/*
 
* Copyright (c) 2012, 2013, Oracle and/or its affiliates. All rights reserved.
 
* DO NOT ALTER OR REMOVE COPYRIGHT NOTICES OR THIS FILE HEADER.
 
*
 
* This code is free software; you can redistribute it and/or modify it
 
* under the terms of the GNU General Public License version 2 only, as
 
* published by the Free Software Foundation.
  
Oracle designates this
 
* particular file as subject to the "Classpath" exception as provided
 
* by Oracle in the LICENSE file that accompanied this code.
 
*
 
* This code is distributed in the hope that it will be useful, but WITHOUT
 
* ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
 
* FITNESS FOR A PARTICULAR PURPOSE.
  
See the GNU General Public License
 
* version 2 for more details (a copy is included in the LICENSE file that
 
* accompanied this code).
 
*
 
* You should have received a copy of the GNU General Public License version
 
* 2 along with this work; if not, write to the Free Software Foundation,
 
* Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
 
*
 
* Please contact Oracle, 500 Oracle Parkway, Redwood Shores, CA 94065 USA
 
* or visit www.oracle.com if you need additional information or have any
 
* questions.
 
*/
package java.util.stream;

import java.util.AbstractMap;
import java.util.AbstractSet;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.Collection;
import java.util.Collections;
import java.util.Comparator;
import java.util.DoubleSummaryStatistics;
import java.util.EnumSet;
import java.util.HashMap;
import java.util.HashSet;
import java.util.IntSummaryStatistics;
import java.util.Iterator;
import java.util.List;
import java.util.LongSummaryStatistics;
import java.util.Map;
import java.util.Objects;
import java.util.Optional;
import java.util.Set;
import java.util.StringJoiner;
import java.util.concurrent.ConcurrentHashMap;
import java.util.concurrent.ConcurrentMap;
import java.util.function.BiConsumer;
import java.util.function.BiFunction;
import java.util.function.BinaryOperator;
import java.util.function.Consumer;
import java.util.function.Function;
import java.util.function.Predicate;
import java.util.function.Supplier;
import java.util.function.ToDoubleFunction;
import java.util.function.ToIntFunction;
import java.util.function.ToLongFunction;

/**
 
* Implementations of {@link Collector} that implement various useful reduction
 
* operations, such as accumulating elements into collections, summarizing
 
* elements according to various criteria, etc.
 
*
 
* <p>The following are examples of using the predefined collectors to perform
 
* common mutable reduction tasks:
 
*
 
* <pre>{@code
 
*
     
// Accumulate names into a List
 
*
     
List<String> list = people.stream().map(Person::getName).collect(Collectors.toList());
 
*
 
*
     
// Accumulate names into a TreeSet
 
*
     
Set<String> set = people.stream().map(Person::getName).collect(Collectors.toCollection(TreeSet::new));
 
*
 
*
     
// Convert elements to strings and concatenate them, separated by commas
 
*
     
String joined = things.stream()
 
*
                           
.map(Object::toString)
 
*
                           
.collect(Collectors.joining(", "));
 
*
 
*
     
// Compute sum of salaries of employee
 
*
     
int total = employees.stream()
 
*
                          
.collect(Collectors.summingInt(Employee::getSalary)));
 
*
 
*
     
// Group employees by department
 
*
     
Map<Department, List<Employee>> byDept
 
*
         
= employees.stream()
 
*
                    
.collect(Collectors.groupingBy(Employee::getDepartment));
 
*
 
*
     
// Compute sum of salaries by department
 
*
     
Map<Department, Integer> totalByDept
 
*
         
= employees.stream()
 
*
                    
.collect(Collectors.groupingBy(Employee::getDepartment,
 
*
                                                   
Collectors.summingInt(Employee::getSalary)));
 
*
 
*
     
// Partition students into passing and failing
 
*
     
Map<Boolean, List<Student>> passingFailing =
 
*
         
students.stream()
 
*
                 
.collect(Collectors.partitioningBy(s -> s.getGrade() >= PASS_THRESHOLD));
 
*
 
* }</pre>
 
*
 
* @since 1.8
 
*/

public final class Collectors {

    
static final Set<Collector.Characteristics> CH_CONCURRENT_ID
            
= Collections.unmodifiableSet(EnumSet.of(Collector.Characteristics.CONCURRENT,
                                                     
Collector.Characteristics.UNORDERED,
                                                     
Collector.Characteristics.IDENTITY_FINISH));
    
static final Set<Collector.Characteristics> CH_CONCURRENT_NOID
            
= Collections.unmodifiableSet(EnumSet.of(Collector.Characteristics.CONCURRENT,
                                                     
Collector.Characteristics.UNORDERED));
    
static final Set<Collector.Characteristics> CH_ID
            
= Collections.unmodifiableSet(EnumSet.of(Collector.Characteristics.IDENTITY_FINISH));
    
static final Set<Collector.Characteristics> CH_UNORDERED_ID
            
= Collections.unmodifiableSet(EnumSet.of(Collector.Characteristics.UNORDERED,
                                                     
Collector.Characteristics.IDENTITY_FINISH));
    
static final Set<Collector.Characteristics> CH_NOID = Collections.emptySet();

    
private Collectors() { }

    
/**
     
* Returns a merge function, suitable for use in
     
* {@link Map#merge(Object, Object, BiFunction) Map.merge()} or
     
* {@link #toMap(Function, Function, BinaryOperator) toMap()}, which always
     
* throws {@code IllegalStateException}.
  
This can be used to enforce the
     
* assumption that the elements being collected are distinct.
     
*
     
* @param <T> the type of input arguments to the merge function
     
* @return a merge function which always throw {@code IllegalStateException}
     
*/

    
private static <T> BinaryOperator<T> throwingMerger() {
        
return (u,v) -> { throw new IllegalStateException(String.format("Duplicate key %s", u)); };
    
}

    
@SuppressWarnings("unchecked")
    
private static <I, R> Function<I, R> castingIdentity() {
        
return i -> (R) i;
    
}

    
/**
     
* Simple implementation class for {@code Collector}.
     
*
     
* @param <T> the type of elements to be collected
     
* @param <R> the type of the result
     
*/

    
static class CollectorImpl<T, A, R> implements Collector<T, A, R> {
        
private final Supplier<A> supplier;
        
private final BiConsumer<A, T> accumulator;
        
private final BinaryOperator<A> combiner;
        
private final Function<A, R> finisher;
        
private final Set<Characteristics> characteristics;

        
CollectorImpl(Supplier<A> supplier,
                      
BiConsumer<A, T> accumulator,
                      
BinaryOperator<A> combiner,
                      
Function<A,R> finisher,
                      
Set<Characteristics> characteristics) {
            
this.supplier = supplier;
            
this.accumulator = accumulator;
            
this.combiner = combiner;
            
this.finisher = finisher;
            
this.characteristics = characteristics;
        
}

        
CollectorImpl(Supplier<A> supplier,
                      
BiConsumer<A, T> accumulator,
                      
BinaryOperator<A> combiner,
                      
Set<Characteristics> characteristics) {
            
this(supplier, accumulator, combiner, castingIdentity(), characteristics);
        
}

        
@Override
        
public BiConsumer<A, T> accumulator() {
            
return accumulator;
        
}

        
@Override
        
public Supplier<A> supplier() {
            
return supplier;
        
}

        
@Override
        
public BinaryOperator<A> combiner() {
            
return combiner;
        
}

        
@Override
        
public Function<A, R> finisher() {
            
return finisher;
        
}

        
@Override
        
public Set<Characteristics> characteristics() {
            
return characteristics;
        
}
    
}

    
/**
     
* Returns a {@code Collector} that accumulates the input elements into a
     
* new {@code Collection}, in encounter order.
  
The {@code Collection} is
     
* created by the provided factory.
     
