# AOT ID: ['0_inference']
from ctypes import c_void_p, c_long, c_int
import torch
import math
import random
import os
import tempfile
from math import inf, nan
from torch._inductor.hooks import run_intermediate_hooks
from torch._inductor.utils import maybe_profile
from torch._inductor.codegen.memory_planning import _align as align
from torch import device, empty_strided
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.select_algorithm import extern_kernels
from torch._inductor.codegen.multi_kernel import MultiKernelCall
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid, split_scan_grid, grid_combo_kernels, start_graph, end_graph
from torch._C import _cuda_getCurrentRawStream as get_raw_stream

aten = torch.ops.aten
inductor_ops = torch.ops.inductor
_quantized = torch.ops._quantized
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cpu = torch._C._dynamo.guards._empty_strided_cpu
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
empty_strided_xpu = torch._C._dynamo.guards._empty_strided_xpu
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor
alloc_from_pool = torch.ops.inductor._alloc_from_pool
async_compile = AsyncCompile()


# kernel path: runs/run_shard_1/inductor_cache/al/cal34tu3pddv3yd2p5douq3hw7xx6ohnho5eciaqpsbz2la5tqlg.py
# Topologically Sorted Source Nodes: [mul, mul_1, v_fused_emb, pow_1, sum_1, sqrt, norm], Original ATen: [aten.mul, aten.add, aten.pow, aten.sum, aten.sqrt]
# Source node to ATen node mapping:
#   mul => mul
#   mul_1 => mul_1
#   norm => add_1
#   pow_1 => pow_1
#   sqrt => sqrt
#   sum_1 => sum_1
#   v_fused_emb => add
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg0_1, 0.75), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg1_1, 0.25), kwargs = {})
#   %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %mul_1), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%add, 2), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_1, [-1], True), kwargs = {})
#   %sqrt : [num_users=1] = call_function[target=torch.ops.aten.sqrt.default](args = (%sum_1,), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%sqrt, 1e-12), kwargs = {})
triton_poi_fused_add_mul_pow_sqrt_sum_0 = async_compile.triton('triton_poi_fused_add_mul_pow_sqrt_sum_0', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[64], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_mul_pow_sqrt_sum_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 8, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_mul_pow_sqrt_sum_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (4*x0), xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + (4*x0), xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr1 + (1 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr0 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp17 = tl.load(in_ptr1 + (2 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp22 = tl.load(in_ptr0 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp24 = tl.load(in_ptr1 + (3 + (4*x0)), xmask, eviction_policy='evict_last')
    tmp1 = 0.75
    tmp2 = tmp0 * tmp1
    tmp4 = 0.25
    tmp5 = tmp3 * tmp4
    tmp6 = tmp2 + tmp5
    tmp7 = tmp6 * tmp6
    tmp9 = tmp8 * tmp1
    tmp11 = tmp10 * tmp4
    tmp12 = tmp9 + tmp11
    tmp13 = tmp12 * tmp12
    tmp14 = tmp7 + tmp13
    tmp16 = tmp15 * tmp1
    tmp18 = tmp17 * tmp4
    tmp19 = tmp16 + tmp18
    tmp20 = tmp19 * tmp19
    tmp21 = tmp14 + tmp20
    tmp23 = tmp22 * tmp1
    tmp25 = tmp24 * tmp4
    tmp26 = tmp23 + tmp25
    tmp27 = tmp26 * tmp26
    tmp28 = tmp21 + tmp27
    tmp29 = libdevice.sqrt(tmp28)
    tmp30 = 1e-12
    tmp31 = tmp29 + tmp30
    tl.store(out_ptr0 + (x0), tmp31, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_1/inductor_cache/ri/cri2dlt42xosseu4ne3wlixshvdq2slgjk73bv3hhadousjrp4up.py
# Topologically Sorted Source Nodes: [mul, mul_1, v_fused_emb, X], Original ATen: [aten.mul, aten.add, aten.div]
# Source node to ATen node mapping:
#   X => div
#   mul => mul
#   mul_1 => mul_1
#   v_fused_emb => add
# Graph fragment:
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg0_1, 0.75), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg1_1, 0.25), kwargs = {})
#   %add : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul, %mul_1), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%add, %add_1), kwargs = {})
triton_poi_fused_add_div_mul_1 = async_compile.triton('triton_poi_fused_add_div_mul_1', '''
import triton
import triton.language as tl
from triton.compiler.compiler import AttrsDescriptor

from torch._inductor.runtime import triton_helpers, triton_heuristics
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
from torch._inductor.runtime.hints import AutotuneHint, ReductionHint, TileHint, instance_descriptor, DeviceProperties

