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.nn import Module
import torch.utils.data
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
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_per_fused_mean_0(in_out_ptr0, in_ptr0, xnumel, rnumel, XBLOCK:
    tl.constexpr):
    xnumel = 16
    RBLOCK: tl.constexpr = 16
    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 + 16 * x0), xmask, other=0.0)
    tmp1 = tl.broadcast_to(tmp0, [XBLOCK, RBLOCK])
    tmp3 = tl.where(xmask, tmp1, 0)
    tmp4 = tl.sum(tmp3, 1)[:, None]
    tmp5 = 16.0
    tmp6 = tmp4 / tmp5
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp6, 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, 4), (4, 1))
    assert_size_stride(primals_3, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 16, 16), torch.float32)
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_per_fused_mean_0[grid(16)](buf1, primals_1, 16, 16, XBLOCK=8,
            num_warps=2, num_stages=1)
        del primals_1
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_3, reinterpret_tensor(buf1, (4, 4), (4,
            1), 0), reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), alpha
            =1, beta=1, out=buf2)
        del primals_2
        del primals_3
    return buf2, reinterpret_tensor(buf1, (4, 4), (4, 1), 0)


def gap2d(_w_in):
    """Helper for building a gap2d layer."""
    return nn.AdaptiveAvgPool2d((1, 1))


def gap2d_cx(cx, _w_in):
    """Accumulates complexity of gap2d into cx = (h, w, flops, params, acts)."""
    flops, params, acts = cx['flops'], cx['params'], cx['acts']
    return {'h': 1, 'w': 1, 'flops': flops, 'params': params, 'acts': acts}


def linear(w_in, w_out, *, bias=False):
    """Helper for building a linear layer."""
    return nn.Linear(w_in, w_out, bias=bias)


def linear_cx(cx, w_in, w_out, *, bias=False):
    """Accumulates complexity of linear into cx = (h, w, flops, params, acts)."""
    h, w, flops, params, acts = cx['h'], cx['w'], cx['flops'], cx['params'
        ], cx['acts']
    flops += w_in * w_out + (w_out if bias else 0)
    params += w_in * w_out + (w_out if bias else 0)
    acts += w_out
    return {'h': h, 'w': w, 'flops': flops, 'params': params, 'acts': acts}


class ResHeadNew(Module):
    """ResNet head: AvgPool, 1x1."""

    def __init__(self, w_in, num_classes):
        super(ResHeadNew, self).__init__()
        self.avg_pool = gap2d(w_in)
        self.fc = linear(w_in, num_classes, bias=True)

    @staticmethod
    def complexity(cx, w_in, num_classes):
        cx = gap2d_cx(cx, w_in)
        cx = linear_cx(cx, w_in, num_classes, bias=True)
        return cx

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


Measured latency_ms: 0.02756