import torch
from torch._inductor.select_algorithm import extern_kernels
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
import numpy as np
import torch.optim
import torch.nn as nn
import torch.nn.init as init
import torch.nn.utils
import torch.autograd
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
reinterpret_tensor = torch._C._dynamo.guards._reinterpret_tensor


@triton.jit
def triton_poi_fused_stack_0(in_ptr0, in_ptr1, out_ptr0, xnumel, XBLOCK: tl
    .constexpr):
    xnumel = 32
    xoffset = tl.program_id(0) * XBLOCK
    xindex = xoffset + tl.arange(0, XBLOCK)[:]
    xmask = xindex < xnumel
    x0 = xindex % 2
    x3 = xindex // 2
    x1 = xindex // 2 % 4
    x4 = xindex
    tmp0 = x0
    tl.full([1], 0, tl.int64)
    tmp3 = tl.full([1], 1, tl.int64)
    tmp4 = tmp0 < tmp3
    tmp5 = tl.load(in_ptr0 + x3, tmp4 & xmask, eviction_policy='evict_last',
        other=0.0)
    tmp6 = tmp0 >= tmp3
    tl.full([1], 2, tl.int64)
    tmp9 = tl.load(in_ptr1 + x1, tmp6 & xmask, eviction_policy='evict_last',
        other=0.0)
    tmp10 = tl.where(tmp4, tmp5, tmp9)
    tl.store(out_ptr0 + x4, tmp10, xmask)


def call(args):
    primals_1, primals_2, primals_3, primals_4 = args
    args.clear()
    assert_size_stride(primals_1, (4, 4), (4, 1))
    assert_size_stride(primals_2, (4,), (1,))
    assert_size_stride(primals_3, (4, 4), (4, 1))
    assert_size_stride(primals_4, (1, 4), (4, 1))
    with torch.cuda._DeviceGuard(0):
        torch.cuda.set_device(0)
        buf0 = empty_strided_cuda((4, 4), (4, 1), torch.float32)
        extern_kernels.addmm(primals_2, primals_3, reinterpret_tensor(
            primals_1, (4, 4), (1, 4), 0), alpha=1, beta=1, out=buf0)
        del primals_1
        del primals_2
        buf1 = empty_strided_cuda((4, 4, 2), (8, 2, 1), torch.float32)
        get_raw_stream(0)
        triton_poi_fused_stack_0[grid(32)](buf0, primals_4, buf1, 32,
            XBLOCK=32, num_warps=1, num_stages=1)
        del buf0
        del primals_4
    return buf1, primals_3


class DiagGaussianActionHeadNew(nn.Module):
    """
    Action head where actions are normally distibuted uncorrelated variables with specific means and variances.

    Means are calculated directly from the network while standard deviation are a parameter of this module
    """
    LOG2PI = np.log(2.0 * np.pi)

    def __init__(self, input_dim, num_dimensions):
        super().__init__()
        self.input_dim = input_dim
        self.num_dimensions = num_dimensions
        self.linear_layer = nn.Linear(input_dim, num_dimensions)
        self.log_std = nn.Parameter(torch.zeros(1, num_dimensions))

    def sample(self, params, argmax_sampling=False):
        """ Sample from a probability space of all actions """
        means = params[:, :, 0]
        log_std = params[:, :, 1]
        if argmax_sampling:
            return means
        else:
            return torch.randn_like(means) * torch.exp(log_std) + means

    def logprob(self, action_sample, pd_params):
        """ Log-likelihood """
        means = pd_params[:, :, 0]
        log_std = pd_params[:, :, 1]
        std = torch.exp(log_std)
        z_score = (action_sample - means) / std
        return -(0.5 * (z_score ** 2 + self.LOG2PI).sum(dim=-1) + log_std.
            sum(dim=-1))

    def reset_weights(self):
        init.orthogonal_(self.linear_layer.weight, gain=0.01)
        init.constant_(self.linear_layer.bias, 0.0)

    def entropy(self, params):
        """
        Categorical distribution entropy calculation - sum probs * log(probs).
        In case of diagonal gaussian distribution - 1/2 log(2 pi e sigma^2)
        """
        log_std = params[:, :, 1]
        return (log_std + 0.5 * (self.LOG2PI + 1)).sum(dim=-1)

    def kl_divergence(self, params_q, params_p):
        """
        Categorical distribution KL divergence calculation
        KL(Q || P) = sum Q_i log (Q_i / P_i)

        Formula is:
        log(sigma_p) - log(sigma_q) + (sigma_q^2 + (mu_q - mu_p)^2))/(2 * sigma_p^2)
        """
        means_q = params_q[:, :, 0]
        log_std_q = params_q[:, :, 1]
        means_p = params_p[:, :, 0]
        log_std_p = params_p[:, :, 1]
        std_q = torch.exp(log_std_q)
        std_p = torch.exp(log_std_p)
        kl_div = log_std_p - log_std_q + (std_q ** 2 + (means_q - means_p) ** 2
            ) / (2.0 * std_p ** 2) - 0.5
        return kl_div.sum(dim=-1)

    def forward(self, input_0):
        primals_4 = self.log_std
        primals_1 = self.linear_layer.weight
        primals_2 = self.linear_layer.bias
        primals_3 = input_0
        output = call([primals_1, primals_2, primals_3, primals_4])
        return output[0]


Measured latency_ms: 0.014993