@triton_heuristics.pointwise(
    size_hints=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: 'i32'}, 'device': DeviceProperties(type='cuda', index=0, cc=90, major=9, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=132), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3, 4), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_div_mul_1', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 3, 'num_reduction': 0, 'backend_hash': 'B098E03CDA7B8ADC90DAFFDF24A2956451D1B13F297756A5DCC209498AA53705', 'are_deterministic_algorithms_enabled': False, 'assert_indirect_indexing': True, 'autotune_local_cache': True, 'autotune_pointwise': True, 'autotune_remote_cache': None, 'force_disable_caches': False, 'dynamic_scale_rblock': True, 'max_autotune': False, 'max_autotune_pointwise': False, 'min_split_scan_rblock': 256, 'spill_threshold': 16, 'store_cubin': False},
    min_elem_per_thread=0
)
@triton.jit
def triton_poi_fused_add_div_mul_1(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
    x2 = xindex
    x1 = (xindex // 4)
    tmp0 = tl.load(in_ptr0 + (x2), xmask)
    tmp3 = tl.load(in_ptr1 + (x2), xmask)
    tmp7 = tl.load(in_ptr2 + (x1), xmask, eviction_policy='evict_last')
    tmp1 = 0.75
    tmp2 = tmp0 * tmp1
    tmp4 = 0.25
    tmp5 = tmp3 * tmp4
    tmp6 = tmp2 + tmp5
    tmp8 = tmp6 / tmp7
    tl.store(out_ptr0 + (x2), tmp8, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1, arg2_1, arg3_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))
    assert_size_stride(arg2_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg3_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        # Topologically Sorted Source Nodes: [mul, mul_1, v_fused_emb, pow_1, sum_1, sqrt, norm], Original ATen: [aten.mul, aten.add, aten.pow, aten.sum, aten.sqrt]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_mul_pow_sqrt_sum_0.run(arg0_1, arg1_1, buf0, 64, grid=grid(64), stream=stream0)
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mul, mul_1, v_fused_emb, X], Original ATen: [aten.mul, aten.add, aten.div]
        triton_poi_fused_add_div_mul_1.run(arg0_1, arg1_1, buf0, buf1, 256, grid=grid(256), stream=stream0)
        del arg0_1
        del arg1_1
        buf2 = buf0; del buf0  # reuse
        # Topologically Sorted Source Nodes: [mul_2, mul_3, t_fused_emb, pow_2, sum_2, sqrt_1, norm_1], Original ATen: [aten.mul, aten.add, aten.pow, aten.sum, aten.sqrt]
        triton_poi_fused_add_mul_pow_sqrt_sum_0.run(arg2_1, arg3_1, buf2, 64, grid=grid(64), stream=stream0)
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mul_2, mul_3, t_fused_emb, X_1], Original ATen: [aten.mul, aten.add, aten.div]
        triton_poi_fused_add_div_mul_1.run(arg2_1, arg3_1, buf2, buf3, 256, grid=grid(256), stream=stream0)
        del arg2_1
        del arg3_1
        del buf2
    return (buf1, buf3, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    arg0_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg2_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    arg3_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1, arg1_1, arg2_1, arg3_1])
    return print_performance(fn, times=times, repeat=repeat)


if __name__ == "__main__":
    from torch._inductor.wrapper_benchmark import compiled_module_main
    compiled_module_main('None', benchmark_compiled_module)

import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init


def l2norm(X, dim=-1, eps=1e-12):
    """L2-normalize columns of X
    """
    norm = torch.pow(X, 2).sum(dim=dim, keepdim=True).sqrt() + eps
    X = torch.div(X, norm)
    return X


class Multi_feature_fusing(nn.Module):
    """
    Emb the features from both modalities to the joint attribute label space.
    """

    def __init__(self, embed_dim, fuse_type='weight_sum'):
        """
        param image_dim: dim of visual feature
        param embed_dim: dim of embedding space
        """
        super(Multi_feature_fusing, self).__init__()
        self.fuse_type = fuse_type
        self.embed_dim = embed_dim
        if fuse_type == 'concat':
            input_dim = int(2 * embed_dim)
            self.joint_emb_v = nn.Linear(input_dim, embed_dim)
            self.joint_emb_t = nn.Linear(input_dim, embed_dim)
            self.init_weights_concat()
        if fuse_type == 'adap_sum':
            self.joint_emb_v = nn.Linear(embed_dim, 1)
            self.joint_emb_t = nn.Linear(embed_dim, 1)
            self.init_weights_adap_sum()

