# 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_6/inductor_cache/xe/cxedqk5kxfk4utnfrixbnyqhrlbgj4i6sv6keatvftozhw66gz74.py
# Topologically Sorted Source Nodes: [sub, pow_1, l2_distance_square, neg, mean, mul, mul_1, truediv, exp], Original ATen: [aten.sub, aten.pow, aten.sum, aten.neg, aten.mean, aten.mul, aten.div, aten.exp]
# Source node to ATen node mapping:
#   exp => exp
#   l2_distance_square => sum_1
#   mean => mean
#   mul => mul
#   mul_1 => mul_1
#   neg => neg
#   pow_1 => pow_1
#   sub => sub
#   truediv => div
# Graph fragment:
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%unsqueeze, %unsqueeze_1), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub, 2), kwargs = {})
#   %sum_1 : [num_users=2] = call_function[target=torch.ops.aten.sum.dim_IntList](args = (%pow_1, [2]), kwargs = {})
#   %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%sum_1,), kwargs = {})
#   %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.default](args = (%sum_1,), kwargs = {})
#   %mul : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mean, 1.0), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul, 2), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%neg, %mul_1), kwargs = {})
#   %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%div,), kwargs = {})
triton_per_fused_div_exp_mean_mul_neg_pow_sub_sum_0 = async_compile.triton('triton_per_fused_div_exp_mean_mul_neg_pow_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.persistent_reduction(
    size_hints=[1, 256],
    reduction_hint=ReductionHint.INNER,
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: 'i32', 4: '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': {3: 1}, 'configs': [AttrsDescriptor(divisible_by_16=(0, 1, 2, 4), equal_to_1=(3,))]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_per_fused_div_exp_mean_mul_neg_pow_sub_sum_0', 'mutated_arg_names': ['in_out_ptr0'], 'no_x_dim': True, 'num_load': 8, 'num_reduction': 1, '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}
)
@triton.jit
def triton_per_fused_div_exp_mean_mul_neg_pow_sub_sum_0(in_out_ptr0, in_ptr0, out_ptr1, xnumel, rnumel):
    xnumel = 1
    XBLOCK: tl.constexpr = 1
    rnumel = 256
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = tl.full([1], xoffset, tl.int32)
    xmask = tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    roffset = 0
    rmask = tl.full([RBLOCK], True, tl.int1)
    r0 = rindex % 16
    r1 = (rindex // 16) % 4
    r2 = (rindex // 64)
    r3 = rindex
    tmp0 = tl.load(in_ptr0 + (r0 + (64*r1)), None, eviction_policy='evict_last')
    tmp1 = tl.load(in_ptr0 + (r0 + (64*r2)), None, eviction_policy='evict_last')
    tmp4 = tl.load(in_ptr0 + (16 + r0 + (64*r1)), None, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr0 + (16 + r0 + (64*r2)), None, eviction_policy='evict_last')
    tmp9 = tl.load(in_ptr0 + (32 + r0 + (64*r1)), None, eviction_policy='evict_last')
    tmp10 = tl.load(in_ptr0 + (32 + r0 + (64*r2)), None, eviction_policy='evict_last')
    tmp14 = tl.load(in_ptr0 + (48 + r0 + (64*r1)), None, eviction_policy='evict_last')
    tmp15 = tl.load(in_ptr0 + (48 + r0 + (64*r2)), None, eviction_policy='evict_last')
    tmp2 = tmp0 - tmp1
    tmp3 = tmp2 * tmp2
    tmp6 = tmp4 - tmp5
    tmp7 = tmp6 * tmp6
    tmp8 = tmp3 + tmp7
    tmp11 = tmp9 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tmp8 + tmp12
    tmp16 = tmp14 - tmp15
    tmp17 = tmp16 * tmp16
    tmp18 = tmp13 + tmp17
    tmp19 = tl.broadcast_to(tmp18, [RBLOCK])
    tmp21 = triton_helpers.promote_to_tensor(tl.sum(tmp19, 0))
    tmp22 = 256.0
    tmp23 = tmp21 / tmp22
    tmp24 = 1.0
    tmp25 = tmp23 * tmp24
    tmp26 = -tmp18
    tmp27 = 2.0
    tmp28 = tmp25 * tmp27
    tmp29 = tmp26 / tmp28
    tmp30 = tl_math.exp(tmp29)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + (tl.full([1], 0, tl.int32)), tmp25, None)
    tl.store(out_ptr1 + (tl.broadcast_to(r3, [RBLOCK])), tmp30, None)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf2 = buf1; del buf1  # reuse
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [sub, pow_1, l2_distance_square, neg, mean, mul, mul_1, truediv, exp], Original ATen: [aten.sub, aten.pow, aten.sum, aten.neg, aten.mean, aten.mul, aten.div, aten.exp]
        stream0 = get_raw_stream(0)
        triton_per_fused_div_exp_mean_mul_neg_pow_sub_sum_0.run(buf2, arg0_1, buf3, 1, 256, grid=grid(1), stream=stream0)
        del arg0_1
    return (buf3, buf2, )


