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
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_add_div_mse_loss_mul_0(in_out_ptr0, in_ptr0, in_ptr1,
    in_ptr2, in_ptr3, in_ptr4, in_ptr5, 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.load(in_ptr1 + r0, None)
    tmp7 = tl.load(in_ptr2 + r0, None)
    tmp8 = tl.load(in_ptr3 + r0, None)
    tmp14 = tl.load(in_ptr4 + r0, None)
    tmp15 = tl.load(in_ptr5 + r0, None)
    tmp2 = tmp0 - tmp1
    tmp3 = tmp2 * tmp2
    tmp4 = tl.broadcast_to(tmp3, [RBLOCK])
    tmp6 = triton_helpers.promote_to_tensor(tl.sum(tmp4, 0))
    tmp9 = tmp7 - tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tl.broadcast_to(tmp10, [RBLOCK])
    tmp13 = triton_helpers.promote_to_tensor(tl.sum(tmp11, 0))
    tmp16 = tmp14 - tmp15
    tmp17 = tmp16 * tmp16
    tmp18 = tl.broadcast_to(tmp17, [RBLOCK])
    tmp20 = triton_helpers.promote_to_tensor(tl.sum(tmp18, 0))
    tmp21 = 256.0
    tmp22 = tmp6 / tmp21
    tmp23 = tmp13 / tmp21
    tmp24 = 1.0
    tmp25 = tmp23 * tmp24
    tmp26 = tmp22 + tmp25
    tmp27 = tmp20 / tmp21
    tmp28 = tmp27 * tmp24
    tmp29 = tmp26 + tmp28
    tmp30 = 0.3333333333333333
    tmp31 = tmp29 * tmp30
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp31, None)


def call(args):
    arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_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))
    assert_size_stride(arg2_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg3_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg4_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg5_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_add_div_mse_loss_mul_0[grid(1)](buf3, arg1_1,
            arg0_1, arg3_1, arg2_1, arg5_1, arg4_1, 1, 256, num_warps=2,
            num_stages=1)
        del arg0_1
        del arg1_1
        del arg2_1
        del arg3_1
        del arg4_1
        del arg5_1
    return buf3,


class myLoss3New(nn.Module):

    def __init__(self, alpha=1.0, beta=1.0):
        super(myLoss3New, self).__init__()
        self.alpha = alpha
        self.beta = beta

    def forward(self, input_0, input_1, input_2, input_3, input_4, input_5):
        arg0_1 = input_0
        arg1_1 = input_1
        arg2_1 = input_2
        arg3_1 = input_3
        arg4_1 = input_4
        arg5_1 = input_5
        output = call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1, arg5_1])
        return output[0]


Measured latency_ms: 0.019311