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


@triton.jit
def triton_poi_fused_convolution_0(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 246016
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x3 = xindex
    x1 = xindex // 3844 % 16
    tmp0 = tl.load(in_out_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr0 + x1, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tl.store(in_out_ptr0 + x3, tmp2, xmask)


@triton.jit
def triton_poi_fused_avg_pool2d_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.
    constexpr):
    xnumel = 61504
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 31
    x1 = xindex // 31
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (2 * x0 + 124 * x1), xmask, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 124 * x1), xmask,
        eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (62 + 2 * x0 + 124 * x1), xmask,
        eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (63 + 2 * x0 + 124 * x1), xmask,
        eviction_policy='evict_last')
    tmp2 = tmp1 + tmp0
    tmp4 = tmp3 + tmp2
    tmp6 = tmp5 + tmp4
    tmp7 = 0.25
    tmp8 = tmp6 * tmp7
    tl.store(out_ptr0 + x2, tmp8, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (16, 10, 3, 3), (90, 9, 3, 1))
    assert_size_stride(primals_2, (16,), (1,))
    assert_size_stride(primals_3, (4, 10, 64, 64), (40960, 4096, 64, 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, 16, 62, 62), (61504, 3844, 62, 1))
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_convolution_0[grid(246016)](buf1, primals_2, 
            246016, XBLOCK=1024, num_warps=4, num_stages=1)
        del primals_2
        buf2 = empty_strided_cuda((4, 16, 31, 31), (15376, 961, 31, 1),
            torch.float32)
        triton_poi_fused_avg_pool2d_1[grid(61504)](buf1, buf2, 61504,
            XBLOCK=512, num_warps=4, num_stages=1)
    return buf2, primals_1, primals_3, buf1


class AvgPoolNew(nn.Module):
    """Module for MaxPool conversion testing
    """

    def __init__(self, inp=10, out=16, kernel_size=3, bias=True):
        super(AvgPoolNew, self).__init__()
        self.conv2d = nn.Conv2d(inp, out, kernel_size=kernel_size, bias=bias)
        self.pool = nn.AvgPool2d(kernel_size=2)

    def forward(self, input_0):
        primals_1 = self.conv2d.weight
        primals_2 = self.conv2d.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]


Measured latency_ms: 0.022592