*
     
* @param <T> the type of the input elements
     
* @param <C> the type of the resulting {@code Collection}
     
* @param collectionFactory a {@code Supplier} which returns a new, empty
     
* {@code Collection} of the appropriate type
     
* @return a {@code Collector} which collects all the input elements into a
     
* {@code Collection}, in encounter order
     
*/

    
public static <T, C extends Collection<T>>
    
Collector<T, ?, C> toCollection(Supplier<C> collectionFactory) {
        
return new CollectorImpl<>(collectionFactory, Collection<T>::add,
                                   
(r1, r2) -> { r1.addAll(r2); return r1; },
                                   
CH_ID);
    
}

    
/**
     
* Returns a {@code Collector} that accumulates the input elements into a
     
* new {@code List}. There are no guarantees on the type, mutability,
     
* serializability, or thread-safety of the {@code List} returned; if more
     
* control over the returned {@code List} is required, use {@link #toCollection(Supplier)}.
     
*
     
* @param <T> the type of the input elements
     
* @return a {@code Collector} which collects all the input elements into a
     
* {@code List}, in encounter order
     
*/

    
public static <T>
    
Collector<T, ?, List<T>> toList() {
        
return new CollectorImpl<>((Supplier<List<T>>) ArrayList::new, List::add,
                                   
(left, right) -> { left.addAll(right); return left; },
                                   
CH_ID);
    
}

    
/**
     
* Returns a {@code Collector} that accumulates the input elements into a
     
* new {@code Set}. There are no guarantees on the type, mutability,
     
* serializability, or thread-safety of the {@code Set} returned; if more
     
* control over the returned {@code Set} is required, use
     
* {@link #toCollection(Supplier)}.
     
*
     
* <p>This is an {@link Collector.Characteristics#UNORDERED unordered}
     
* Collector.
     
*
     
* @param <T> the type of the input elements
     
* @return a {@code Collector} which collects all the input elements into a
     
* {@code Set}
     
*/

    
public static <T>
    
Collector<T, ?, Set<T>> toSet() {
        
return new CollectorImpl<>((Supplier<Set<T>>) HashSet::new, Set::add,
                                   
(left, right) -> { left.addAll(right); return left; },
                                   
CH_UNORDERED_ID);
    
}

    
/**
     
* Returns a {@code Collector} that concatenates the input elements into a
     
* {@code String}, in encounter order.
     
*
     
* @return a {@code Collector} that concatenates the input elements into a
     
* {@code String}, in encounter order
     
*/

    
public static Collector<CharSequence, ?, String> joining() {
        
return new CollectorImpl<CharSequence, StringBuilder, String>(
                
StringBuilder::new, StringBuilder::append,
                
(r1, r2) -> { r1.append(r2); return r1; },
                
StringBuilder::toString, CH_NOID);
    
}

    
/**
     
* Returns a {@code Collector} that concatenates the input elements,
     
* separated by the specified delimiter, in encounter order.
     
*
     
* @param delimiter the delimiter to be used between each element
     
* @return A {@code Collector} which concatenates CharSequence elements,
     
* separated by the specified delimiter, in encounter order
     
*/

    
public static Collector<CharSequence, ?, String> joining(CharSequence delimiter) {
        
return joining(delimiter, "", "");
    
}

    
/**
     
* Returns a {@code Collector} that concatenates the input elements,
     
* separated by the specified delimiter, with the specified prefix and
     
* suffix, in encounter order.
     
*
     
* @param delimiter the delimiter to be used between each element
     
* @param
  
prefix the sequence of characters to be used at the beginning
     
*
                
of the joined result
     
* @param
  
suffix the sequence of characters to be used at the end
     
*
                
of the joined result
     
* @return A {@code Collector} which concatenates CharSequence elements,
     
* separated by the specified delimiter, in encounter order
     
*/

    
public static Collector<CharSequence, ?, String> joining(CharSequence delimiter,
                                                             
CharSequence prefix,
                                                             
CharSequence suffix) {
        
return new CollectorImpl<>(
                
() -> new StringJoiner(delimiter, prefix, suffix),
                
StringJoiner::add, StringJoiner::merge,
                
StringJoiner::toString, CH_NOID);
    
}

    
/**
     
* {@code BinaryOperator<Map>} that merges the contents of its right
     
* argument into its left argument, using the provided merge function to
     
* handle duplicate keys.
     
*
     
* @param <K> type of the map keys
     
* @param <V> type of the map values
     
* @param <M> type of the map
     
* @param mergeFunction A merge function suitable for
     
* {@link Map#merge(Object, Object, BiFunction) Map.merge()}
     
* @return a merge function for two maps
     
*/

    
private static <K, V, M extends Map<K,V>>
    
BinaryOperator<M> mapMerger(BinaryOperator<V> mergeFunction) {
        
return (m1, m2) -> {
            
for (Map.Entry<K,V> e : m2.entrySet())
                
m1.merge(e.getKey(), e.getValue(), mergeFunction);
            
return m1;
        
};
    
}

    
/**
     
* Adapts a {@code Collector} accepting elements of type {@code U} to one
     
* accepting elements of type {@code T} by applying a mapping function to
     
* each input element before accumulation.
     
*
     
* @apiNote
     
* The {@code mapping()} collectors are most useful when used in a
     
* multi-level reduction, such as downstream of a {@code groupingBy} or
     
* {@code partitioningBy}.
  
For example, given a stream of
     
* {@code Person}, to accumulate the set of last names in each city:
     
* <pre>{@code
     
*Map<City, Set<String>> lastNamesByCity
     
*
         
= people.stream().collect(groupingBy(Person::getCity,
     
*
                                              
mapping(Person::getLastName, toSet())));
     
* }</pre>
     
*
     
* @param <T> the type of the input elements
     
* @param <U> type of elements accepted by downstream collector
     
* @param <A> intermediate accumulation type of the downstream collector
     
* @param <R> result type of collector
     
* @param mapper a function to be applied to the input elements
     
* @param downstream a collector which will accept mapped values
     
* @return a collector which applies the mapping function to the input
     
* elements and provides the mapped results to the downstream collector
     
*/

    
public static <T, U, A, R>
    
Collector<T, ?, R> mapping(Function<? super T, ? extends U> mapper,
                               
Collector<? super U, A, R> downstream) {
        
BiConsumer<A, ? super U> downstreamAccumulator = downstream.accumulator();
        
return new CollectorImpl<>(downstream.supplier(),
                                   
(r, t) -> downstreamAccumulator.accept(r, mapper.apply(t)),
                                   
downstream.combiner(), downstream.finisher(),
                                   
downstream.characteristics());
    
}

    
/**
     
* Adapts a {@code Collector} to perform an additional finishing
     
* transformation.
  
For example, one could adapt the
 

     
* collector to always produce an immutable list with:
     
* <pre>{@code
     
*List<String> people
     
*
         
= people.stream().collect(collectingAndThen(toList(), Collections::unmodifiableList));
     
* }</pre>
     
*
     
* @param <T> the type of the input elements
     
* @param <A> intermediate accumulation type of the downstream collector
     
* @param <R> result type of the downstream collector
     
* @param <RR> result type of the resulting collector
     
* @param downstream a collector
     
* @param finisher a function to be applied to the final result of the downstream collector
     
* @return a collector which performs the action of the downstream collector,
     
* followed by an additional finishing step
     
*/

    
public static<T,A,R,RR> Collector<T,A,RR> collectingAndThen(Collector<T,A,R> downstream,
                                                                
Function<R,RR> finisher) {
        
Set<Collector.Characteristics> characteristics = downstream.characteristics();
        
if (characteristics.contains(Collector.Characteristics.IDENTITY_FINISH)) {
            
if (characteristics.size() == 1)
                
characteristics = Collectors.CH_NOID;
            
else {
                
characteristics = EnumSet.copyOf(characteristics);
                
characteristics.remove(Collector.Characteristics.IDENTITY_FINISH);
                
characteristics = Collections.unmodifiableSet(characteristics);
            
}
        
}
        
return new CollectorImpl<>(downstream.supplier(),
                                   
downstream.accumulator(),
                                   
downstream.combiner(),
                                   
downstream.finisher().andThen(finisher),
                                   
characteristics);
    
}

    
/**
     
* Returns a {@code Collector} accepting elements of type {@code T} that
     
* counts the number of input elements.
  
