import torch


class GainesMul(torch.nn.Module):
    """
    this module is for Gaines stochastic multiplication, supporting unipolar/bipolar
    """

    def __init__(self, mode='bipolar', stype=torch.float):
        super(GainesMul, self).__init__()
        self.mode = mode
        self.stype = stype

    def UnaryMul_forward(self, input_0, input_1):
        if self.mode == 'unipolar':
            return input_0.type(torch.int8) & input_1.type(torch.int8)
        elif self.mode == 'bipolar':
            return 1 - (input_0.type(torch.int8) ^ input_1.type(torch.int8))
        else:
            raise ValueError('UnaryMul mode is not implemented.')

    def forward(self, input_0, input_1):
        return self.UnaryMul_forward(input_0, input_1).type(self.stype)


def get_inputs():
    return [torch.rand([4, 4, 4, 4]), 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
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_bitwise_xor_rsub_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)
    tmp2 = tl.load(in_ptr1 + x0, xmask)
    tmp1 = tmp0.to(tl.int8)
    tmp3 = tmp2.to(tl.int8)
    tmp4 = tmp1 ^ tmp3
    tmp5 = tl.full([1], 1, tl.int8)
    tmp6 = tmp5 - tmp4
    tmp7 = tmp6.to(tl.float32)
    tl.store(out_ptr0 + x0, tmp7, 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, (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__to_copy_bitwise_xor_rsub_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 GainesMulNew(torch.nn.Module):
    """
    this module is for Gaines stochastic multiplication, supporting unipolar/bipolar
    """

    def __init__(self, mode='bipolar', stype=torch.float):
        super(GainesMulNew, self).__init__()
        self.mode = mode
        self.stype = stype

    def UnaryMul_forward(self, input_0, input_1):
        if self.mode == 'unipolar':
            return input_0.type(torch.int8) & input_1.type(torch.int8)
        elif self.mode == 'bipolar':
            return 1 - (input_0.type(torch.int8) ^ input_1.type(torch.int8))
        else:
            raise ValueError('UnaryMul mode is not implemented.')

    def forward(self, input_0, input_1):
        arg0_1 = input_0
        arg1_1 = input_1
        output = call([arg0_1, arg1_1])
        return output[0]
