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 import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
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_relu_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x2, tmp4, xmask)


@triton.jit
def triton_poi_fused_abs_add_div_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 1024
    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 = 1.0
    tmp3 = tmp1 + tmp2
    tmp4 = tmp0 / tmp3
    tl.store(out_ptr0 + x0, tmp4, xmask)


@triton.jit
def triton_poi_fused_tanh_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 1024
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = libdevice.tanh(tmp2)
    tl.store(in_out_ptr0 + x2, tmp3, xmask)


@triton.jit
def triton_poi_fused_tanh_3(in_out_ptr0, in_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_out_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr0 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tmp4 = libdevice.tanh(tmp3)
    tl.store(in_out_ptr0 + x0, tmp4, xmask)


def call(args):
    (primals_1, primals_2, primals_3, primals_4, primals_5, primals_6,
        primals_7, primals_8, primals_9) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 1), (1, 1))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4, 4), (4, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 4), (4, 1))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (1, 4), (4, 1))
    assert_size_stride(primals_9, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((256, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(primals_1, (256, 1), (1, 1), 0
            ), reinterpret_tensor(primals_2, (1, 4), (1, 1), 0), out=buf0)
        del primals_2
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_relu_0[grid(1024)](buf1, primals_3, 1024, XBLOCK=
            256, num_warps=4, num_stages=1)
        del primals_3
        buf2 = empty_strided_cuda((256, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_5, buf1, reinterpret_tensor(primals_4,
            (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf2)
        del primals_5
        buf3 = empty_strided_cuda((256, 4), (4, 1), torch.float32)
        triton_poi_fused_abs_add_div_1[grid(1024)](buf2, buf3, 1024, XBLOCK
            =256, num_warps=4, num_stages=1)
        buf4 = empty_strided_cuda((256, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf3, reinterpret_tensor(primals_6, (4, 4), (1, 4
            ), 0), out=buf4)
        buf5 = buf4
        del buf4
        triton_poi_fused_tanh_2[grid(1024)](buf5, primals_7, 1024, XBLOCK=
            128, num_warps=4, num_stages=1)
        del primals_7
        buf6 = empty_strided_cuda((256, 1), (1, 1), torch.float32)
        extern_kernels.mm(buf5, reinterpret_tensor(primals_8, (4, 1), (1, 4
            ), 0), out=buf6)
        buf7 = buf6
        del buf6
        triton_poi_fused_tanh_3[grid(256)](buf7, primals_9, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        del primals_9
    return buf7, reinterpret_tensor(primals_1, (256, 1), (1, 1), 0
        ), buf1, buf2, buf3, buf5, buf7, primals_8, primals_6, primals_4


class DeepNeuralNetworkNew(nn.Module):

    def __init__(self, u):
        super(DeepNeuralNetworkNew, self).__init__()
        self.fc1 = nn.Linear(1, u)
        self.fc2 = nn.Linear(u, u)
        self.fc3 = nn.Linear(u, u)
        self.fc4 = nn.Linear(u, 1)
        self.ReLu = nn.ReLU()
        self.Sigmoid = nn.Softsign()
        self.Tanhshrink = nn.Tanh()
        self.Softplus = nn.ELU()

    def __repr__(self):
        return json.dumps(self.__dict__)

    def forward(self, input_0):
        primals_2 = self.fc1.weight
        primals_3 = self.fc1.bias
        primals_4 = self.fc2.weight
        primals_5 = self.fc2.bias
        primals_6 = self.fc3.weight
        primals_7 = self.fc3.bias
        primals_8 = self.fc4.weight
        primals_9 = self.fc4.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6, primals_7, primals_8, primals_9])
        return output[0]


Measured latency_ms: 0.078477