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._inductor.runtime import triton_helpers
import torch.nn as nn
import torch.utils.data
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_max_pool2d_with_indices_0(in_ptr0, out_ptr0, xnumel,
    XBLOCK: tl.constexpr):
    xnumel = 2304
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 12
    x1 = xindex // 12
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (2 * x0 + 48 * x1), xmask, eviction_policy=
        'evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 2 * x0 + 48 * x1), xmask, eviction_policy
        ='evict_last')
    tmp3 = tl.load(in_ptr0 + (24 + 2 * x0 + 48 * x1), xmask,
        eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (25 + 2 * x0 + 48 * x1), xmask,
        eviction_policy='evict_last')
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tl.store(out_ptr0 + x2, tmp6, xmask)


@triton.jit
def triton_poi_fused_relu_1(in_out_ptr0, in_ptr0, xnumel, XBLOCK: tl.constexpr
    ):
    xnumel = 192
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 64
    tmp0 = tl.load(in_out_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tmp0 + tmp1
    tmp3 = tl.full([1], 0, tl.int32)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tl.store(in_out_ptr0 + x2, tmp4, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4, primals_5 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 24, 24), (2304, 576, 24, 1))
    assert_size_stride(primals_2, (64, 768), (768, 1))
    assert_size_stride(primals_3, (64,), (1,))
    assert_size_stride(primals_4, (10, 64), (64, 1))
    assert_size_stride(primals_5, (10,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 12, 12), (576, 144, 12, 1), torch.
            float32)
        get_raw_stream(0)
        triton_poi_fused_max_pool2d_with_indices_0[grid(2304)](primals_1,
            buf0, 2304, XBLOCK=256, num_warps=4, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((3, 64), (64, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf0, (3, 768), (768, 1), 0),
            reinterpret_tensor(primals_2, (768, 64), (1, 768), 0), out=buf1)
        del primals_2
        buf2 = buf1
        del buf1
        triton_poi_fused_relu_1[grid(192)](buf2, primals_3, 192, XBLOCK=256,
            num_warps=4, num_stages=1)
        del primals_3
        buf3 = empty_strided_cuda((3, 10), (10, 1), torch.float32)
        extern_kernels.addmm(primals_5, buf2, reinterpret_tensor(primals_4,
            (64, 10), (1, 64), 0), alpha=1, beta=1, out=buf3)
        del primals_5
    return buf3, reinterpret_tensor(buf0, (3, 768), (768, 1), 0
        ), buf2, primals_4


class NetNew(nn.Module):

    def __init__(self):
        super(NetNew, self).__init__()
        self.pool = torch.nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
        self.fc1 = torch.nn.Linear(3 * 16 * 16, 64)
        self.fc2 = torch.nn.Linear(64, 10)

    def forward(self, input_0):
        primals_2 = self.fc1.weight
        primals_3 = self.fc1.bias
        primals_4 = self.fc2.weight
        primals_5 = self.fc2.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4, primals_5])
        return output[0]


Measured latency_ms: 0.042077