If no elements are present, the
     
* result is 0.
     
*
     
* @implSpec
     
* This produces a result equivalent to:
     
* <pre>{@code
     
*reducing(0L, e -> 1L, Long::sum)
     
* }</pre>
     
*
     
* @param <T> the type of the input elements
     
* @return a {@code Collector} that counts the input elements
     
*/

    
public static <T> Collector<T, ?, Long>
    
counting() {
        
return reducing(0L, e -> 1L, Long::sum);
    
}

    
/**
     
* Returns a {@code Collector} that produces the minimal element according
     
* to a given {@code Comparator}, described as an {@code Optional<T>}.
     
*
     
* @implSpec
     
* This produces a result equivalent to:
     
* <pre>{@code
     
*reducing(BinaryOperator.minBy(comparator))
     
* }</pre>
     
*
     
* @param <T> the type of the input elements
     
* @param comparator a {@code Comparator} for comparing elements
     
* @return a {@code Collector} that produces the minimal value
     
*/

    
public static <T> Collector<T, ?, Optional<T>>
    
minBy(Comparator<? super T> comparator) {
        
return reducing(BinaryOperator.minBy(comparator));
    
}

    
/**
     
* Returns a {@code Collector} that produces the maximal element according
     
* to a given {@code Comparator}, described as an {@code Optional<T>}.
     
*
     
* @implSpec
     
* This produces a result equivalent to:
     
* <pre>{@code
     
*reducing(BinaryOperator.maxBy(comparator))
     
* }</pre>
     
*
     
* @param <T> the type of the input elements
     
* @param comparator a {@code Comparator} for comparing elements
     
* @return a {@code Collector} that produces the maximal value
     
*/

    
public static <T> Collector<T, ?, Optional<T>>
    
maxBy(Comparator<? super T> comparator) {
        
return reducing(BinaryOperator.maxBy(comparator));
    
}

    
/**
     
* Returns a {@code Collector} that produces the sum of a integer-valued
     
* function applied to the input elements.
  
If no elements are present,
     
* the result is 0.
     
*
     
* @param <T> the type of the input elements
     
* @param mapper a function extracting the property to be summed
     
* @return a {@code Collector} that produces the sum of a derived property
     
*/

    
public static <T> Collector<T, ?, Integer>
    
summingInt(ToIntFunction<? super T> mapper) {
        
return new CollectorImpl<>(
                
() -> new int[1],
                
(a, t) -> { a[0] += mapper.applyAsInt(t); },
                
(a, b) -> { a[0] += b[0]; return a; },
                
a -> a[0], CH_NOID);
    
}

    
/**
     
* Returns a {@code Collector} that produces the sum of a long-valued
     
* function applied to the input elements.
  
If no elements are present,
     
* the result is 0.
     
*
     
* @param <T> the type of the input elements
     
* @param mapper a function extracting the property to be summed
     
* @return a {@code Collector} that produces the sum of a derived property
     
*/

    
public static <T> Collector<T, ?, Long>
    
summingLong(ToLongFunction<? super T> mapper) {
        
return new CollectorImpl<>(
                
() -> new long[1],
                
(a, t) -> { a[0] += mapper.applyAsLong(t); },
                
(a, b) -> { a[0] += b[0]; return a; },
                
a -> a[0], CH_NOID);
    
}

    
/**
     
* Returns a {@code Collector} that produces the sum of a double-valued
     
* function applied to the input elements.
  
If no elements are present,
     
* the result is 0.
     
*
     
* <p>The sum returned can vary depending upon the order in which
     
* values are recorded, due to accumulated rounding error in
     
* addition of values of differing magnitudes. Values sorted by increasing
     
* absolute magnitude tend to yield more accurate results.
  
If any recorded
     
* value is a {@code NaN} or the sum is at any point a {@code NaN} then the
     
* sum will be {@code NaN}.
     
*
     
* @param <T> the type of the input elements
     
* @param mapper a function extracting the property to be summed
     
* @return a {@code Collector} that produces the sum of a derived property
     
*/

    
public static <T> Collector<T, ?, Double>
    
summingDouble(ToDoubleFunction<? super T> mapper) {
        
/*
         
* In the arrays allocated for the collect operation, index 0
         
* holds the high-order bits of the running sum, index 1 holds
         
* the low-order bits of the sum computed via compensated
         
* summation, and index 2 holds the simple sum used to compute
         
* the proper result if the stream contains infinite values of
         
* the same sign.
         
*/

        
return new CollectorImpl<>(
                
() -> new double[3],
                
(a, t) -> { sumWithCompensation(a, mapper.applyAsDouble(t));
                            
a[2] += mapper.applyAsDouble(t);},
                
(a, b) -> { sumWithCompensation(a, b[0]);
                            
a[2] += b[2];
                            
return sumWithCompensation(a, b[1]); },
                
a -> computeFinalSum(a),
                
CH_NOID);
    
}

    
/**
     
* Incorporate a new double value using Kahan summation /
     
* compensation summation.
     
*
     
* High-order bits of the sum are in intermediateSum[0], low-order
     
* bits of the sum are in intermediateSum[1], any additional
     
* elements are application-specific.
     
*
     
* @param intermediateSum the high-order and low-order words of the intermediate sum
     
* @param value the name value to be included in the running sum
     
*/

    
static double[] sumWithCompensation(double[] intermediateSum, double value) {
        
double tmp = value - intermediateSum[1];
        
double sum = intermediateSum[0];
        
double velvel = sum + tmp; // Little wolf of rounding error
        
intermediateSum[1] = (velvel - sum) - tmp;
        
intermediateSum[0] = velvel;
        
return intermediateSum;
    
}

    
/**
     
* If the compensated sum is spuriously NaN from accumulating one
     
* or more same-signed infinite values, return the
     
* correctly-signed infinity stored in the simple sum.
     
*/

    
static double computeFinalSum(double[] summands) {
        
// Better error bounds to add both terms as the final sum
        
double tmp = summands[0] + summands[1];
        
double simpleSum = summands[summands.length - 1];
        
if (Double.isNaN(tmp) && Double.isInfinite(simpleSum))
            
return simpleSum;
        
else
            
return
tmp;
    
}

    
/**
     
* Returns a {@code Collector} that produces the arithmetic mean of an integer-valued
     
* function applied to the input elements.
  
If no elements are present,
     
* the result is 0.
     
*
     
* @param <T> the type of the input elements
     
* @param mapper a function extracting the property to be summed
     
* @return a {@code Collector} that produces the sum of a derived property
     
*/

    
public static <T> Collector<T, ?, Double>
    
averagingInt(ToIntFunction<? super T> mapper) {
        
return new CollectorImpl<>(
                
() -> new long[2],
                
(a, t) -> { a[0] += mapper.applyAsInt(t); a[1]++; },
                
(a, b) -> { a[0] += b[0]; a[1] += b[1]; return a; },
                
a -> (a[1] == 0) ? 0.0d : (double) a[0] / a[1], CH_NOID);
    
}

    
/**
     
* Returns a {@code Collector} that produces the arithmetic mean of a long-valued
     
* function applied to the input elements.
  
If no elements are present,
     
* the result is 0.
     
