package eu.dnetlib.dhp.oa.graph.dump.skgif;

import static eu.dnetlib.dhp.common.SparkSessionSupport.runWithSparkSession;

import java.io.Serializable;
import java.util.*;
import java.util.stream.Collectors;

import org.apache.commons.io.IOUtils;
import org.apache.spark.SparkConf;
import org.apache.spark.api.java.function.FilterFunction;
import org.apache.spark.api.java.function.FlatMapFunction;
import org.apache.spark.api.java.function.MapFunction;
import org.apache.spark.api.java.function.MapGroupsFunction;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Encoders;
import org.apache.spark.sql.SaveMode;
import org.apache.spark.sql.SparkSession;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import eu.dnetlib.dhp.application.ArgumentApplicationParser;
import eu.dnetlib.dhp.oa.graph.dump.skgif.beans.EmitPerManifestation;
import eu.dnetlib.dhp.schema.common.EntityType;
import eu.dnetlib.dhp.schema.common.ModelSupport;
import eu.dnetlib.dhp.schema.oaf.*;
import eu.dnetlib.dhp.schema.oaf.Datasource;
import eu.dnetlib.dhp.skgif.model.*;
import eu.dnetlib.dhp.utils.DHPUtils;
import scala.Tuple2;

/**
 * @author miriam.baglioni
 * @Date 06/02/24
 */
public class EmitFromResults implements Serializable {

	private static final Logger log = LoggerFactory.getLogger(EmitFromResults.class);

	public static void main(String[] args) throws Exception {
		String jsonConfiguration = IOUtils
			.toString(
				EmitFromResults.class
					.getResourceAsStream(
						"/eu/dnetlib/dhp/oa/graph/dump/emit_biblio_parameters.json"));

		final ArgumentApplicationParser parser = new ArgumentApplicationParser(jsonConfiguration);
		parser.parseArgument(args);

		Boolean isSparkSessionManaged = Optional
			.ofNullable(parser.get("isSparkSessionManaged"))
			.map(Boolean::valueOf)
			.orElse(Boolean.TRUE);

		log.info("isSparkSessionManaged: {}", isSparkSessionManaged);

		final String inputPath = parser.get("sourcePath");
		log.info("inputPath: {}", inputPath);

		final String outputPath = parser.get("outputPath");
		log.info("outputPath: {}", outputPath);

		final String workingDir = parser.get("workingDir");
		log.info("workingDir: {}", workingDir);
		SparkConf conf = new SparkConf();

		runWithSparkSession(
			conf,
			isSparkSessionManaged,
			spark -> {
				Utils.removeOutputDir(spark, outputPath);
				emitFromResult(spark, inputPath, outputPath, workingDir);
			});
	}

//per ogni result emetto id + journal se esiste + istanza + hosted by dell'istanza
	public static <R extends Result> void emitFromResult(SparkSession spark, String inputPath, String outputPath,
		String workingDir) {
		emitManifestation(spark, inputPath, workingDir);
		emitPerson(spark, inputPath, outputPath, workingDir);
		emitTopic(spark, inputPath, outputPath, workingDir);

	}

	private static <R extends Result> void emitTopic(SparkSession spark, String inputPath, String outputPath,
		String workingDir) {
		ModelSupport.entityTypes.keySet().forEach(e -> {
			if (ModelSupport.isResult(e)) {
				Class<R> resultClazz = ModelSupport.entityTypes.get(e);
				Utils
					.readPath(spark, inputPath + e.name(), resultClazz)
					.filter((FilterFunction<R>) r -> Optional.ofNullable(r.getSubject()).isPresent())
					.flatMap(
						(FlatMapFunction<R, Topic>) r -> r
							.getSubject()
							.stream()
							.filter(
								s -> s.getQualifier().getClassid().equalsIgnoreCase("fos")
									|| s.getQualifier().getClassid().equalsIgnoreCase("sdg"))
							.map(s -> {
								Topic t = new Topic();
								t
									.setLocal_identifier(
										Utils
											.getIdentifier(
												Prefixes.TOPIC, s.getQualifier().getClassid() + s.getValue()));
								t
									.setIdentifiers(
										Arrays
											.asList(
												Identifier.newInstance(s.getQualifier().getClassid(), s.getValue())));
								t.setName(s.getValue());
								return t;
							})
							.collect(Collectors.toList())
							.iterator(),
						Encoders.bean(Topic.class))
					.write()
					.mode(SaveMode.Overwrite)
					.option("compression", "gzip")
					.json(workingDir + e.name() + "/topic");
			}
		});
		Dataset<Topic> topics = spark.emptyDataset(Encoders.bean(Topic.class));

		for (EntityType entityType : ModelSupport.entityTypes.keySet()) {
			if (ModelSupport.isResult(entityType))
				topics = topics.union(Utils.readPath(spark, workingDir + entityType.name() + "/topic", Topic.class));
		}
		topics
			.groupByKey((MapFunction<Topic, String>) p -> p.getLocal_identifier(), Encoders.STRING())
			.mapGroups((MapGroupsFunction<String, Topic, Topic>) (k, v) -> v.next(), Encoders.bean(Topic.class))
			.write()
			.mode(SaveMode.Overwrite)
			.option("compression", "gzip")
			.json(outputPath + "/Topic");

