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
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride


@triton.jit
def triton_poi_fused_convolution_0(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)
    x3 = xindex
    x1 = xindex // 2 % 512
    tmp0 = tl.load(in_out_ptr0 + x3, None)
    tmp1 = tl.load(in_ptr0 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, None)


@triton.jit
def triton_poi_fused_convolution_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 256
    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
    tl.store(in_out_ptr0 + x0, tmp3, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 1, 64), (64, 64, 1))
    assert_size_stride(primals_2, (512, 1, 64), (64, 64, 1))
    assert_size_stride(primals_3, (512,), (1,))
    assert_size_stride(primals_4, (512, 1, 64), (64, 64, 1))
    assert_size_stride(primals_5, (1,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_1, primals_2, stride=(32,
            ), padding=(16,), dilation=(1,), transposed=False,
            output_padding=(0,), groups=1, bias=None)
        assert_size_stride(buf0, (4, 512, 2), (1024, 2, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(4096)](buf1, primals_3, 4096,
            XBLOCK=128, num_warps=4, num_stages=1)
        del primals_3
        buf2 = extern_kernels.convolution(buf1, primals_4, stride=(32,),
            padding=(16,), dilation=(1,), transposed=True, output_padding=(
            0,), groups=1, bias=None)
        assert_size_stride(buf2, (4, 1, 64), (64, 64, 1))
        buf3 = buf2
        del buf2
        triton_poi_fused_convolution_1[grid(256)](buf3, primals_5, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del primals_5
    return buf3, primals_1, primals_2, primals_4, buf1


class BaseCNNNew(nn.Module):

    def __init__(self):
        super(BaseCNNNew, self).__init__()
        self.conv = nn.Conv1d(in_channels=1, out_channels=512, kernel_size=
            64, stride=32, padding=16)
        self.deconv = nn.ConvTranspose1d(in_channels=512, out_channels=1,
            kernel_size=64, stride=32, padding=16)

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


Measured latency_ms: 0.015295