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import os
import json

import torch
import torch.distributed as dist

from cuda.bindings import runtime, driver


def check_errors(result, success, err_fn):

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# from torch.nn import *
# class Repro(torch.nn.Module):
#     def __init__(self) -> None:
#         super().__init__()
#         self.true_graph_3 = GraphModule()
#         self.false_graph_3 = GraphModule()
#         self.true_graph_4 = GraphModule()
#         self.false_graph_4 = GraphModule()

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import requests

ACCESS_TOKEN = 'TOKEN'
r = requests.get('https://zenodo.org/api/deposit/depositions',
                  params={'access_token': ACCESS_TOKEN})
r.status_code
# 200
# print(r.json())
# []

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import os
import importlib
import pickle

import torch
from torch.fx._symbolic_trace import symbolic_trace
from torch.profiler import profile, ProfilerActivity
from torch.fx.partitioner.partitioner import CapabilityBasedPartitioner
from torch.fx.partitioner.nvfuser_operator_support import NvFuserOperatorSupport

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#include <vector>
#include <limits>
#include <string>
#include <fstream>
#include <sstream>
// #include "utils/dendro.h"
#include "parlay/parallel.h"

using namespace std;
// g++ -std=c++17 -I../../external/gbbs/external/parlaylib/include dendrogram_conversion.cpp -o dendro

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// EXAMPLE USE 1:
//
// fork_join_scheduler fj;
//
// long fib(long i) {
//   if (i <= 1) return 1;
//   long l,r;
//   fj.pardo([&] () { l = fib(i-1);},
//            [&] () { r = fib(i-2);});
//   return l + r;

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#include "parlay/scheduler.h"
#include "parlay/parallel.h"
#include <unistd.h>


//to compile: g++ -Iexternal/parlaylib/include -DPARLAY_OPENMP -std=c++17 -pthread parlayschtest.cpp
using namespace std;
int main(int argc, char* argv[]) {

    parlay::fork_join_scheduler fj;

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import torch
import torch.nn.functional as F
import gpytorch
from gpytorch.kernels import Kernel
from typing import List
import numpy as np
# import abc
import exkern

from exkern import ElementwiseExKern