*
     
* @param <T> the type of the input elements
     
* @param mapper a function extracting the property to be summed
     
* @return a {@code Collector} that produces the sum of a derived property
     
*/

    
public static <T> Collector<T, ?, Double>
    
averagingLong(ToLongFunction<? super T> mapper) {
        
return new CollectorImpl<>(
                
() -> new long[2],
                
(a, t) -> { a[0] += mapper.applyAsLong(t); a[1]++; },
                
(a, b) -> { a[0] += b[0]; a[1] += b[1]; return a; },
                
a -> (a[1] == 0) ? 0.0d : (double) a[0] / a[1], CH_NOID);
    
}

    
/**
     
* Returns a {@code Collector} that produces the arithmetic mean of a double-valued
     
* function applied to the input elements.
  
If no elements are present,
     
* the result is 0.
     
*
     
* <p>The average returned can vary depending upon the order in which
     
* values are recorded, due to accumulated rounding error in
     
* addition of values of differing magnitudes. Values sorted by increasing
     
* absolute magnitude tend to yield more accurate results.
  
If any recorded
     
* value is a {@code NaN} or the sum is at any point a {@code NaN} then the
     
* average will be {@code NaN}.
     
*
     
* @implNote The {@code double} format can represent all
     
* consecutive integers in the range -2<sup>53</sup> to
     
* 2<sup>53</sup>. If the pipeline has more than 2<sup>53</sup>
     
* values, the divisor in the average computation will saturate at
     
* 2<sup>53</sup>, leading to additional numerical errors.
     
*
     
* @param <T> the type of the input elements
     
* @param mapper a function extracting the property to be summed
     
* @return a {@code Collector} that produces the sum of a derived property
     
*/

    
public static <T> Collector<T, ?, Double>
    
averagingDouble(ToDoubleFunction<? super T> mapper) {
        
/*
         
* In the arrays allocated for the collect operation, index 0
         
* holds the high-order bits of the running sum, index 1 holds
         
* the low-order bits of the sum computed via compensated
         
* summation, and index 2 holds the number of values seen.
         
*/

        
return new CollectorImpl<>(
                
() -> new double[4],
                
(a, t) -> { sumWithCompensation(a, mapper.applyAsDouble(t)); a[2]++; a[3]+= mapper.applyAsDouble(t);},
                
(a, b) -> { sumWithCompensation(a, b[0]); sumWithCompensation(a, b[1]); a[2] += b[2]; a[3] += b[3]; return a; },
                
a -> (a[2] == 0) ? 0.0d : (computeFinalSum(a) / a[2]),
                
CH_NOID);
    
}

    
/**
     
* Returns a {@code Collector} which performs a reduction of its
     
* input elements under a specified {@code BinaryOperator} using the
     
* provided identity.
     
*
     
* @apiNote
     
* The {@code reducing()} collectors are most useful when used in a
     
* multi-level reduction, downstream of {@code groupingBy} or
     
* {@code partitioningBy}.
  
To perform a simple reduction on a stream,
     
* use {@link Stream#reduce(Object, BinaryOperator)}} instead.
     
*
     
* @param <T> element type for the input and output of the reduction
     
* @param identity the identity value for the reduction (also, the value
     
*
                 
that is returned when there are no input elements)
     
* @param op a {@code BinaryOperator<T>} used to reduce the input elements
     
* @return a {@code Collector} which implements the reduction operation
     
*
     
* @see #reducing(BinaryOperator)
     
* @see #reducing(Object, Function, BinaryOperator)
     
*/

    
public static <T> Collector<T, ?, T>
    
reducing(T identity, BinaryOperator<T> op) {
        
return new CollectorImpl<>(
                
boxSupplier(identity),
                
(a, t) -> { a[0] = op.apply(a[0], t); },
                
(a, b) -> { a[0] = op.apply(a[0], b[0]); return a; },
                
a -> a[0],
                
CH_NOID);
    
}

    
@SuppressWarnings("unchecked")
    
private static <T> Supplier<T[]> boxSupplier(T identity) {
        
return () -> (T[]) new Object[] { identity };
    
}

    
/**
     
* Returns a {@code Collector} which performs a reduction of its
     
* input elements under a specified {@code BinaryOperator}.
  
The result
     
* is described as an {@code Optional<T>}.
     
*
     
* @apiNote
     
* The {@code reducing()} collectors are most useful when used in a
     
* multi-level reduction, downstream of {@code groupingBy} or
     
* {@code partitioningBy}.
  
To perform a simple reduction on a stream,
     
* use {@link Stream#reduce(BinaryOperator)} instead.
     
*
     
* <p>For example, given a stream of {@code Person}, to calculate tallest
     
* person in each city:
     
* <pre>{@code
     
*Comparator<Person> byHeight = Comparator.comparing(Person::getHeight);
     
*Map<City, Person> tallestByCity
     
*
         
= people.stream().collect(groupingBy(Person::getCity, reducing(BinaryOperator.maxBy(byHeight))));
     
* }</pre>
     
*
     
* @param <T> element type for the input and output of the reduction
     
* @param op a {@code BinaryOperator<T>} used to reduce the input elements
     
* @return a {@code Collector} which implements the reduction operation
     
*
     
* @see #reducing(Object, BinaryOperator)
     
* @see #reducing(Object, Function, BinaryOperator)
     
*/

    
public static <T> Collector<T, ?, Optional<T>>
    
reducing(BinaryOperator<T> op) {
        
class OptionalBox implements Consumer<T> {
            
T value = null;
            
boolean present = false;

            
@Override
            
public void accept(T t) {
                
if (present) {
                    
value = op.apply(value, t);
                
}
                
else {
                    
value = t;
                    
present = true;
                
}
            
}
        
}

        
return new CollectorImpl<T, OptionalBox, Optional<T>>(
                
OptionalBox::new, OptionalBox::accept,
                
(a, b) -> { if (b.present) a.accept(b.value); return a; },
                
a -> Optional.ofNullable(a.value), CH_NOID);
    
}

    
/**
     
* Returns a {@code Collector} which performs a reduction of its
     
* input elements under a specified mapping function and
     
* {@code BinaryOperator}. This is a generalization of
     
* {@link #reducing(Object, BinaryOperator)} which allows a transformation
     
* of the elements before reduction.
     
*
     
* @apiNote
     
* The {@code reducing()} collectors are most useful when used in a
     
* multi-level reduction, downstream of {@code groupingBy} or
     
* {@code partitioningBy}.
  
To perform a simple map-reduce on a stream,
     
* use {@link Stream#map(Function)} and {@link Stream#reduce(Object, BinaryOperator)}
     
* instead.
     
*
     
* <p>For example, given a stream of {@code Person}, to calculate the longest
     
* last name of residents in each city:
     
* <pre>{@code
     
*Comparator<String> byLength = Comparator.comparing(String::length);
     
*Map<City, String> longestLastNameByCity
     
*
         
= people.stream().collect(groupingBy(Person::getCity,
     
*
                                              
reducing(Person::getLastName, BinaryOperator.maxBy(byLength))));
     
* }</pre>
     
*
     
* @param <T> the type of the input elements
     
* @param <U> the type of the mapped values
     
* @param identity the identity value for the reduction (also, the value
     
*
                 
that is returned when there are no input elements)
     
* @param mapper a mapping function to apply to each input value
     
* @param op a {@code BinaryOperator<U>} used to reduce the mapped values
     
* @return a {@code Collector} implementing the map-reduce operation
     
*
     
* @see #reducing(Object, BinaryOperator)
     
* @see #reducing(BinaryOperator)
     
*/

    
public static <T, U>
    
Collector<T, ?, U> reducing(U identity,
                                
Function<? super T, ? extends U> mapper,
                                
BinaryOperator<U> op) {
        
return new CollectorImpl<>(
                
boxSupplier(identity),
                
(a, t) -> { a[0] = op.apply(a[0], mapper.apply(t)); },
                
(a, b) -> { a[0] = op.apply(a[0], b[0]); return a; },
                
a -> a[0], CH_NOID);
    
}

    
/**
     
* Returns a {@code Collector} implementing a "group by" operation on
     
* input elements of type {@code T}, grouping elements according to a
     
* classification function, and returning the results in a {@code Map}.
     
