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.triton_helpers import libdevice
import torch.nn as nn
import torch.utils.data
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_mean_mul_neg_sqrt_sub_0(in_out_ptr0, in_ptr0,
    in_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr):
    rnumel = 192
    RBLOCK: tl.constexpr = 256
    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, :]
    rmask = rindex < rnumel
    r0 = rindex % 12
    r1 = rindex // 12
    r2 = rindex % 3
    r3 = rindex // 3
    tmp0 = tl.load(in_ptr0 + (4 + r0 + 16 * r1), rmask, other=0.0)
    tmp1 = tl.load(in_ptr0 + (r0 + 16 * r1), rmask, other=0.0)
    tmp3 = tl.load(in_ptr1 + (4 + r0 + 16 * r1), rmask, other=0.0)
    tmp4 = tl.load(in_ptr1 + (r0 + 16 * r1), rmask, other=0.0)
    tmp16 = tl.load(in_ptr0 + (1 + r2 + 4 * r3), rmask, other=0.0)
    tmp17 = tl.load(in_ptr0 + (r2 + 4 * r3), rmask, other=0.0)
    tmp19 = tl.load(in_ptr1 + (1 + r2 + 4 * r3), rmask, other=0.0)
    tmp20 = tl.load(in_ptr1 + (r2 + 4 * r3), rmask, other=0.0)
    tmp2 = tmp0 - tmp1
    tmp5 = tmp3 - tmp4
    tmp6 = -tmp5
    tmp7 = tmp2 + tmp6
    tmp8 = tmp7 * tmp7
    tmp9 = 1e-06
    tmp10 = tmp8 + tmp9
    tmp11 = libdevice.sqrt(tmp10)
    tmp12 = tl.broadcast_to(tmp11, [XBLOCK, RBLOCK])
    tmp14 = tl.where(rmask, tmp12, 0)
    tmp15 = tl.sum(tmp14, 1)[:, None]
    tmp18 = tmp16 - tmp17
    tmp21 = tmp19 - tmp20
    tmp22 = -tmp21
    tmp23 = tmp18 + tmp22
    tmp24 = tmp23 * tmp23
    tmp25 = tmp24 + tmp9
    tmp26 = libdevice.sqrt(tmp25)
    tmp27 = tl.broadcast_to(tmp26, [XBLOCK, RBLOCK])
    tmp29 = tl.where(rmask, tmp27, 0)
    tmp30 = tl.sum(tmp29, 1)[:, None]
    tmp31 = 192.0
    tmp32 = tmp15 / tmp31
    tmp33 = tmp30 / tmp31
    tmp34 = tmp32 + tmp33
    tmp35 = 0.5
    tmp36 = tmp34 * tmp35
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp36, 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)
        buf2 = buf0
        del buf0
        get_raw_stream(0)
        triton_per_fused_add_mean_mul_neg_sqrt_sub_0[grid(1)](buf2, arg0_1,
            arg1_1, 1, 192, XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
    return buf2,


class L1_Charbonnier_loss(nn.Module):
    """L1 Charbonnierloss."""

    def __init__(self):
        super(L1_Charbonnier_loss, self).__init__()
        self.eps = 1e-06

    def forward(self, X, Y):
        diff = torch.add(X, -Y)
        error = torch.sqrt(diff * diff + self.eps)
        loss = torch.mean(error)
        return loss


class L1_Gradient_lossNew(nn.Module):

    def __init__(self):
        super(L1_Gradient_lossNew, self).__init__()
        self.eps = 1e-06
        self.crit = L1_Charbonnier_loss()

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


Measured latency_ms: 0.015046