# AOT ID: ['0_forward']
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_6/inductor_cache/3f/c3fgygdgagldpgudukq7p5yd3kohfjvgvg5m6am26npejtme7sj6.py
# Topologically Sorted Source Nodes: [sub, abs_1, pow_1, sum_1, mu, sum_2, mu_1], Original ATen: [aten.sub, aten.abs, aten.pow, aten.sum, aten.reciprocal, aten.mul, aten.div]
# Source node to ATen node mapping:
#   abs_1 => abs_1
#   mu => mul, reciprocal
#   mu_1 => div
#   pow_1 => pow_1
#   sub => sub
#   sum_1 => sum_1
#   sum_2 => sum_2
# Graph fragment:
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%unsqueeze, %primals_2), kwargs = {})
#   %abs_1 : [num_users=1] = call_function[target=torch.ops.aten.abs.default](args = (%sub,), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%abs_1, 0.6666666666666666), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_1, [2]), kwargs = {})
#   %reciprocal : [num_users=1] = call_function[target=torch.ops.aten.reciprocal.default](args = (%sum_1,), kwargs = {})
#   %mul : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%reciprocal, 1.0), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%mul, [1], True), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%mul, %sum_2), kwargs = {})
triton_poi_fused_abs_div_mul_pow_reciprocal_sub_sum_0 = async_compile.triton('triton_poi_fused_abs_div_mul_pow_reciprocal_sub_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=80, major=8, regs_per_multiprocessor=65536, max_threads_per_multi_processor=2048, multi_processor_count=108), 'constants': {}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 3), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_abs_div_mul_pow_reciprocal_sub_sum_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': False, 'num_load': 5, 'num_reduction': 0, 'backend_hash': 'A9C866B4A14FD3277824029365D703C2427B2E685E54EC9B3EF4ADC8D1EEAC1D', '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_abs_div_mul_pow_reciprocal_sub_sum_0(in_out_ptr0, in_ptr0, in_ptr1, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x1 = (xindex // 16)
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + (64*x1)), xmask)
    tmp1 = tl.load(in_ptr1 + (x0), xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (16 + x0 + (64*x1)), xmask)
    tmp11 = tl.load(in_ptr0 + (32 + x0 + (64*x1)), xmask)
    tmp16 = tl.load(in_ptr0 + (48 + x0 + (64*x1)), xmask)
    tmp2 = tmp0 - tmp1
    tmp3 = tl_math.abs(tmp2)
    tmp4 = 0.6666666666666666
    tmp5 = libdevice.pow(tmp3, tmp4)
    tmp7 = tmp6 - tmp1
    tmp8 = tl_math.abs(tmp7)
    tmp9 = libdevice.pow(tmp8, tmp4)
    tmp10 = tmp5 + tmp9
    tmp12 = tmp11 - tmp1
    tmp13 = tl_math.abs(tmp12)
    tmp14 = libdevice.pow(tmp13, tmp4)
    tmp15 = tmp10 + tmp14
    tmp17 = tmp16 - tmp1
    tmp18 = tl_math.abs(tmp17)
    tmp19 = libdevice.pow(tmp18, tmp4)
    tmp20 = tmp15 + tmp19
    tmp21 = tl.full([1], 1, tl.int32)
    tmp22 = tmp21 / tmp20
    tmp23 = 1.0
    tmp24 = tmp22 * tmp23
    tmp25 = tmp24 / tmp24
    tl.store(in_out_ptr0 + (x2), tmp25, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1, 4, 4), (16, 64, 4, 1), torch.float32)
        buf1 = reinterpret_tensor(buf0, (4, 1, 4, 4), (16, 16, 4, 1), 0); del buf0  # reuse
        # Topologically Sorted Source Nodes: [sub, abs_1, pow_1, sum_1, mu, sum_2, mu_1], Original ATen: [aten.sub, aten.abs, aten.pow, aten.sum, aten.reciprocal, aten.mul, aten.div]
        stream0 = get_raw_stream(0)
        triton_poi_fused_abs_div_mul_pow_reciprocal_sub_sum_0.run(buf1, primals_1, primals_2, 64, grid=grid(64), stream=stream0)
    return (buf1, primals_1, primals_2, )