*
     
* <p>The classification function maps elements to some key type {@code K}.
     
* The collector produces a {@code Map<K, List<T>>} whose keys are the
     
* values resulting from applying the classification function to the input
     
* elements, and whose corresponding values are {@code List}s containing the
     
* input elements which map to the associated key under the classification
     
* function.
     
*
     
* <p>There are no guarantees on the type, mutability, serializability, or
     
* thread-safety of the {@code Map} or {@code List} objects returned.
     
* @implSpec
     
* This produces a result similar to:
     
* <pre>{@code
     
*groupingBy(classifier, toList());
     
* }</pre>
     
*
     
* @implNote
     
* The returned {@code Collector} is not concurrent.
  
For parallel stream
     
* pipelines, the {@code combiner} function operates by merging the keys
     
* from one map into another, which can be an expensive operation.
  
If
     
* preservation of the order in which elements appear in the resulting {@code Map}
     
* collector is not required, using {@link #groupingByConcurrent(Function)}
     
* may offer better parallel performance.
     
*
     
* @param <T> the type of the input elements
     
* @param <K> the type of the keys
     
* @param classifier the classifier function mapping input elements to keys
     
* @return a {@code Collector} implementing the group-by operation
     
*
     
* @see #groupingBy(Function, Collector)
     
* @see #groupingBy(Function, Supplier, Collector)
     
* @see #groupingByConcurrent(Function)
     
*/

    
public static <T, K> Collector<T, ?, Map<K, List<T>>>
    
groupingBy(Function<? super T, ? extends K> classifier) {
        
return groupingBy(classifier, toList());
    
}

    
/**
     
* Returns a {@code Collector} implementing a cascaded "group by" operation
     
* on input elements of type {@code T}, grouping elements according to a
     
* classification function, and then performing a reduction operation on
     
* the values associated with a given key using the specified downstream
     
* {@code Collector}.
     
*
     
* <p>The classification function maps elements to some key type {@code K}.
     
* The downstream collector operates on elements of type {@code T} and
     
* produces a result of type {@code D}. The resulting collector produces a
     
* {@code Map<K, D>}.
     
*
     
* <p>There are no guarantees on the type, mutability,
     
* serializability, or thread-safety of the {@code Map} returned.
     
*
     
* <p>For example, to compute the set of last names of people in each city:
     
* <pre>{@code
     
*Map<City, Set<String>> namesByCity
     
*
         
= people.stream().collect(groupingBy(Person::getCity,
     
*
                                              
mapping(Person::getLastName, toSet())));
     
* }</pre>
     
*
     
* @implNote
     
* The returned {@code Collector} is not concurrent.
  
For parallel stream
     
* pipelines, the {@code combiner} function operates by merging the keys
     
* from one map into another, which can be an expensive operation.
  
If
     
* preservation of the order in which elements are presented to the downstream
     
* collector is not required, using {@link #groupingByConcurrent(Function, Collector)}
     
* may offer better parallel performance.
     
*
     
* @param <T> the type of the input elements
     
* @param <K> the type of the keys
     
* @param <A> the intermediate accumulation type of the downstream collector
     
* @param <D> the result type of the downstream reduction
     
* @param classifier a classifier function mapping input elements to keys
     
* @param downstream a {@code Collector} implementing the downstream reduction
     
* @return a {@code Collector} implementing the cascaded group-by operation
     
* @see #groupingBy(Function)
     
*
     
* @see #groupingBy(Function, Supplier, Collector)
     
* @see #groupingByConcurrent(Function, Collector)
     
*/

    
public static <T, K, A, D>
    
Collector<T, ?, Map<K, D>> groupingBy(Function<? super T, ? extends K> classifier,
                                          
Collector<? super T, A, D> downstream) {
        
return groupingBy(classifier, HashMap::new, downstream);
    
}

    
/**
     
* Returns a {@code Collector} implementing a cascaded "group by" operation
     
* on input elements of type {@code T}, grouping elements according to a
     
* classification function, and then performing a reduction operation on
     
* the values associated with a given key using the specified downstream
     
* {@code Collector}.
  
The {@code Map} produced by the Collector is created
     
* with the supplied factory function.
     
*
     
* <p>The classification function maps elements to some key type {@code K}.
     
* The downstream collector operates on elements of type {@code T} and
     
* produces a result of type {@code D}. The resulting collector produces a
     
* {@code Map<K, D>}.
     
*
     
* <p>For example, to compute the set of last names of people in each city,
     
* where the city names are sorted:
     
* <pre>{@code
     
*Map<City, Set<String>> namesByCity
     
*
         
= people.stream().collect(groupingBy(Person::getCity, TreeMap::new,
     
*
                                              
mapping(Person::getLastName, toSet())));
     
* }</pre>
     
*
     
* @implNote
     
* The returned {@code Collector} is not concurrent.
  
For parallel stream
     
* pipelines, the {@code combiner} function operates by merging the keys
     
* from one map into another, which can be an expensive operation.
  
If
     
* preservation of the order in which elements are presented to the downstream
     
* collector is not required, using {@link #groupingByConcurrent(Function, Supplier, Collector)}
     
* may offer better parallel performance.
     
*
     
* @param <T> the type of the input elements
     
* @param <K> the type of the keys
     
* @param <A> the intermediate accumulation type of the downstream collector
     
* @param <D> the result type of the downstream reduction
     
* @param <M> the type of the resulting {@code Map}
     
* @param classifier a classifier function mapping input elements to keys
     
* @param downstream a {@code Collector} implementing the downstream reduction
     
* @param mapFactory a function which, when called, produces a new empty
     
*
                   
{@code Map} of the desired type
     
* @return a {@code Collector} implementing the cascaded group-by operation
     
*
     
* @see #groupingBy(Function, Collector)
     
* @see #groupingBy(Function)
     
* @see #groupingByConcurrent(Function, Supplier, Collector)
     
*/

    
public static <T, K, D, A, M extends Map<K, D>>
    
Collector<T, ?, M> groupingBy(Function<? super T, ? extends K> classifier,
                                  
Supplier<M> mapFactory,
                                  
Collector<? super T, A, D> downstream) {
        
Supplier<A> downstreamSupplier = downstream.supplier();
        
BiConsumer<A, ? super T> downstreamAccumulator = downstream.accumulator();
        
BiConsumer<Map<K, A>, T> accumulator = (m, t) -> {
            
K key = Objects.requireNonNull(classifier.apply(t), "element cannot be mapped to a null key");
            
A container = m.computeIfAbsent(key, k -> downstreamSupplier.get());
            
downstreamAccumulator.accept(container, t);
        
};
        
BinaryOperator<Map<K, A>> merger = Collectors.<K, A, Map<K, A>>mapMerger(downstream.combiner());
        
@SuppressWarnings("unchecked")
        
Supplier<Map<K, A>> mangledFactory = (Supplier<Map<K, A>>) mapFactory;

        
if (downstream.characteristics().contains(Collector.Characteristics.IDENTITY_FINISH)) {
            
return new CollectorImpl<>(mangledFactory, accumulator, merger, CH_ID);
        
}
        
else {
            
@SuppressWarnings("unchecked")
            
Function<A, A> downstreamFinisher = (Function<A, A>) downstream.finisher();
            
Function<Map<K, A>, M> finisher = intermediate -> {
                
intermediate.replaceAll((k, v) -> downstreamFinisher.apply(v));
                
@SuppressWarnings("unchecked")
                
M castResult = (M) intermediate;
                
return castResult;
            
};
            
return new CollectorImpl<>(mangledFactory, accumulator, merger, finisher, CH_NOID);
        
}
    
}

    
/**
     
* Returns a concurrent {@code Collector} implementing a "group by"
     
* operation on input elements of type {@code T}, grouping elements
     
* according to a classification function.
     
