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 time
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
from time import time as time
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_cat_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 1296
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x1 = xindex // 81 % 4
    x0 = xindex % 81
    x2 = xindex // 324
    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 + 81 * x1 + 162 * x2), tmp4 & xmask, other=0.0
        )
    tmp6 = tmp0 >= tmp3
    tl.full([1], 4, tl.int64)
    tmp9 = tl.load(in_ptr1 + (x0 + 81 * (-2 + x1) + 162 * 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, 2, 4, 4), (32, 16, 4, 1))
    assert_size_stride(primals_3, (2, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(reinterpret_tensor(primals_1, (4,
            2, 4, 4), (64, 16, 4, 1), 0), primals_2, stride=(1, 1), padding
            =(4, 4), dilation=(1, 1), transposed=False, output_padding=(0, 
            0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 2, 9, 9), (162, 81, 9, 1))
        buf1 = extern_kernels.convolution(primals_1, primals_3, stride=(1, 
            1), padding=(4, 4), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 2, 9, 9), (162, 81, 9, 1))
        buf2 = empty_strided_cuda((4, 4, 9, 9), (324, 81, 9, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_cat_0[grid(1296)](buf0, buf1, buf2, 1296, XBLOCK=
            128, num_warps=4, num_stages=1)
        del buf0
        del buf1
    return buf2, primals_1, primals_2, primals_3


class Cnv2d_separableNew(nn.Module):

    def __init__(self, n_input_ch, n_output_ch, kernel_size, stride,
        padding, bias=False, red_portion=0.5):
        super(Cnv2d_separableNew, self).__init__()
        self.n_input_ch = n_input_ch
        self.n_input_ch_red = int(n_input_ch * red_portion)
        self.n_output_ch = n_output_ch
        self.n_output_ch_red = int(n_output_ch * red_portion)
        self.n_output_ch_green = n_output_ch - self.n_output_ch_red
        self.conv_half = nn.Conv2d(self.n_input_ch_red, self.
            n_output_ch_red, kernel_size, stride, padding, bias=bias)
        self.conv_all = nn.Conv2d(self.n_input_ch, self.n_output_ch_green,
            kernel_size, stride, padding, bias=bias)

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


Measured latency_ms: 0.036887