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Statistical sampling library for use in virtdata libraries, based
on apache commons math 4
/*
* Copyright (c) 2012-2017 The ANTLR Project. All rights reserved.
* Use of this file is governed by the BSD 3-clause license that
* can be found in the LICENSE.txt file in the project root.
*/
package org.antlr.v4.runtime.atn;
import org.antlr.v4.runtime.Recognizer;
import org.antlr.v4.runtime.RuleContext;
import org.antlr.v4.runtime.misc.DoubleKeyMap;
import org.antlr.v4.runtime.misc.MurmurHash;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.Collections;
import java.util.Comparator;
import java.util.HashMap;
import java.util.IdentityHashMap;
import java.util.List;
import java.util.Map;
public abstract class PredictionContext {
/**
* Represents {@code $} in local context prediction, which means wildcard.
* {@code *+x = *}.
*/
public static final EmptyPredictionContext EMPTY = new EmptyPredictionContext();
/**
* Represents {@code $} in an array in full context mode, when {@code $}
* doesn't mean wildcard: {@code $ + x = [$,x]}. Here,
* {@code $} = {@link #EMPTY_RETURN_STATE}.
*/
public static final int EMPTY_RETURN_STATE = Integer.MAX_VALUE;
private static final int INITIAL_HASH = 1;
public static int globalNodeCount = 0;
public final int id = globalNodeCount++;
/**
* Stores the computed hash code of this {@link PredictionContext}. The hash
* code is computed in parts to match the following reference algorithm.
*
*
* private int referenceHashCode() {
* int hash = {@link MurmurHash#initialize MurmurHash.initialize}({@link #INITIAL_HASH});
*
* for (int i = 0; i < {@link #size()}; i++) {
* hash = {@link MurmurHash#update MurmurHash.update}(hash, {@link #getParent getParent}(i));
* }
*
* for (int i = 0; i < {@link #size()}; i++) {
* hash = {@link MurmurHash#update MurmurHash.update}(hash, {@link #getReturnState getReturnState}(i));
* }
*
* hash = {@link MurmurHash#finish MurmurHash.finish}(hash, 2 * {@link #size()});
* return hash;
* }
*
*/
public final int cachedHashCode;
protected PredictionContext(int cachedHashCode) {
this.cachedHashCode = cachedHashCode;
}
/** Convert a {@link RuleContext} tree to a {@link PredictionContext} graph.
* Return {@link #EMPTY} if {@code outerContext} is empty or null.
*/
public static PredictionContext fromRuleContext(ATN atn, RuleContext outerContext) {
if ( outerContext==null ) outerContext = RuleContext.EMPTY;
// if we are in RuleContext of start rule, s, then PredictionContext
// is EMPTY. Nobody called us. (if we are empty, return empty)
if ( outerContext.parent==null || outerContext==RuleContext.EMPTY ) {
return PredictionContext.EMPTY;
}
// If we have a parent, convert it to a PredictionContext graph
PredictionContext parent = EMPTY;
parent = PredictionContext.fromRuleContext(atn, outerContext.parent);
ATNState state = atn.states.get(outerContext.invokingState);
RuleTransition transition = (RuleTransition)state.transition(0);
return SingletonPredictionContext.create(parent, transition.followState.stateNumber);
}
public abstract int size();
public abstract PredictionContext getParent(int index);
public abstract int getReturnState(int index);
/** This means only the {@link #EMPTY} (wildcard? not sure) context is in set. */
public boolean isEmpty() {
return this == EMPTY;
}
public boolean hasEmptyPath() {
// since EMPTY_RETURN_STATE can only appear in the last position, we check last one
return getReturnState(size() - 1) == EMPTY_RETURN_STATE;
}
@Override
public final int hashCode() {
return cachedHashCode;
}
@Override
public abstract boolean equals(Object obj);
protected static int calculateEmptyHashCode() {
int hash = MurmurHash.initialize(INITIAL_HASH);
hash = MurmurHash.finish(hash, 0);
return hash;
}
protected static int calculateHashCode(PredictionContext parent, int returnState) {
int hash = MurmurHash.initialize(INITIAL_HASH);
hash = MurmurHash.update(hash, parent);
hash = MurmurHash.update(hash, returnState);
hash = MurmurHash.finish(hash, 2);
return hash;
}
protected static int calculateHashCode(PredictionContext[] parents, int[] returnStates) {
int hash = MurmurHash.initialize(INITIAL_HASH);
for (PredictionContext parent : parents) {
hash = MurmurHash.update(hash, parent);
}
for (int returnState : returnStates) {
hash = MurmurHash.update(hash, returnState);
}
hash = MurmurHash.finish(hash, 2 * parents.length);
return hash;
}
// dispatch
public static PredictionContext merge(
PredictionContext a, PredictionContext b,
boolean rootIsWildcard,
DoubleKeyMap mergeCache)
{
assert a!=null && b!=null; // must be empty context, never null
// share same graph if both same
if ( a==b || a.equals(b) ) return a;
if ( a instanceof SingletonPredictionContext && b instanceof SingletonPredictionContext) {
return mergeSingletons((SingletonPredictionContext)a,
(SingletonPredictionContext)b,
rootIsWildcard, mergeCache);
}
// At least one of a or b is array
// If one is $ and rootIsWildcard, return $ as * wildcard
if ( rootIsWildcard ) {
if ( a instanceof EmptyPredictionContext ) return a;
if ( b instanceof EmptyPredictionContext ) return b;
}
// convert singleton so both are arrays to normalize
if ( a instanceof SingletonPredictionContext ) {
a = new ArrayPredictionContext((SingletonPredictionContext)a);
}
if ( b instanceof SingletonPredictionContext) {
b = new ArrayPredictionContext((SingletonPredictionContext)b);
}
return mergeArrays((ArrayPredictionContext) a, (ArrayPredictionContext) b,
rootIsWildcard, mergeCache);
}
/**
* Merge two {@link SingletonPredictionContext} instances.