*
     
* <p>This is a {@link Collector.Characteristics#CONCURRENT concurrent} and
     
* {@link Collector.Characteristics#UNORDERED unordered} Collector.
     
*
     
* <p>The classification function maps elements to some key type {@code K}.
     
* The collector produces a {@code ConcurrentMap<K, List<T>>} whose keys are the
     
* values resulting from applying the classification function to the input
     
* elements, and whose corresponding values are {@code List}s containing the
     
* input elements which map to the associated key under the classification
     
* function.
     
*
     
* <p>There are no guarantees on the type, mutability, or serializability
     
* of the {@code Map} or {@code List} objects returned, or of the
     
* thread-safety of the {@code List} objects returned.
     
* @implSpec
     
* This produces a result similar to:
     
* <pre>{@code
     
*groupingByConcurrent(classifier, toList());
     
* }</pre>
     
*
     
* @param <T> the type of the input elements
     
* @param <K> the type of the keys
     
* @param classifier a classifier function mapping input elements to keys
     
* @return a concurrent, unordered {@code Collector} implementing the group-by operation
     
*
     
* @see #groupingBy(Function)
     
* @see #groupingByConcurrent(Function, Collector)
     
* @see #groupingByConcurrent(Function, Supplier, Collector)
     
*/

    
public static <T, K>
    
Collector<T, ?, ConcurrentMap<K, List<T>>>
    
groupingByConcurrent(Function<? super T, ? extends K> classifier) {
        
return groupingByConcurrent(classifier, ConcurrentHashMap::new, toList());
    
}

    
/**
     
* Returns a concurrent {@code Collector} implementing a cascaded "group by"
     
* operation on input elements of type {@code T}, grouping elements
     
* according to a classification function, and then performing a reduction
     
* operation on the values associated with a given key using the specified
     
* downstream {@code Collector}.
     
*
     
* <p>This is a {@link Collector.Characteristics#CONCURRENT concurrent} and
     
* {@link Collector.Characteristics#UNORDERED unordered} Collector.
     
*
     
* <p>The classification function maps elements to some key type {@code K}.
     
* The downstream collector operates on elements of type {@code T} and
     
* produces a result of type {@code D}. The resulting collector produces a
     
* {@code Map<K, D>}.
     
*
     
* <p>For example, to compute the set of last names of people in each city,
     
* where the city names are sorted:
     
* <pre>{@code
     
*ConcurrentMap<City, Set<String>> namesByCity
     
*
         
= people.stream().collect(groupingByConcurrent(Person::getCity,
     
*
                                                        
mapping(Person::getLastName, toSet())));
     
* }</pre>
     
*
     
* @param <T> the type of the input elements
     
* @param <K> the type of the keys
     
* @param <A> the intermediate accumulation type of the downstream collector
     
* @param <D> the result type of the downstream reduction
     
* @param classifier a classifier function mapping input elements to keys
     
* @param downstream a {@code Collector} implementing the downstream reduction
     
* @return a concurrent, unordered {@code Collector} implementing the cascaded group-by operation
     
*
     
* @see #groupingBy(Function, Collector)
     
* @see #groupingByConcurrent(Function)
     
* @see #groupingByConcurrent(Function, Supplier, Collector)
     
*/

    
public static <T, K, A, D>
    
Collector<T, ?, ConcurrentMap<K, D>> groupingByConcurrent(Function<? super T, ? extends K> classifier,
                                                              
Collector<? super T, A, D> downstream) {
        
return groupingByConcurrent(classifier, ConcurrentHashMap::new, downstream);
    
}

    
/**
     
* Returns a concurrent {@code Collector} implementing a cascaded "group by"
     
* operation on input elements of type {@code T}, grouping elements
     
* according to a classification function, and then performing a reduction
     
* operation on the values associated with a given key using the specified
     
* downstream {@code Collector}.
  
The {@code ConcurrentMap} produced by the
     
* Collector is created with the supplied factory function.
     
*
     
* <p>This is a {@link Collector.Characteristics#CONCURRENT concurrent} and
     
* {@link Collector.Characteristics#UNORDERED unordered} Collector.
     
*
     
* <p>The classification function maps elements to some key type {@code K}.
     
* The downstream collector operates on elements of type {@code T} and
     
* produces a result of type {@code D}. The resulting collector produces a
     
* {@code Map<K, D>}.
     
*
     
* <p>For example, to compute the set of last names of people in each city,
     
* where the city names are sorted:
     
* <pre>{@code
     
*ConcurrentMap<City, Set<String>> namesByCity
     
*
         
= people.stream().collect(groupingBy(Person::getCity, ConcurrentSkipListMap::new,
     
*
                                              
mapping(Person::getLastName, toSet())));
     
* }</pre>
     
*
     
*
     
* @param <T> the type of the input elements
     
* @param <K> the type of the keys
     
* @param <A> the intermediate accumulation type of the downstream collector
     
* @param <D> the result type of the downstream reduction
     
* @param <M> the type of the resulting {@code ConcurrentMap}
     
* @param classifier a classifier function mapping input elements to keys
     
* @param downstream a {@code Collector} implementing the downstream reduction
     
* @param mapFactory a function which, when called, produces a new empty
     
*
                   
{@code ConcurrentMap} of the desired type
     
* @return a concurrent, unordered {@code Collector} implementing the cascaded group-by operation
     
*
     
* @see #groupingByConcurrent(Function)
     
* @see #groupingByConcurrent(Function, Collector)
     
* @see #groupingBy(Function, Supplier, Collector)
     
*/

    
public static <T, K, A, D, M extends ConcurrentMap<K, D>>
    
Collector<T, ?, M> groupingByConcurrent(Function<? super T, ? extends K> classifier,
                                            
Supplier<M> mapFactory,
                                            
Collector<? super T, A, D> downstream) {
        
Supplier<A> downstreamSupplier = downstream.supplier();
        
BiConsumer<A, ? super T> downstreamAccumulator = downstream.accumulator();
        
BinaryOperator<ConcurrentMap<K, A>> merger = Collectors.<K, A, ConcurrentMap<K, A>>mapMerger(downstream.combiner());
        
@SuppressWarnings("unchecked")
        
Supplier<ConcurrentMap<K, A>> mangledFactory = (Supplier<ConcurrentMap<K, A>>) mapFactory;
        
BiConsumer<ConcurrentMap<K, A>, T> accumulator;
        
if (downstream.characteristics().contains(Collector.Characteristics.CONCURRENT)) {
            
accumulator = (m, t) -> {
                
K key = Objects.requireNonNull(classifier.apply(t), "element cannot be mapped to a null key");
                
A resultContainer = m.computeIfAbsent(key, k -> downstreamSupplier.get());
                
downstreamAccumulator.accept(resultContainer, t);
            
};
        
}
        
else {
            
accumulator = (m, t) -> {
                
K key = Objects.requireNonNull(classifier.apply(t), "element cannot be mapped to a null key");
                
A resultContainer = m.computeIfAbsent(key, k -> downstreamSupplier.get());
                
synchronized (resultContainer) {
                    
downstreamAccumulator.accept(resultContainer, t);
                
}
            
};
        
}

        
if (downstream.characteristics().contains(Collector.Characteristics.IDENTITY_FINISH)) {
            
return new CollectorImpl<>(mangledFactory, accumulator, merger, CH_CONCURRENT_ID);
        
}
        
else {
            
@SuppressWarnings("unchecked")
            
Function<A, A> downstreamFinisher = (Function<A, A>) downstream.finisher();
            
Function<ConcurrentMap<K, A>, M> finisher = intermediate -> {
                
intermediate.replaceAll((k, v) -> downstreamFinisher.apply(v));
                
@SuppressWarnings("unchecked")
                
M castResult = (M) intermediate;
                
return castResult;
            
};
            
return new CollectorImpl<>(mangledFactory, accumulator, merger, finisher, CH_CONCURRENT_NOID);
        
}
    
}

    
/**
     
* Returns a {@code Collector} which partitions the input elements according
     
* to a {@code Predicate}, and organizes them into a
     
* {@code Map<Boolean, List<T>>}.
     
