# 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_8/inductor_cache/7d/c7dlf7xsdr4eyx4wciw5byptm4lpdngi52fdrrqzbrpcywi4eu2a.py
# Topologically Sorted Source Nodes: [pow_2, add_2, features_stddev, div, sub_3, mul, wrapped_sqrt_1, truediv_2, erf, add, cdf, mul_4, mul_5, sub, pow_1, neg, sub_1, wrapped_log_1, sub_2, pdf, mul_6, add_3, mul_2, mul_3, outputs_mean, pow_3, outputs_variance], Original ATen: [aten.pow, aten.add, aten.sqrt, aten.div, aten.sub, aten.mul, aten.erf, aten.neg, aten.log, aten.exp]
# Source node to ATen node mapping:
#   add => add
#   add_2 => add_2
#   add_3 => add_3
#   cdf => mul_1
#   div => div
#   erf => erf
#   features_stddev => sqrt
#   mul => mul
#   mul_2 => mul_2
#   mul_3 => mul_3
#   mul_4 => mul_4
#   mul_5 => mul_5
#   mul_6 => mul_6
#   neg => neg
#   outputs_mean => add_1
#   outputs_variance => sub_4
#   pdf => exp
#   pow_1 => pow_1
#   pow_2 => pow_2
#   pow_3 => pow_3
#   sub => sub
#   sub_1 => div_1
#   sub_2 => sub_2
#   sub_3 => sub_3
#   truediv_2 => div_2
#   wrapped_log_1 => full_default_1
#   wrapped_sqrt_1 => full_default_2
# Graph fragment:
#   %pow_2 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%arg1_1, 2), kwargs = {})
#   %add_2 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%pow_2, %arg0_1), kwargs = {})
#   %sqrt : [num_users=3] = call_function[target=torch.ops.aten.sqrt.default](args = (%arg0_1,), kwargs = {})
#   %div : [num_users=2] = call_function[target=torch.ops.aten.div.Tensor](args = (%arg1_1, %sqrt), kwargs = {})
#   %sub_3 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%div, 0.0), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sub_3, 1.0), kwargs = {})
#   %full_default_2 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 1.414213562373095), kwargs = {dtype: torch.float64, layout: torch.strided, device: cpu, pin_memory: False})
#   %div_2 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%mul, %full_default_2), kwargs = {})
#   %erf : [num_users=1] = call_function[target=torch.ops.aten.erf.default](args = (%div_2,), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%erf, 1.0), kwargs = {})
#   %mul_1 : [num_users=2] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add, 0.5), kwargs = {})
#   %mul_4 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%add_2, %mul_1), kwargs = {})
#   %mul_5 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg1_1, %sqrt), kwargs = {})
#   %sub : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%div, 0.0), kwargs = {})
#   %pow_1 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%sub, 2), kwargs = {})
#   %neg : [num_users=1] = call_function[target=torch.ops.aten.neg.default](args = (%pow_1,), kwargs = {})
#   %div_1 : [num_users=1] = call_function[target=torch.ops.aten.div.Tensor](args = (%neg, 2.0), kwargs = {})
#   %full_default_1 : [num_users=1] = call_function[target=torch.ops.aten.full.default](args = ([], 0.9189385332046727), kwargs = {dtype: torch.float64, layout: torch.strided, device: cpu, pin_memory: False})
