import torch
import torch.nn as nn


class ActivationFunction(nn.Module):

    def __init__(self):
        super().__init__()
        self.name = self.__class__.__name__
        self.config = {'name': self.name}


class LeakyReLU(ActivationFunction):

    def __init__(self, alpha=0.1):
        super().__init__()
        self.config['alpha'] = alpha

    def forward(self, x):
        return torch.where(x > 0, x, self.config['alpha'] * x)


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
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_gt_mul_where_0(in_ptr0, 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)
    tmp1 = 0.0
    tmp2 = tmp0 > tmp1
    tmp3 = 0.1
    tmp4 = tmp0 * tmp3
    tmp5 = tl.where(tmp2, tmp0, tmp4)
    tl.store(out_ptr0 + x0, tmp5, 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_gt_mul_where_0[grid(256)](arg0_1, buf0, 256,
            XBLOCK=128, num_warps=4, num_stages=1)
        del arg0_1
    return buf0,


class ActivationFunction(nn.Module):

    def __init__(self):
        super().__init__()
        self.name = self.__class__.__name__
        self.config = {'name': self.name}


class LeakyReLUNew(ActivationFunction):

    def __init__(self, alpha=0.1):
        super().__init__()
        self.config['alpha'] = alpha

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