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
from torch.nn.modules.loss import *
from torch.nn.modules import *
from torch.optim import *
from torch.optim.lr_scheduler import *
import torch.backends
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_add_div_mean_pow_sub_var_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 = 1e-12
    tmp25 = tmp23 + tmp24
    tmp26 = libdevice.sqrt(tmp25)
    tmp27 = tmp10 / tmp26
    tl.store(out_ptr0 + x2, tmp27, xmask)


@triton.jit
def triton_poi_fused_div_1(in_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_out_ptr0 + x0, xmask)
    tmp1 = 0.25
    tmp2 = tmp0 * tmp1
    tl.store(in_out_ptr0 + x0, tmp2, xmask)


@triton.jit
def triton_poi_fused_2(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)
    tl.store(out_ptr0 + x0, tmp0, xmask)


@triton.jit
def triton_poi_fused_add_3(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 = -1.0
    tmp2 = tmp0 + tmp1
    tl.store(out_ptr0 + 5 * x0, tmp2, xmask)


@triton.jit
def triton_per_fused_pow_sum_4(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_poi_fused_fill_5(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 = 0.0
    tl.store(out_ptr0 + 5 * x0, tmp0, xmask)


@triton.jit
def triton_per_fused_add_mul_pow_sum_6(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)
    tmp5 = tl.load(in_ptr1 + 0)
    tmp6 = tl.broadcast_to(tmp5, [XBLOCK, 1])
    tmp1 = tmp0 * tmp0
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp4 = tl.sum(tmp2, 1)[:, None]
    tmp7 = 1.0
    tmp8 = tmp4 * tmp7
    tmp9 = tmp6 + tmp8
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp9, 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_add_div_mean_pow_sub_var_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_add_div_mean_pow_sub_var_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 = buf2
        del buf2
        triton_poi_fused_div_1[grid(16)](buf3, 16, XBLOCK=16, num_warps=1,
            num_stages=1)
        buf4 = buf1
        del buf1
        triton_poi_fused_2[grid(16)](buf3, buf4, 16, XBLOCK=16, num_warps=1,
            num_stages=1)
        triton_poi_fused_add_3[grid(4)](buf3, buf4, 4, XBLOCK=4, num_warps=
            1, num_stages=1)
        buf8 = empty_strided_cuda((), (), torch.float32)
        triton_per_fused_pow_sum_4[grid(1)](buf4, buf4, buf8, 1, 4, XBLOCK=
            1, num_warps=2, num_stages=1)
        triton_poi_fused_fill_5[grid(4)](buf3, 4, XBLOCK=4, num_warps=1,
            num_stages=1)
        del buf4
        buf11 = empty_strided_cuda((), (), torch.float32)
        buf12 = buf11
        del buf11
        triton_per_fused_add_mul_pow_sum_6[grid(1)](buf12, buf3, buf8, 1, 
            16, XBLOCK=1, num_warps=2, num_stages=1)
        del buf3
        del buf8
    return buf12,


class BarlowTwinsLossNew(nn.Module):
    """The Contrastive embedding loss.

    It has been proposed in `Barlow Twins:
    Self-Supervised Learning via Redundancy Reduction`_.

    Example:

    .. code-block:: python

        import torch
        from torch.nn import functional as F
        from catalyst.contrib.nn import BarlowTwinsLoss

        embeddings_left = F.normalize(torch.rand(256, 64, requires_grad=True))
        embeddings_right = F.normalize(torch.rand(256, 64, requires_grad=True))
        criterion = BarlowTwinsLoss(offdiag_lambda = 1)
        criterion(embeddings_left, embeddings_right)

    .. _`Barlow Twins: Self-Supervised Learning via Redundancy Reduction`:
        https://arxiv.org/abs/2103.03230
    """

    def __init__(self, offdiag_lambda=1.0, eps=1e-12):
        """
        Args:
            offdiag_lambda: trade-off parameter
            eps: shift for the varience (var + eps)
        """
        super().__init__()
        self.offdiag_lambda = offdiag_lambda
        self.eps = eps

    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: 0.01501