import torch
import torch.nn as nn


class MaskUpdate(nn.Module):

    def __init__(self, alpha):
        super(MaskUpdate, self).__init__()
        self.updateFunc = nn.ReLU(True)
        self.alpha = alpha

    def forward(self, inputMaskMap):
        """ self.alpha.data = torch.clamp(self.alpha.data, 0.6, 0.8)
        print(self.alpha) """
        return torch.pow(self.updateFunc(inputMaskMap), self.alpha)


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


def get_init_inputs():
    return [[], {'alpha': 4}]

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
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


@triton.jit
def triton_poi_fused_pow_relu_0(in_ptr0, out_ptr0, out_ptr2, 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)
    tmp1 = tl.full([1], 0, tl.int32)
    tmp2 = triton_helpers.maximum(tmp1, tmp0)
    tmp3 = tmp2 * tmp2
    tmp4 = tmp3 * tmp3
    tl.store(out_ptr0 + x0, tmp4, xmask)
    tl.store(out_ptr2 + x0, tmp2, 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, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_pow_relu_0[grid(256)](arg0_1, buf0, arg0_1, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        del arg0_1
    return buf0,


class MaskUpdateNew(nn.Module):

    def __init__(self, alpha):
        super(MaskUpdateNew, self).__init__()
        self.updateFunc = nn.ReLU(True)
        self.alpha = alpha

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