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
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 = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 4
    x1 = xindex // 4
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 64 * x1), xmask)
    tmp1 = tl.load(in_ptr0 + (4 + x0 + 64 * x1), xmask)
    tmp3 = tl.load(in_ptr0 + (8 + x0 + 64 * x1), xmask)
    tmp5 = tl.load(in_ptr0 + (12 + x0 + 64 * x1), xmask)
    tmp7 = tl.load(in_ptr0 + (16 + x0 + 64 * x1), xmask)
    tmp9 = tl.load(in_ptr0 + (20 + x0 + 64 * x1), xmask)
    tmp11 = tl.load(in_ptr0 + (24 + x0 + 64 * x1), xmask)
    tmp13 = tl.load(in_ptr0 + (28 + x0 + 64 * x1), xmask)
    tmp15 = tl.load(in_ptr0 + (32 + x0 + 64 * x1), xmask)
    tmp17 = tl.load(in_ptr0 + (36 + x0 + 64 * x1), xmask)
    tmp19 = tl.load(in_ptr0 + (40 + x0 + 64 * x1), xmask)
    tmp21 = tl.load(in_ptr0 + (44 + x0 + 64 * x1), xmask)
    tmp23 = tl.load(in_ptr0 + (48 + x0 + 64 * x1), xmask)
    tmp25 = tl.load(in_ptr0 + (52 + x0 + 64 * x1), xmask)
    tmp27 = tl.load(in_ptr0 + (56 + x0 + 64 * x1), xmask)
    tmp29 = tl.load(in_ptr0 + (60 + x0 + 64 * x1), xmask)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp4 = triton_helpers.maximum(tmp3, tmp2)
    tmp6 = triton_helpers.maximum(tmp5, tmp4)
    tmp8 = triton_helpers.maximum(tmp7, tmp6)
    tmp10 = triton_helpers.maximum(tmp9, tmp8)
    tmp12 = triton_helpers.maximum(tmp11, tmp10)
    tmp14 = triton_helpers.maximum(tmp13, tmp12)
    tmp16 = triton_helpers.maximum(tmp15, tmp14)
    tmp18 = triton_helpers.maximum(tmp17, tmp16)
    tmp20 = triton_helpers.maximum(tmp19, tmp18)
    tmp22 = triton_helpers.maximum(tmp21, tmp20)
    tmp24 = triton_helpers.maximum(tmp23, tmp22)
    tmp26 = triton_helpers.maximum(tmp25, tmp24)
    tmp28 = triton_helpers.maximum(tmp27, tmp26)
    tmp30 = triton_helpers.maximum(tmp29, tmp28)
    tl.store(out_ptr0 + x2, tmp30, 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, 1, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_max_pool2d_with_indices_0[grid(16)](primals_1,
            buf0, 16, XBLOCK=16, num_warps=1, num_stages=1)
        del primals_1
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_3, reinterpret_tensor(buf0, (4, 4), (4,
            1), 0), reinterpret_tensor(primals_2, (4, 4), (1, 4), 0), alpha
            =1, beta=1, out=buf1)
        del primals_2
        del primals_3
    return buf1, reinterpret_tensor(buf0, (4, 4), (4, 1), 0)


class SimpleNetNew(nn.Module):

    def __init__(self, width, input_size, output_size, pool='max'):
        super(SimpleNetNew, self).__init__()
        self.pool = nn.MaxPool2d(width, stride=width
            ) if pool == 'max' else nn.AvgPool2d(width, stride=width)
        self.fc1 = nn.Linear(input_size, output_size)

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


Measured latency_ms: 0.022986