# 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_4/inductor_cache/yr/cyrgm4aqzilrmqiictogb5g4uftoozm5popx7beh2trkl46a6noy.py
# Topologically Sorted Source Nodes: [mu, largerThanMu, masked_fill_, a, sigma1, neg, sub, pow_1, mul, exp, leftValuesActiv, lowerThanMu, masked_fill__1, sub_1, sigma2, neg_1, sub_2, pow_2, mul_2, exp_1, mul_3, rightValueActiv, output], Original ATen: [aten.clamp, aten.ge, aten.masked_fill, aten.neg, aten.sub, aten.pow, aten.mul, aten.exp, aten.lt, aten.add]
# Source node to ATen node mapping:
#   a => clamp_max, clamp_min
#   exp => exp
#   exp_1 => exp_1
#   largerThanMu => ge
#   leftValuesActiv => mul_1
#   lowerThanMu => lt
#   masked_fill_ => full_default, where
#   masked_fill__1 => full_default_1, where_1
#   mu => clamp_max_1, clamp_min_1
#   mul => mul
#   mul_2 => mul_2
#   mul_3 => mul_3
#   neg => neg
#   neg_1 => neg_1
#   output => add_1
#   pow_1 => pow_1
#   pow_2 => pow_2
#   rightValueActiv => add
#   sigma1 => clamp_max_2, clamp_min_2
#   sigma2 => clamp_max_3, clamp_min_3
#   sub => sub
#   sub_1 => sub_1
#   sub_2 => sub_2
# Graph fragment:
#   %clamp_min_1 : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%arg1_1, 0.1), kwargs = {})
#   %clamp_max_1 : [num_users=4] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min_1, 3.0), kwargs = {})
#   %ge : [num_users=1] = call_function[target=torch.ops.aten.ge.Tensor](args = (%arg4_1, %clamp_max_1), kwargs = {})
#   %full_default : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 0.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %clamp_min : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%arg0_1, 1.01), kwargs = {})
#   %clamp_max : [num_users=2] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min, 6.0), kwargs = {})
#   %clamp_min_2 : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%arg2_1, 0.5), kwargs = {})
#   %clamp_max_2 : [num_users=1] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min_2, 2.0), kwargs = {})
#   %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%clamp_max_2,), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg4_1, %clamp_max_1), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub, 2), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%neg, %pow_1), kwargs = {})
#   %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%mul,), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%clamp_max, %exp), kwargs = {})
#   %where : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%ge, %full_default, %mul_1), kwargs = {})
#   %lt : [num_users=1] = call_function[target=torch.ops.aten.lt.Tensor](args = (%arg4_1, %clamp_max_1), kwargs = {})
#   %full_default_1 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 0.0), kwargs = {dtype: torch.float32, layout: torch.strided, device: cuda:0, pin_memory: False})
#   %sub_1 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%clamp_max, 1), kwargs = {})
#   %clamp_min_3 : [num_users=1] = call_function[target=torch.ops.aten.clamp_min.default](args = (%arg3_1, 0.5), kwargs = {})
#   %clamp_max_3 : [num_users=1] = call_function[target=torch.ops.aten.clamp_max.default](args = (%clamp_min_3, 2.0), kwargs = {})
#   %neg_1 : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%clamp_max_3,), kwargs = {})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%arg4_1, %clamp_max_1), kwargs = {})
#   %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub_2, 2), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%neg_1, %pow_2), kwargs = {})
#   %exp_1 : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%mul_2,), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_1, %exp_1), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_3, 1), kwargs = {})
#   %where_1 : [num_users=1] = call_function[target=torch.ops.aten.where.self](args = (%lt, %full_default_1, %add), kwargs = {})
#   %add_1 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%where, %where_1), kwargs = {})
triton_poi_fused_add_clamp_exp_ge_lt_masked_fill_mul_neg_pow_sub_0 = async_compile.triton('triton_poi_fused_add_clamp_exp_ge_lt_masked_fill_mul_neg_pow_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: '*fp32', 6: '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, 6), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_clamp_exp_ge_lt_masked_fill_mul_neg_pow_sub_0', 'mutated_arg_names': [], '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_clamp_exp_ge_lt_masked_fill_mul_neg_pow_sub_0(in_ptr0, in_ptr1, in_ptr2, in_ptr3, in_ptr4, 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])
    tmp8 = tl.load(in_ptr2 + (0))
    tmp9 = tl.broadcast_to(tmp8, [XBLOCK])
    tmp14 = tl.load(in_ptr3 + (0))
    tmp15 = tl.broadcast_to(tmp14, [XBLOCK])
    tmp31 = tl.load(in_ptr4 + (0))
    tmp32 = tl.broadcast_to(tmp31, [XBLOCK])
    tmp3 = 0.1
    tmp4 = triton_helpers.maximum(tmp2, tmp3)
    tmp5 = 3.0
    tmp6 = triton_helpers.minimum(tmp4, tmp5)
    tmp7 = tmp0 >= tmp6
    tmp10 = 1.01
    tmp11 = triton_helpers.maximum(tmp9, tmp10)
    tmp12 = 6.0
    tmp13 = triton_helpers.minimum(tmp11, tmp12)
    tmp16 = 0.5
    tmp17 = triton_helpers.maximum(tmp15, tmp16)
    tmp18 = 2.0
    tmp19 = triton_helpers.minimum(tmp17, tmp18)
    tmp20 = -tmp19
    tmp21 = tmp0 - tmp6
    tmp22 = tmp21 * tmp21
    tmp23 = tmp20 * tmp22
    tmp24 = tl_math.exp(tmp23)
    tmp25 = tmp13 * tmp24
    tmp26 = 0.0
    tmp27 = tl.where(tmp7, tmp26, tmp25)
    tmp28 = tmp0 < tmp6
    tmp29 = 1.0
    tmp30 = tmp13 - tmp29
    tmp33 = triton_helpers.maximum(tmp32, tmp16)
    tmp34 = triton_helpers.minimum(tmp33, tmp18)
    tmp35 = -tmp34
    tmp36 = tmp35 * tmp22
    tmp37 = tl_math.exp(tmp36)
    tmp38 = tmp30 * tmp37
    tmp39 = tmp38 + tmp29
    tmp40 = tl.where(tmp28, tmp26, tmp39)
    tmp41 = tmp27 + tmp40
    tl.store(out_ptr0 + (x0), tmp41, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1, arg2_1, arg3_1, arg4_1 = args
    args.clear()
    assert_size_stride(arg0_1, (), ())
    assert_size_stride(arg1_1, (), ())
    assert_size_stride(arg2_1, (), ())
    assert_size_stride(arg3_1, (), ())
    assert_size_stride(arg4_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, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [mu, largerThanMu, masked_fill_, a, sigma1, neg, sub, pow_1, mul, exp, leftValuesActiv, lowerThanMu, masked_fill__1, sub_1, sigma2, neg_1, sub_2, pow_2, mul_2, exp_1, mul_3, rightValueActiv, output], Original ATen: [aten.clamp, aten.ge, aten.masked_fill, aten.neg, aten.sub, aten.pow, aten.mul, aten.exp, aten.lt, aten.add]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_clamp_exp_ge_lt_masked_fill_mul_neg_pow_sub_0.run(arg4_1, arg1_1, arg0_1, arg2_1, arg3_1, buf0, 256, grid=grid(256), stream=stream0)
        del arg0_1
        del arg1_1
        del arg2_1
        del arg3_1
        del arg4_1
    return (buf0, )


