# 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_4/inductor_cache/os/cosmcerpidzwa6sjwrma6sckbev7edvqrnpoottn3y2xknmtustb.py
# Topologically Sorted Source Nodes: [std], Original ATen: [aten.std]
# Source node to ATen node mapping:
#   std => sqrt, var
# Graph fragment:
#   %var : [num_users=1] = call_function[target=torch.ops.aten.var.correction](args = (%primals_2, [0]), kwargs = {correction: 1.0})
#   %sqrt : [num_users=2] = call_function[target=torch.ops.aten.sqrt.default](args = (%var,), kwargs = {})
triton_poi_fused_std_0 = async_compile.triton('triton_poi_fused_std_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: '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), equal_to_1=())]},
    inductor_meta={'autotune_hints': set(), 'kernel_name': 'triton_poi_fused_std_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_std_0(in_ptr0, out_ptr0, xnumel, XBLOCK : tl.constexpr):
    xnumel = 64
    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_ptr0 + (64 + x0), xmask)
    tmp3 = tl.load(in_ptr0 + (128 + x0), xmask)
    tmp5 = tl.load(in_ptr0 + (192 + x0), xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tmp9 = tmp0 - tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tmp1 - tmp8
    tmp12 = tmp11 * tmp11
    tmp13 = tmp10 + tmp12
    tmp14 = tmp3 - tmp8
    tmp15 = tmp14 * tmp14
    tmp16 = tmp13 + tmp15
    tmp17 = tmp5 - tmp8
    tmp18 = tmp17 * tmp17
    tmp19 = tmp16 + tmp18
    tmp20 = 3.0
    tmp21 = tmp19 / tmp20
    tmp22 = libdevice.sqrt(tmp21)
    tl.store(out_ptr0 + (x0), tmp22, xmask)
''', device_str='cuda')


# kernel path: runs/run_shard_4/inductor_cache/dk/cdkg7zys5y3gqtiwl2zlfbqbq5tru7ravxon55tbwigoo34fkspd.py
# Topologically Sorted Source Nodes: [exp, mul, mul_1, sample], Original ATen: [aten.exp, aten.mul, aten.add]
# Source node to ATen node mapping:
#   exp => exp
#   mul => mul
#   mul_1 => mul_1
#   sample => add
# Graph fragment:
#   %exp : [num_users=1] = call_function[target=torch.ops.aten.exp.default](args = (%expand,), kwargs = {})
#   %mul : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%expand_1, %exp), kwargs = {})
#   %mul_1 : [num_users=1] = call_function[target=torch.ops.aten.mul.Tensor](args = (%mul, %primals_3), kwargs = {})
#   %add : [num_users=1] = call_function[target=torch.ops.aten.add.Tensor](args = (%primals_2, %mul_1), kwargs = {})
triton_poi_fused_add_exp_mul_1 = async_compile.triton('triton_poi_fused_add_exp_mul_1', '''
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_exp_mul_1', '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_exp_mul_1(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
    x3 = xindex
    x4 = xindex % 64
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + (x3), xmask)
    tmp1 = tl.load(in_ptr1 + (x4), xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr2 + (x0), xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + (x3), xmask)
    tmp3 = tl_math.exp(tmp2)
    tmp4 = tmp1 * tmp3
    tmp6 = tmp4 * tmp5
    tmp7 = tmp0 + tmp6
    tl.store(out_ptr0 + (x3), tmp7, xmask)
''', device_str='cuda')


async_compile.wait(globals())
del async_compile

def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4, ), (1, ))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (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), (16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [std], Original ATen: [aten.std]
        stream0 = get_raw_stream(0)
        triton_poi_fused_std_0.run(primals_2, buf0, 64, grid=grid(64), stream=stream0)
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        # Topologically Sorted Source Nodes: [exp, mul, mul_1, sample], Original ATen: [aten.exp, aten.mul, aten.add]
        triton_poi_fused_add_exp_mul_1.run(primals_2, buf0, primals_1, primals_3, buf1, 256, grid=grid(256), stream=stream0)
        del primals_2
    return (buf1, primals_1, primals_3, 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((4, ), (1, ), 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((4, 4, 4, 4), (64, 16, 4, 1), device='cuda:0', dtype=torch.float32)
    fn = lambda: call([primals_1, primals_2, primals_3])
    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 torch.nn as nn


class Selection(nn.Module):
    """
    Selection neurons to sample from a latent representation for a decoder agent.
    An abstract representation :math:`l_i` is disturbed by a value :math:`r_i` sampled from a normal 
    standard distribution which is scaled by the selection neuron :math:`s_i`.

    ..math::
        n_i \\sim l_i + \\sigma_{l_i} 	imes \\exp(s_i) 	imes r_i

    where :math:`\\sigma_{l_i}` is the standard deviation over the batch. 
    If the selection neuron has a low (i.e. negative) value, the latent variable is passed to the agent. 
    If the selection neuron has a high value (i.e. close to zero), the latent variable is rendered useless to the agent.

    Args:
        num_selectors (int): Number of selection neurons, i.e. latent variables.

