import torch
from torch._inductor.select_algorithm import extern_kernels
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
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_div_mean_std_sub_0(in_ptr0, out_ptr0, xnumel, XBLOCK:
    tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + x0, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr0 + (4 + x0), xmask, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (8 + x0), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (12 + x0), xmask, eviction_policy='evict_last')
    tmp3 = tmp1 + tmp2
    tmp5 = tmp3 + tmp4
    tmp7 = tmp5 + tmp6
    tmp8 = 4.0
    tmp9 = tmp7 / tmp8
    tmp10 = tmp0 - tmp9
    tmp11 = tmp1 - tmp9
    tmp12 = tmp11 * tmp11
    tmp13 = tmp2 - tmp9
    tmp14 = tmp13 * tmp13
    tmp15 = tmp12 + tmp14
    tmp16 = tmp4 - tmp9
    tmp17 = tmp16 * tmp16
    tmp18 = tmp15 + tmp17
    tmp19 = tmp6 - tmp9
    tmp20 = tmp19 * tmp19
    tmp21 = tmp18 + tmp20
    tmp22 = 3.0
    tmp23 = tmp21 / tmp22
    tmp24 = libdevice.sqrt(tmp23)
    tmp25 = tmp10 / tmp24
    tl.store(out_ptr0 + x2, tmp25, xmask)


@triton.jit
def triton_poi_fused_div_1(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = 0.25
    tmp2 = tmp0 * tmp1
    tl.store(out_ptr0 + x0, tmp2, xmask)


@triton.jit
def triton_poi_fused_add_2(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 5 * x0, xmask, eviction_policy='evict_last')
    tmp1 = 0.25
    tmp2 = tmp0 * tmp1
    tmp3 = -1.0
    tmp4 = tmp2 + tmp3
    tl.store(out_ptr0 + 5 * x0, tmp4, xmask)


@triton.jit
def triton_per_fused_pow_sum_3(in_ptr0, out_ptr1, out_ptr2, xnumel, rnumel,
    XBLOCK: tl.constexpr):
    RBLOCK: tl.constexpr = 4
    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
    tmp0 = tl.load(in_ptr0 + 5 * r0, None, eviction_policy='evict_last')
    tmp1 = tmp0 * tmp0
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp4 = tl.sum(tmp2, 1)[:, None]
    tl.store(out_ptr1 + tl.broadcast_to(5 * r0, [XBLOCK, RBLOCK]), tmp1, None)
    tl.store(out_ptr2 + tl.full([XBLOCK, 1], 0, tl.int32), tmp4, None)


@triton.jit
def triton_per_fused_add_mul_sum_4(in_out_ptr0, in_ptr0, xnumel, rnumel,
    XBLOCK: tl.constexpr):
    rnumel = 12
    RBLOCK: tl.constexpr = 16
    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
    tmp0 = tl.load(in_ptr0 + (1 + 5 * (r0 // 4) + 5 * (r0 % 4 // 4) + r0 % 
        4), rmask, other=0.0)
    tmp6 = tl.load(in_out_ptr0 + 0)
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK, 1])
    tmp1 = tmp0 * tmp0
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp4 = tl.where(rmask, tmp2, 0)
    tmp5 = tl.sum(tmp4, 1)[:, None]
    tmp8 = 0.005
    tmp9 = tmp5 * tmp8
    tmp10 = tmp7 + tmp9
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp10, None)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4), (4, 1))
    assert_size_stride(arg1_1, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_div_mean_std_sub_0[grid(16)](arg0_1, buf0, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        del arg0_1
        buf1 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        triton_poi_fused_div_mean_std_sub_0[grid(16)](arg1_1, buf1, 16,
            XBLOCK=16, num_warps=1, num_stages=1)
        del arg1_1
        buf2 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(reinterpret_tensor(buf0, (4, 4), (1, 4), 0), buf1,
            out=buf2)
        del buf0
        buf3 = buf1
        del buf1
        triton_poi_fused_div_1[grid(16)](buf2, buf3, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        triton_poi_fused_add_2[grid(4)](buf2, buf3, 4, XBLOCK=4, num_warps=
            1, num_stages=1)
        del buf2
        buf7 = empty_strided_cuda((), (), torch.float32)
        triton_per_fused_pow_sum_3[grid(1)](buf3, buf3, buf7, 1, 4, XBLOCK=
            1, num_warps=2, num_stages=1)
        buf9 = buf7
        del buf7
        triton_per_fused_add_mul_sum_4[grid(1)](buf9, buf3, 1, 12, XBLOCK=1,
            num_warps=2, num_stages=1)
        del buf3
    return buf9,


class BarlowTwinsLossNew(nn.Module):

    def __init__(self, batch_size, lambda_coeff=0.005, z_dim=128):
        super().__init__()
        self.z_dim = z_dim
        self.batch_size = batch_size
        self.lambda_coeff = lambda_coeff

    def off_diagonal_ele(self, x):
        n, m = x.shape
        assert n == m
        return x.flatten()[:-1].view(n - 1, n + 1)[:, 1:].flatten()

    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: 0.014415