# 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/eb/cebvdz62qer4oz5rwrmgolin6f7xce7tjvzcl2p4jvitglhropzk.py
# Topologically Sorted Source Nodes: [sub, truediv, dist, pow_2, add, reciprocal], Original ATen: [aten.sub, aten.div, aten.pow, aten.add, aten.reciprocal]
# Source node to ATen node mapping:
#   add => add
#   dist => pow_1
#   pow_2 => pow_2
#   reciprocal => reciprocal
#   sub => sub
#   truediv => div
# Graph fragment:
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%primals_2, %primals_1), kwargs = {})
#   %div : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%sub, %primals_3), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%div, 2), kwargs = {})
#   %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Tensor](args = (%pow_1, %primals_4), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%pow_2, 1), kwargs = {})
#   %reciprocal : [num_users=1] = call_function[target=torch.ops.aten.reciprocal.default](args = (%add,), kwargs = {})
triton_poi_fused_add_div_pow_reciprocal_sub_0 = async_compile.triton('triton_poi_fused_add_div_pow_reciprocal_sub_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=[256], 
    filename=__file__,
    triton_meta={'signature': {0: '*fp32', 1: '*fp32', 2: '*fp32', 3: '*fp32', 4: '*fp32', 5: '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, 4, 5), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_div_pow_reciprocal_sub_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 4, '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_pow_reciprocal_sub_0(in_ptr0, in_ptr1, in_ptr2, in_ptr3, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + (x0), xmask)
    tmp1 = tl.load(in_ptr1 + (0))
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp4 = tl.load(in_ptr2 + (0))
    tmp5 = tl.broadcast_to(tmp4, [XBLOCK])
    tmp8 = tl.load(in_ptr3 + (0))
    tmp9 = tl.broadcast_to(tmp8, [XBLOCK])
    tmp3 = tmp0 - tmp2
    tmp6 = tmp3 / tmp5
    tmp7 = tmp6 * tmp6
    tmp10 = libdevice.pow(tmp7, tmp9)
    tmp11 = 1.0
    tmp12 = tmp10 + tmp11
    tmp13 = tl.full([1], 1, tl.int32)
    tmp14 = tmp13 / tmp12
    tl.store(out_ptr0 + (x0), tmp14, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (), ())
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (), ())
    assert_size_stride(primals_4, (), ())
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [sub, truediv, dist, pow_2, add, reciprocal], Original ATen: [aten.sub, aten.div, aten.pow, aten.add, aten.reciprocal]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_div_pow_reciprocal_sub_0.run(primals_2, primals_1, primals_3, primals_4, buf0, 256, grid=grid(256), stream=stream0)
    return (buf0, primals_1, primals_2, primals_3, primals_4, buf0, )


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((), (), device='cuda:0', dtype=torch.float32)
    primals_2 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    primals_3 = rand_strided((), (), device='cuda:0', dtype=torch.float32)
    primals_4 = rand_strided((), (), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3, primals_4])
    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


def _mk_param(val):
    """Make a torch parameter from a scalar value"""
    if isinstance(val, torch.Tensor):
        val = val.item()
    return torch.nn.Parameter(torch.tensor(val, dtype=torch.float))


class BellMembFunc(torch.nn.Module):
    """
        Generalised Bell membership function; defined by three parameters:
            a, the half-width (at the crossover point)
            b, controls the slope at the crossover point (which is -b/2a)
            c, the center point
    """

    def __init__(self, a, b, c):
        super(BellMembFunc, self).__init__()
        self.register_parameter('a', _mk_param(a))
        self.register_parameter('b', _mk_param(b))
        self.register_parameter('c', _mk_param(c))
        self.b.register_hook(BellMembFunc.b_log_hook)

    @staticmethod
    def b_log_hook(grad):
        """
            Possibility of a log(0) in the grad for b, giving a nan.
            Fix this by replacing any nan in the grad with ~0.
        """
        grad[torch.isnan(grad)] = 1e-09
        return grad

    def forward(self, x):
        dist = torch.pow((x - self.c) / self.a, 2)
        return torch.reciprocal(1 + torch.pow(dist, self.b))

    def pretty(self):
        return 'BellMembFunc {} {} {}'.format(self.a, self.b, self.c)


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


def get_init_inputs():
    return [[], {'a': 4, 'b': 4, 'c': 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
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_pow_reciprocal_sub_0(in_ptr0, in_ptr1, in_ptr2,
    in_ptr3, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 256
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex
    tmp0 = tl.load(in_ptr0 + x0, xmask)
    tmp1 = tl.load(in_ptr1 + 0)
    tmp2 = tl.broadcast_to(tmp1, [XBLOCK])
    tmp4 = tl.load(in_ptr2 + 0)
    tmp5 = tl.broadcast_to(tmp4, [XBLOCK])
    tmp8 = tl.load(in_ptr3 + 0)
    tmp9 = tl.broadcast_to(tmp8, [XBLOCK])
    tmp3 = tmp0 - tmp2
    tmp6 = tmp3 / tmp5
    tmp7 = tmp6 * tmp6
    tmp10 = libdevice.pow(tmp7, tmp9)
    tmp11 = 1.0
    tmp12 = tmp10 + tmp11
    tmp13 = tl.full([1], 1, tl.int32)
    tmp14 = tmp13 / tmp12
    tl.store(out_ptr0 + x0, tmp14, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (), ())
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (), ())
    assert_size_stride(primals_4, (), ())
    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_pow_reciprocal_sub_0[grid(256)](primals_2,
            primals_1, primals_3, primals_4, buf0, 256, XBLOCK=256,
            num_warps=4, num_stages=1)
    return buf0, primals_1, primals_2, primals_3, primals_4, buf0


def _mk_param(val):
    """Make a torch parameter from a scalar value"""
    if isinstance(val, torch.Tensor):
        val = val.item()
    return torch.nn.Parameter(torch.tensor(val, dtype=torch.float))


class BellMembFuncNew(torch.nn.Module):
    """
        Generalised Bell membership function; defined by three parameters:
            a, the half-width (at the crossover point)
            b, controls the slope at the crossover point (which is -b/2a)
            c, the center point
    """

    def __init__(self, a, b, c):
        super(BellMembFuncNew, self).__init__()
        self.register_parameter('a', _mk_param(a))
        self.register_parameter('b', _mk_param(b))
        self.register_parameter('c', _mk_param(c))
        self.b.register_hook(BellMembFuncNew.b_log_hook)

    @staticmethod
    def b_log_hook(grad):
        """
            Possibility of a log(0) in the grad for b, giving a nan.
            Fix this by replacing any nan in the grad with ~0.
        """
        grad[torch.isnan(grad)] = 1e-09
        return grad

    def pretty(self):
        return 'BellMembFunc {} {} {}'.format(self.a, self.b, self.c)

    def forward(self, input_0):
        primals_1 = self.a
        primals_3 = self.b
        primals_4 = self.c
        primals_2 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]
