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 libdevice
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_mean_0(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.broadcast_to(tmp0, [RBLOCK])
    tmp3 = triton_helpers.promote_to_tensor(tl.sum(tmp1, 0))
    tl.store(out_ptr0 + tl.full([1], 0, tl.int32), tmp3, None)


@triton.jit
def triton_per_fused_linalg_vector_norm_mean_mul_neg_pow_sub_1(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)
    tmp2 = tl.load(in_ptr0 + (16 + r0 + 64 * r1), None)
    tmp5 = tl.load(in_ptr0 + (32 + r0 + 64 * r1), None)
    tmp8 = tl.load(in_ptr0 + (48 + r0 + 64 * r1), None)
    tmp18 = tl.load(in_out_ptr0 + 0)
    tmp19 = tl.broadcast_to(tmp18, [XBLOCK, 1])
    tmp22 = tl.load(in_ptr1 + 0)
    tmp23 = tl.broadcast_to(tmp22, [XBLOCK, 1])
    tmp1 = tmp0 * tmp0
    tmp3 = tmp2 * tmp2
    tmp4 = tmp1 + tmp3
    tmp6 = tmp5 * tmp5
    tmp7 = tmp4 + tmp6
    tmp9 = tmp8 * tmp8
    tmp10 = tmp7 + tmp9
    tmp11 = libdevice.sqrt(tmp10)
    tmp12 = 1.0
    tmp13 = tmp11 - tmp12
    tmp14 = tmp13 * tmp13
    tmp15 = tl.broadcast_to(tmp14, [XBLOCK, RBLOCK])
    tmp17 = tl.sum(tmp15, 1)[:, None]
    tmp20 = 256.0
    tmp21 = tmp19 / tmp20
    tmp24 = tmp23 / tmp20
    tmp25 = tmp21 - tmp24
    tmp26 = 64.0
    tmp27 = tmp17 / tmp26
    tmp28 = 4.0
    tmp29 = tmp27 * tmp28
    tmp30 = tmp25 - tmp29
    tmp31 = -tmp30
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp31, None)


def call(args):
    arg0_1, arg1_1, arg2_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))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((), (), torch.float32)
        get_raw_stream(0)
        triton_per_fused_mean_0[grid(1)](arg0_1, buf0, 1, 256, num_warps=2,
            num_stages=1)
        del arg0_1
        buf1 = empty_strided_cuda((), (), torch.float32)
        triton_per_fused_mean_0[grid(1)](arg1_1, buf1, 1, 256, num_warps=2,
            num_stages=1)
        del arg1_1
        buf3 = buf0
        del buf0
        triton_per_fused_linalg_vector_norm_mean_mul_neg_pow_sub_1[grid(1)](
            buf3, arg2_1, buf1, 1, 64, XBLOCK=1, num_warps=2, num_stages=1)
        del arg2_1
        del buf1
    return buf3,


class WDLossNew(nn.Module):

    def __init__(self, _lambda):
        super(WDLossNew, self).__init__()
        self._lambda = _lambda

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


Measured latency_ms: 0.038845