import torch


class GreedyHashLoss(torch.nn.Module):

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

    def forward(self, u):
        b = GreedyHashLoss.Hash.apply(u)
        loss = (u.abs() - 1).pow(3).abs().mean()
        return b, loss


    class Hash(torch.autograd.Function):

        @staticmethod
        def forward(ctx, input):
            return input.sign()

        @staticmethod
        def backward(ctx, grad_output):
            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
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_per_fused_abs_mean_pow_sign_sub_0(in_out_ptr0, in_ptr0, out_ptr0,
    xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, None)
    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 = tl_math.abs(tmp0)
    tmp9 = 1.0
    tmp10 = tmp8 - tmp9
    tmp11 = tmp10 * tmp10
    tmp12 = tmp11 * tmp10
    tmp13 = tl_math.abs(tmp12)
    tmp14 = tl.broadcast_to(tmp13, [RBLOCK])
    tmp16 = triton_helpers.promote_to_tensor(tl.sum(tmp14, 0))
    tmp17 = 256.0
    tmp18 = tmp16 / tmp17
    tl.store(out_ptr0 + tl.broadcast_to(r0, [RBLOCK]), tmp7, None)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp18, None)


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 = empty_strided_cuda((), (), torch.float32)
        buf2 = buf1
        del buf1
        get_raw_stream(0)
        triton_per_fused_abs_mean_pow_sign_sub_0[grid(1)](buf2, arg0_1,
            buf0, 1, 256, num_warps=2, num_stages=1)
        del arg0_1
    return buf0, buf2


class GreedyHashLossNew(torch.nn.Module):

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

    class Hash(torch.autograd.Function):

        @staticmethod
        def forward(ctx, input):
            return input.sign()

        @staticmethod
        def backward(ctx, grad_output):
            return grad_output

    def forward(self, input_0):
        arg0_1 = input_0
        output = call([arg0_1])
        return output[0], output[1]
