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
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_relu_threshold_backward_0(in_out_ptr0, in_ptr0,
    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_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp5 = 0.0
    tmp6 = tmp4 <= tmp5
    tl.store(in_out_ptr0 + x2, tmp4, xmask)
    tl.store(out_ptr0 + x2, tmp6, xmask)


@triton.jit
def triton_poi_fused_add_1(in_out_ptr0, in_ptr0, in_ptr1, 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_out_ptr0 + x2, xmask)
    tmp2 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp4 = tmp0 + tmp3
    tl.store(in_out_ptr0 + x2, tmp4, xmask)


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, (4, 4, 3), (12, 3, 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,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(reinterpret_tensor(primals_1, (1,
            4, 4), (16, 4, 1), 0), primals_2, stride=(1,), padding=(1,),
            dilation=(1,), transposed=False, output_padding=(0,), groups=1,
            bias=None)
        assert_size_stride(buf0, (1, 4, 4), (16, 4, 1))
        buf1 = reinterpret_tensor(buf0, (4, 4), (4, 1), 0)
        del buf0
        buf4 = empty_strided_cuda((4, 4), (4, 1), torch.bool)
        get_raw_stream(0)
        triton_poi_fused_relu_threshold_backward_0[grid(16)](buf1,
            primals_3, buf4, 16, XBLOCK=16, num_warps=1, num_stages=1)
        del primals_3
        buf2 = extern_kernels.convolution(reinterpret_tensor(buf1, (1, 4, 4
            ), (0, 4, 1), 0), primals_4, stride=(1,), padding=(0,),
            dilation=(1,), transposed=False, output_padding=(0,), groups=1,
            bias=None)
        assert_size_stride(buf2, (1, 4, 4), (16, 4, 1))
        buf3 = reinterpret_tensor(buf2, (4, 4), (4, 1), 0)
        del buf2
        triton_poi_fused_add_1[grid(16)](buf3, primals_1, primals_5, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        del primals_5
    return buf3, primals_2, primals_4, reinterpret_tensor(primals_1, (1, 4,
        4), (16, 4, 1), 0), reinterpret_tensor(buf1, (1, 4, 4), (16, 4, 1), 0
        ), buf4


class DilatedResConvNew(nn.Module):

    def __init__(self, channels, dilation=1, activation='relu', padding=1,
        kernel_size=3, left_pad=0):
        super().__init__()
        in_channels = channels
        if activation == 'relu':
            self.activation = lambda *args, **kwargs: F.relu(*args, **
                kwargs, inplace=True)
        elif activation == 'tanh':
            self.activation = F.tanh
        elif activation == 'glu':
            self.activation = F.glu
            in_channels = channels // 2
        self.left_pad = left_pad
        self.dilated_conv = nn.Conv1d(in_channels, channels, kernel_size=
            kernel_size, stride=1, padding=dilation * padding, dilation=
            dilation, bias=True)
        self.conv_1x1 = nn.Conv1d(in_channels, channels, kernel_size=1,
            bias=True)

    def forward(self, input_0):
        primals_2 = self.dilated_conv.weight
        primals_3 = self.dilated_conv.bias
        primals_4 = self.conv_1x1.weight
        primals_5 = self.conv_1x1.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]


Measured latency_ms: 0.023216