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
import torch.nn.functional as F
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_native_layer_norm_0(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 16 * x1), xmask)
    tmp1 = tl.load(in_ptr0 + (4 + x0 + 16 * x1), xmask)
    tmp3 = tl.load(in_ptr0 + (8 + x0 + 16 * x1), xmask)
    tmp5 = tl.load(in_ptr0 + (12 + x0 + 16 * x1), xmask)
    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 = tmp19 / tmp7
    tmp21 = 1e-05
    tmp22 = tmp20 + tmp21
    tmp23 = libdevice.rsqrt(tmp22)
    tl.store(out_ptr0 + x2, tmp8, xmask)
    tl.store(out_ptr1 + x2, tmp23, xmask)


@triton.jit
def triton_poi_fused_convolution_leaky_relu_native_layer_norm_1(in_ptr0,
    in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr1, out_ptr2, ynumel, xnumel,
    YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y1 = yindex // 4
    y0 = yindex % 4
    tmp0 = tl.load(in_ptr0 + (x2 + 4 * y3), xmask & ymask, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr1 + (x2 + 4 * y1), xmask & ymask, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr2 + (x2 + 4 * y1), xmask & ymask, eviction_policy=
        'evict_last')
    tmp5 = tl.load(in_ptr3 + y0, ymask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + y0, ymask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 * tmp5
    tmp8 = tmp6 + tmp7
    tmp9 = 0.0
    tmp10 = tmp8 > tmp9
    tmp11 = 0.01
    tmp12 = tmp8 * tmp11
    tmp13 = tl.where(tmp10, tmp8, tmp12)
    tl.store(out_ptr1 + (y0 + 4 * x2 + 16 * y1), tmp13, xmask & ymask)
    tl.store(out_ptr2 + (x2 + 4 * y3), tmp13, xmask & ymask)


@triton.jit
def triton_poi_fused_convolution_2(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 4 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, xmask)


@triton.jit
def triton_poi_fused_convolution_3(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 80
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 5 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, xmask)


@triton.jit
def triton_poi_fused_native_layer_norm_4(in_ptr0, out_ptr0, out_ptr1,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 20 * x1), xmask)
    tmp1 = tl.load(in_ptr0 + (5 + x0 + 20 * x1), xmask)
    tmp3 = tl.load(in_ptr0 + (10 + x0 + 20 * x1), xmask)
    tmp5 = tl.load(in_ptr0 + (15 + x0 + 20 * x1), xmask)
    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 = tmp19 / tmp7
    tmp21 = 1e-05
    tmp22 = tmp20 + tmp21
    tmp23 = libdevice.rsqrt(tmp22)
    tl.store(out_ptr0 + x2, tmp8, xmask)
    tl.store(out_ptr1 + x2, tmp23, xmask)


@triton.jit
def triton_poi_fused_convolution_leaky_relu_native_layer_norm_5(in_ptr0,
    in_ptr1, in_ptr2, in_ptr3, in_ptr4, out_ptr1, out_ptr2, out_ptr3,
    ynumel, xnumel, YBLOCK: tl.constexpr, XBLOCK: tl.constexpr):
    ynumel = 16
    xnumel = 4
    yoffset = tl.program_id(1) * YBLOCK
    yindex = yoffset + tl.arange(0, YBLOCK)[None, :]
    ymask = yindex < ynumel
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    x2 = xindex
    y3 = yindex
    y1 = yindex // 4
    y0 = yindex % 4
    tmp0 = tl.load(in_ptr0 + (x2 + 5 * y3), xmask & ymask, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr1 + (x2 + 4 * y1), xmask & ymask, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr2 + (x2 + 4 * y1), xmask & ymask, eviction_policy=
        'evict_last')
    tmp5 = tl.load(in_ptr3 + y0, ymask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr4 + y0, ymask, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp4 = tmp2 * tmp3
    tmp6 = tmp4 * tmp5
    tmp8 = tmp6 + tmp7
    tmp9 = 0.0
    tmp10 = tmp8 > tmp9
    tmp11 = 0.01
    tmp12 = tmp8 * tmp11
    tmp13 = tl.where(tmp10, tmp8, tmp12)
    tl.store(out_ptr1 + (y0 + 4 * x2 + 16 * y1), tmp13, xmask & ymask)
    tl.store(out_ptr2 + (x2 + 4 * y3), tmp13, xmask & ymask)
    tl.store(out_ptr3 + (x2 + 4 * y3), tmp13, xmask & ymask)