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((), (), device='cuda:0', dtype=torch.float32)
    arg1_1 = rand_strided((), (), device='cuda:0', dtype=torch.float32)
    arg2_1 = rand_strided((), (), device='cuda:0', dtype=torch.float32)
    arg3_1 = rand_strided((), (), device='cuda:0', dtype=torch.float32)
    arg4_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, arg4_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 torch import nn
from torch.nn.parameter import Parameter


class GaussActivation(nn.Module):

    def __init__(self, a, mu, sigma1, sigma2):
        super(GaussActivation, self).__init__()
        self.a = Parameter(torch.tensor(a, dtype=torch.float32))
        self.mu = Parameter(torch.tensor(mu, dtype=torch.float32))
        self.sigma1 = Parameter(torch.tensor(sigma1, dtype=torch.float32))
        self.sigma2 = Parameter(torch.tensor(sigma2, dtype=torch.float32))

    def forward(self, inputFeatures):
        a = torch.clamp(self.a.data, 1.01, 6.0)
        mu = torch.clamp(self.mu.data, 0.1, 3.0)
        sigma1 = torch.clamp(self.sigma1.data, 0.5, 2.0)
        sigma2 = torch.clamp(self.sigma2.data, 0.5, 2.0)
        lowerThanMu = inputFeatures < mu
        largerThanMu = inputFeatures >= mu
        leftValuesActiv = a * torch.exp(-sigma1 * (inputFeatures - mu) ** 2)
        leftValuesActiv.masked_fill_(largerThanMu, 0.0)
        rightValueActiv = 1 + (a - 1) * torch.exp(-sigma2 * (inputFeatures -
            mu) ** 2)
        rightValueActiv.masked_fill_(lowerThanMu, 0.0)
        output = leftValuesActiv + rightValueActiv
        return output


