import torch
import torch.nn as nn
import torch.nn.parallel


class Shifted_softplus(nn.Module):
    """
  Performs a Shifter softplus loss, which modifies with a value of log(2)
  """

    def __init__(self):
        super(Shifted_softplus, self).__init__()
        self.act = nn.Softplus()
        self.shift = nn.Parameter(torch.tensor([0.6931]), False)

    def forward(self, X):
        """
    Applies the Activation function

    Parameters
    ----------
    node_feats: torch.Tensor
        The node features.

    Returns
    -------
    node_feats: torch.Tensor
        The updated node features.

    """
        node_feats = self.act(X) - self.shift
        return node_feats


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.triton_helpers import libdevice, math as tl_math
import torch.nn as nn
import torch.nn.parallel
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_softplus_sub_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)
    tmp9 = tl.load(in_ptr1 + 0)
    tmp10 = tl.broadcast_to(tmp9, [XBLOCK])
    tmp1 = 1.0
    tmp2 = tmp0 * tmp1
    tmp3 = 20.0
    tmp4 = tmp2 > tmp3
    tmp5 = tl_math.exp(tmp2)
    tmp6 = libdevice.log1p(tmp5)
    tmp7 = tmp6 * tmp1
    tmp8 = tl.where(tmp4, tmp0, tmp7)
    tmp11 = tmp8 - tmp10
    tl.store(out_ptr0 + x0, tmp11, xmask)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_1, (1,), (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_softplus_sub_0[grid(256)](arg0_1, arg1_1, buf0, 
            256, XBLOCK=256, num_warps=4, num_stages=1)
        del arg0_1
        del arg1_1
    return buf0,


class Shifted_softplusNew(nn.Module):
    """
  Performs a Shifter softplus loss, which modifies with a value of log(2)
  """

    def __init__(self):
        super(Shifted_softplusNew, self).__init__()
        self.act = nn.Softplus()
        self.shift = nn.Parameter(torch.tensor([0.6931]), False)

    def forward(self, input_0):
        arg1_1 = self.shift
        arg0_1 = input_0
        output = call([arg0_1, arg1_1])
        return output[0]
