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]


Measured latency_ms: 0.029617