@triton.jit
def triton_poi_fused_add_convolution_mul_sigmoid_6(in_out_ptr0, in_out_ptr1,
    in_ptr0, in_ptr1, in_ptr2, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 4 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_out_ptr1 + x3, xmask)
    tmp4 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr2 + x3, xmask)
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tl.sigmoid(tmp5)
    tmp7 = tmp2 * tmp6
    tmp9 = tmp7 + tmp8
    tl.store(in_out_ptr0 + x3, tmp2, xmask)
    tl.store(in_out_ptr1 + x3, tmp5, xmask)
    tl.store(out_ptr0 + x3, tmp9, 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) = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4), (16, 4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4,), (1,))
    assert_size_stride(primals_4, (4, 4, 1), (4, 1, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4,), (1,))
    assert_size_stride(primals_7, (4,), (1,))
    assert_size_stride(primals_8, (4, 4, 2), (8, 2, 1))
    assert_size_stride(primals_9, (4,), (1,))
    assert_size_stride(primals_10, (4,), (1,))
    assert_size_stride(primals_11, (4,), (1,))
    assert_size_stride(primals_12, (4, 4, 1), (4, 1, 1))
    assert_size_stride(primals_13, (4,), (1,))
    assert_size_stride(primals_14, (4, 4, 1), (4, 1, 1))
    assert_size_stride(primals_15, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        buf1 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_native_layer_norm_0[grid(16)](primals_1, buf0,
            buf1, 16, XBLOCK=16, num_warps=1, num_stages=1)
        buf3 = empty_strided_cuda((4, 4, 4), (16, 1, 4), torch.float32)
        buf4 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_convolution_leaky_relu_native_layer_norm_1[grid(16, 4)
            ](primals_1, buf0, buf1, primals_2, primals_3, buf3, buf4, 16, 
            4, XBLOCK=4, YBLOCK=8, num_warps=1, num_stages=1)
        buf5 = extern_kernels.convolution(buf4, primals_4, stride=(1,),
            padding=(0,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf5, (4, 4, 4), (16, 4, 1))
        buf6 = buf5
        del buf5
        triton_poi_fused_convolution_2[grid(64)](buf6, primals_5, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del primals_5
        buf7 = buf1
        del buf1
        buf8 = buf0
        del buf0
        triton_poi_fused_native_layer_norm_0[grid(16)](buf6, buf7, buf8, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        buf10 = reinterpret_tensor(buf4, (4, 4, 4), (16, 1, 4), 0)
        del buf4
        buf11 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_convolution_leaky_relu_native_layer_norm_1[grid(16, 4)
            ](buf6, buf7, buf8, primals_6, primals_7, buf10, buf11, 16, 4,
            XBLOCK=4, YBLOCK=8, num_warps=1, num_stages=1)
        buf12 = extern_kernels.convolution(buf11, primals_8, stride=(1,),
            padding=(1,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf12, (4, 4, 5), (20, 5, 1))
        buf13 = buf12
        del buf12
        triton_poi_fused_convolution_3[grid(80)](buf13, primals_9, 80,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_9
        buf14 = buf8
        del buf8
        buf15 = buf7
        del buf7
        triton_poi_fused_native_layer_norm_4[grid(16)](buf13, buf14, buf15,
            16, XBLOCK=16, num_warps=1, num_stages=1)
        buf17 = reinterpret_tensor(buf11, (4, 4, 4), (16, 1, 4), 0)
        del buf11
        buf18 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        buf21 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_convolution_leaky_relu_native_layer_norm_5[grid(16, 4)
            ](buf13, buf14, buf15, primals_10, primals_11, buf17, buf18,
            buf21, 16, 4, XBLOCK=4, YBLOCK=16, num_warps=1, num_stages=1)
        del buf14
        del buf15
        buf19 = extern_kernels.convolution(buf18, primals_12, stride=(1,),
            padding=(0,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf19, (4, 4, 4), (16, 4, 1))
        del buf18
        buf22 = extern_kernels.convolution(buf21, primals_14, stride=(1,),
            padding=(0,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf22, (4, 4, 4), (16, 4, 1))
        buf20 = buf19
        del buf19
        buf23 = buf22
        del buf22
        buf24 = buf21
        del buf21
        triton_poi_fused_add_convolution_mul_sigmoid_6[grid(64)](buf20,
            buf23, primals_13, primals_15, primals_1, buf24, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        del primals_13
        del primals_15
    return (buf24, primals_1, primals_2, primals_3, primals_4, primals_6,
        primals_7, primals_8, primals_10, primals_11, primals_12,
        primals_14, buf3, buf6, buf10, buf13, buf17, buf20, buf23)


class DilConv1dWithGLUNew(nn.Module):

    def __init__(self, num_channels, dilation, lenght=100, kernel_size=2,
        activation=F.leaky_relu, residual_connection=True, dropout=0.2):
        super(DilConv1dWithGLUNew, self).__init__()
        self.dilation = dilation
        self.start_ln = nn.LayerNorm(num_channels)
        self.start_conv1x1 = nn.Conv1d(num_channels, num_channels,
            kernel_size=1)
        self.dilconv_ln = nn.LayerNorm(num_channels)
        self.dilated_conv = nn.Conv1d(num_channels, num_channels, dilation=
            dilation, kernel_size=kernel_size, padding=dilation)
        self.gate_ln = nn.LayerNorm(num_channels)
        self.end_conv1x1 = nn.Conv1d(num_channels, num_channels, kernel_size=1)
        self.gated_conv1x1 = nn.Conv1d(num_channels, num_channels,
            kernel_size=1)
        self.activation = activation
        self.buffer = None
        self.residual_connection = residual_connection

    def clear_buffer(self):
        self.buffer = None

    def forward(self, input_0):
        primals_2 = self.start_ln.weight
        primals_3 = self.start_ln.bias
        primals_4 = self.start_conv1x1.weight
        primals_5 = self.start_conv1x1.bias
        primals_6 = self.dilconv_ln.weight
        primals_7 = self.dilconv_ln.bias
        primals_8 = self.dilated_conv.weight
        primals_9 = self.dilated_conv.bias
        primals_10 = self.gate_ln.weight
        primals_11 = self.gate_ln.bias
        primals_12 = self.end_conv1x1.weight
        primals_13 = self.end_conv1x1.bias
        primals_14 = self.gated_conv1x1.weight
        primals_15 = self.gated_conv1x1.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,
            primals_10, primals_11, primals_12, primals_13, primals_14,
            primals_15])
        return output[0]


Measured latency_ms: 0.18469