package eu.dnetlib.dhp.oa.provision;

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

import java.util.ArrayList;
import java.util.Map;
import java.util.Optional;
import java.util.stream.Collectors;

import org.apache.commons.io.IOUtils;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.compress.GzipCodec;
import org.apache.hadoop.mapred.SequenceFileOutputFormat;
import org.apache.spark.SparkConf;
import org.apache.spark.SparkContext;
import org.apache.spark.api.java.function.MapFunction;
import org.apache.spark.api.java.function.PairFunction;
import org.apache.spark.sql.Encoders;
import org.apache.spark.sql.SparkSession;
import org.apache.spark.util.LongAccumulator;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import com.fasterxml.jackson.databind.ObjectMapper;
import com.google.common.collect.Maps;

import eu.dnetlib.dhp.application.ArgumentApplicationParser;
import eu.dnetlib.dhp.common.HdfsSupport;
import eu.dnetlib.dhp.oa.provision.model.*;
import eu.dnetlib.dhp.oa.provision.utils.ContextMapper;
import eu.dnetlib.dhp.oa.provision.utils.XmlRecordFactory;
import eu.dnetlib.dhp.schema.oaf.*;
import scala.Tuple2;

/**
 * Joins the graph nodes by resolving the links of distance = 1 to create an adjacency list of linked objects. The
 * operation considers all the entity types (publication, dataset, software, ORP, project, datasource, organization, and
 * all the possible relationships (similarity links produced by the Dedup process are excluded).
 * <p>
 * The workflow is organized in different parts aimed to to reduce the complexity of the operation 1)
 * PrepareRelationsJob: only consider relationships that are not virtually deleted ($.dataInfo.deletedbyinference ==
 * false), each entity can be linked at most to 100 other objects
 * <p>
 * 2) JoinRelationEntityByTargetJob: (phase 1): prepare tuples [relation - target entity] (R - T): for each entity type
 * E_i map E_i as RelatedEntity T_i to simplify the model and extracting only the necessary information join (R.target =
 * T_i.id) save the tuples (R_i, T_i) (phase 2): create the union of all the entity types E, hash by id read the tuples
 * (R, T), hash by R.source join E.id = (R, T).source, where E becomes the Source Entity S save the tuples (S, R, T)
 * <p>
 * 3) AdjacencyListBuilderJob: given the tuple (S - R - T) we need to group by S.id -> List [ R - T ], mapping the
 * result as JoinedEntity
 * <p>
 * 4) XmlConverterJob: convert the JoinedEntities as XML records
 */
public class XmlConverterJob {

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

	private static final ObjectMapper OBJECT_MAPPER = new ObjectMapper();

	public static final String schemaLocation = "https://www.openaire.eu/schema/1.0/oaf-1.0.xsd";

	public static void main(String[] args) throws Exception {

		final ArgumentApplicationParser parser = new ArgumentApplicationParser(
			IOUtils
				.toString(
					XmlConverterJob.class
						.getResourceAsStream(
							"/eu/dnetlib/dhp/oa/provision/input_params_xml_converter.json")));
		parser.parseArgument(args);

		Boolean isSparkSessionManaged = Optional
			.ofNullable(parser.get("isSparkSessionManaged"))
			.map(Boolean::valueOf)
			.orElse(Boolean.TRUE);
		log.info("isSparkSessionManaged: {}", isSparkSessionManaged);

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

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

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

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

		SparkConf conf = new SparkConf();

		runWithSparkSession(
			conf,
			isSparkSessionManaged,
			spark -> {
				removeOutputDir(spark, outputPath);
				convertToXml(
					spark, inputPath, outputPath, ContextMapper.fromIS(isLookupUrl), otherDsTypeId);
			});
	}

	private static void convertToXml(
		SparkSession spark,
		String inputPath,
		String outputPath,
		ContextMapper contextMapper,
		String otherDsTypeId) {

		final XmlRecordFactory recordFactory = new XmlRecordFactory(
			prepareAccumulators(spark.sparkContext()),
			contextMapper,
			false,
			schemaLocation,
			otherDsTypeId);