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


def get_init_inputs():
    return [[], {'a': 4, 'mu': 4, 'sigma1': 4, 'sigma2': 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 import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
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


@triton.jit
def triton_poi_fused_add_clamp_exp_ge_lt_masked_fill_mul_neg_pow_sub_0(in_ptr0,
    in_ptr1, in_ptr2, in_ptr3, in_ptr4, 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])
    tmp8 = tl.load(in_ptr2 + 0)
    tmp9 = tl.broadcast_to(tmp8, [XBLOCK])
    tmp14 = tl.load(in_ptr3 + 0)
    tmp15 = tl.broadcast_to(tmp14, [XBLOCK])
    tmp31 = tl.load(in_ptr4 + 0)
    tmp32 = tl.broadcast_to(tmp31, [XBLOCK])
    tmp3 = 0.1
    tmp4 = triton_helpers.maximum(tmp2, tmp3)
    tmp5 = 3.0
    tmp6 = triton_helpers.minimum(tmp4, tmp5)
    tmp7 = tmp0 >= tmp6
    tmp10 = 1.01
    tmp11 = triton_helpers.maximum(tmp9, tmp10)
    tmp12 = 6.0
    tmp13 = triton_helpers.minimum(tmp11, tmp12)
    tmp16 = 0.5
    tmp17 = triton_helpers.maximum(tmp15, tmp16)
    tmp18 = 2.0
    tmp19 = triton_helpers.minimum(tmp17, tmp18)
    tmp20 = -tmp19
    tmp21 = tmp0 - tmp6
    tmp22 = tmp21 * tmp21
    tmp23 = tmp20 * tmp22
    tmp24 = tl_math.exp(tmp23)
    tmp25 = tmp13 * tmp24
    tmp26 = 0.0
    tmp27 = tl.where(tmp7, tmp26, tmp25)
    tmp28 = tmp0 < tmp6
    tmp29 = 1.0
    tmp30 = tmp13 - tmp29
    tmp33 = triton_helpers.maximum(tmp32, tmp16)
    tmp34 = triton_helpers.minimum(tmp33, tmp18)
    tmp35 = -tmp34
    tmp36 = tmp35 * tmp22
    tmp37 = tl_math.exp(tmp36)
    tmp38 = tmp30 * tmp37
    tmp39 = tmp38 + tmp29
    tmp40 = tl.where(tmp28, tmp26, tmp39)
    tmp41 = tmp27 + tmp40
    tl.store(out_ptr0 + x0, tmp41, xmask)


def call(args):
    arg0_1, arg1_1, arg2_1, arg3_1, arg4_1 = args
    args.clear()
    assert_size_stride(arg0_1, (), ())
    assert_size_stride(arg1_1, (), ())
    assert_size_stride(arg2_1, (), ())
    assert_size_stride(arg3_1, (), ())
    assert_size_stride(arg4_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, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_clamp_exp_ge_lt_masked_fill_mul_neg_pow_sub_0[grid
            (256)](arg4_1, arg1_1, arg0_1, arg2_1, arg3_1, buf0, 256,
            XBLOCK=256, num_warps=4, num_stages=1)
        del arg0_1
        del arg1_1
        del arg2_1
        del arg3_1
        del arg4_1
    return buf0,


class GaussActivationNew(nn.Module):

    def __init__(self, a, mu, sigma1, sigma2):
        super(GaussActivationNew, self).__init__()
        self.a = Parameter(torch.tensor(a, dtype=torch.float32))
        self.mu = Parameter(torch.tensor(mu, dtype=torch.float32))
        self.sigma1 = Parameter(torch.tensor(sigma1, dtype=torch.float32))
        self.sigma2 = Parameter(torch.tensor(sigma2, dtype=torch.float32))

    def forward(self, input_0):
        arg0_1 = self.a
        arg1_1 = self.mu
        arg2_1 = self.sigma1
        arg3_1 = self.sigma2
        arg4_1 = input_0
        output = call([arg0_1, arg1_1, arg2_1, arg3_1, arg4_1])
        return output[0]
