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
import torch.nn.functional as F
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_poi_fused_clamp_pow_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp3 = tl.load(in_ptr1 + 0)
    tmp4 = tl.broadcast_to(tmp3, [XBLOCK])
    tmp1 = 1e-06
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp5 = libdevice.pow(tmp2, tmp4)
    tl.store(out_ptr0 + x0, tmp5, xmask)


@triton.jit
def triton_poi_fused_avg_pool2d_mul_pow_reciprocal_1(in_ptr0, in_ptr1,
    out_ptr0, out_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 16 * x0, xmask, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (1 + 16 * x0), xmask, eviction_policy='evict_last'
        )
    tmp3 = tl.load(in_ptr0 + (2 + 16 * x0), xmask, eviction_policy='evict_last'
        )
    tmp5 = tl.load(in_ptr0 + (3 + 16 * x0), xmask, eviction_policy='evict_last'
        )
    tmp7 = tl.load(in_ptr0 + (4 + 16 * x0), xmask, eviction_policy='evict_last'
        )
    tmp9 = tl.load(in_ptr0 + (5 + 16 * x0), xmask, eviction_policy='evict_last'
        )
    tmp11 = tl.load(in_ptr0 + (6 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp13 = tl.load(in_ptr0 + (7 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp15 = tl.load(in_ptr0 + (8 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp17 = tl.load(in_ptr0 + (9 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp19 = tl.load(in_ptr0 + (10 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp21 = tl.load(in_ptr0 + (11 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp23 = tl.load(in_ptr0 + (12 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp25 = tl.load(in_ptr0 + (13 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp27 = tl.load(in_ptr0 + (14 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp29 = tl.load(in_ptr0 + (15 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp33 = tl.load(in_ptr1 + 0)
    tmp34 = tl.broadcast_to(tmp33, [XBLOCK])
    tmp2 = tmp1 + tmp0
    tmp4 = tmp3 + tmp2
    tmp6 = tmp5 + tmp4
    tmp8 = tmp7 + tmp6
    tmp10 = tmp9 + tmp8
    tmp12 = tmp11 + tmp10
    tmp14 = tmp13 + tmp12
    tmp16 = tmp15 + tmp14
    tmp18 = tmp17 + tmp16
    tmp20 = tmp19 + tmp18
    tmp22 = tmp21 + tmp20
    tmp24 = tmp23 + tmp22
    tmp26 = tmp25 + tmp24
    tmp28 = tmp27 + tmp26
    tmp30 = tmp29 + tmp28
    tmp31 = 0.0625
    tmp32 = tmp30 * tmp31
    tmp35 = tl.full([1], 1, tl.int32)
    tmp36 = tmp35 / tmp34
    tmp37 = 1.0
    tmp38 = tmp36 * tmp37
    tmp39 = libdevice.pow(tmp32, tmp38)
    tl.store(out_ptr0 + x0, tmp32, xmask)
    tl.store(out_ptr1 + x0, tmp39, xmask)


def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (1,), (1,))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_clamp_pow_0[grid(256)](primals_2, primals_1, buf0,
            256, XBLOCK=128, num_warps=4, num_stages=1)
        buf1 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.float32)
        buf2 = empty_strided_cuda((4, 4, 1, 1), (4, 1, 1, 1), torch.float32)
        triton_poi_fused_avg_pool2d_mul_pow_reciprocal_1[grid(16)](buf0,
            primals_1, buf1, buf2, 16, XBLOCK=16, num_warps=1, num_stages=1)
    return buf2, primals_1, primals_2, buf0, buf1, buf2


def gem(x, p=3, eps=1e-06):
    return F.avg_pool2d(x.clamp(min=eps).pow(p), (x.size(-2), x.size(-1))).pow(
        1.0 / p)


class GeMNew(nn.Module):

    def __init__(self, p=3, eps=1e-06, requires_grad: 'bool'=True):
        super().__init__()
        self.p = nn.Parameter(data=torch.ones(1) * p, requires_grad=
            requires_grad)
        self.eps = eps

    def __repr__(self):
        return (self.__class__.__name__ +
            f'(p={self.p.data.tolist()[0]:.4f}, eps={self.eps})')

    def forward(self, input_0):
        primals_1 = self.p
        primals_2 = input_0
        output = call([primals_1, primals_2])
        return output[0]


Measured latency_ms: 0.03064