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 import nn
from matplotlib import pyplot as pyplot
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_poi_fused_add_div_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 2
    x1 = xindex // 2 % 2
    x4 = xindex // 4
    x2 = xindex // 4 % 4
    x6 = xindex
    tmp0 = tl.load(in_ptr0 + (2 * x0 + 6 * x1 + 9 * x4), xmask,
        eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr1 + x2, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (3 + 2 * x0 + 9 * x4), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (1 + 6 * x1 + 9 * x4), xmask, eviction_policy=
        'evict_last')
    tmp9 = tl.load(in_ptr0 + (4 + 9 * x4), xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp4 = tmp3 + tmp1
    tmp5 = tmp2 + tmp4
    tmp7 = tmp6 + tmp1
    tmp8 = tmp5 + tmp7
    tmp10 = tmp9 + tmp1
    tmp11 = tmp8 + tmp10
    tmp12 = 0.25
    tmp13 = tmp11 * tmp12
    tl.store(out_ptr0 + x6, tmp13, 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,), (1,))
    assert_size_stride(primals_3, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = extern_kernels.convolution(primals_3, primals_1, stride=(1, 
            1), padding=(1, 1), dilation=(1, 1), transposed=False,
            output_padding=(0, 0), groups=1, bias=None)
        assert_size_stride(buf0, (4, 4, 3, 3), (36, 9, 3, 1))
        buf1 = empty_strided_cuda((4, 4, 2, 2), (16, 4, 2, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_div_0[grid(64)](buf0, primals_2, buf1, 64,
            XBLOCK=64, num_warps=1, num_stages=1)
        del buf0
        del primals_2
    return buf1, primals_1, primals_3


class MyConvo2d(nn.Module):

    def __init__(self, input_dim, output_dim, kernel_size, he_init=True,
        stride=1, bias=True):
        super(MyConvo2d, self).__init__()
        self.he_init = he_init
        self.padding = int((kernel_size - 1) / 2)
        self.conv = nn.Conv2d(input_dim, output_dim, kernel_size, stride=1,
            padding=self.padding, bias=bias)

    def forward(self, input):
        output = self.conv(input)
        return output


class ConvMeanPoolNew(nn.Module):

    def __init__(self, input_dim, output_dim, kernel_size, he_init=True):
        super(ConvMeanPoolNew, self).__init__()
        self.he_init = he_init
        self.conv = MyConvo2d(input_dim, output_dim, kernel_size, he_init=
            self.he_init)

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


Measured latency_ms: 0.023225