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
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_per_fused_abs_add_div_mean_sign_sum_0(in_out_ptr0, in_ptr0,
    in_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 64
    xoffset = tl.program_id(0) * XBLOCK
    xoffset + tl.arange(0, XBLOCK)[:, None]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex % 16
    r1 = rindex // 16
    tmp0 = tl.load(in_ptr0 + (r0 + 64 * r1), None)
    tmp8 = tl.load(in_ptr0 + (16 + r0 + 64 * r1), None)
    tmp16 = tl.load(in_ptr0 + (32 + r0 + 64 * r1), None)
    tmp24 = tl.load(in_ptr0 + (48 + r0 + 64 * r1), None)
    tmp36 = tl.load(in_ptr1 + (r0 + 64 * r1), None)
    tmp43 = tl.load(in_ptr1 + (16 + r0 + 64 * r1), None)
    tmp51 = tl.load(in_ptr1 + (32 + r0 + 64 * r1), None)
    tmp59 = tl.load(in_ptr1 + (48 + r0 + 64 * r1), None)
    tmp1 = tl.full([1, 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)
    tmp9 = tmp1 < tmp8
    tmp10 = tmp9.to(tl.int8)
    tmp11 = tmp8 < tmp1
    tmp12 = tmp11.to(tl.int8)
    tmp13 = tmp10 - tmp12
    tmp14 = tmp13.to(tmp8.dtype)
    tmp15 = tmp7 + tmp14
    tmp17 = tmp1 < tmp16
    tmp18 = tmp17.to(tl.int8)
    tmp19 = tmp16 < tmp1
    tmp20 = tmp19.to(tl.int8)
    tmp21 = tmp18 - tmp20
    tmp22 = tmp21.to(tmp16.dtype)
    tmp23 = tmp15 + tmp22
    tmp25 = tmp1 < tmp24
    tmp26 = tmp25.to(tl.int8)
    tmp27 = tmp24 < tmp1
    tmp28 = tmp27.to(tl.int8)
    tmp29 = tmp26 - tmp28
    tmp30 = tmp29.to(tmp24.dtype)
    tmp31 = tmp23 + tmp30
    tmp32 = tl_math.abs(tmp31)
    tmp33 = tl.broadcast_to(tmp32, [XBLOCK, RBLOCK])
    tmp35 = tl.sum(tmp33, 1)[:, None]
    tmp37 = tmp1 < tmp36
    tmp38 = tmp37.to(tl.int8)
    tmp39 = tmp36 < tmp1
    tmp40 = tmp39.to(tl.int8)
    tmp41 = tmp38 - tmp40
    tmp42 = tmp41.to(tmp36.dtype)
    tmp44 = tmp1 < tmp43
    tmp45 = tmp44.to(tl.int8)
    tmp46 = tmp43 < tmp1
    tmp47 = tmp46.to(tl.int8)
    tmp48 = tmp45 - tmp47
    tmp49 = tmp48.to(tmp43.dtype)
    tmp50 = tmp42 + tmp49
    tmp52 = tmp1 < tmp51
    tmp53 = tmp52.to(tl.int8)
    tmp54 = tmp51 < tmp1
    tmp55 = tmp54.to(tl.int8)
    tmp56 = tmp53 - tmp55
    tmp57 = tmp56.to(tmp51.dtype)
    tmp58 = tmp50 + tmp57
    tmp60 = tmp1 < tmp59
    tmp61 = tmp60.to(tl.int8)
    tmp62 = tmp59 < tmp1
    tmp63 = tmp62.to(tl.int8)
    tmp64 = tmp61 - tmp63
    tmp65 = tmp64.to(tmp59.dtype)
    tmp66 = tmp58 + tmp65
    tmp67 = tl_math.abs(tmp66)
    tmp68 = tl.broadcast_to(tmp67, [XBLOCK, RBLOCK])
    tmp70 = tl.sum(tmp68, 1)[:, None]
    tmp71 = 64.0
    tmp72 = tmp35 / tmp71
    tmp73 = tmp70 / tmp71
    tmp74 = tmp72 + tmp73
    tmp75 = 0.5
    tmp76 = tmp74 * tmp75
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp76, None)


@triton.jit
def triton_per_fused_mse_loss_sign_1(in_out_ptr0, in_ptr0, in_ptr1, 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)
    tmp8 = tl.load(in_ptr1 + 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)
    tmp9 = tmp1 < tmp8
    tmp10 = tmp9.to(tl.int8)
    tmp11 = tmp8 < tmp1
    tmp12 = tmp11.to(tl.int8)
    tmp13 = tmp10 - tmp12
    tmp14 = tmp13.to(tmp8.dtype)
    tmp15 = tmp7 - tmp14
    tmp16 = tmp15 * tmp15
    tmp17 = tl.broadcast_to(tmp16, [RBLOCK])
    tmp19 = triton_helpers.promote_to_tensor(tl.sum(tmp17, 0))
    tmp20 = 256.0
    tmp21 = tmp19 / tmp20
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp21, None)


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((), (), torch.float32)
        buf3 = buf0
        del buf0
        get_raw_stream(0)
        triton_per_fused_abs_add_div_mean_sign_sum_0[grid(1)](buf3, arg0_1,
            arg1_1, 1, 64, XBLOCK=1, num_warps=2, num_stages=1)
        buf2 = empty_strided_cuda((), (), torch.float32)
        buf4 = buf2
        del buf2
        triton_per_fused_mse_loss_sign_1[grid(1)](buf4, arg1_1, arg0_1, 1, 
            256, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
    return buf3, buf4


class CodeLossNew(nn.Module):

    def __init__(self):
        super().__init__()
        self.loss = nn.MSELoss()

    def forward(self, input_0, input_1):
        arg0_1 = input_0
        arg1_1 = input_1
        output = call([arg0_1, arg1_1])
        return output[0], output[1]


Measured latency_ms: 0.028297