        **kwargs:
            init_selectors (float): Initial value for selection neurons. Default: -10.
    """

    def __init__(self, num_selectors, init_selectors=-10.0):
        super(Selection, self).__init__()
        select = torch.Tensor([init_selectors for _ in range(num_selectors)])
        self.selectors = nn.Parameter(select)

    def forward(self, x, rand, std_dev=None):
        """
        The forward pass for the selection neurons.

        Args:
            x (torch.Tensor): The input array of shape (batch_size, size_latent).
            rand (torch.Tensor): Random samples from standard normal distribution of size (batch_size, size_latent).

            **kwargs:
                std_dev (:class:`torch.Tensor` or :class:`NoneType`): The standard deviation calculated throughout 
                                                                      episodes. Needs to be specified for prediction. 
                                                                      Default: None.
        
        Returns:
            sample (torch.Tensor): Sample from a distribution around latent variables.
        """
        selectors = self.selectors.expand_as(x)
        if std_dev is None:
            std = x.std(dim=0).expand_as(x)
        else:
            std = std_dev
        sample = x + std * torch.exp(selectors) * rand
        return sample


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


def get_init_inputs():
    return [[], {'num_selectors': 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, math as tl_math
import torch.nn as nn
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_std_0(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
    xnumel = 64
    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_ptr0 + (64 + x0), xmask)
    tmp3 = tl.load(in_ptr0 + (128 + x0), xmask)
    tmp5 = tl.load(in_ptr0 + (192 + x0), xmask)
    tmp2 = tmp0 + tmp1
    tmp4 = tmp2 + tmp3
    tmp6 = tmp4 + tmp5
    tmp7 = 4.0
    tmp8 = tmp6 / tmp7
    tmp9 = tmp0 - tmp8
    tmp10 = tmp9 * tmp9
    tmp11 = tmp1 - tmp8
    tmp12 = tmp11 * tmp11
    tmp13 = tmp10 + tmp12
    tmp14 = tmp3 - tmp8
    tmp15 = tmp14 * tmp14
    tmp16 = tmp13 + tmp15
    tmp17 = tmp5 - tmp8
    tmp18 = tmp17 * tmp17
    tmp19 = tmp16 + tmp18
    tmp20 = 3.0
    tmp21 = tmp19 / tmp20
    tmp22 = libdevice.sqrt(tmp21)
    tl.store(out_ptr0 + x0, tmp22, xmask)


@triton.jit
def triton_poi_fused_add_exp_mul_1(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
    x3 = xindex
    x4 = xindex % 64
    x0 = xindex % 4
    tmp0 = tl.load(in_ptr0 + x3, xmask)
    tmp1 = tl.load(in_ptr1 + x4, xmask, eviction_policy='evict_last')
    tmp2 = tl.load(in_ptr2 + x0, xmask, eviction_policy='evict_last')
    tmp5 = tl.load(in_ptr3 + x3, xmask)
    tmp3 = tl_math.exp(tmp2)
    tmp4 = tmp1 * tmp3
    tmp6 = tmp4 * tmp5
    tmp7 = tmp0 + tmp6
    tl.store(out_ptr0 + x3, tmp7, xmask)


def call(args):
    primals_1, primals_2, primals_3 = args
    args.clear()
    assert_size_stride(primals_1, (4,), (1,))
    assert_size_stride(primals_2, (4, 4, 4, 4), (64, 16, 4, 1))
    assert_size_stride(primals_3, (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), (16, 4, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_std_0[grid(64)](primals_2, buf0, 64, XBLOCK=64,
            num_warps=1, num_stages=1)
        buf1 = empty_strided_cuda((4, 4, 4, 4), (64, 16, 4, 1), torch.float32)
        triton_poi_fused_add_exp_mul_1[grid(256)](primals_2, buf0,
            primals_1, primals_3, buf1, 256, XBLOCK=128, num_warps=4,
            num_stages=1)
        del primals_2
    return buf1, primals_1, primals_3, buf0


class SelectionNew(nn.Module):
    """
    Selection neurons to sample from a latent representation for a decoder agent.
    An abstract representation :math:`l_i` is disturbed by a value :math:`r_i` sampled from a normal 
    standard distribution which is scaled by the selection neuron :math:`s_i`.

    ..math::
        n_i \\sim l_i + \\sigma_{l_i} 	imes \\exp(s_i) 	imes r_i

    where :math:`\\sigma_{l_i}` is the standard deviation over the batch. 
    If the selection neuron has a low (i.e. negative) value, the latent variable is passed to the agent. 
    If the selection neuron has a high value (i.e. close to zero), the latent variable is rendered useless to the agent.

    Args:
        num_selectors (int): Number of selection neurons, i.e. latent variables.

        **kwargs:
            init_selectors (float): Initial value for selection neurons. Default: -10.
    """

    def __init__(self, num_selectors, init_selectors=-10.0):
        super(SelectionNew, self).__init__()
        select = torch.Tensor([init_selectors for _ in range(num_selectors)])
        self.selectors = nn.Parameter(select)

    def forward(self, input_0, input_1):
        primals_1 = self.selectors
        primals_2 = input_0
        primals_3 = input_1
        output = call([primals_1, primals_2, primals_3])
        return output[0]