    def init_weights_concat(self):
        """Xavier initialization"""
        r = np.sqrt(6.0) / np.sqrt(self.embed_dim + 2 * self.embed_dim)
        self.joint_emb_v.weight.data.uniform_(-r, r)
        self.joint_emb_v.bias.data.fill_(0)
        self.joint_emb_t.weight.data.uniform_(-r, r)
        self.joint_emb_t.bias.data.fill_(0)

    def init_weights_adap_sum(self):
        """Xavier initialization"""
        r = np.sqrt(6.0) / np.sqrt(self.embed_dim + 1)
        self.joint_emb_v.weight.data.uniform_(-r, r)
        self.joint_emb_v.bias.data.fill_(0)
        self.joint_emb_t.weight.data.uniform_(-r, r)
        self.joint_emb_t.bias.data.fill_(0)

    def forward(self, v_emb_instance, t_emb_instance, v_emb_concept,
        t_emb_concept):
        """
        Forward propagation.
        :param v_emb_instance, t_emb_instance: instance-level visual or textual features, shape: (batch_size, emb_dim)
        :param v_emb_concept, t_emb_concept: consensus-level concept features, shape: (batch_size, emb_dim)
        :return: joint embbeding features for both modalities
        """
        if self.fuse_type == 'multiple':
            v_fused_emb = v_emb_instance.mul(v_emb_concept)
            v_fused_emb = l2norm(v_fused_emb)
            t_fused_emb = t_emb_instance.mul(t_emb_concept)
            t_fused_emb = l2norm(t_fused_emb)
        elif self.fuse_type == 'concat':
            v_fused_emb = torch.cat([v_emb_instance, v_emb_concept], dim=1)
            v_fused_emb = self.joint_emb_instance_v(v_fused_emb)
            v_fused_emb = l2norm(v_fused_emb)
            t_fused_emb = torch.cat([t_emb_instance, t_emb_concept], dim=1)
            t_fused_emb = self.joint_emb_instance_v(t_fused_emb)
            t_fused_emb = l2norm(t_fused_emb)
        elif self.fuse_type == 'adap_sum':
            v_mean = (v_emb_instance + v_emb_concept) / 2
            v_emb_instance_mat = self.joint_emb_instance_v(v_mean)
            alpha_v = F.sigmoid(v_emb_instance_mat)
            v_fused_emb = alpha_v * v_emb_instance + (1 - alpha_v
                ) * v_emb_concept
            v_fused_emb = l2norm(v_fused_emb)
            t_mean = (t_emb_instance + t_emb_concept) / 2
            t_emb_instance_mat = self.joint_emb_instance_t(t_mean)
            alpha_t = F.sigmoid(t_emb_instance_mat)
            t_fused_emb = alpha_t * t_emb_instance + (1 - alpha_t
                ) * t_emb_concept
            t_fused_emb = l2norm(t_fused_emb)
        elif self.fuse_type == 'weight_sum':
            alpha = 0.75
            v_fused_emb = alpha * v_emb_instance + (1 - alpha) * v_emb_concept
            v_fused_emb = l2norm(v_fused_emb)
            t_fused_emb = alpha * t_emb_instance + (1 - alpha) * t_emb_concept
            t_fused_emb = l2norm(t_fused_emb)
        return v_fused_emb, t_fused_emb


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


def get_init_inputs():
    return [[], {'embed_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 numpy as np
import torch.nn as nn
import torch.nn.init
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_mul_pow_sqrt_sum_0(in_ptr0, in_ptr1, out_ptr0,
    xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp3 = tl.load(in_ptr1 + 4 * x0, xmask, eviction_policy='evict_last')
    tmp8 = tl.load(in_ptr0 + (1 + 4 * x0), xmask, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr1 + (1 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp15 = tl.load(in_ptr0 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp17 = tl.load(in_ptr1 + (2 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp22 = tl.load(in_ptr0 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp24 = tl.load(in_ptr1 + (3 + 4 * x0), xmask, eviction_policy='evict_last'
        )
    tmp1 = 0.75
    tmp2 = tmp0 * tmp1
    tmp4 = 0.25
    tmp5 = tmp3 * tmp4
    tmp6 = tmp2 + tmp5
    tmp7 = tmp6 * tmp6
    tmp9 = tmp8 * tmp1
    tmp11 = tmp10 * tmp4
    tmp12 = tmp9 + tmp11
    tmp13 = tmp12 * tmp12
    tmp14 = tmp7 + tmp13
    tmp16 = tmp15 * tmp1
    tmp18 = tmp17 * tmp4
    tmp19 = tmp16 + tmp18
    tmp20 = tmp19 * tmp19
    tmp21 = tmp14 + tmp20
    tmp23 = tmp22 * tmp1
    tmp25 = tmp24 * tmp4
    tmp26 = tmp23 + tmp25
    tmp27 = tmp26 * tmp26
    tmp28 = tmp21 + tmp27
    tmp29 = libdevice.sqrt(tmp28)
    tmp30 = 1e-12
    tmp31 = tmp29 + tmp30
    tl.store(out_ptr0 + x0, tmp31, xmask)


