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
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_leaky_relu_0(in_ptr0, in_ptr1, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 400
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 4 % 25
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.05
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + x3, tmp4, xmask)
    tl.store(out_ptr1 + x3, tmp7, xmask)


@triton.jit
def triton_poi_fused_convolution_leaky_relu_1(in_ptr0, in_ptr1, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 4 % 16
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.05
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + x3, tmp4, xmask)
    tl.store(out_ptr1 + x3, tmp7, xmask)


@triton.jit
def triton_poi_fused_convolution_leaky_relu_2(in_ptr0, in_ptr1, out_ptr0,
    out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 160
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 4 % 10
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = 0.0
    tmp4 = tmp2 > tmp3
    tmp5 = 0.05
    tmp6 = tmp2 * tmp5
    tmp7 = tl.where(tmp4, tmp2, tmp6)
    tl.store(out_ptr0 + x3, tmp4, xmask)
    tl.store(out_ptr1 + x3, tmp7, xmask)


@triton.jit
def triton_poi_fused_convolution_relu_threshold_backward_3(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
    x0 = xindex
    tmp0 = tl.load(in_out_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr0 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp3 = tmp0 + tmp2
    tmp4 = tl.full([1], 0, tl.int32)
    tmp5 = triton_helpers.maximum(tmp4, tmp3)
    tmp6 = 0.0
    tmp7 = tmp5 <= tmp6
    tl.store(in_out_ptr0 + x0, tmp5, xmask)
    tl.store(out_ptr0 + x0, tmp7, xmask)


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, 1))
    assert_size_stride(primals_2, (25, 1, 9), (9, 9, 1))
    assert_size_stride(primals_3, (25,), (1,))
    assert_size_stride(primals_4, (16, 25, 7), (175, 7, 1))
    assert_size_stride(primals_5, (16,), (1,))
    assert_size_stride(primals_6, (10, 16, 7), (112, 7, 1))
    assert_size_stride(primals_7, (10,), (1,))
    assert_size_stride(primals_8, (1, 10, 1), (10, 1, 1))
    assert_size_stride(primals_9, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(reinterpret_tensor(primals_1, (4,
            1, 4), (4, 4, 1), 0), primals_2, stride=(1,), padding=(4,),
            dilation=(1,), transposed=False, output_padding=(0,), groups=1,
            bias=None)
        assert_size_stride(buf0, (4, 25, 4), (100, 4, 1))
        buf1 = empty_strided_cuda((4, 25, 4), (100, 4, 1), torch.bool)
        buf2 = empty_strided_cuda((4, 25, 4), (100, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_convolution_leaky_relu_0[grid(400)](buf0,
            primals_3, buf1, buf2, 400, XBLOCK=128, num_warps=4, num_stages=1)
        del buf0
        del primals_3
        buf3 = extern_kernels.convolution(buf2, primals_4, stride=(1,),
            padding=(3,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf3, (4, 16, 4), (64, 4, 1))
        buf4 = empty_strided_cuda((4, 16, 4), (64, 4, 1), torch.bool)
        buf5 = empty_strided_cuda((4, 16, 4), (64, 4, 1), torch.float32)
        triton_poi_fused_convolution_leaky_relu_1[grid(256)](buf3,
            primals_5, buf4, buf5, 256, XBLOCK=256, num_warps=4, num_stages=1)
        del buf3
        del primals_5
        buf6 = extern_kernels.convolution(buf5, primals_6, stride=(1,),
            padding=(3,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf6, (4, 10, 4), (40, 4, 1))
        buf7 = empty_strided_cuda((4, 10, 4), (40, 4, 1), torch.bool)
        buf8 = empty_strided_cuda((4, 10, 4), (40, 4, 1), torch.float32)
        triton_poi_fused_convolution_leaky_relu_2[grid(160)](buf6,
            primals_7, buf7, buf8, 160, XBLOCK=128, num_warps=4, num_stages=1)
        del buf6
        del primals_7
        buf9 = extern_kernels.convolution(buf8, primals_8, stride=(1,),
            padding=(0,), dilation=(1,), transposed=False, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf9, (4, 1, 4), (4, 4, 1))
        buf10 = buf9
        del buf9
        buf11 = empty_strided_cuda((4, 1, 4), (4, 4, 1), torch.bool)
        triton_poi_fused_convolution_relu_threshold_backward_3[grid(16)](buf10,
            primals_9, buf11, 16, XBLOCK=16, num_warps=1, num_stages=1)
        del primals_9
    return reinterpret_tensor(buf10, (4, 4), (4, 1), 0
        ), primals_2, primals_4, primals_6, primals_8, reinterpret_tensor(
        primals_1, (4, 1, 4), (4, 4, 1), 0
        ), buf1, buf2, buf4, buf5, buf7, buf8, buf11


class Net_1New(nn.Module):

    def __init__(self):
        super(Net_1New, self).__init__()
        self.conv1 = nn.Conv1d(1, 25, 9, padding=4)
        self.conv2 = nn.Conv1d(25, 16, 7, padding=3)
        self.conv3 = nn.Conv1d(16, 10, 7, padding=3)
        self.conv4 = nn.Conv1d(10, 1, 1)

    def forward(self, input_0):
        primals_2 = self.conv1.weight
        primals_3 = self.conv1.bias
        primals_4 = self.conv2.weight
        primals_5 = self.conv2.bias
        primals_6 = self.conv3.weight
        primals_7 = self.conv3.bias
        primals_8 = self.conv4.weight
        primals_9 = self.conv4.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: 0.105875