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.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_add_mean_std_0(in_ptr0, out_ptr0, out_ptr1, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tmp9 = tmp0 - tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tmp1 - tmp8
    tmp12 = tmp11 * tmp11
    tmp13 = tmp10 + tmp12
    tmp14 = tmp3 - tmp8
    tmp15 = tmp14 * tmp14
    tmp16 = tmp13 + tmp15
    tmp17 = tmp5 - tmp8
    tmp18 = tmp17 * tmp17
    tmp19 = tmp16 + tmp18
    tmp20 = 3.0
    tmp21 = tmp19 / tmp20
    tmp22 = libdevice.sqrt(tmp21)
    tmp23 = 1e-05
    tmp24 = tmp22 + tmp23
    tl.store(out_ptr0 + x0, tmp8, xmask)
    tl.store(out_ptr1 + x0, tmp24, xmask)


@triton.jit
def triton_poi_fused_div_sub_1(in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = tmp2 / tmp3
    tl.store(out_ptr0 + x2, tmp4, xmask)


@triton.jit
def triton_poi_fused_sigmoid_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    tl.full([XBLOCK], True, tl.int1)
    x2 = xindex
    x0 = xindex % 2048
    tmp0 = tl.load(in_out_ptr0 + x2, None)
    tmp1 = tl.load(in_ptr0 + x0, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.sigmoid(tmp2)
    tl.store(in_out_ptr0 + x2, tmp3, None)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (2048, 4), (4, 1))
    assert_size_stride(primals_3, (2048,), (1,))
    assert_size_stride(primals_4, (4, 2048), (2048, 1))
    assert_size_stride(primals_5, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        buf1 = empty_strided_cuda((4, 1), (1, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_mean_std_0[grid(4)](primals_1, buf0, buf1, 4,
            XBLOCK=4, num_warps=1, num_stages=1)
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_div_sub_1[grid(16)](primals_1, buf0, buf1, buf2, 
            16, XBLOCK=16, num_warps=1, num_stages=1)
        del primals_1
        buf3 = empty_strided_cuda((4, 2048), (2048, 1), torch.float32)
        extern_kernels.mm(buf2, reinterpret_tensor(primals_2, (4, 2048), (1,
            4), 0), out=buf3)
        del primals_2
        buf4 = buf3
        del buf3
        triton_poi_fused_sigmoid_2[grid(8192)](buf4, primals_3, 8192,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_3
        buf5 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_5, buf4, reinterpret_tensor(primals_4,
            (2048, 4), (1, 2048), 0), alpha=1, beta=1, out=buf5)
        del primals_5
    return buf5, buf1, buf0, buf2, buf4, primals_4


class DNNnetNew(torch.nn.Module):

    def __init__(self, n_layer, n_in_channel, n_out_channel):
        super(DNNnetNew, self).__init__()
        self.n_layer = n_layer
        self.fc_layers = torch.nn.ModuleList()
        self.act_func = torch.nn.Sigmoid()
        start_layer = torch.nn.Linear(n_in_channel, 2048)
        self.start = start_layer
        for i in range(self.n_layer - 1):
            fc_layer = torch.nn.Linear(2048, 2048)
            self.fc_layers.append(fc_layer)
        end_layer = torch.nn.Linear(2048, n_out_channel)
        self.end = end_layer

    def scale(self, x):
        """
        x = [batchsize , n_mel_channels x frames]
        """
        self.mm = torch.mean(x, dim=1, keepdim=True)
        self.std = torch.std(x, dim=1, keepdim=True) + 1e-05
        return (x - self.mm) / self.std

    def forward(self, input_0):
        primals_2 = self.start.weight
        primals_3 = self.start.bias
        primals_4 = self.end.weight
        primals_5 = self.end.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]


Measured latency_ms: 0.058401