@triton.jit
def triton_poi_fused_add_div_mul_1(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
    x2 = xindex
    x1 = xindex // 4
    tmp0 = tl.load(in_ptr0 + x2, xmask)
    tmp3 = tl.load(in_ptr1 + x2, xmask)
    tmp7 = tl.load(in_ptr2 + x1, xmask, eviction_policy='evict_last')
    tmp1 = 0.75
    tmp2 = tmp0 * tmp1
    tmp4 = 0.25
    tmp5 = tmp3 * tmp4
    tmp6 = tmp2 + tmp5
    tmp8 = tmp6 / tmp7
    tl.store(out_ptr0 + x2, tmp8, xmask)


def call(args):
    arg0_1, arg1_1, arg2_1, arg3_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))
    assert_size_stride(arg2_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg3_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 1), (16, 4, 1, 64), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_mul_pow_sqrt_sum_0[grid(64)](arg0_1, arg1_1,
            buf0, 64, XBLOCK=64, num_warps=1, num_stages=1)
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_add_div_mul_1[grid(256)](arg0_1, arg1_1, buf0,
            buf1, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del arg0_1
        del arg1_1
        buf2 = buf0
        del buf0
        triton_poi_fused_add_mul_pow_sqrt_sum_0[grid(64)](arg2_1, arg3_1,
            buf2, 64, XBLOCK=64, num_warps=1, num_stages=1)
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_add_div_mul_1[grid(256)](arg2_1, arg3_1, buf2,
            buf3, 256, XBLOCK=128, num_warps=4, num_stages=1)
        del arg2_1
        del arg3_1
        del buf2
    return buf1, buf3


def l2norm(X, dim=-1, eps=1e-12):
    """L2-normalize columns of X
    """
    norm = torch.pow(X, 2).sum(dim=dim, keepdim=True).sqrt() + eps
    X = torch.div(X, norm)
    return X


class Multi_feature_fusingNew(nn.Module):
    """
    Emb the features from both modalities to the joint attribute label space.
    """

    def __init__(self, embed_dim, fuse_type='weight_sum'):
        """
        param image_dim: dim of visual feature
        param embed_dim: dim of embedding space
        """
        super(Multi_feature_fusingNew, self).__init__()
        self.fuse_type = fuse_type
        self.embed_dim = embed_dim
        if fuse_type == 'concat':
            input_dim = int(2 * embed_dim)
            self.joint_emb_v = nn.Linear(input_dim, embed_dim)
            self.joint_emb_t = nn.Linear(input_dim, embed_dim)
            self.init_weights_concat()
        if fuse_type == 'adap_sum':
            self.joint_emb_v = nn.Linear(embed_dim, 1)
            self.joint_emb_t = nn.Linear(embed_dim, 1)
            self.init_weights_adap_sum()

    def init_weights_concat(self):
        """Xavier initialization"""
        r = np.sqrt(6.0) / np.sqrt(self.embed_dim + 2 * self.embed_dim)
        self.joint_emb_v.weight.data.uniform_(-r, r)
        self.joint_emb_v.bias.data.fill_(0)
        self.joint_emb_t.weight.data.uniform_(-r, r)
        self.joint_emb_t.bias.data.fill_(0)

    def init_weights_adap_sum(self):
        """Xavier initialization"""
        r = np.sqrt(6.0) / np.sqrt(self.embed_dim + 1)
        self.joint_emb_v.weight.data.uniform_(-r, r)
        self.joint_emb_v.bias.data.fill_(0)
        self.joint_emb_t.weight.data.uniform_(-r, r)
        self.joint_emb_t.bias.data.fill_(0)

    def forward(self, input_0, input_1, input_2, input_3):
        arg0_1 = input_0
        arg1_1 = input_1
        arg2_1 = input_2
        arg3_1 = input_3
        output = call([arg0_1, arg1_1, arg2_1, arg3_1])
        return output[0], output[1]