#   %sub_2 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%div_1, %full_default_1), kwargs = {})
#   %exp : [num_users=2] = call_function[target=torch.ops.aten.exp.default](args = (%sub_2,), kwargs = {})
#   %mul_6 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul_5, %exp), kwargs = {})
#   %add_3 : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_4, %mul_6), kwargs = {})
#   %mul_2 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%arg1_1, %mul_1), kwargs = {})
#   %mul_3 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%sqrt, %exp), kwargs = {})
#   %add_1 : [num_users=2] = call_function[target=torch.ops.aten.add.Tensor](args = (%mul_2, %mul_3), kwargs = {})
#   %pow_3 : [num_users=1] = call_function[target=torch.ops.aten.pow.Tensor_Scalar](args = (%add_1, 2), kwargs = {})
#   %sub_4 : [num_users=1] = call_function[target=torch.ops.aten.sub.Tensor](args = (%add_3, %pow_3), kwargs = {})
triton_poi_fused_add_div_erf_exp_log_mul_neg_pow_sqrt_sub_0 = async_compile.triton('triton_poi_fused_add_div_erf_exp_log_mul_neg_pow_sqrt_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: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_add_div_erf_exp_log_mul_neg_pow_sqrt_sub_0', 'mutated_arg_names': [], 'no_x_dim': False, 'num_load': 2, '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_erf_exp_log_mul_neg_pow_sqrt_sub_0(in_ptr0, in_ptr1, out_ptr0, out_ptr1, 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 + (x0), xmask)
    tmp2 = libdevice.sqrt(tmp1)
    tmp3 = tmp0 / tmp2
    tmp4 = 0.0
    tmp5 = tmp3 - tmp4
    tmp6 = 1.0
    tmp7 = tmp5 * tmp6
    tmp8 = 1.414213562373095
    tmp9 = tmp7 / tmp8
    tmp10 = libdevice.erf(tmp9)
    tmp11 = tmp10 + tmp6
    tmp12 = 0.5
    tmp13 = tmp11 * tmp12
    tmp14 = tmp0 * tmp13
    tmp15 = tmp5 * tmp5
    tmp16 = -tmp15
    tmp17 = tmp16 * tmp12
    tmp18 = 0.9189385332046727
    tmp19 = tmp17 - tmp18
    tmp20 = tl_math.exp(tmp19)
    tmp21 = tmp2 * tmp20
    tmp22 = tmp14 + tmp21
    tmp23 = tmp0 * tmp0
    tmp24 = tmp23 + tmp1
    tmp25 = tmp24 * tmp13
    tmp26 = tmp0 * tmp2
    tmp27 = tmp26 * tmp20
    tmp28 = tmp25 + tmp27
    tmp29 = tmp22 * tmp22
    tmp30 = tmp28 - tmp29
    tl.store(out_ptr0 + (x0), tmp22, xmask)
    tl.store(out_ptr1 + (x0), tmp30, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_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)
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [pow_2, add_2, features_stddev, div, sub_3, mul, wrapped_sqrt_1, truediv_2, erf, add, cdf, mul_4, mul_5, sub, pow_1, neg, sub_1, wrapped_log_1, sub_2, pdf, mul_6, add_3, mul_2, mul_3, outputs_mean, pow_3, outputs_variance], Original ATen: [aten.pow, aten.add, aten.sqrt, aten.div, aten.sub, aten.mul, aten.erf, aten.neg, aten.log, aten.exp]
        stream0 = get_raw_stream(0)
        triton_poi_fused_add_div_erf_exp_log_mul_neg_pow_sqrt_sub_0.run(arg1_1, arg0_1, buf0, buf1, 256, grid=grid(256), stream=stream0)
        del arg0_1
        del arg1_1
    return (buf0, buf1, )


