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, math as tl_math
from torch import nn
import torch.nn.functional as F
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_div_linalg_vector_norm_sub_0(in_ptr0, in_ptr1,
    out_ptr2, xnumel, rnumel, XBLOCK: tl.constexpr):
    xnumel = 4
    RBLOCK: tl.constexpr = 16
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:, None]
    xmask = xindex < xnumel
    rindex = tl.arange(0, RBLOCK)[None, :]
    tl.full([XBLOCK, RBLOCK], True, tl.int1)
    r1 = rindex
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (r1 + 64 * x0), xmask, other=0.0)
    tmp2 = tl.load(in_ptr0 + (16 + r1 + 64 * x0), xmask, other=0.0)
    tmp5 = tl.load(in_ptr0 + (32 + r1 + 64 * x0), xmask, other=0.0)
    tmp8 = tl.load(in_ptr0 + (48 + r1 + 64 * x0), xmask, other=0.0)
    tmp18 = tl.load(in_ptr1 + (r1 + 64 * x0), xmask, other=0.0)
    tmp20 = tl.load(in_ptr1 + (16 + r1 + 64 * x0), xmask, other=0.0)
    tmp23 = tl.load(in_ptr1 + (32 + r1 + 64 * x0), xmask, other=0.0)
    tmp26 = tl.load(in_ptr1 + (48 + r1 + 64 * x0), xmask, other=0.0)
    tmp1 = tmp0 * tmp0
    tmp3 = tmp2 * tmp2
    tmp4 = tmp1 + tmp3
    tmp6 = tmp5 * tmp5
    tmp7 = tmp4 + tmp6
    tmp9 = tmp8 * tmp8
    tmp10 = tmp7 + tmp9
    tmp11 = 4.0
    tmp12 = tmp10 / tmp11
    tmp13 = tmp12 * tmp12
    tmp14 = tl.broadcast_to(tmp13, [XBLOCK, RBLOCK])
    tmp16 = tl.where(xmask, tmp14, 0)
    tmp17 = tl.sum(tmp16, 1)[:, None]
    tmp19 = tmp18 * tmp18
    tmp21 = tmp20 * tmp20
    tmp22 = tmp19 + tmp21
    tmp24 = tmp23 * tmp23
    tmp25 = tmp22 + tmp24
    tmp27 = tmp26 * tmp26
    tmp28 = tmp25 + tmp27
    tmp29 = tmp28 / tmp11
    tmp30 = tmp29 * tmp29
    tmp31 = tl.broadcast_to(tmp30, [XBLOCK, RBLOCK])
    tmp33 = tl.where(xmask, tmp31, 0)
    tmp34 = tl.sum(tmp33, 1)[:, None]
    tmp35 = libdevice.sqrt(tmp17)
    tmp36 = 1e-12
    tmp37 = triton_helpers.maximum(tmp35, tmp36)
    tmp38 = tmp12 / tmp37
    tmp39 = libdevice.sqrt(tmp34)
    tmp40 = triton_helpers.maximum(tmp39, tmp36)
    tmp41 = tmp29 / tmp40
    tmp42 = tmp38 - tmp41
    tl.store(out_ptr2 + (r1 + 16 * x0), tmp42, xmask)


@triton.jit
def triton_per_fused_abs_mean_1(in_out_ptr0, in_ptr0, 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
    tmp0 = tl.load(in_ptr0 + r0, None)
    tmp1 = tl_math.abs(tmp0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK, RBLOCK])
    tmp4 = tl.sum(tmp2, 1)[:, None]
    tmp5 = 64.0
    tmp6 = tmp4 / tmp5
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([XBLOCK, 1], 0, tl.int32), tmp6, None)


def call(args):
    arg0_1, arg1_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))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf2 = empty_strided_cuda((4, 16), (16, 1), torch.float32)
        get_raw_stream(0)
        triton_per_fused_div_linalg_vector_norm_sub_0[grid(4)](arg0_1,
            arg1_1, buf2, 4, 16, XBLOCK=1, num_warps=2, num_stages=1)
        del arg0_1
        del arg1_1
        buf3 = empty_strided_cuda((), (), torch.float32)
        buf4 = buf3
        del buf3
        triton_per_fused_abs_mean_1[grid(1)](buf4, buf2, 1, 64, XBLOCK=1,
            num_warps=2, num_stages=1)
        del buf2
    return buf4,


class FactorTransferNew(nn.Module):
    """Paraphrasing Complex Network: Network Compression via Factor Transfer, NeurIPS 2018"""

    def __init__(self, p1=2, p2=1):
        super(FactorTransferNew, self).__init__()
        self.p1 = p1
        self.p2 = p2

    def factor_loss(self, f_s, f_t):
        s_H, t_H = f_s.shape[2], f_t.shape[2]
        if s_H > t_H:
            f_s = F.adaptive_avg_pool2d(f_s, (t_H, t_H))
        elif s_H < t_H:
            f_t = F.adaptive_avg_pool2d(f_t, (s_H, s_H))
        else:
            pass
        if self.p2 == 1:
            return (self.factor(f_s) - self.factor(f_t)).abs().mean()
        else:
            return (self.factor(f_s) - self.factor(f_t)).pow(self.p2).mean()

    def factor(self, f):
        return F.normalize(f.pow(self.p1).mean(1).view(f.size(0), -1))

    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: 0.058155