import torch
import torch.nn as nn
import torch.utils.data


class CustomizedLayer(nn.Module):

    def __init__(self, in_dim):
        super().__init__()
        self.in_dim = in_dim
        self.scale = nn.Parameter(torch.Tensor(self.in_dim))
        self.bias = nn.Parameter(torch.Tensor(self.in_dim))

    def forward(self, x):
        norm = x.pow(2).sum(dim=1, keepdim=True).sqrt()
        x = torch.div(x, norm)
        return x * self.scale + self.bias

    def __repr__(self):
        return 'CustomizedLayer(in_dim=%d)' % self.in_dim


def get_inputs():
    return [torch.rand([4, 4, 4, 4])]


def get_init_inputs():
    return [[], {'in_dim': 4}]

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.triton_helpers import libdevice
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda


@triton.jit
def triton_poi_fused_add_div_mul_pow_sqrt_sum_0(in_ptr0, in_ptr1, in_ptr2,
    out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x4 = xindex
    x3 = xindex // 64
    x5 = xindex % 16
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x4, xmask)
    tmp1 = tl.load(in_ptr0 + (x5 + 64 * x3), xmask, eviction_policy=
        'evict_last')
    tmp3 = tl.load(in_ptr0 + (16 + x5 + 64 * x3), xmask, eviction_policy=
        'evict_last')
    tmp6 = tl.load(in_ptr0 + (32 + x5 + 64 * x3), xmask, eviction_policy=
        'evict_last')
    tmp9 = tl.load(in_ptr0 + (48 + x5 + 64 * x3), xmask, eviction_policy=
        'evict_last')
    tmp14 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp16 = tl.load(in_ptr2 + x0, 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 = tmp0 / tmp12
    tmp15 = tmp13 * tmp14
    tmp17 = tmp15 + tmp16
    tl.store(out_ptr0 + x4, tmp17, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4,), (1,))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_div_mul_pow_sqrt_sum_0[grid(256)](primals_1,
            primals_2, primals_3, buf0, 256, XBLOCK=128, num_warps=4,
            num_stages=1)
        del primals_2
        del primals_3
    return buf0, primals_1


class CustomizedLayerNew(nn.Module):

    def __init__(self, in_dim):
        super().__init__()
        self.in_dim = in_dim
        self.scale = nn.Parameter(torch.Tensor(self.in_dim))
        self.bias = nn.Parameter(torch.Tensor(self.in_dim))

    def __repr__(self):
        return 'CustomizedLayer(in_dim=%d)' % self.in_dim

    def forward(self, input_0):
        primals_2 = self.scale
        primals_3 = self.bias
        primals_1 = input_0
        output = call([primals_1, primals_2, primals_3])
        return output[0]
