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.nn
import torch.optim
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_per_fused_convolution_mean_0(in_ptr0, in_ptr1, out_ptr0, xnumel,
    rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r3 = rindex
    x4 = xindex
    x1 = xindex // 64 % 16
    tmp0 = tl.load(in_ptr0 + (r3 + 64 * x4), None)
    tmp1 = tl.load(in_ptr1 + x1, None, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp5 = tl.sum(tmp3, 1)[:, None]
    tl.store(out_ptr0 + x4, tmp5, None)


@triton.jit
def triton_per_fused_convolution_mean_1(in_out_ptr0, in_ptr0, xnumel,
    rnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    RBLOCK: tl.constexpr = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 64 * x0), xmask, other=0.0)
    tmp1 = 64.0
    tmp2 = tmp0 / tmp1
    tmp3 = tl.broadcast_to(tmp2, [XBLOCK, RBLOCK])
    tmp5 = tl.where(xmask, tmp3, 0)
    tmp6 = tl.sum(tmp5, 1)[:, None]
    tmp7 = tmp6 / tmp1
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp7, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (16, 3, 1, 1), (3, 1, 1, 1))
    assert_size_stride(primals_2, (16,), (1,))
    assert_size_stride(primals_3, (4, 3, 64, 64), (12288, 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, 64, 64), (65536, 4096, 64, 1))
        buf1 = empty_strided_cuda((4, 16, 64), (1024, 64, 1), torch.float32)
        get_raw_stream(0)
        triton_per_fused_convolution_mean_0[grid(4096)](buf0, primals_2,
            buf1, 4096, 64, XBLOCK=32, num_warps=8, num_stages=1)
        del buf0
        del primals_2
        buf2 = empty_strided_cuda((4, 16), (16, 1), torch.float32)
        buf3 = buf2
        del buf2
        triton_per_fused_convolution_mean_1[grid(64)](buf3, buf1, 64, 64,
            XBLOCK=8, num_warps=4, num_stages=1)
        del buf1
    return buf3, primals_1, primals_3


class MockModuleNew(nn.Module):

    def __init__(self):
        super().__init__()
        self.conv = nn.Conv2d(3, 16, kernel_size=1)

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


Measured latency_ms: 0.038323