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)
    fn = lambda: call([arg0_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
from typing import Optional
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed


class GaussianKernel(nn.Module):
    """Gaussian Kernel Matrix

    Gaussian Kernel k is defined by

    .. math::
        k(x_1, x_2) = \\exp \\left( - \\dfrac{\\| x_1 - x_2 \\|^2}{2\\sigma^2} \\right)

    where :math:`x_1, x_2 \\in R^d` are 1-d tensors.

    Gaussian Kernel Matrix K is defined on input group :math:`X=(x_1, x_2, ..., x_m),`

    .. math::
        K(X)_{i,j} = k(x_i, x_j)

    Also by default, during training this layer keeps running estimates of the
    mean of L2 distances, which are then used to set hyperparameter  :math:`\\sigma`.
    Mathematically, the estimation is :math:`\\sigma^2 = \\dfrac{\\alpha}{n^2}\\sum_{i,j} \\| x_i - x_j \\|^2`.
    If :attr:`track_running_stats` is set to ``False``, this layer then does not
    keep running estimates, and use a fixed :math:`\\sigma` instead.

    Parameters:
        - sigma (float, optional): bandwidth :math:`\\sigma`. Default: None
        - track_running_stats (bool, optional): If ``True``, this module tracks the running mean of :math:`\\sigma^2`.
          Otherwise, it won't track such statistics and always uses fix :math:`\\sigma^2`. Default: ``True``
        - alpha (float, optional): :math:`\\alpha` which decides the magnitude of :math:`\\sigma^2` when track_running_stats is set to ``True``

    Inputs:
        - X (tensor): input group :math:`X`

    Shape:
        - Inputs: :math:`(minibatch, F)` where F means the dimension of input features.
        - Outputs: :math:`(minibatch, minibatch)`
    """

    def __init__(self, sigma: 'Optional[float]'=None, track_running_stats:
        'Optional[bool]'=True, alpha: 'Optional[float]'=1.0):
        super(GaussianKernel, self).__init__()
        assert track_running_stats or sigma is not None
        self.sigma_square = torch.tensor(sigma * sigma
            ) if sigma is not None else None
        self.track_running_stats = track_running_stats
        self.alpha = alpha

    def forward(self, X: 'torch.Tensor') ->torch.Tensor:
        l2_distance_square = ((X.unsqueeze(0) - X.unsqueeze(1)) ** 2).sum(2)
        if self.track_running_stats:
            self.sigma_square = self.alpha * torch.mean(l2_distance_square.
                detach())
        return torch.exp(-l2_distance_square / (2 * self.sigma_square))


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


def get_init_inputs():
    return [[], {}]