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)
    arg1_1 = rand_strided((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([arg0_1, arg1_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
import numpy as np
import torch.nn as nn
from numbers import Number


def normcdf(value, mu=0.0, stddev=1.0):
    sinv = 1.0 / stddev if isinstance(stddev, Number) else stddev.reciprocal()
    return 0.5 * (1.0 + torch.erf((value - mu) * sinv / np.sqrt(2.0)))


def _normal_log_pdf(value, mu, stddev):
    var = stddev ** 2
    log_scale = np.log(stddev) if isinstance(stddev, Number) else torch.log(
        stddev)
    return -(value - mu) ** 2 / (2.0 * var) - log_scale - np.log(np.sqrt(
        2.0 * np.pi))


def normpdf(value, mu=0.0, stddev=1.0):
    return torch.exp(_normal_log_pdf(value, mu, stddev))


class ReLU(nn.Module):

    def __init__(self, keep_variance_fn=None):
        super(ReLU, self).__init__()
        self._keep_variance_fn = keep_variance_fn

    def forward(self, features_mean, features_variance):
        features_stddev = torch.sqrt(features_variance)
        div = features_mean / features_stddev
        pdf = normpdf(div)
        cdf = normcdf(div)
        outputs_mean = features_mean * cdf + features_stddev * pdf
        outputs_variance = (features_mean ** 2 + features_variance
            ) * cdf + features_mean * features_stddev * pdf - outputs_mean ** 2
        if self._keep_variance_fn is not None:
            outputs_variance = self._keep_variance_fn(outputs_variance)
        return outputs_mean, outputs_variance


def get_inputs():
    return [torch.rand([4, 4, 4, 4]), 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.triton_helpers import libdevice, math as tl_math
import numpy as np
import torch.nn as nn
from numbers import Number
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_erf_exp_log_mul_neg_pow_sqrt_sub_0(in_ptr0,
    in_ptr1, out_ptr0, out_ptr1, 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 + x0, xmask)
    tmp2 = libdevice.sqrt(tmp1)
    tmp3 = tmp0 / tmp2
    tmp4 = 0.0
    tmp5 = tmp3 - tmp4
    tmp6 = 1.0
    tmp7 = tmp5 * tmp6
    tmp8 = 1.414213562373095
    tmp9 = tmp7 / tmp8
    tmp10 = libdevice.erf(tmp9)
    tmp11 = tmp10 + tmp6
    tmp12 = 0.5
    tmp13 = tmp11 * tmp12
    tmp14 = tmp0 * tmp13
    tmp15 = tmp5 * tmp5
    tmp16 = -tmp15
    tmp17 = tmp16 * tmp12
    tmp18 = 0.9189385332046727
    tmp19 = tmp17 - tmp18
    tmp20 = tl_math.exp(tmp19)
    tmp21 = tmp2 * tmp20
    tmp22 = tmp14 + tmp21
    tmp23 = tmp0 * tmp0
    tmp24 = tmp23 + tmp1
    tmp25 = tmp24 * tmp13
    tmp26 = tmp0 * tmp2
    tmp27 = tmp26 * tmp20
    tmp28 = tmp25 + tmp27
    tmp29 = tmp22 * tmp22
    tmp30 = tmp28 - tmp29
    tl.store(out_ptr0 + x0, tmp22, xmask)
    tl.store(out_ptr1 + x0, tmp30, xmask)


def call(args):
    arg0_1, arg1_1 = args
    args.clear()
    assert_size_stride(arg0_1, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(arg1_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)
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_add_div_erf_exp_log_mul_neg_pow_sqrt_sub_0[grid(256)](
            arg1_1, arg0_1, buf0, buf1, 256, XBLOCK=128, num_warps=4,
            num_stages=1)
        del arg0_1
        del arg1_1
    return buf0, buf1


def normcdf(value, mu=0.0, stddev=1.0):
    sinv = 1.0 / stddev if isinstance(stddev, Number) else stddev.reciprocal()
    return 0.5 * (1.0 + torch.erf((value - mu) * sinv / np.sqrt(2.0)))


def _normal_log_pdf(value, mu, stddev):
    var = stddev ** 2
    log_scale = np.log(stddev) if isinstance(stddev, Number) else torch.log(
        stddev)
    return -(value - mu) ** 2 / (2.0 * var) - log_scale - np.log(np.sqrt(
        2.0 * np.pi))


def normpdf(value, mu=0.0, stddev=1.0):
    return torch.exp(_normal_log_pdf(value, mu, stddev))


class ReLUNew(nn.Module):

    def __init__(self, keep_variance_fn=None):
        super(ReLUNew, self).__init__()
        self._keep_variance_fn = keep_variance_fn

    def forward(self, input_0, input_1):
        arg0_1 = input_0
        arg1_1 = input_1
        output = call([arg0_1, arg1_1])
        return output[0], output[1]
