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
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_leaky_relu_0(in_ptr0, out_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_ptr0 + x0, xmask)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 0.2
    tmp4 = tmp0 * tmp3
    tmp5 = tl.where(tmp2, tmp0, tmp4)
    tl.store(out_ptr0 + x0, tmp5, xmask)


@triton.jit
def triton_poi_fused_leaky_relu_1(in_ptr0, 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
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 0.2
    tmp4 = tmp0 * tmp3
    tmp5 = tl.where(tmp2, tmp0, tmp4)
    tl.store(out_ptr0 + x0, tmp2, xmask)
    tl.store(out_ptr1 + x0, tmp5, xmask)


@triton.jit
def triton_poi_fused_add_2(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 + x0, xmask)
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x0, tmp2, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4, 3, 3, 3), (108, 27, 9, 3, 1))
    assert_size_stride(primals_3, (4, 4, 3, 3, 3), (108, 27, 9, 3, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_leaky_relu_0[grid(256)](primals_1, buf0, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        buf1 = extern_kernels.convolution(reinterpret_tensor(buf0, (1, 4, 4,
            4, 4), (0, 64, 16, 4, 1), 0), primals_2, stride=(1, 1, 1),
            padding=(1, 1, 1), dilation=(1, 1, 1), transposed=False,
            output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (1, 4, 4, 4, 4), (256, 64, 16, 4, 1))
        buf2 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.bool)
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_leaky_relu_1[grid(256)](buf1, buf2, buf3, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del buf1
        buf4 = extern_kernels.convolution(reinterpret_tensor(buf3, (1, 4, 4,
            4, 4), (0, 64, 16, 4, 1), 0), primals_3, stride=(1, 1, 1),
            padding=(1, 1, 1), dilation=(1, 1, 1), transposed=False,
            output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf4, (1, 4, 4, 4, 4), (256, 64, 16, 4, 1))
        buf5 = reinterpret_tensor(buf4, (4, 4, 4, 4), (64, 16, 4, 1), 0)
        del buf4
        triton_poi_fused_add_2[grid(256)](buf5, primals_1, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
        del primals_1
    return buf5, primals_2, primals_3, reinterpret_tensor(buf0, (1, 4, 4, 4,
        4), (256, 64, 16, 4, 1), 0), buf2, reinterpret_tensor(buf3, (1, 4, 
        4, 4, 4), (256, 64, 16, 4, 1), 0)


class PreActBlockNew(nn.Module):
    """Pre-activation version of the BasicBlock."""
    expansion = 1

    def __init__(self, in_planes, planes, num_group=4, stride=1, bias=False):
        super(PreActBlockNew, self).__init__()
        self.conv1 = nn.Conv3d(in_planes, planes, kernel_size=3, stride=
            stride, padding=1, bias=bias)
        self.conv2 = nn.Conv3d(planes, planes, kernel_size=3, stride=1,
            padding=1, bias=bias)
        if stride != 1 or in_planes != self.expansion * planes:
            self.shortcut = nn.Sequential(nn.Conv3d(in_planes, self.
                expansion * planes, kernel_size=1, stride=stride, bias=bias))

    def forward(self, input_0):
        primals_2 = self.conv1.weight
        primals_3 = self.conv2.weight
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]


Measured latency_ms: 0.077471