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 import 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__to_copy_mean_ne_sum_0(in_ptr0, in_ptr1, out_ptr0,
    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)
    tmp1 = tl.load(in_ptr1 + (r0 + 64 * r1), None)
    tmp4 = tl.load(in_ptr0 + (16 + r0 + 64 * r1), None)
    tmp5 = tl.load(in_ptr1 + (16 + r0 + 64 * r1), None)
    tmp9 = tl.load(in_ptr0 + (32 + r0 + 64 * r1), None)
    tmp10 = tl.load(in_ptr1 + (32 + r0 + 64 * r1), None)
    tmp14 = tl.load(in_ptr0 + (48 + r0 + 64 * r1), None)
    tmp15 = tl.load(in_ptr1 + (48 + r0 + 64 * r1), None)
    tmp2 = tmp0 != tmp1
    tmp3 = tmp2.to(tl.float32)
    tmp6 = tmp4 != tmp5
    tmp7 = tmp6.to(tl.float32)
    tmp8 = tmp3 + tmp7
    tmp11 = tmp9 != tmp10
    tmp12 = tmp11.to(tl.float32)
    tmp13 = tmp8 + tmp12
    tmp16 = tmp14 != tmp15
    tmp17 = tmp16.to(tl.float32)
    tmp18 = tmp13 + tmp17
    tmp19 = tl.broadcast_to(tmp18, [XBLOCK, RBLOCK])
    tmp21 = tl.sum(tmp19, 1)[:, None]
    tl.store(out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp21, None)


@triton.jit
def triton_per_fused__to_copy_add_div_mean_mse_loss_mul_ne_sum_1(in_out_ptr0,
    in_ptr0, in_ptr1, in_ptr2, in_ptr3, 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_out_ptr0 + 0)
    tmp15 = tl.broadcast_to(tmp14, [1])
    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 = 64.0
    tmp17 = tmp15 / tmp16
    tmp18 = 256.0
    tmp19 = tmp6 / tmp18
    tmp20 = tmp13 / tmp18
    tmp21 = tmp19 + tmp20
    tmp22 = 0.5
    tmp23 = tmp21 * tmp22
    tmp24 = 0.001
    tmp25 = tmp23 * tmp24
    tmp26 = tmp17 + tmp25
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp26, None)


def call(args):
    arg0_1, arg1_1, arg2_1, arg3_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))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((), (), torch.float32)
        get_raw_stream(0)
        triton_per_fused__to_copy_mean_ne_sum_0[grid(1)](arg0_1, arg1_1,
            buf0, 1, 64, XBLOCK=1, num_warps=2, num_stages=1)
        buf3 = buf0
        del buf0
        triton_per_fused__to_copy_add_div_mean_mse_loss_mul_ne_sum_1[grid(1)](
            buf3, arg2_1, arg0_1, arg3_1, arg1_1, 1, 256, num_warps=2,
            num_stages=1)
        del arg0_1
        del arg1_1
        del arg2_1
        del arg3_1
    return buf3,


class CodeLossNew(nn.Module):

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

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


Measured latency_ms: 0.02856