def benchmark_compiled_module(times=10, repeat=10):
    from torch._dynamo.testing import rand_strided
    from torch._inductor.utils import print_performance
    primals_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4), (4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2])
    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 torch.nn as nn
import torch.nn.parallel
import torch.utils.data
import torch.utils.data.distributed
from torch.nn.parameter import Parameter


class ClusterLayer(nn.Module):

    def __init__(self, n_cluster, expansion, cluster_m):
        super(ClusterLayer, self).__init__()
        self.center = Parameter(torch.Tensor(n_cluster, expansion))
        self.m = cluster_m

    def forward(self, x):
        mu = 1.0 / torch.sum(torch.abs(x.unsqueeze(1) - self.center) ** (
            2.0 / (self.m - 1.0)), dim=2)
        mu = mu / torch.sum(mu, dim=1, keepdim=True)
        return mu


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


def get_init_inputs():
    return [[], {'n_cluster': 4, 'expansion': 4, 'cluster_m': 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, math as tl_math
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
import torch.utils.data.distributed
from torch.nn.parameter import Parameter
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_abs_div_mul_pow_reciprocal_sub_sum_0(in_out_ptr0,
    in_ptr0, in_ptr1, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 16
    x1 = xindex // 16
    x2 = xindex
    tmp0 = tl.load(in_ptr0 + (x0 + 64 * x1), xmask)
    tmp1 = tl.load(in_ptr1 + x0, xmask, eviction_policy='evict_last')
    tmp6 = tl.load(in_ptr0 + (16 + x0 + 64 * x1), xmask)
    tmp11 = tl.load(in_ptr0 + (32 + x0 + 64 * x1), xmask)
    tmp16 = tl.load(in_ptr0 + (48 + x0 + 64 * x1), xmask)
    tmp2 = tmp0 - tmp1
    tmp3 = tl_math.abs(tmp2)
    tmp4 = 0.6666666666666666
    tmp5 = libdevice.pow(tmp3, tmp4)
    tmp7 = tmp6 - tmp1
    tmp8 = tl_math.abs(tmp7)
    tmp9 = libdevice.pow(tmp8, tmp4)
    tmp10 = tmp5 + tmp9
    tmp12 = tmp11 - tmp1
    tmp13 = tl_math.abs(tmp12)
    tmp14 = libdevice.pow(tmp13, tmp4)
    tmp15 = tmp10 + tmp14
    tmp17 = tmp16 - tmp1
    tmp18 = tl_math.abs(tmp17)
    tmp19 = libdevice.pow(tmp18, tmp4)
    tmp20 = tmp15 + tmp19
    tmp21 = tl.full([1], 1, tl.int32)
    tmp22 = tmp21 / tmp20
    tmp23 = 1.0
    tmp24 = tmp22 * tmp23
    tmp25 = tmp24 / tmp24
    tl.store(in_out_ptr0 + x2, tmp25, xmask)


def call(args):
    primals_1, primals_2 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_2, (4, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 1, 4, 4), (16, 64, 4, 1), torch.float32)
        buf1 = reinterpret_tensor(buf0, (4, 1, 4, 4), (16, 16, 4, 1), 0)
        del buf0
        get_raw_stream(0)
        triton_poi_fused_abs_div_mul_pow_reciprocal_sub_sum_0[grid(64)](buf1,
            primals_1, primals_2, 64, XBLOCK=64, num_warps=1, num_stages=1)
    return buf1, primals_1, primals_2


class ClusterLayerNew(nn.Module):

    def __init__(self, n_cluster, expansion, cluster_m):
        super(ClusterLayerNew, self).__init__()
        self.center = Parameter(torch.Tensor(n_cluster, expansion))
        self.m = cluster_m

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