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._inductor.runtime.triton_helpers import libdevice
from torch import nn
from torch.optim.lr_scheduler import *
from torch.optim import *
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_clamp_mean_pow_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 = 1e-06
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp3 = tmp2 * tmp2
    tmp4 = tmp3 * tmp2
    tmp5 = tl.broadcast_to(tmp4, [XBLOCK, RBLOCK])
    tmp7 = tl.where(xmask, tmp5, 0)
    tmp8 = tl.sum(tmp7, 1)[:, None]
    tmp9 = 16.0
    tmp10 = tmp8 / tmp9
    tmp11 = 0.3333333333333333
    tmp12 = libdevice.pow(tmp10, tmp11)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + x0, tmp12, 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)
        buf1 = reinterpret_tensor(buf0, (4, 4, 1, 1), (4, 1, 1, 1), 0)
        del buf0
        get_raw_stream(0)
        triton_per_fused_clamp_mean_pow_0[grid(16)](buf1, arg0_1, 16, 16,
            XBLOCK=8, num_warps=2, num_stages=1)
        del arg0_1
    return buf1,


class GeneralizedMeanPoolingNew(nn.Module):
    """Applies a 2D power-average adaptive pooling over an input signal composed of several input planes.
    The function computed is: :math:`f(X) = pow(sum(pow(X, p)), 1/p)`
        - At p = infinity, one gets Max Pooling
        - At p = 1, one gets Average Pooling
    The output is of size H x W, for any input size.
    The number of output features is equal to the number of input planes.
    Args:
        output_size: the target output size of the image of the form H x W.
                     Can be a tuple (H, W) or a single H for a square image H x H
                     H and W can be either a ``int``, or ``None`` which means the size will
                     be the same as that of the input.
    """

    def __init__(self, norm=3, output_size=1, eps=1e-06):
        super(GeneralizedMeanPoolingNew, self).__init__()
        assert norm > 0
        self.p = float(norm)
        self.output_size = output_size
        self.eps = eps

    def __repr__(self):
        return self.__class__.__name__ + '(' + str(self.p
            ) + ', ' + 'output_size=' + str(self.output_size) + ')'

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


Measured latency_ms: 0.015167