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.utils
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_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 = tl.full([1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tl.store(out_ptr0 + x0, tmp2, xmask)


@triton.jit
def triton_poi_fused_cat_1(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 4 % 4
    x0 = xindex % 4
    x2 = xindex // 16
    x3 = xindex
    tmp0 = x1
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 2, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + (x0 + 4 * x1 + 8 * x2), tmp4 & xmask, other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 4, tl.int64)
    tmp9 = tl.load(in_ptr1 + (x0 + 4 * (-2 + x1) + 8 * x2), tmp6 & xmask,
        other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + x3, tmp10, 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, (2, 4, 1, 1, 1), (4, 1, 1, 1, 1))
    assert_size_stride(primals_3, (2, 4, 1, 1, 1), (4, 1, 1, 1, 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_relu_0[grid(256)](primals_1, buf0, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        del primals_1
        buf1 = extern_kernels.convolution(reinterpret_tensor(buf0, (1, 4, 4,
            4, 4), (0, 64, 16, 4, 1), 0), primals_2, stride=(2, 2, 2),
            padding=(0, 0, 0), dilation=(1, 1, 1), transposed=False,
            output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (1, 2, 2, 2, 2), (16, 8, 4, 2, 1))
        buf2 = extern_kernels.convolution(reinterpret_tensor(buf0, (1, 4, 4,
            3, 3), (0, 64, 16, 4, 1), 5), primals_3, stride=(2, 2, 2),
            padding=(0, 0, 0), dilation=(1, 1, 1), transposed=False,
            output_padding=(0, 0, 0), groups=1, bias=None)
        assert_size_stride(buf2, (1, 2, 2, 2, 2), (16, 8, 4, 2, 1))
        buf3 = empty_strided_cuda((2, 4, 2, 2), (16, 4, 2, 1), torch.float32)
        triton_poi_fused_cat_1[grid(32)](buf1, buf2, buf3, 32, XBLOCK=32,
            num_warps=1, num_stages=1)
        del buf1
        del buf2
    return buf3, primals_2, primals_3, reinterpret_tensor(buf0, (1, 4, 4, 4,
        4), (256, 64, 16, 4, 1), 0), reinterpret_tensor(buf0, (1, 4, 4, 3, 
        3), (256, 64, 16, 4, 1), 5)


class FactorizedReduceNew(nn.Module):

    def __init__(self, C_in, C_out, affine=True):
        super(FactorizedReduceNew, self).__init__()
        assert C_out % 2 == 0
        self.relu = nn.ReLU(inplace=False)
        self.conv_1 = nn.Conv3d(C_in, C_out // 2, 1, stride=2, padding=0,
            bias=False)
        self.conv_2 = nn.Conv3d(C_in, C_out // 2, 1, stride=2, padding=0,
            bias=False)

    def forward(self, input_0):
        primals_2 = self.conv_1.weight
        primals_3 = self.conv_2.weight
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]


Measured latency_ms: 0.013886