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 math as tl_math
from typing import Optional
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
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_exp_mean_mul_neg_pow_sub_sum_0(in_out_ptr0,
    in_ptr0, out_ptr1, xnumel, rnumel):
    XBLOCK: tl.constexpr = 1
    RBLOCK: tl.constexpr = 256
    xoffset = tl.program_id(0) * XBLOCK
    tl.full([1], xoffset, tl.int32)
    tl.full([RBLOCK], True, tl.int1)
    rindex = tl.arange(0, RBLOCK)[:]
    tl.full([RBLOCK], True, tl.int1)
    r0 = rindex % 16
    r1 = rindex // 16 % 4
    r2 = rindex // 64
    r3 = rindex
    tmp0 = tl.load(in_ptr0 + (r0 + 64 * r1), None, eviction_policy='evict_last'
        )
    tmp1 = tl.load(in_ptr0 + (r0 + 64 * r2), None, eviction_policy='evict_last'
        )
    tmp4 = tl.load(in_ptr0 + (16 + r0 + 64 * r1), None, eviction_policy=
        'evict_last')
    tmp5 = tl.load(in_ptr0 + (16 + r0 + 64 * r2), None, eviction_policy=
        'evict_last')
    tmp9 = tl.load(in_ptr0 + (32 + r0 + 64 * r1), None, eviction_policy=
        'evict_last')
    tmp10 = tl.load(in_ptr0 + (32 + r0 + 64 * r2), None, eviction_policy=
        'evict_last')
    tmp14 = tl.load(in_ptr0 + (48 + r0 + 64 * r1), None, eviction_policy=
        'evict_last')
    tmp15 = tl.load(in_ptr0 + (48 + r0 + 64 * r2), None, eviction_policy=
        'evict_last')
    tmp2 = tmp0 - tmp1
    tmp3 = tmp2 * tmp2
    tmp6 = tmp4 - tmp5
    tmp7 = tmp6 * tmp6
    tmp8 = tmp3 + tmp7
    tmp11 = tmp9 - tmp10
    tmp12 = tmp11 * tmp11
    tmp13 = tmp8 + tmp12
    tmp16 = tmp14 - tmp15
    tmp17 = tmp16 * tmp16
    tmp18 = tmp13 + tmp17
    tmp19 = tl.broadcast_to(tmp18, [RBLOCK])
    tmp21 = triton_helpers.promote_to_tensor(tl.sum(tmp19, 0))
    tmp22 = 256.0
    tmp23 = tmp21 / tmp22
    tmp24 = 1.0
    tmp25 = tmp23 * tmp24
    tmp26 = -tmp18
    tmp27 = 2.0
    tmp28 = tmp25 * tmp27
    tmp29 = tmp26 / tmp28
    tmp30 = tl_math.exp(tmp29)
    tl.debug_barrier()
    tl.store(in_out_ptr0 + tl.full([1], 0, tl.int32), tmp25, None)
    tl.store(out_ptr1 + tl.broadcast_to(r3, [RBLOCK]), tmp30, None)


def call(args):
    arg0_1, = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf1 = empty_strided_cuda((), (), torch.float32)
        buf2 = buf1
        del buf1
        buf3 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_per_fused_div_exp_mean_mul_neg_pow_sub_sum_0[grid(1)](buf2,
            arg0_1, buf3, 1, 256, num_warps=2, num_stages=1)
        del arg0_1
    return buf3, buf2


class GaussianKernelNew(nn.Module):
    """Gaussian Kernel Matrix

    Gaussian Kernel k is defined by

    .. math::
        k(x_1, x_2) = \\exp \\left( - \\dfrac{\\| x_1 - x_2 \\|^2}{2\\sigma^2} \\right)

    where :math:`x_1, x_2 \\in R^d` are 1-d tensors.

    Gaussian Kernel Matrix K is defined on input group :math:`X=(x_1, x_2, ..., x_m),`

    .. math::
        K(X)_{i,j} = k(x_i, x_j)

    Also by default, during training this layer keeps running estimates of the
    mean of L2 distances, which are then used to set hyperparameter  :math:`\\sigma`.
    Mathematically, the estimation is :math:`\\sigma^2 = \\dfrac{\\alpha}{n^2}\\sum_{i,j} \\| x_i - x_j \\|^2`.
    If :attr:`track_running_stats` is set to ``False``, this layer then does not
    keep running estimates, and use a fixed :math:`\\sigma` instead.

    Parameters:
        - sigma (float, optional): bandwidth :math:`\\sigma`. Default: None
        - track_running_stats (bool, optional): If ``True``, this module tracks the running mean of :math:`\\sigma^2`.
          Otherwise, it won't track such statistics and always uses fix :math:`\\sigma^2`. Default: ``True``
        - alpha (float, optional): :math:`\\alpha` which decides the magnitude of :math:`\\sigma^2` when track_running_stats is set to ``True``

    Inputs:
        - X (tensor): input group :math:`X`

    Shape:
        - Inputs: :math:`(minibatch, F)` where F means the dimension of input features.
        - Outputs: :math:`(minibatch, minibatch)`
    """

    def __init__(self, sigma: 'Optional[float]'=None, track_running_stats:
        'Optional[bool]'=True, alpha: 'Optional[float]'=1.0):
        super(GaussianKernelNew, self).__init__()
        assert track_running_stats or sigma is not None
        self.sigma_square = torch.tensor(sigma * sigma
            ) if sigma is not None else None
        self.track_running_stats = track_running_stats
        self.alpha = alpha

    def forward(self, input_0):
        arg0_1 = input_0
        output = call([arg0_1])
        return output[0]
