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
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride


@triton.jit
def triton_poi_fused_add_convolution_mul_sub_0(in_out_ptr0, in_ptr0,
    in_ptr1, in_ptr2, in_ptr3, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 4 % 4
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + x3, xmask)
    tmp4 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp7 = tl.load(in_ptr3 + x3, xmask)
    tmp2 = tmp0 + tmp1
    tmp5 = tmp3 + tmp4
    tmp6 = tmp5 - tmp2
    tmp8 = tmp6 * tmp7
    tmp9 = tmp2 + tmp8
    tl.store(in_out_ptr0 + x3, tmp9, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5, primals_6 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_4, (4, 4, 3, 3), (36, 9, 3, 1))
    assert_size_stride(primals_5, (4,), (1,))
    assert_size_stride(primals_6, (4, 4, 2, 2), (16, 4, 2, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 2, 2), (16, 4, 2, 1))
        buf1 = extern_kernels.convolution(primals_3, primals_4, stride=(1, 
            1), padding=(0, 0), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf1, (4, 4, 2, 2), (16, 4, 2, 1))
        buf2 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_add_convolution_mul_sub_0[grid(64)](buf2,
            primals_2, buf1, primals_5, primals_6, 64, XBLOCK=64, num_warps
            =1, num_stages=1)
        del buf1
        del primals_2
        del primals_5
    return buf2, primals_1, primals_3, primals_4, primals_6


class BlendConv2dNew(nn.Module):

    def __init__(self, dim_in, dim_out, ksize=3, stride=1, padding=0,
        dilation=1, groups=1, bias=True, transpose=False, **unused_kwargs):
        super(BlendConv2dNew, self).__init__()
        module = nn.ConvTranspose2d if transpose else nn.Conv2d
        self._layer0 = module(dim_in, dim_out, kernel_size=ksize, stride=
            stride, padding=padding, dilation=dilation, groups=groups, bias
            =bias)
        self._layer1 = module(dim_in, dim_out, kernel_size=ksize, stride=
            stride, padding=padding, dilation=dilation, groups=groups, bias
            =bias)

    def forward(self, input_0, input_1):
        primals_1 = self._layer0.weight
        primals_2 = self._layer0.bias
        primals_4 = self._layer1.weight
        primals_5 = self._layer1.bias
        primals_6 = input_0
        primals_3 = input_1
        output = call([primals_1, primals_2, primals_3, primals_4,
            primals_5, primals_6])
        return output[0]


Measured latency_ms: 0.089075