*
     
* There are no guarantees on the type, mutability,
     
* serializability, or thread-safety of the {@code Map} returned.
     
*
     
* @param <T> the type of the input elements
     
* @param predicate a predicate used for classifying input elements
     
* @return a {@code Collector} implementing the partitioning operation
     
*
     
* @see #partitioningBy(Predicate, Collector)
     
*/

    
public static <T>
    
Collector<T, ?, Map<Boolean, List<T>>> partitioningBy(Predicate<? super T> predicate) {
        
return partitioningBy(predicate, toList());
    
}

    
/**
     
* Returns a {@code Collector} which partitions the input elements according
     
* to a {@code Predicate}, reduces the values in each partition according to
     
* another {@code Collector}, and organizes them into a
     
* {@code Map<Boolean, D>} whose values are the result of the downstream
     
* reduction.
     
*
     
* <p>There are no guarantees on the type, mutability,
     
* serializability, or thread-safety of the {@code Map} returned.
     
*
     
* @param <T> the type of the input elements
     
* @param <A> the intermediate accumulation type of the downstream collector
     
* @param <D> the result type of the downstream reduction
     
* @param predicate a predicate used for classifying input elements
     
* @param downstream a {@code Collector} implementing the downstream
     
*
                   
reduction
     
* @return a {@code Collector} implementing the cascaded partitioning
     
*
         
operation
     
*
     
* @see #partitioningBy(Predicate)
     
*/

    
public static <T, D, A>
    
Collector<T, ?, Map<Boolean, D>> partitioningBy(Predicate<? super T> predicate,
                                                    
Collector<? super T, A, D> downstream) {
        
BiConsumer<A, ? super T> downstreamAccumulator = downstream.accumulator();
        
BiConsumer<Partition<A>, T> accumulator = (result, t) ->
                
downstreamAccumulator.accept(predicate.test(t) ? result.forTrue : result.forFalse, t);
        
BinaryOperator<A> op = downstream.combiner();
        
BinaryOperator<Partition<A>> merger = (left, right) ->
                
new Partition<>(op.apply(left.forTrue, right.forTrue),
                                
op.apply(left.forFalse, right.forFalse));
        
Supplier<Partition<A>> supplier = () ->
                
new Partition<>(downstream.supplier().get(),
                                
downstream.supplier().get());
        
if (downstream.characteristics().contains(Collector.Characteristics.IDENTITY_FINISH)) {
            
return new CollectorImpl<>(supplier, accumulator, merger, CH_ID);
        
}
        
else {
            
Function<Partition<A>, Map<Boolean, D>> finisher = par ->
                    
new Partition<>(downstream.finisher().apply(par.forTrue),
                                    
downstream.finisher().apply(par.forFalse));
            
return new CollectorImpl<>(supplier, accumulator, merger, finisher, CH_NOID);
        
}
    
}

    
/**
     
* Returns a {@code Collector} that accumulates elements into a
     
* {@code Map} whose keys and values are the result of applying the provided
     
* mapping functions to the input elements.
     
*
     
* <p>If the mapped keys contains duplicates (according to
     
* {@link Object#equals(Object)}), an {@code IllegalStateException} is
     
* thrown when the collection operation is performed.
  
If the mapped keys
     
* may have duplicates, use {@link #toMap(Function, Function, BinaryOperator)}
     
* instead.
     
*
     
* @apiNote
     
* It is common for either the key or the value to be the input elements.
     
* In this case, the utility method
     
* {@link java.util.function.Function#identity()} may be helpful.
     
* For example, the following produces a {@code Map} mapping
     
* students to their grade point average:
     
* <pre>{@code
     
*Map<Student, Double> studentToGPA
     
*
         
students.stream().collect(toMap(Functions.identity(),
     
*
                                         
student -> computeGPA(student)));
     
* }</pre>
     
* And the following produces a {@code Map} mapping a unique identifier to
     
* students:
     
* <pre>{@code
     
*Map<String, Student> studentIdToStudent
     
*
         
students.stream().collect(toMap(Student::getId,
     
*
                                         
Functions.identity());
     
* }</pre>
     
*
     
* @implNote
     
* The returned {@code Collector} is not concurrent.
  
For parallel stream
     
* pipelines, the {@code combiner} function operates by merging the keys
     
* from one map into another, which can be an expensive operation.
  
If it is
     
* not required that results are inserted into the {@code Map} in encounter
     
* order, using {@link #toConcurrentMap(Function, Function)}
     
* may offer better parallel performance.
     
*
     
* @param <T> the type of the input elements
     
* @param <K> the output type of the key mapping function
     
* @param <U> the output type of the value mapping function
     
* @param keyMapper a mapping function to produce keys
     
* @param valueMapper a mapping function to produce values
     
* @return a {@code Collector} which collects elements into a {@code Map}
     
* whose keys and values are the result of applying mapping functions to
     
* the input elements
     
*
     
* @see #toMap(Function, Function, BinaryOperator)
     
* @see #toMap(Function, Function, BinaryOperator, Supplier)
     
* @see #toConcurrentMap(Function, Function)
     
*/

    
public static <T, K, U>
    
Collector<T, ?, Map<K,U>> toMap(Function<? super T, ? extends K> keyMapper,
                                    
Function<? super T, ? extends U> valueMapper) {
        
return toMap(keyMapper, valueMapper, throwingMerger(), HashMap::new);
    
}

    
/**
     
* Returns a {@code Collector} that accumulates elements into a
     
* {@code Map} whose keys and values are the result of applying the provided
     
* mapping functions to the input elements.
     
*
     
* <p>If the mapped
     
* keys contains duplicates (according to {@link Object#equals(Object)}),
     
* the value mapping function is applied to each equal element, and the
     
* results are merged using the provided merging function.
     
*
     
* @apiNote
     
* There are multiple ways to deal with collisions between multiple elements
     
* mapping to the same key.
  
The other forms of {@code toMap} simply use
     
* a merge function that throws unconditionally, but you can easily write
     
* more flexible merge policies.
  
For example, if you have a stream
     
* of {@code Person}, and you want to produce a "phone book" mapping name to
     
* address, but it is possible that two persons have the same name, you can
     
* do as follows to gracefully deals with these collisions, and produce a
     
* {@code Map} mapping names to a concatenated list of addresses:
     
* <pre>{@code
     
*Map<String, String> phoneBook
     
*
         
people.stream().collect(toMap(Person::getName,
     
*
                                       
Person::getAddress,
     
*
                                       
(s, a) -> s + ", " + a));
     
* }</pre>
     
*
     
* @implNote
     
* The returned {@code Collector} is not concurrent.
  
For parallel stream
     
* pipelines, the {@code combiner} function operates by merging the keys
     
* from one map into another, which can be an expensive operation.
  
If it is
     
* not required that results are merged into the {@code Map} in encounter
     
* order, using {@link #toConcurrentMap(Function, Function, BinaryOperator)}
     
* may offer better parallel performance.
     
*
     
* @param <T> the type of the input elements
     
* @param <K> the output type of the key mapping function
     
* @param <U> the output type of the value mapping function
     
* @param keyMapper a mapping function to produce keys
     
* @param valueMapper a mapping function to produce values
     
* @param mergeFunction a merge function, used to resolve collisions between
     
*
                      
values associated with the same key, as supplied
     
*
                      
to {@link Map#merge(Object, Object, BiFunction)}
     
* @return a {@code Collector} which collects elements into a {@code Map}
     
* whose keys are the result of applying a key mapping function to the input
     
* elements, and whose values are the result of applying a value mapping
     
* function to all input elements equal to the key and combining them
     
* using the merge function
     
*
     
* @see #toMap(Function, Function)
     
* @see #toMap(Function, Function, BinaryOperator, Supplier)
     
* @see #toConcurrentMap(Function, Function, BinaryOperator)
     
*/

    
public static <T, K, U>
    
Collector<T, ?, Map<K,U>> toMap(Function<? super T, ? extends K> keyMapper,
                                    
Function<? super T, ? extends U> valueMapper,
                                    
BinaryOperator<U> mergeFunction) {
        
return toMap(keyMapper, valueMapper, mergeFunction, HashMap::new);
    
}

    
/**
     
* Returns a {@code Collector} that accumulates elements into a
     
* {@code Map} whose keys and values are the result of applying the provided
     
* mapping functions to the input elements.
     
