import torch
import torch.nn as nn


class BinaryLayer(nn.Module):

    def forward(self, input):
        """Forward function for binary layer

        :param input: data
        :returns: sign of data
        :rtype: Tensor

        """
        return torch.sign(input)

    def backward(self, grad_output):
        """Straight through estimator

        :param grad_output: gradient tensor
        :returns: truncated gradient tensor
        :rtype: Tensor

        """
        input = self.saved_tensors
        grad_output[input > 1] = 0
        grad_output[input < -1] = 0
        return grad_output


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_sign_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 = 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)
    tl.store(out_ptr0 + x0, tmp7, 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_sign_0[grid(256)](arg0_1, buf0, 256, XBLOCK=128,
            num_warps=4, num_stages=1)
        del arg0_1
    return buf0,


class BinaryLayerNew(nn.Module):

    def backward(self, grad_output):
        """Straight through estimator

        :param grad_output: gradient tensor
        :returns: truncated gradient tensor
        :rtype: Tensor

        """
        input = self.saved_tensors
        grad_output[input > 1] = 0
        grad_output[input < -1] = 0
        return grad_output

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