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.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):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, None)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 1.0507009873554805
    tmp4 = tmp0 * tmp3
    tmp5 = 1.0
    tmp6 = tmp0 * tmp5
    tmp7 = libdevice.expm1(tmp6)
    tmp8 = 1.7580993408473766
    tmp9 = tmp7 * tmp8
    tmp10 = tl.where(tmp2, tmp4, tmp9)
    tl.store(out_ptr0 + x0, tmp10, None)


@triton.jit
def triton_poi_fused_elu_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, None)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 1.0507009873554805
    tmp4 = tmp0 * tmp3
    tmp5 = 1.0
    tmp6 = tmp0 * tmp5
    tmp7 = libdevice.expm1(tmp6)
    tmp8 = 1.7580993408473766
    tmp9 = tmp7 * tmp8
    tmp10 = tl.where(tmp2, tmp4, tmp9)
    tl.store(out_ptr0 + x0, tmp10, None)


@triton.jit
def triton_poi_fused_elu_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, None)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 1.0507009873554805
    tmp4 = tmp0 * tmp3
    tmp5 = 1.0
    tmp6 = tmp0 * tmp5
    tmp7 = libdevice.expm1(tmp6)
    tmp8 = 1.7580993408473766
    tmp9 = tmp7 * tmp8
    tmp10 = tl.where(tmp2, tmp4, tmp9)
    tl.store(out_ptr0 + x0, tmp10, None)


@triton.jit
def triton_poi_fused_elu_3(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, None)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 1.0507009873554805
    tmp4 = tmp0 * tmp3
    tmp5 = 1.0
    tmp6 = tmp0 * tmp5
    tmp7 = libdevice.expm1(tmp6)
    tmp8 = 1.7580993408473766
    tmp9 = tmp7 * tmp8
    tmp10 = tl.where(tmp2, tmp4, tmp9)
    tl.store(out_ptr0 + x0, tmp10, None)


@triton.jit
def triton_poi_fused_sigmoid_4(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 128
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 2
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.sigmoid(tmp2)
    tl.store(in_out_ptr0 + x2, tmp3, 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) = args
    args.clear()
    assert_size_stride(primals_1, (2048, 4), (4, 1))
    assert_size_stride(primals_2, (2048,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (1024, 2048), (2048, 1))
    assert_size_stride(primals_5, (1024,), (1,))
    assert_size_stride(primals_6, (640, 1024), (1024, 1))
    assert_size_stride(primals_7, (640,), (1,))
    assert_size_stride(primals_8, (512, 640), (640, 1))
    assert_size_stride(primals_9, (512,), (1,))
    assert_size_stride(primals_10, (2, 512), (512, 1))
    assert_size_stride(primals_11, (2,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((64, 2048), (2048, 1), torch.float32)
        extern_kernels.addmm(primals_2, reinterpret_tensor(primals_3, (64, 
            4), (4, 1), 0), reinterpret_tensor(primals_1, (4, 2048), (1, 4),
            0), alpha=1, beta=1, out=buf0)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((4, 4, 4, 2048), (32768, 8192, 2048, 1),
            torch.float32)
        get_raw_stream(0)
        triton_poi_fused_elu_0[grid(131072)](buf0, buf1, 131072, XBLOCK=512,
            num_warps=8, num_stages=1)
        buf2 = empty_strided_cuda((64, 1024), (1024, 1), torch.float32)
        extern_kernels.addmm(primals_5, reinterpret_tensor(buf1, (64, 2048),
            (2048, 1), 0), reinterpret_tensor(primals_4, (2048, 1024), (1, 
            2048), 0), alpha=1, beta=1, out=buf2)
        del primals_5
        buf3 = empty_strided_cuda((4, 4, 4, 1024), (16384, 4096, 1024, 1),
            torch.float32)
        triton_poi_fused_elu_1[grid(65536)](buf2, buf3, 65536, XBLOCK=512,
            num_warps=4, num_stages=1)
        buf4 = empty_strided_cuda((64, 640), (640, 1), torch.float32)
        extern_kernels.addmm(primals_7, reinterpret_tensor(buf3, (64, 1024),
            (1024, 1), 0), reinterpret_tensor(primals_6, (1024, 640), (1, 
            1024), 0), alpha=1, beta=1, out=buf4)
        del primals_7
        buf5 = empty_strided_cuda((4, 4, 4, 640), (10240, 2560, 640, 1),
            torch.float32)
        triton_poi_fused_elu_2[grid(40960)](buf4, buf5, 40960, XBLOCK=256,
            num_warps=4, num_stages=1)
        buf6 = empty_strided_cuda((64, 512), (512, 1), torch.float32)
        extern_kernels.addmm(primals_9, reinterpret_tensor(buf5, (64, 640),
            (640, 1), 0), reinterpret_tensor(primals_8, (640, 512), (1, 640
            ), 0), alpha=1, beta=1, out=buf6)
        del primals_9
        buf7 = empty_strided_cuda((4, 4, 4, 512), (8192, 2048, 512, 1),
            torch.float32)
        triton_poi_fused_elu_3[grid(32768)](buf6, buf7, 32768, XBLOCK=128,
            num_warps=4, num_stages=1)
        buf8 = empty_strided_cuda((64, 2), (2, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf7, (64, 512), (512, 1), 0),
            reinterpret_tensor(primals_10, (512, 2), (1, 512), 0), out=buf8)
        buf9 = reinterpret_tensor(buf8, (4, 4, 4, 2), (32, 8, 2, 1), 0)
        del buf8
        triton_poi_fused_sigmoid_4[grid(128)](buf9, primals_11, 128, XBLOCK
            =128, num_warps=4, num_stages=1)
        del primals_11
    return buf9, reinterpret_tensor(primals_3, (64, 4), (4, 1), 0
        ), buf0, reinterpret_tensor(buf1, (64, 2048), (2048, 1), 0
        ), buf2, reinterpret_tensor(buf3, (64, 1024), (1024, 1), 0
        ), buf4, reinterpret_tensor(buf5, (64, 640), (640, 1), 0
        ), buf6, reinterpret_tensor(buf7, (64, 512), (512, 1), 0
        ), buf9, primals_10, primals_8, primals_6, primals_4


class FiveNetNew(nn.Module):

    def __init__(self, n_features, e1=1024, e2=2048, e3=1024, e4=640, e5=
        512, p=0.4):
        super(FiveNetNew, self).__init__()
        self.a1 = nn.Linear(n_features, e2)
        self.a2 = nn.Linear(e2, e3)
        self.a3 = nn.Linear(e3, e4)
        self.a4 = nn.Linear(e4, e5)
        self.a5 = nn.Linear(e5, 2)
        self.dropout = nn.Dropout(p)

    def forward(self, input_0):
        primals_1 = self.a1.weight
        primals_2 = self.a1.bias
        primals_4 = self.a2.weight
        primals_5 = self.a2.bias
        primals_6 = self.a3.weight
        primals_7 = self.a3.bias
        primals_8 = self.a4.weight
        primals_9 = self.a4.bias
        primals_10 = self.a5.weight
        primals_11 = self.a5.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])
        return output[0]


Measured latency_ms: 0.0217