal_(self.l6.weight)
        torch.nn.init.zeros_(self.l6.bias)
        torch.nn.init.xavier_normal_(self.l_out.weight)
        torch.nn.init.zeros_(self.l_out.bias)

    def forward(self, x):
        x = self.act(self.l_in(x))
        x = self.act(self.l1(x))
        x = self.act(self.l2(x))
        x = self.act(self.l3(x))
        x = self.act(self.l4(x))
        x = self.act(self.l5(x))
        x = self.act(self.l6(x))
        x = self.l_out(x)
        return x


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


def get_init_inputs():
    return [[], {'num_in_features': 4, 'num_out_features': 4,
        'neurons_per_layer': 1}]

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
import torch.optim
import torch.jit
import torch.nn as nn
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_elu_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    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 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 1.0
    tmp4 = tmp0 * tmp3
    tmp5 = libdevice.expm1(tmp4)
    tmp6 = tmp5 * tmp3
    tmp7 = tl.where(tmp2, tmp4, tmp6)
    tl.store(out_ptr0 + x0, tmp7, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9, primals_10, primals_11, primals_12,
        primals_13, primals_14, primals_15, primals_16, primals_17) = args
    args.clear()
    assert_size_stride(primals_1, (1, 4), (4, 1))
    assert_size_stride(primals_2, (1,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (1, 1), (1, 1))
    assert_size_stride(primals_5, (1,), (1,))
    assert_size_stride(primals_6, (1, 1), (1, 1))
    assert_size_stride(primals_7, (1,), (1,))
    assert_size_stride(primals_8, (1, 1), (1, 1))
    assert_size_stride(primals_9, (1,), (1,))
    assert_size_stride(primals_10, (1, 1), (1, 1))
    assert_size_stride(primals_11, (1,), (1,))
    assert_size_stride(primals_12, (1, 1), (1, 1))
    assert_size_stride(primals_13, (1,), (1,))
    assert_size_stride(primals_14, (1, 1), (1, 1))
    assert_size_stride(primals_15, (1,), (1,))
    assert_size_stride(primals_16, (4, 1), (1, 1))
    assert_size_stride(primals_17, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf1 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64, 
            4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 1), (1, 4), 0
            ), alpha=1, beta=1, out=buf1)
        del primals_1
        del primals_2
        buf2 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_elu_0[grid(64)](buf1, buf2, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf4 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_5, reinterpret_tensor(buf2, (64, 1), (
            1, 0), 0), primals_4, alpha=1, beta=1, out=buf4)
        del primals_5
        buf5 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        triton_poi_fused_elu_0[grid(64)](buf4, buf5, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf7 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_7, reinterpret_tensor(buf5, (64, 1), (
            1, 0), 0), primals_6, alpha=1, beta=1, out=buf7)
        del primals_7
        buf8 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        triton_poi_fused_elu_0[grid(64)](buf7, buf8, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf10 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_9, reinterpret_tensor(buf8, (64, 1), (
            1, 0), 0), primals_8, alpha=1, beta=1, out=buf10)
        del primals_9
        buf11 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        triton_poi_fused_elu_0[grid(64)](buf10, buf11, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf13 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_11, reinterpret_tensor(buf11, (64, 1),
            (1, 0), 0), primals_10, alpha=1, beta=1, out=buf13)
        del primals_11
        buf14 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        triton_poi_fused_elu_0[grid(64)](buf13, buf14, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf16 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_13, reinterpret_tensor(buf14, (64, 1),
            (1, 0), 0), primals_12, alpha=1, beta=1, out=buf16)
        del primals_13
        buf17 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        triton_poi_fused_elu_0[grid(64)](buf16, buf17, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf19 = empty_strided_cuda((64, 1), (1, 1), torch.float32)
        extern_kernels.addmm(primals_15, reinterpret_tensor(buf17, (64, 1),
            (1, 0), 0), primals_14, alpha=1, beta=1, out=buf19)
        del primals_15
        buf20 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 1), torch.float32)
        triton_poi_fused_elu_0[grid(64)](buf19, buf20, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf21 = empty_strided_cuda((64, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_17, reinterpret_tensor(buf20, (64, 1),
            (1, 0), 0), reinterpret_tensor(primals_16, (1, 4), (1, 1), 0),
            alpha=1, beta=1, out=buf21)
        del primals_17
    return (reinterpret_tensor(buf21, (4, 4, 4, 4), (64, 16, 4, 1), 0),
        reinterpret_tensor(primals_3, (64, 4), (4, 1), 0), buf1,
        reinterpret_tensor(buf2, (64, 1), (1, 1), 0), buf4,
        reinterpret_tensor(buf5, (64, 1), (1, 1), 0), buf7,
        reinterpret_tensor(buf8, (64, 1), (1, 1), 0), buf10,
        reinterpret_tensor(buf11, (64, 1), (1, 1), 0), buf13,
        reinterpret_tensor(buf14, (64, 1), (1, 1), 0), buf16,
        reinterpret_tensor(buf17, (64, 1), (1, 1), 0), buf19,
        reinterpret_tensor(buf20, (64, 1), (1, 1), 0), primals_16,
        primals_14, primals_12, primals_10, primals_8, primals_6, primals_4)


