import torch
import torch.nn.functional as F


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 GeM(torch.nn.Module):
    """
    Implementation of GeM pooling.
    https://paperswithcode.com/method/generalized-mean-pooling

    NOTE:
    p is learnable, but there is a consensus that it is better to fix the p value at 3.
    """

    def __init__(self, p=3, eps=1e-06):
        super(GeM, self).__init__()
        self.p = p
        self.eps = eps

    def forward(self, x):
        return gem(x, p=self.p, eps=self.eps)


def get_inputs():
    return [torch.rand([4, 4, 4, 4])]


def get_init_inputs():
    return [[], {}]

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
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_avg_pool2d_clamp_pow_0(in_out_ptr0, in_ptr0, 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')
    tmp5 = tl.load(in_ptr0 + (1 + 16 * x0), xmask, eviction_policy='evict_last'
        )
    tmp10 = tl.load(in_ptr0 + (2 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp15 = tl.load(in_ptr0 + (3 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp20 = tl.load(in_ptr0 + (4 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp25 = tl.load(in_ptr0 + (5 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp30 = tl.load(in_ptr0 + (6 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp35 = tl.load(in_ptr0 + (7 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp40 = tl.load(in_ptr0 + (8 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp45 = tl.load(in_ptr0 + (9 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp50 = tl.load(in_ptr0 + (10 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp55 = tl.load(in_ptr0 + (11 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp60 = tl.load(in_ptr0 + (12 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp65 = tl.load(in_ptr0 + (13 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp70 = tl.load(in_ptr0 + (14 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp75 = tl.load(in_ptr0 + (15 + 16 * x0), xmask, eviction_policy=
        'evict_last')
    tmp1 = 1e-06
    tmp2 = triton_helpers.maximum(tmp0, tmp1)
    tmp3 = tmp2 * tmp2
    tmp4 = tmp3 * tmp2
    tmp6 = triton_helpers.maximum(tmp5, tmp1)
    tmp7 = tmp6 * tmp6
    tmp8 = tmp7 * tmp6
    tmp9 = tmp8 + tmp4
    tmp11 = triton_helpers.maximum(tmp10, tmp1)
    tmp12 = tmp11 * tmp11
    tmp13 = tmp12 * tmp11
    tmp14 = tmp13 + tmp9
    tmp16 = triton_helpers.maximum(tmp15, tmp1)
    tmp17 = tmp16 * tmp16
    tmp18 = tmp17 * tmp16
    tmp19 = tmp18 + tmp14
    tmp21 = triton_helpers.maximum(tmp20, tmp1)
    tmp22 = tmp21 * tmp21
    tmp23 = tmp22 * tmp21
    tmp24 = tmp23 + tmp19
    tmp26 = triton_helpers.maximum(tmp25, tmp1)
    tmp27 = tmp26 * tmp26
    tmp28 = tmp27 * tmp26
    tmp29 = tmp28 + tmp24
    tmp31 = triton_helpers.maximum(tmp30, tmp1)
    tmp32 = tmp31 * tmp31
    tmp33 = tmp32 * tmp31
    tmp34 = tmp33 + tmp29
    tmp36 = triton_helpers.maximum(tmp35, tmp1)
    tmp37 = tmp36 * tmp36
    tmp38 = tmp37 * tmp36
    tmp39 = tmp38 + tmp34
    tmp41 = triton_helpers.maximum(tmp40, tmp1)
    tmp42 = tmp41 * tmp41
    tmp43 = tmp42 * tmp41
    tmp44 = tmp43 + tmp39
    tmp46 = triton_helpers.maximum(tmp45, tmp1)
    tmp47 = tmp46 * tmp46
    tmp48 = tmp47 * tmp46
    tmp49 = tmp48 + tmp44
    tmp51 = triton_helpers.maximum(tmp50, tmp1)
    tmp52 = tmp51 * tmp51
    tmp53 = tmp52 * tmp51
    tmp54 = tmp53 + tmp49
    tmp56 = triton_helpers.maximum(tmp55, tmp1)
    tmp57 = tmp56 * tmp56
    tmp58 = tmp57 * tmp56
    tmp59 = tmp58 + tmp54
    tmp61 = triton_helpers.maximum(tmp60, tmp1)
    tmp62 = tmp61 * tmp61
    tmp63 = tmp62 * tmp61
    tmp64 = tmp63 + tmp59
    tmp66 = triton_helpers.maximum(tmp65, tmp1)
    tmp67 = tmp66 * tmp66
    tmp68 = tmp67 * tmp66
    tmp69 = tmp68 + tmp64
    tmp71 = triton_helpers.maximum(tmp70, tmp1)
    tmp72 = tmp71 * tmp71
    tmp73 = tmp72 * tmp71
    tmp74 = tmp73 + tmp69
    tmp76 = triton_helpers.maximum(tmp75, tmp1)
    tmp77 = tmp76 * tmp76
    tmp78 = tmp77 * tmp76
    tmp79 = tmp78 + tmp74
    tmp80 = 0.0625
    tmp81 = tmp79 * tmp80
    tmp82 = 0.3333333333333333
    tmp83 = libdevice.pow(tmp81, tmp82)
    tl.store(in_out_ptr0 + x0, tmp83, 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_poi_fused_avg_pool2d_clamp_pow_0[grid(16)](buf1, arg0_1, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        del arg0_1
    return buf1,


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(torch.nn.Module):
    """
    Implementation of GeM pooling.
    https://paperswithcode.com/method/generalized-mean-pooling

    NOTE:
    p is learnable, but there is a consensus that it is better to fix the p value at 3.
    """

    def __init__(self, p=3, eps=1e-06):
        super(GeMNew, self).__init__()
        self.p = p
        self.eps = eps

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