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import pypeln as pl
import time
from random import random

def slow_add1(x):
  time.sleep(random()) # <= some slow computation
  return x + 1

def slow_gt3(x):
  time.sleep(random()) # <= some slow computation

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def get_model(params) -> tf.keras.Model:

    x0 = tf.keras.Input(shape=(1,), name="x0")
    x1 = tf.keras.Input(shape=(1,), name="x1")

    inputs = [x0, x1]

    # x0 embeddings

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#-------------------------------------------------------------
# types
#-------------------------------------------------------------
type C = concept type C
    proc name(x: C, msg: string): string

type AnyC = object
    name: proc(msg: string): string # doesn't contain C

type A = object

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using Base.Threads
using LoopVectorization
using BenchmarkTools

const None = [CartesianIndex()] 

function distances(data1, data2)
    data1 = deg2rad.(data1)
    data2 = deg2rad.(data2)
    lat1 = @view data1[:, 1]

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using Base.Threads
using Distributions
using BenchmarkTools

ENV["PYCALL_JL_RUNTIME_PYTHON"] = Sys.which("python")

using PyCall

py"""
import sys

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import typing as tp

from jax import numpy as jnp
import jax
import numpy as np
import time

@jax.jit
def _distances_jax(data1, data2):

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# client-pypeln-pl.task.py

from aiohttp import ClientSession, TCPConnector
import asyncio
import sys
import pypeln as pl


limit = 1000
urls = ("http://localhost:8080/{}".format(i) for i in range(int(sys.argv[1])))

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# client-task-pool.py

from aiohttp import ClientSession, TCPConnector
import asyncio
import sys
from pypeln.task import TaskPool

limit = 1000

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import asyncio

class TaskPool(object):

    def __init__(self, workers):
        self._semaphore = asyncio.Semaphore(workers)
        self._tasks = set()
        

    async def put(self, coro):

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# client-async-as-completed.py

from aiohttp import ClientSession, TCPConnector
import asyncio
from itertools import islice
import sys

def limited_as_completed(coros, limit):
    futures = [
        asyncio.ensure_future(c)