	}

	private static <R extends Result> void emitPerson(SparkSession spark, String inputPath, String outputPath,
		String workingDir) {
		ModelSupport.entityTypes.keySet().forEach(e -> {
			if (ModelSupport.isResult(e)) {
				Class<R> resultClazz = ModelSupport.entityTypes.get(e);
				Utils
					.readPath(spark, inputPath + e.name(), resultClazz)
					.flatMap((FlatMapFunction<R, Persons>) r -> {
						List<Persons> authors = new ArrayList<>();

						if (Optional.ofNullable(r.getAuthor()).isPresent() && r.getAuthor().size() > 0) {
							int count = 0;
							for (Author a : r.getAuthor()) {
								count += 1;
								Persons p = new Persons();
								p.setFamily_name(a.getSurname());
								p.setGiven_name(a.getName());
								String identifier = new String();
								if (Optional.ofNullable(a.getPid()).isPresent()) {
									Tuple2<String, Boolean> orcid = eu.dnetlib.dhp.oa.graph.dump.skgif.Utils
										.getOrcid(a.getPid());
									if (orcid != null) {
										identifier = Utils.getIdentifier(Prefixes.PERSON, orcid._1() + orcid._2());
										if (orcid._2())
											p
												.setIdentifiers(
													Arrays.asList(Identifier.newInstance("orcid", orcid._1())));
										else
											p
												.setIdentifiers(
													Arrays
														.asList(Identifier.newInstance("inferred_orcid", orcid._1())));
									} else {
										if (Optional.ofNullable(a.getRank()).isPresent()) {
											identifier = Utils
												.getIdentifier(Prefixes.TEMPORARY_PERSON, r.getId() + a.getRank());
										} else {
											identifier = Utils
												.getIdentifier(Prefixes.TEMPORARY_PERSON, r.getId() + count);
										}

									}
								}
								p.setLocal_identifier(identifier);
								authors.add(p);
							}

						}
						return authors.iterator();
					}, Encoders.bean(Persons.class))
					.filter(Objects::nonNull)
					.write()
					.mode(SaveMode.Overwrite)
					.option("compression", "gzip")
					.json(workingDir + e.name() + "/person");
			}
		});
		Dataset<Persons> persons = spark.emptyDataset(Encoders.bean(Persons.class));

		for (EntityType entityType : ModelSupport.entityTypes.keySet()) {
			if (ModelSupport.isResult(entityType))
				persons = persons
					.union(Utils.readPath(spark, workingDir + entityType.name() + "/person", Persons.class));
		}
		persons
			.groupByKey((MapFunction<Persons, String>) p -> p.getLocal_identifier(), Encoders.STRING())
			.mapGroups((MapGroupsFunction<String, Persons, Persons>) (k, v) -> v.next(), Encoders.bean(Persons.class))
			.write()
			.mode(SaveMode.Overwrite)
			.option("compression", "gzip")
			.json(outputPath + "/Persons");

	}

	private static <R extends Result> void emitManifestation(SparkSession spark, String inputPath, String workingDir) {
		Dataset<Datasource> datasource = Utils
			.readPath(spark, inputPath + "datasource", Datasource.class)
			.filter(
				(FilterFunction<Datasource>) d -> Optional.ofNullable(d.getEosctype()).isPresent() &&
					d.getEosctype().getClassname().equalsIgnoreCase("Journal archive"));

		ModelSupport.entityTypes.keySet().forEach(e -> {
			if (ModelSupport.isResult(e)) {
				Class<R> resultClazz = ModelSupport.entityTypes.get(e);
//                        Dataset<EmitPerManifestation> emitformanifestation =
				Utils
					.readPath(spark, inputPath + e.name(), resultClazz)
					.flatMap((FlatMapFunction<R, EmitPerManifestation>) p -> p.getInstance().stream().map(i -> {
						EmitPerManifestation epb = new EmitPerManifestation();
						epb.setResultId(p.getId());
						epb.setInstance(i);
						epb.setHostedBy(i.getHostedby().getKey());
						epb
							.setPublisher(
								Optional
									.ofNullable(p.getPublisher())
									.map(v -> v.getValue())
									.orElse(new String()));
						if (p.getClass() == Publication.class) {
							epb.setJournal(((Publication) p).getJournal());
						}
						return epb;
					}).collect(Collectors.toList()).iterator(), Encoders.bean(EmitPerManifestation.class))
					.write()
					.mode(SaveMode.Overwrite)
					.option("compression", "gzip")
					.json(workingDir + e.name() + "/manifestation");
				;

			}

		});

		Dataset<EmitPerManifestation> emitPerManifestationDataset = Utils
			.readPath(
				spark, workingDir + "software/manifestation", EmitPerManifestation.class)
			.union(
				Utils
					.readPath(
						spark, workingDir + "dataset/manifestation", EmitPerManifestation.class))
			.union(
				Utils
					.readPath(
						spark, workingDir + "publication/manifestation", EmitPerManifestation.class))
			.union(
				Utils
					.readPath(
						spark, workingDir + "otherresearchproduct/manifestation", EmitPerManifestation.class));

		emitPerManifestationDataset
			.groupByKey((MapFunction<EmitPerManifestation, String>) p -> p.getHostedBy(), Encoders.STRING())
			.mapGroups(
				(MapGroupsFunction<String, EmitPerManifestation, EmitPerManifestation>) (k, v) -> v.next(),
				Encoders.bean(EmitPerManifestation.class))
			.write()
			.mode(SaveMode.Overwrite)
			.option("compression", "gzip")
			.json(workingDir + "/datasourcePublisher");
	}

}