import torch
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
from torch.nn import Module
from torch.nn.modules.module import Module
from torch.nn.modules.utils import _pair
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 + (16 + x0 + 64 * x1), xmask)
    tmp3 = tl.load(in_ptr0 + (32 + x0 + 64 * x1), xmask)
    tmp5 = tl.load(in_ptr0 + (48 + x0 + 64 * x1), xmask)
    tmp7 = tl.load(in_ptr0 + (4 + x0 + 64 * x1), xmask)
    tmp9 = tl.load(in_ptr0 + (20 + x0 + 64 * x1), xmask)
    tmp11 = tl.load(in_ptr0 + (36 + x0 + 64 * x1), xmask)
    tmp13 = tl.load(in_ptr0 + (52 + x0 + 64 * x1), xmask)
    tmp15 = tl.load(in_ptr0 + (8 + x0 + 64 * x1), xmask)
    tmp17 = tl.load(in_ptr0 + (24 + x0 + 64 * x1), xmask)
    tmp19 = tl.load(in_ptr0 + (40 + x0 + 64 * x1), xmask)
    tmp21 = tl.load(in_ptr0 + (56 + x0 + 64 * x1), xmask)
    tmp23 = tl.load(in_ptr0 + (12 + x0 + 64 * x1), xmask)
    tmp25 = tl.load(in_ptr0 + (28 + x0 + 64 * x1), xmask)
    tmp27 = tl.load(in_ptr0 + (44 + 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):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 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)
        get_raw_stream(0)
        triton_poi_fused_max_pool2d_with_indices_0[grid(16)](arg0_1, buf0, 
            16, XBLOCK=16, num_warps=1, num_stages=1)
        del arg0_1
    return reinterpret_tensor(buf0, (4, 1, 1, 4), (4, 1, 1, 1), 0),


class my_MaxPool2dNew(Module):

    def __init__(self, kernel_size, stride=None, padding=0, dilation=1,
        return_indices=False, ceil_mode=False):
        super(my_MaxPool2dNew, self).__init__()
        self.kernel_size = kernel_size
        self.stride = stride or kernel_size
        self.padding = padding
        self.dilation = dilation
        self.return_indices = return_indices
        self.ceil_mode = ceil_mode

    def __repr__(self):
        kh, kw = _pair(self.kernel_size)
        dh, dw = _pair(self.stride)
        padh, padw = _pair(self.padding)
        dilh, dilw = _pair(self.dilation)
        padding_str = ', padding=(' + str(padh) + ', ' + str(padw
            ) + ')' if padh != 0 or padw != 0 else ''
        dilation_str = ', dilation=(' + str(dilh) + ', ' + str(dilw
            ) + ')' if dilh != 0 and dilw != 0 else ''
        ceil_str = ', ceil_mode=' + str(self.ceil_mode)
        return self.__class__.__name__ + '(' + 'kernel_size=(' + str(kh
            ) + ', ' + str(kw) + ')' + ', stride=(' + str(dh) + ', ' + str(dw
            ) + ')' + padding_str + dilation_str + ceil_str + ')'

    def forward(self, input_0):
        arg0_1 = input_0
        output = call([arg0_1])
        return output[0]


Measured latency_ms: 0.014862