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
from torch import Tensor
from torch import 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_convolution_0(in_ptr0, out_ptr0, 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
    x1 = xindex
    y0 = yindex
    tmp0 = tl.load(in_ptr0 + (y0 + 16 * x1), xmask & ymask, eviction_policy
        ='evict_last')
    tl.store(out_ptr0 + (x1 + 4 * y0), tmp0, xmask & ymask)


@triton.jit
def triton_poi_fused_convolution_1(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
    x3 = xindex
    x1 = xindex // 4 % 8
    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_glu_2(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 % 16
    x1 = xindex // 16
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 32 * x1), xmask)
    tmp1 = tl.load(in_ptr0 + (16 + x0 + 32 * x1), xmask)
    tmp2 = tl.sigmoid(tmp1)
    tmp3 = tmp0 * tmp2
    tl.store(out_ptr0 + x2, tmp3, xmask)


@triton.jit
def triton_poi_fused_convolution_3(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_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 + 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_mul_native_layer_norm_sigmoid_5(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 = tl.sigmoid(tmp8)
    tmp10 = tmp8 * tmp9
    tl.store(out_ptr1 + (y0 + 4 * x2 + 16 * y1), tmp10, xmask & ymask)
    tl.store(out_ptr2 + (x2 + 4 * y3), tmp10, xmask & ymask)


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), (16, 4, 1))
    assert_size_stride(primals_2, (8, 4, 1), (4, 1, 1))
    assert_size_stride(primals_3, (8,), (1,))
    assert_size_stride(primals_4, (4, 1, 1), (1, 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, 1), (4, 1, 1))
    assert_size_stride(primals_9, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(16, 4)](primals_1, buf0, 16, 4,
            XBLOCK=4, YBLOCK=16, num_warps=1, num_stages=1)
        buf1 = extern_kernels.convolution(buf0, primals_2, stride=(1,),
            padding=(0,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf1, (4, 8, 4), (32, 4, 1))
        buf2 = buf1
        del buf1
        triton_poi_fused_convolution_1[grid(128)](buf2, primals_3, 128,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_3
        buf3 = buf0
        del buf0
        triton_poi_fused_glu_2[grid(64)](buf2, buf3, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf4 = extern_kernels.convolution(buf3, primals_4, stride=(1,),
            padding=(0,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=4, bias=None)
        assert_size_stride(buf4, (4, 4, 4), (16, 4, 1))
        buf5 = buf4
        del buf4
        triton_poi_fused_convolution_3[grid(64)](buf5, primals_5, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del primals_5
        buf6 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        buf7 = empty_strided_cuda((4, 4, 1), (4, 1, 16), torch.float32)
        triton_poi_fused_native_layer_norm_4[grid(16)](buf5, buf6, buf7, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        buf9 = empty_strided_cuda((4, 4, 4), (16, 1, 4), torch.float32)
        buf10 = empty_strided_cuda((4, 4, 4), (16, 4, 1), torch.float32)
        triton_poi_fused_convolution_mul_native_layer_norm_sigmoid_5[grid(
            16, 4)](buf5, buf6, buf7, primals_6, primals_7, buf9, buf10, 16,
            4, XBLOCK=4, YBLOCK=8, num_warps=1, num_stages=1)
        del buf6
        del buf7
        buf11 = extern_kernels.convolution(buf10, primals_8, stride=(1,),
            padding=(0,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf11, (4, 4, 4), (16, 4, 1))
        del buf10
        buf12 = buf11
        del buf11
        triton_poi_fused_convolution_3[grid(64)](buf12, primals_9, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del primals_9
    return (reinterpret_tensor(buf12, (4, 4, 4), (1, 16, 4), 0), primals_2,
        primals_4, primals_6, primals_7, primals_8, reinterpret_tensor(
        primals_1, (4, 4, 4), (4, 1, 16), 0), buf2, buf3, buf5, buf9)


class Swish(torch.nn.Module):
    """Construct an Swish object."""

    def forward(self, x: 'Tensor') ->Tensor:
        """Return Swich activation function."""
        return x * torch.sigmoid(x)


class ConvolutionModuleNew(nn.Module):
    """ConvolutionModule in Conformer model.
    Modified from https://github.com/espnet/espnet/blob/master/espnet/nets/pytorch_backend/conformer/convolution.py

    Args:
        channels (int): The number of channels of conv layers.
        kernel_size (int): Kernerl size of conv layers.
        bias (bool): Whether to use bias in conv layers (default=True).

    """

    def __init__(self, channels: 'int', kernel_size: 'int', bias: 'bool'=True
        ) ->None:
        """Construct an ConvolutionModule object."""
        super(ConvolutionModuleNew, self).__init__()
        assert (kernel_size - 1) % 2 == 0
        self.pointwise_conv1 = nn.Conv1d(channels, 2 * channels,
            kernel_size=1, stride=1, padding=0, bias=bias)
        self.depthwise_conv = nn.Conv1d(channels, channels, kernel_size,
            stride=1, padding=(kernel_size - 1) // 2, groups=channels, bias
            =bias)
        self.norm = nn.LayerNorm(channels)
        self.pointwise_conv2 = nn.Conv1d(channels, channels, kernel_size=1,
            stride=1, padding=0, bias=bias)
        self.activation = Swish()

    def forward(self, input_0):
        primals_2 = self.pointwise_conv1.weight
        primals_3 = self.pointwise_conv1.bias
        primals_4 = self.depthwise_conv.weight
        primals_5 = self.depthwise_conv.bias
        primals_6 = self.norm.weight
        primals_7 = self.norm.bias
        primals_8 = self.pointwise_conv2.weight
        primals_9 = self.pointwise_conv2.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: 0.02583