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
from torch import 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_mean_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
    tl.store(out_ptr0 + x2, tmp10, xmask)


@triton.jit
def triton_per_fused_linalg_vector_norm_mul_pow_sub_1(in_out_ptr0, in_ptr0,
    in_ptr1, xnumel, rnumel, XBLOCK: tl.constexpr):
    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, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r0 = rindex
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp3 = tl.load(in_ptr1 + r0, None)
    tmp1 = 0.25
    tmp2 = tmp0 * tmp1
    tmp4 = tmp3 * tmp1
    tmp5 = tmp2 - tmp4
    tmp6 = tmp5 * tmp5
    tmp7 = tl.broadcast_to(tmp6, [XBLOCK, RBLOCK])
    tmp9 = tl.sum(tmp7, 1)[:, None]
    tmp10 = libdevice.sqrt(tmp9)
    tmp11 = tmp10 * tmp10
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp11, 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), (1, 4), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_mean_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)
        extern_kernels.mm(buf0, reinterpret_tensor(buf0, (4, 4), (4, 1), 0),
            out=buf1)
        buf2 = buf0
        del buf0
        triton_poi_fused_mean_sub_0[grid(16)](arg1_1, buf2, 16, XBLOCK=16,
            num_warps=1, num_stages=1)
        del arg1_1
        buf3 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf2, reinterpret_tensor(buf2, (4, 4), (4, 1), 0),
            out=buf3)
        del buf2
        buf4 = empty_strided_cuda((), (), torch.float32)
        buf5 = buf4
        del buf4
        triton_per_fused_linalg_vector_norm_mul_pow_sub_1[grid(1)](buf5,
            buf1, buf3, 1, 16, XBLOCK=1, num_warps=2, num_stages=1)
        del buf1
        del buf3
    return buf5,


def Covariance(m, bias=False, rowvar=True, inplace=False):
    """ Estimate a covariance matrix given data(tensor).
    Covariance indicates the level to which two variables vary together.
    If we examine N-dimensional samples, `X = [x_1, x_2, ... x_N]^T`,
    then the covariance matrix element `C_{ij}` is the covariance of
    `x_i` and `x_j`. The element `C_{ii}` is the variance of `x_i`.

    Args:
        m: numpy array - A 1-D or 2-D array containing multiple variables and observations.
            Each row of `m` represents a variable, and each column a single
            observation of all those variables.
        rowvar: bool - If `rowvar` is True, then each row represents a
            variable, with observations in the columns. Otherwise, the
            relationship is transposed: each column represents a variable,
            while the rows contain observations.

    Returns:
        The covariance matrix of the variables.
    """
    if m.dim() > 2:
        raise ValueError('m has more than 2 dimensions')
    if m.dim() < 2:
        m = m.view(1, -1)
    if not rowvar and m.size(0) != 1:
        m = m.t()
    fact = 1.0 / (m.size(1) - 1) if not bias else 1.0 / m.size(1)
    if inplace:
        m -= torch.mean(m, dim=1, keepdim=True)
    else:
        m = m - torch.mean(m, dim=1, keepdim=True)
    mt = m.t()
    return fact * m.matmul(mt).squeeze()


class CMDS_LossNew(nn.Module):
    """Equation(1) in Self-calibrating Neural Networks for Dimensionality Reduction

    Attributes:
        X: tensor - original datas.
        Y: tensor - encoded datas.
    Returns:
        cmds: float - The cmds loss.
    """

    def __init__(self):
        super(CMDS_LossNew, self).__init__()

    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: 0.055538