blvp

FROM python:3.9-slim as builder
COPY requirements.txt .
COPY src/ src/
COPY setup.py .
RUN python setup.py bdist_wheel


FROM public.ecr.aws/lambda/python:3.9
COPY --from=builder dist/<package_name>-1.0-py3-none-any.whl <package_name>-1.0-py3-none-any.whl
RUN python3.9 -m pip install <package_name>-1.0-py3-none-any.whl && rm -f <package_name>-1.0-py3-none-any.whl

blvp

package com.jiwire.pca.tasks.output.views

import mist.api.data.{JsData, JsList, JsMap, JsString}
import mist.api.encoding.JsEncoder
import shadedshapeless._
import shadedshapeless.labelled.FieldType
import shadedshapeless.ops.hlist.{Mapper, ToTraversable}
import shadedshapeless.ops.record._

case class Sheet(

blvp

import java.lang.*;
import java.lang.reflect.*;


public class TestJava {
    public static void main(String... args) throws Throwable {
        
        org.apache.spark.util.Utils$ instance = org.apache.spark.util.Utils$.MODULE$;
        
        Method method = instance.getClass().getDeclaredMethod("findLocalInetAddress");

blvp

object ScalingFactorFunc extends MistFn {
  implicit val sfDailyOutMessageEnc = mist.api.encoding.generic.encoder[ScalingFactorDailyOutMessage]
  implicit val sfCommonConfigExt = mist.api.encoding.generic.extractor[CommonConfig]
  implicit val sfDailyInMessageExt = mist.api.encoding.generic.extractor[ScalingFactorDailyInMessage]
  implicit val sfConfigExt = mist.api.encoding.generic.extractor[ScalingFactorConfig]
  
  def handle: Handle = withArgs(
    arg[CommonConfig]("cfg"),
    arg[ScalingFactorDailyInMessage]("params"),
    arg[ScalingFactorConfig]("sfCfg")

blvp

object PcaLvmOutput extends MistFn {
  implicit val resultMessageEnc = mist.api.encoding.generic.encoder[ResultMessage]
  override def handle: Handle = {
      withArgs(
        arg[String]("table_prefix"),
        arg[Int]("nod_histogram_length", 31)
          .validated(nodHistogramLength => nodHistogramLength > 0, "nod_histogram_length value should be positive"),
        arg[Int]("limit_value", 250),
        arg[String]("sources", " 'ai_6010' "),
        arg[Int]("scaling_factor_days_processed", 90),

blvp

object VisitRateMist extends MistFn with Logging {
  implicit val vmArgExt = mist.api.encoding.generic.extractor[VisitMetricsResultMessage]
  implicit val vmMetricsEnc = mist.api.encoding.generic.encoder[VisitMetricsResultMessage]
  
  override def handle: Handle = {
    withArgs(arg[VisitRateArguments])
      .withMistExtras
      .onSparkSession((args: VisitRateArguments, extras: MistExtras, spark: SparkSession) => {
        //computation
        val result: VisitMetricsResultMessage = ??? 

blvp

import functools
from abc import abstractmethod


def with_args(*args_decorator):
    def wrapper(f):
        for arg_decorator in args_decorator:
            f = arg_decorator(f)
        f.arg_defs = args_decorator
        return f

blvp

$(document).ready ->

  class TaskFour
    MAX_VALUE = Number.MAX_VALUE

    constructor: (n = 3) ->
      @n = n
      console.log "N = #{@n}"
      @s = @_setArray(@n + 1)
      @s[0] = 3.4

blvp

import gzip
import pickle

import numpy as np
import tensorflow as tf
import tflearn
from gensim.models.word2vec import Word2Vec
from tflearn.data_utils import to_categorical, pad_sequences
from tflearn.datasets import imdb
from tflearn.layers.conv import conv_1d, global_max_pool, conv_2d

blvp

import argparse as arg
import os

import cv2
import numpy as np


def generate_cropped_by_white(img_path):
    img = cv2.imread(img_path)
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)