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, math as tl_math
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_add_div_index_put_lift_fresh_pow_sqrt_sum_0(in_out_ptr0,
    in_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp1 = tl.load(in_ptr0 + 4 * x1, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr0 + (1 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (2 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (3 + 4 * x1), xmask, eviction_policy='evict_last')
    tmp2 = tmp1 * tmp1
    tmp4 = tmp3 * tmp3
    tmp5 = tmp2 + tmp4
    tmp7 = tmp6 * tmp6
    tmp8 = tmp5 + tmp7
    tmp10 = tmp9 * tmp9
    tmp11 = tmp8 + tmp10
    tmp12 = libdevice.sqrt(tmp11)
    tmp13 = 1e-07
    tmp14 = tmp12 + tmp13
    tmp15 = tmp0 / tmp14
    tmp16 = tmp15 != tmp15
    tmp17 = 0.0
    tmp18 = tl.where(tmp16, tmp17, tmp15)
    tl.store(in_out_ptr0 + x2, tmp18, xmask)


@triton.jit
def triton_per_fused_add_div_log_mean_mul_sum_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)
    r2 = rindex
    r1 = rindex // 4
    tmp0 = tl.load(in_ptr0 + r2, None)
    tmp5 = tl.load(in_ptr0 + 4 * r1, None, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (1 + 4 * r1), None, eviction_policy='evict_last')
    tmp12 = tl.load(in_ptr0 + (2 + 4 * r1), None, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr0 + (3 + 4 * r1), None, eviction_policy='evict_last')
    tmp23 = tl.load(in_ptr1 + r2, None)
    tmp26 = tl.load(in_ptr1 + 4 * r1, None, eviction_policy='evict_last')
    tmp29 = tl.load(in_ptr1 + (1 + 4 * r1), None, eviction_policy='evict_last')
    tmp33 = tl.load(in_ptr1 + (2 + 4 * r1), None, eviction_policy='evict_last')
    tmp37 = tl.load(in_ptr1 + (3 + 4 * r1), None, eviction_policy='evict_last')
    tmp1 = 1.0
    tmp2 = tmp0 + tmp1
    tmp3 = 0.5
    tmp4 = tmp2 * tmp3
    tmp6 = tmp5 + tmp1
    tmp7 = tmp6 * tmp3
    tmp9 = tmp8 + tmp1
    tmp10 = tmp9 * tmp3
    tmp11 = tmp7 + tmp10
    tmp13 = tmp12 + tmp1
    tmp14 = tmp13 * tmp3
    tmp15 = tmp11 + tmp14
    tmp17 = tmp16 + tmp1
    tmp18 = tmp17 * tmp3
    tmp19 = tmp15 + tmp18
    tmp20 = tmp4 / tmp19
    tmp21 = 1e-07
    tmp22 = tmp20 + tmp21
    tmp24 = tmp23 + tmp1
    tmp25 = tmp24 * tmp3
    tmp27 = tmp26 + tmp1
    tmp28 = tmp27 * tmp3
    tmp30 = tmp29 + tmp1
    tmp31 = tmp30 * tmp3
    tmp32 = tmp28 + tmp31
    tmp34 = tmp33 + tmp1
    tmp35 = tmp34 * tmp3
    tmp36 = tmp32 + tmp35
    tmp38 = tmp37 + tmp1
    tmp39 = tmp38 * tmp3
    tmp40 = tmp36 + tmp39
    tmp41 = tmp25 / tmp40
    tmp42 = tmp41 + tmp21
    tmp43 = tmp22 / tmp42
    tmp44 = tl_math.log(tmp43)
    tmp45 = tmp20 * tmp44
    tmp46 = tl.broadcast_to(tmp45, [XBLOCK, RBLOCK])
    tmp48 = tl.sum(tmp46, 1)[:, None]
    tmp49 = 16.0
    tmp50 = tmp48 / tmp49
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp50, 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)
        buf1 = buf0
        del buf0
        get_raw_stream(0)
        triton_poi_fused_add_div_index_put_lift_fresh_pow_sqrt_sum_0[grid(16)](
            buf1, arg1_1, 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(buf1, reinterpret_tensor(buf1, (4, 4), (1, 4), 0),
            out=buf2)
        buf4 = buf1
        del buf1
        buf5 = buf4
        del buf4
        triton_poi_fused_add_div_index_put_lift_fresh_pow_sqrt_sum_0[grid(16)](
            buf5, arg0_1, 16, XBLOCK=16, num_warps=1, num_stages=1)
        del arg0_1
        buf6 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.mm(buf5, reinterpret_tensor(buf5, (4, 4), (1, 4), 0),
            out=buf6)
        del buf5
        buf7 = empty_strided_cuda((), (), torch.float32)
        buf8 = buf7
        del buf7
        triton_per_fused_add_div_log_mean_mul_sum_1[grid(1)](buf8, buf2,
            buf6, 1, 16, XBLOCK=1, num_warps=2, num_stages=1)
        del buf2
        del buf6
    return buf8,


class PKTNew(nn.Module):
    """Probabilistic Knowledge Transfer for deep representation learning
    Code from author: https://github.com/passalis/probabilistic_kt"""

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

    @staticmethod
    def cosine_similarity_loss(output_net, target_net, eps=1e-07):
        output_net_norm = torch.sqrt(torch.sum(output_net ** 2, dim=1,
            keepdim=True))
        output_net = output_net / (output_net_norm + eps)
        output_net[output_net != output_net] = 0
        target_net_norm = torch.sqrt(torch.sum(target_net ** 2, dim=1,
            keepdim=True))
        target_net = target_net / (target_net_norm + eps)
        target_net[target_net != target_net] = 0
        model_similarity = torch.mm(output_net, output_net.transpose(0, 1))
        target_similarity = torch.mm(target_net, target_net.transpose(0, 1))
        model_similarity = (model_similarity + 1.0) / 2.0
        target_similarity = (target_similarity + 1.0) / 2.0
        model_similarity = model_similarity / torch.sum(model_similarity,
            dim=1, keepdim=True)
        target_similarity = target_similarity / torch.sum(target_similarity,
            dim=1, keepdim=True)
        loss = torch.mean(target_similarity * torch.log((target_similarity +
            eps) / (model_similarity + eps)))
        return 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: 0.0155