# 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_7/inductor_cache/qr/cqramfwzchcxgkcint325vw2hnfsaipzt53x3qxrfqbhcxae7edr.py
# Topologically Sorted Source Nodes: [sub, pow_1, norm_squared, truediv, add, numerator, numerator_1, sum_2, truediv_2], Original ATen: [aten.sub, aten.pow, aten.sum, aten.div, aten.add, aten.reciprocal, aten.mul]
# Source node to ATen node mapping:
#   add => add
#   norm_squared => sum_1
#   numerator => mul, reciprocal
#   numerator_1 => pow_2
#   pow_1 => pow_1
#   sub => sub
#   sum_2 => sum_2
#   truediv => div
#   truediv_2 => div_1
# Graph fragment:
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%unsqueeze, %primals_2), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub, 2), kwargs = {})
#   %sum_1 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_1, [2]), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sum_1, 1.0), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%div, 1.0), kwargs = {})
#   %reciprocal : [num_users=1] = call_function[target=torch.ops.aten.reciprocal.default](args = (%add,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%reciprocal, 1.0), kwargs = {})
#   %pow_2 : [num_users=2] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%mul, 1.0), kwargs = {})
#   %sum_2 : [num_users=1] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_2, [1], True), kwargs = {})
#   %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%pow_2, %sum_2), kwargs = {})
triton_poi_fused_add_div_mul_pow_reciprocal_sub_sum_0 = async_compile.triton('triton_poi_fused_add_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_add_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_add_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')
    tmp4 = tl.load(in_ptr0 + (16 + x0 + (64*x1)), xmask)
    tmp8 = tl.load(in_ptr0 + (32 + x0 + (64*x1)), xmask)
    tmp12 = tl.load(in_ptr0 + (48 + x0 + (64*x1)), xmask)
    tmp2 = tmp0 - tmp1
    tmp3 = tmp2 * tmp2
    tmp5 = tmp4 - tmp1
    tmp6 = tmp5 * tmp5
    tmp7 = tmp3 + tmp6
    tmp9 = tmp8 - tmp1
    tmp10 = tmp9 * tmp9
    tmp11 = tmp7 + tmp10
    tmp13 = tmp12 - tmp1
    tmp14 = tmp13 * tmp13
    tmp15 = tmp11 + tmp14
    tmp16 = 1.0
    tmp17 = tmp15 * tmp16
    tmp18 = tmp17 + tmp16
    tmp19 = tl.full([1], 1, tl.int32)
    tmp20 = tmp19 / tmp18
    tmp21 = tmp20 * tmp16
    tmp22 = tmp21 / tmp21
    tl.store(in_out_ptr0 + (x2), tmp22, 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, pow_1, norm_squared, truediv, add, numerator, numerator_1, sum_2, truediv_2], Original ATen: [aten.sub, aten.pow, aten.sum, aten.div, aten.add, aten.reciprocal, aten.mul]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_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
from torch import nn
from typing import Optional


class ClusterAssignment(nn.Module):

    def __init__(self, n_classes: 'int', enc_shape: 'int', alpha: 'float'=
        1.0, cluster_centers: 'Optional[torch.Tensor]'=None) ->None:
        """
        Module to handle the soft assignment, for a description see in 3.1.1. in Xie/Girshick/Farhadi,
        where the Student's t-distribution is used to measure similarity between feature vector and each
        cluster centroid.
        :param n_classes: number of clusters
        :param enc_shape: embedding dimension of feature vectors
        :param alpha: parameter representing the degrees of freedom in the t-distribution, default 1.0
        :param cluster_centers: clusters centers to initialise, if None then use Xavier uniform
        """
        super().__init__()
        self.enc_shape = enc_shape
        self.n_classes = n_classes
        self.alpha = alpha
        if cluster_centers is None:
            initial_cluster_centers = torch.zeros(self.n_classes, self.
                enc_shape, dtype=torch.float)
            nn.init.xavier_uniform_(initial_cluster_centers)
        else:
            initial_cluster_centers = cluster_centers
        self.cluster_centers = nn.Parameter(initial_cluster_centers)

    def forward(self, batch: 'torch.Tensor') ->torch.Tensor:
        """
        Compute the soft assignment for a batch of feature vectors, returning a batch of assignments
        for each cluster.
        :param batch: FloatTensor of [batch size, embedding dimension]
        :return: FloatTensor [batch size, number of clusters]
        """
        norm_squared = torch.sum((batch.unsqueeze(1) - self.cluster_centers
            ) ** 2, 2)
        numerator = 1.0 / (1.0 + norm_squared / self.alpha)
        power = float(self.alpha + 1) / 2
        numerator = numerator ** power
        return numerator / torch.sum(numerator, dim=1, keepdim=True)


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


def get_init_inputs():
    return [[], {'n_classes': 4, 'enc_shape': 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 import nn
from typing import Optional
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_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')
    tmp4 = tl.load(in_ptr0 + (16 + x0 + 64 * x1), xmask)
    tmp8 = tl.load(in_ptr0 + (32 + x0 + 64 * x1), xmask)
    tmp12 = tl.load(in_ptr0 + (48 + x0 + 64 * x1), xmask)
    tmp2 = tmp0 - tmp1
    tmp3 = tmp2 * tmp2
    tmp5 = tmp4 - tmp1
    tmp6 = tmp5 * tmp5
    tmp7 = tmp3 + tmp6
    tmp9 = tmp8 - tmp1
    tmp10 = tmp9 * tmp9
    tmp11 = tmp7 + tmp10
    tmp13 = tmp12 - tmp1
    tmp14 = tmp13 * tmp13
    tmp15 = tmp11 + tmp14
    tmp16 = 1.0
    tmp17 = tmp15 * tmp16
    tmp18 = tmp17 + tmp16
    tmp19 = tl.full([1], 1, tl.int32)
    tmp20 = tmp19 / tmp18
    tmp21 = tmp20 * tmp16
    tmp22 = tmp21 / tmp21
    tl.store(in_out_ptr0 + x2, tmp22, 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_add_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 ClusterAssignmentNew(nn.Module):

    def __init__(self, n_classes: 'int', enc_shape: 'int', alpha: 'float'=
        1.0, cluster_centers: 'Optional[torch.Tensor]'=None) ->None:
        """
        Module to handle the soft assignment, for a description see in 3.1.1. in Xie/Girshick/Farhadi,
        where the Student's t-distribution is used to measure similarity between feature vector and each
        cluster centroid.
        :param n_classes: number of clusters
        :param enc_shape: embedding dimension of feature vectors
        :param alpha: parameter representing the degrees of freedom in the t-distribution, default 1.0
        :param cluster_centers: clusters centers to initialise, if None then use Xavier uniform
        """
        super().__init__()
        self.enc_shape = enc_shape
        self.n_classes = n_classes
        self.alpha = alpha
        if cluster_centers is None:
            initial_cluster_centers = torch.zeros(self.n_classes, self.
                enc_shape, dtype=torch.float)
            nn.init.xavier_uniform_(initial_cluster_centers)
        else:
            initial_cluster_centers = cluster_centers
        self.cluster_centers = nn.Parameter(initial_cluster_centers)

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