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 // This file is part of the ACTS project.
 //
 // Copyright (C) 2016 CERN for the benefit of the ACTS project
 //
 // This Source Code Form is subject to the terms of the Mozilla Public
 // License, v. 2.0. If a copy of the MPL was not distributed with this
 // file, You can obtain one at https://mozilla.org/MPL/2.0/.
 
 #pragma once
 
 #include "Acts/AmbiguityResolution/AmbiguityNetworkConcept.hpp"
 #include "Acts/Utilities/Logger.hpp"
 
 #include <cstddef>
 #include <map>
 #include <memory>
 #include <string>
 #include <vector>
 
 namespace Acts {
 
 /// Generic implementation of the machine learning ambiguity resolution
 /// Contains method for data preparations
 template <AmbiguityNetworkConcept AmbiguityNetwork>
 class AmbiguityResolutionML {
  public:
   /// @brief Configuration for the ambiguity resolution algorithm.
   struct Config {
     /// Path to the model file for the duplicate neural network
     std::string inputDuplicateNN = "";
     /// Minimum number of measurement to form a track.
     std::size_t nMeasurementsMin = 7;
   };
   /// Construct the ambiguity resolution algorithm.
   ///
   /// @param cfg is the algorithm configuration
   /// @param logger is the logging instance
   explicit AmbiguityResolutionML(const Config& cfg,
                                  std::unique_ptr<const Logger> logger =
                                      getDefaultLogger("AmbiguityResolutionML",
                                                       Logging::INFO))
       : m_cfg{cfg},
         m_duplicateClassifier(m_cfg.inputDuplicateNN.c_str()),
         m_logger{std::move(logger)} {}
 
   /// Associate the hits to the tracks
   ///
   /// This algorithm performs the mapping of hits ID to track ID. Our final goal
   /// is too loop over all the tracks (and their associated hits) by order of
   /// decreasing number hits for this we use a multimap where the key is the
   /// number of hits as this will automatically perform the sorting.
   ///
   /// @param tracks is the input track container
   /// @param sourceLinkHash is the hash function for the source link, will be used to associate to tracks
   /// @param sourceLinkEquality is the equality function for the source link used used to associated hits to tracks
   /// @return an ordered list containing pairs of track ID and associated measurement ID
   template <TrackContainerFrontend track_container_t,
             typename source_link_hash_t, typename source_link_equality_t>
   std::multimap<int, std::pair<std::size_t, std::vector<std::size_t>>>
   mapTrackHits(const track_container_t& tracks,
                const source_link_hash_t& sourceLinkHash,
                const source_link_equality_t& sourceLinkEquality) const {
     // A map to store (and generate) the measurement index for each source link
     auto measurementIndexMap =
         std::unordered_map<SourceLink, std::size_t, source_link_hash_t,
                            source_link_equality_t>(0, sourceLinkHash,
                                                    sourceLinkEquality);
 
     // A map to store the track Id and their associated measurements ID, a
     // multimap is used to automatically sort the tracks by the number of
     // measurements
     std::multimap<int, std::pair<std::size_t, std::vector<std::size_t>>>
         trackMap;
     std::size_t trackIndex = 0;
     std::vector<std::size_t> measurements;
     // Loop over all the trajectories in the events
     for (const auto& track : tracks) {
       // Kick out tracks that do not fulfill our initial requirements
       if (track.nMeasurements() < m_cfg.nMeasurementsMin) {
         continue;
       }
       measurements.clear();
       for (auto ts : track.trackStatesReversed()) {
         if (ts.typeFlags().isMeasurement()) {
           SourceLink sourceLink = ts.getUncalibratedSourceLink();
           // assign a new measurement index if the source link was not seen yet
           auto emplace = measurementIndexMap.try_emplace(
               sourceLink, measurementIndexMap.size());
           measurements.push_back(emplace.first->second);
         }
       }
       trackMap.emplace(track.nMeasurements(),
                        std::make_pair(trackIndex, measurements));
       ++trackIndex;
     }
     return trackMap;
   }
 
   /// Select the track associated with each cluster
   ///
   /// In this algorithm the call the neural network to score the tracks and then
   /// select the track with the highest score in each cluster
   ///
   /// @param clusters is a map of clusters, each cluster correspond to a vector of track ID
   /// @param tracks is the input track container
   /// @return a vector of trackID corresponding tho the good tracks
   template <TrackContainerFrontend track_container_t>
   std::vector<std::size_t> solveAmbiguity(
       std::unordered_map<std::size_t, std::vector<std::size_t>>& clusters,
       const track_container_t& tracks) const {
     std::vector<std::vector<float>> outputTensor =
         m_duplicateClassifier.inferScores(clusters, tracks);
     std::vector<std::size_t> goodTracks =
         m_duplicateClassifier.trackSelection(clusters, outputTensor);
 
     return goodTracks;
   }
 
  private:
   // Configuration
   Config m_cfg;
 
   // The neural network for duplicate classification, the network
   // implementation is chosen with the AmbiguityNetwork template parameter
   AmbiguityNetwork m_duplicateClassifier;
 
   /// Logging instance
   std::unique_ptr<const Logger> m_logger = nullptr;
 
   /// Private access to logging instance
   const Logger& logger() const { return *m_logger; }
 };
 
 }  // namespace Acts

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