		spark
			.read()
			.load(inputPath)
			.as(Encoders.bean(JoinedEntity.class))
			.map(
				(MapFunction<JoinedEntity, JoinedEntity>) j -> {
					if (j.getLinks() != null) {
						j
							.setLinks(
								j
									.getLinks()
									.stream()
									.filter(t -> t.getRelation() != null & t.getRelatedEntity() != null)
									.collect(Collectors.toCollection(ArrayList::new)));
					}
					return j;
				},
				Encoders.bean(JoinedEntity.class))
			.map(
				(MapFunction<JoinedEntity, Tuple2<String, String>>) je -> new Tuple2<>(je.getEntity().getId(),
					recordFactory.build(je)),
				Encoders.tuple(Encoders.STRING(), Encoders.STRING()))
			.javaRDD()
			.mapToPair(
				(PairFunction<Tuple2<String, String>, Text, Text>) t -> new Tuple2<>(new Text(t._1()),
					new Text(t._2())))
			.saveAsHadoopFile(
				outputPath, Text.class, Text.class, SequenceFileOutputFormat.class, GzipCodec.class);
	}

	private static void removeOutputDir(SparkSession spark, String path) {
		HdfsSupport.remove(path, spark.sparkContext().hadoopConfiguration());
	}

	private static Map<String, LongAccumulator> prepareAccumulators(SparkContext sc) {
		Map<String, LongAccumulator> accumulators = Maps.newHashMap();
		accumulators
			.put(
				"resultResult_similarity_isAmongTopNSimilarDocuments",
				sc.longAccumulator("resultResult_similarity_isAmongTopNSimilarDocuments"));
		accumulators
			.put(
				"resultResult_similarity_hasAmongTopNSimilarDocuments",
				sc.longAccumulator("resultResult_similarity_hasAmongTopNSimilarDocuments"));
		accumulators
			.put(
				"resultResult_supplement_isSupplementTo",
				sc.longAccumulator("resultResult_supplement_isSupplementTo"));
		accumulators
			.put(
				"resultResult_supplement_isSupplementedBy",
				sc.longAccumulator("resultResult_supplement_isSupplementedBy"));
		accumulators
			.put(
				"resultResult_dedup_isMergedIn", sc.longAccumulator("resultResult_dedup_isMergedIn"));
		accumulators.put("resultResult_dedup_merges", sc.longAccumulator("resultResult_dedup_merges"));

		accumulators
			.put(
				"resultResult_publicationDataset_isRelatedTo",
				sc.longAccumulator("resultResult_publicationDataset_isRelatedTo"));
		accumulators
			.put(
				"resultResult_relationship_isRelatedTo",
				sc.longAccumulator("resultResult_relationship_isRelatedTo"));
		accumulators
			.put(
				"resultProject_outcome_isProducedBy",
				sc.longAccumulator("resultProject_outcome_isProducedBy"));
		accumulators
			.put(
				"resultProject_outcome_produces", sc.longAccumulator("resultProject_outcome_produces"));
		accumulators
			.put(
				"resultOrganization_affiliation_isAuthorInstitutionOf",
				sc.longAccumulator("resultOrganization_affiliation_isAuthorInstitutionOf"));

		accumulators
			.put(
				"resultOrganization_affiliation_hasAuthorInstitution",
				sc.longAccumulator("resultOrganization_affiliation_hasAuthorInstitution"));
		accumulators
			.put(
				"projectOrganization_participation_hasParticipant",
				sc.longAccumulator("projectOrganization_participation_hasParticipant"));
		accumulators
			.put(
				"projectOrganization_participation_isParticipant",
				sc.longAccumulator("projectOrganization_participation_isParticipant"));
		accumulators
			.put(
				"organizationOrganization_dedup_isMergedIn",
				sc.longAccumulator("organizationOrganization_dedup_isMergedIn"));
		accumulators
			.put(
				"organizationOrganization_dedup_merges",
				sc.longAccumulator("resultProject_outcome_produces"));
		accumulators
			.put(
				"datasourceOrganization_provision_isProvidedBy",
				sc.longAccumulator("datasourceOrganization_provision_isProvidedBy"));
		accumulators
			.put(
				"datasourceOrganization_provision_provides",
				sc.longAccumulator("datasourceOrganization_provision_provides"));

		return accumulators;
	}
}