class SimpleMLPNew(nn.Module):

    def __init__(self, num_in_features: 'int', num_out_features: 'int',
        neurons_per_layer: 'int'):
        super(SimpleMLPNew, self).__init__()
        self.act = nn.ELU()
        self.l_in = nn.Linear(in_features=num_in_features, out_features=
            neurons_per_layer)
        self.l1 = nn.Linear(in_features=neurons_per_layer, out_features=
            neurons_per_layer)
        self.l2 = nn.Linear(in_features=neurons_per_layer, out_features=
            neurons_per_layer)
        self.l3 = nn.Linear(in_features=neurons_per_layer, out_features=
            neurons_per_layer)
        self.l4 = nn.Linear(in_features=neurons_per_layer, out_features=
            neurons_per_layer)
        self.l5 = nn.Linear(in_features=neurons_per_layer, out_features=
            neurons_per_layer)
        self.l6 = nn.Linear(in_features=neurons_per_layer, out_features=
            neurons_per_layer)
        self.l_out = nn.Linear(in_features=neurons_per_layer, out_features=
            num_out_features)
        torch.nn.init.xavier_normal_(self.l_in.weight)
        torch.nn.init.zeros_(self.l_in.bias)
        torch.nn.init.xavier_normal_(self.l1.weight)
        torch.nn.init.zeros_(self.l1.bias)
        torch.nn.init.xavier_normal_(self.l2.weight)
        torch.nn.init.zeros_(self.l2.bias)
        torch.nn.init.xavier_normal_(self.l3.weight)
        torch.nn.init.zeros_(self.l3.bias)
        torch.nn.init.xavier_normal_(self.l4.weight)
        torch.nn.init.zeros_(self.l4.bias)
        torch.nn.init.xavier_normal_(self.l5.weight)
        torch.nn.init.zeros_(self.l5.bias)
        torch.nn.init.xavier_normal_(self.l6.weight)
        torch.nn.init.zeros_(self.l6.bias)
        torch.nn.init.xavier_normal_(self.l_out.weight)
        torch.nn.init.zeros_(self.l_out.bias)

    def forward(self, input_0):
        primals_1 = self.l_in.weight
        primals_2 = self.l_in.bias
        primals_4 = self.l1.weight
        primals_5 = self.l1.bias
        primals_6 = self.l2.weight
        primals_7 = self.l2.bias
        primals_8 = self.l3.weight
        primals_9 = self.l3.bias
        primals_10 = self.l4.weight
        primals_11 = self.l4.bias
        primals_12 = self.l5.weight
        primals_13 = self.l5.bias
        primals_14 = self.l6.weight
        primals_15 = self.l6.bias
        primals_16 = self.l_out.weight
        primals_17 = self.l_out.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9,
            primals_10, primals_11, primals_12, primals_13, primals_14,
            primals_15, primals_16, primals_17])
        return output[0]