*
     
* <p>If the mapped
     
* keys contains duplicates (according to {@link Object#equals(Object)}),
     
* the value mapping function is applied to each equal element, and the
     
* results are merged using the provided merging function.
  
The {@code Map}
     
* is created by a provided supplier function.
     
*
     
* @implNote
     
* The returned {@code Collector} is not concurrent.
  
For parallel stream
     
* pipelines, the {@code combiner} function operates by merging the keys
     
* from one map into another, which can be an expensive operation.
  
If it is
     
* not required that results are merged into the {@code Map} in encounter
     
* order, using {@link #toConcurrentMap(Function, Function, BinaryOperator, Supplier)}
     
* may offer better parallel performance.
     
*
     
* @param <T> the type of the input elements
     
* @param <K> the output type of the key mapping function
     
* @param <U> the output type of the value mapping function
     
* @param <M> the type of the resulting {@code Map}
     
* @param keyMapper a mapping function to produce keys
     
* @param valueMapper a mapping function to produce values
     
* @param mergeFunction a merge function, used to resolve collisions between
     
*
                      
values associated with the same key, as supplied
     
*
                      
to {@link Map#merge(Object, Object, BiFunction)}
     
* @param mapSupplier a function which returns a new, empty {@code Map} into
     
*
                    
which the results will be inserted
     
* @return a {@code Collector} which collects elements into a {@code Map}
     
* whose keys are the result of applying a key mapping function to the input
     
* elements, and whose values are the result of applying a value mapping
     
* function to all input elements equal to the key and combining them
     
* using the merge function
     
*
     
* @see #toMap(Function, Function)
     
* @see #toMap(Function, Function, BinaryOperator)
     
* @see #toConcurrentMap(Function, Function, BinaryOperator, Supplier)
     
*/

    
public static <T, K, U, M extends Map<K, U>>
    
Collector<T, ?, M> toMap(Function<? super T, ? extends K> keyMapper,
                                
Function<? super T, ? extends U> valueMapper,
                                
BinaryOperator<U> mergeFunction,
                                
Supplier<M> mapSupplier) {
        
BiConsumer<M, T> accumulator
                
= (map, element) -> map.merge(keyMapper.apply(element),
                                              
valueMapper.apply(element), mergeFunction);
        
return new CollectorImpl<>(mapSupplier, accumulator, mapMerger(mergeFunction), CH_ID);
    
}

    
/**
     
* Returns a concurrent {@code Collector} that accumulates elements into a
     
* {@code ConcurrentMap} whose keys and values are the result of applying
     
* the provided mapping functions to the input elements.
     
*
     
* <p>If the mapped keys contains duplicates (according to
     
* {@link Object#equals(Object)}), an {@code IllegalStateException} is
     
* thrown when the collection operation is performed.
  
If the mapped keys
     
* may have duplicates, use
     
* {@link #toConcurrentMap(Function, Function, BinaryOperator)} instead.
     
*
     
* @apiNote
     
* It is common for either the key or the value to be the input elements.
     
* In this case, the utility method
     
* {@link java.util.function.Function#identity()} may be helpful.
     
* For example, the following produces a {@code Map} mapping
     
* students to their grade point average:
     
* <pre>{@code
     
*Map<Student, Double> studentToGPA
     
*
         
students.stream().collect(toMap(Functions.identity(),
     
*
                                         
student -> computeGPA(student)));
     
* }</pre>
     
* And the following produces a {@code Map} mapping a unique identifier to
     
* students:
     
* <pre>{@code
     
*Map<String, Student> studentIdToStudent
     
*
         
students.stream().collect(toConcurrentMap(Student::getId,
     
*
                                                   
Functions.identity());
     
* }</pre>
     
*
     
* <p>This is a {@link Collector.Characteristics#CONCURRENT concurrent} and
     
* {@link Collector.Characteristics#UNORDERED unordered} Collector.
     
*
     
* @param <T> the type of the input elements
     
* @param <K> the output type of the key mapping function
     
* @param <U> the output type of the value mapping function
     
* @param keyMapper the mapping function to produce keys
     
* @param valueMapper the mapping function to produce values
     
* @return a concurrent, unordered {@code Collector} which collects elements into a
     
* {@code ConcurrentMap} whose keys are the result of applying a key mapping
     
* function to the input elements, and whose values are the result of
     
* applying a value mapping function to the input elements
     
*
     
* @see #toMap(Function, Function)
     
* @see #toConcurrentMap(Function, Function, BinaryOperator)
     
* @see #toConcurrentMap(Function, Function, BinaryOperator, Supplier)
     
*/

    
public static <T, K, U>
    
Collector<T, ?, ConcurrentMap<K,U>> toConcurrentMap(Function<? super T, ? extends K> keyMapper,
                                                        
Function<? super T, ? extends U> valueMapper) {
        
return toConcurrentMap(keyMapper, valueMapper, throwingMerger(), ConcurrentHashMap::new);
    
}

    
/**
     
* Returns a concurrent {@code Collector} that accumulates elements into a
     
* {@code ConcurrentMap} whose keys and values are the result of applying
     
* the provided mapping functions to the input elements.
     
*
     
* <p>If the mapped keys contains duplicates (according to {@link Object#equals(Object)}),
     
* the value mapping function is applied to each equal element, and the
     
* results are merged using the provided merging function.
     
*
     
* @apiNote
     
* There are multiple ways to deal with collisions between multiple elements
     
* mapping to the same key.
  
The other forms of {@code toConcurrentMap} simply use
     
* a merge function that throws unconditionally, but you can easily write
     
* more flexible merge policies.
  
For example, if you have a stream
     
* of {@code Person}, and you want to produce a "phone book" mapping name to
     
* address, but it is possible that two persons have the same name, you can
     
* do as follows to gracefully deals with these collisions, and produce a
     
* {@code Map} mapping names to a concatenated list of addresses:
     
* <pre>{@code
     
*Map<String, String> phoneBook
     
*
         
people.stream().collect(toConcurrentMap(Person::getName,
     
*
                                                 
Person::getAddress,
     
*
                                                 
(s, a) -> s + ", " + a));
     
* }</pre>
     
*
     
* <p>This is a {@link Collector.Characteristics#CONCURRENT concurrent} and
     
* {@link Collector.Characteristics#UNORDERED unordered} Collector.
     
*
     
* @param <T> the type of the input elements
     
* @param <K> the output type of the key mapping function
     
* @param <U> the output type of the value mapping function
     
* @param keyMapper a mapping function to produce keys
     
* @param valueMapper a mapping function to produce values
     
* @param mergeFunction a merge function, used to resolve collisions between
     
*
                      
values associated with the same key, as supplied
     
*
                      
to {@link Map#merge(Object, Object, BiFunction)}
     
* @return a concurrent, unordered {@code Collector} which collects elements into a
     
* {@code ConcurrentMap} whose keys are the result of applying a key mapping
     
* function to the input elements, and whose values are the result of
     
* applying a value mapping function to all input elements equal to the key
     
* and combining them using the merge function
     
*
     
* @see #toConcurrentMap(Function, Function)
     
* @see #toConcurrentMap(Function, Function, BinaryOperator, Supplier)
     
* @see #toMap(Function, Function, BinaryOperator)
     
*/

    
public static <T, K, U>