import torch
import torch.nn as nn
import torch.utils
import torch.utils.data.distributed


class BinaryActivation(nn.Module):

    def __init__(self):
        super(BinaryActivation, self).__init__()

    def forward(self, x):
        out_forward = torch.sign(x)
        mask1 = x < -1
        mask2 = x < 0
        mask3 = x < 1
        out1 = -1 * mask1.type(torch.float32) + (x * x + 2 * x) * (1 -
            mask1.type(torch.float32))
        out2 = out1 * mask2.type(torch.float32) + (-x * x + 2 * x) * (1 -
            mask2.type(torch.float32))
        out3 = out2 * mask3.type(torch.float32) + 1 * (1 - mask3.type(torch
            .float32))
        out = out_forward.detach() - out3.detach() + out3
        return out


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
import torch.utils
import torch.utils.data.distributed
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__to_copy_add_lt_mul_neg_rsub_sign_sub_0(in_out_ptr0,
    in_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 = tl.full([1], 0, tl.int32)
    tmp2 = tmp1 < tmp0
    tmp3 = tmp2.to(tl.int8)
    tmp4 = tmp0 < tmp1
    tmp5 = tmp4.to(tl.int8)
    tmp6 = tmp3 - tmp5
    tmp7 = tmp6.to(tmp0.dtype)
    tmp8 = -1.0
    tmp9 = tmp0 < tmp8
    tmp10 = tmp9.to(tl.float32)
    tmp11 = tmp10 * tmp8
    tmp12 = tmp0 * tmp0
    tmp13 = 2.0
    tmp14 = tmp0 * tmp13
    tmp15 = tmp12 + tmp14
    tmp16 = 1.0
    tmp17 = tmp16 - tmp10
    tmp18 = tmp15 * tmp17
    tmp19 = tmp11 + tmp18
    tmp20 = 0.0
    tmp21 = tmp0 < tmp20
    tmp22 = tmp21.to(tl.float32)
    tmp23 = tmp19 * tmp22
    tmp24 = -tmp0
    tmp25 = tmp24 * tmp0
    tmp26 = tmp25 + tmp14
    tmp27 = tmp16 - tmp22
    tmp28 = tmp26 * tmp27
    tmp29 = tmp23 + tmp28
    tmp30 = tmp0 < tmp16
    tmp31 = tmp30.to(tl.float32)
    tmp32 = tmp29 * tmp31
    tmp33 = tmp16 - tmp31
    tmp34 = tmp33 * tmp16
    tmp35 = tmp32 + tmp34
    tmp36 = tmp7 - tmp35
    tmp37 = tmp36 + tmp35
    tl.store(in_out_ptr0 + x0, tmp37, 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)
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused__to_copy_add_lt_mul_neg_rsub_sign_sub_0[grid(256)](
            buf1, arg0_1, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del arg0_1
    return buf1,


class BinaryActivationNew(nn.Module):

    def __init__(self):
        super(BinaryActivationNew, self).__init__()

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