*
* Stack tops equal, parents merge is same; return left graph.
*
*
* Same stack top, parents differ; merge parents giving array node, then
* remainders of those graphs. A new root node is created to point to the
* merged parents.
*
*
* Different stack tops pointing to same parent. Make array node for the
* root where both element in the root point to the same (original)
* parent.
*
*
* Different stack tops pointing to different parents. Make array node for
* the root where each element points to the corresponding original
* parent.
*
*
* @param a the first {@link SingletonPredictionContext}
* @param b the second {@link SingletonPredictionContext}
* @param rootIsWildcard {@code true} if this is a local-context merge,
* otherwise false to indicate a full-context merge
* @param mergeCache
*/
public static PredictionContext mergeSingletons(
SingletonPredictionContext a,
SingletonPredictionContext b,
boolean rootIsWildcard,
DoubleKeyMap mergeCache)
{
if ( mergeCache!=null ) {
PredictionContext previous = mergeCache.get(a,b);
if ( previous!=null ) return previous;
previous = mergeCache.get(b,a);
if ( previous!=null ) return previous;
}
PredictionContext rootMerge = mergeRoot(a, b, rootIsWildcard);
if ( rootMerge!=null ) {
if ( mergeCache!=null ) mergeCache.put(a, b, rootMerge);
return rootMerge;
}
if ( a.returnState==b.returnState ) { // a == b
PredictionContext parent = merge(a.parent, b.parent, rootIsWildcard, mergeCache);
// if parent is same as existing a or b parent or reduced to a parent, return it
if ( parent == a.parent ) return a; // ax + bx = ax, if a=b
if ( parent == b.parent ) return b; // ax + bx = bx, if a=b
// else: ax + ay = a'[x,y]
// merge parents x and y, giving array node with x,y then remainders
// of those graphs. dup a, a' points at merged array
// new joined parent so create new singleton pointing to it, a'
PredictionContext a_ = SingletonPredictionContext.create(parent, a.returnState);
if ( mergeCache!=null ) mergeCache.put(a, b, a_);
return a_;
}
else { // a != b payloads differ
// see if we can collapse parents due to $+x parents if local ctx
PredictionContext singleParent = null;
if ( a==b || (a.parent!=null && a.parent.equals(b.parent)) ) { // ax + bx = [a,b]x
singleParent = a.parent;
}
if ( singleParent!=null ) { // parents are same
// sort payloads and use same parent
int[] payloads = {a.returnState, b.returnState};
if ( a.returnState > b.returnState ) {
payloads[0] = b.returnState;
payloads[1] = a.returnState;
}
PredictionContext[] parents = {singleParent, singleParent};
PredictionContext a_ = new ArrayPredictionContext(parents, payloads);
if ( mergeCache!=null ) mergeCache.put(a, b, a_);
return a_;
}
// parents differ and can't merge them. Just pack together
// into array; can't merge.
// ax + by = [ax,by]
int[] payloads = {a.returnState, b.returnState};
PredictionContext[] parents = {a.parent, b.parent};
if ( a.returnState > b.returnState ) { // sort by payload
payloads[0] = b.returnState;
payloads[1] = a.returnState;
parents = new PredictionContext[] {b.parent, a.parent};
}
PredictionContext a_ = new ArrayPredictionContext(parents, payloads);
if ( mergeCache!=null ) mergeCache.put(a, b, a_);
return a_;
}
}
/**
* Handle case where at least one of {@code a} or {@code b} is
* {@link #EMPTY}. In the following diagrams, the symbol {@code $} is used
* to represent {@link #EMPTY}.
*
* Local-Context Merges
*
* These local-context merge operations are used when {@code rootIsWildcard}
* is true.
*
* {@link #EMPTY} is superset of any graph; return {@link #EMPTY}.
*
*
* {@link #EMPTY} and anything is {@code #EMPTY}, so merged parent is
* {@code #EMPTY}; return left graph.
*
*
* Special case of last merge if local context.
*
*
* Full-Context Merges
*
* These full-context merge operations are used when {@code rootIsWildcard}
* is false.
*
*
*
* Must keep all contexts; {@link #EMPTY} in array is a special value (and
* null parent).
*
*
*
*
* @param a the first {@link SingletonPredictionContext}
* @param b the second {@link SingletonPredictionContext}
* @param rootIsWildcard {@code true} if this is a local-context merge,
* otherwise false to indicate a full-context merge
*/
public static PredictionContext mergeRoot(SingletonPredictionContext a,
SingletonPredictionContext b,
boolean rootIsWildcard)
{
if ( rootIsWildcard ) {
if ( a == EMPTY ) return EMPTY; // * + b = *
if ( b == EMPTY ) return EMPTY; // a + * = *
}
else {
if ( a == EMPTY && b == EMPTY ) return EMPTY; // $ + $ = $
if ( a == EMPTY ) { // $ + x = [x,$]
int[] payloads = {b.returnState, EMPTY_RETURN_STATE};
PredictionContext[] parents = {b.parent, null};
PredictionContext joined =
new ArrayPredictionContext(parents, payloads);
return joined;
}
if ( b == EMPTY ) { // x + $ = [x,$] ($ is always last if present)
int[] payloads = {a.returnState, EMPTY_RETURN_STATE};
PredictionContext[] parents = {a.parent, null};
PredictionContext joined =
new ArrayPredictionContext(parents, payloads);
return joined;
}
}
return null;
}
/**
* Merge two {@link ArrayPredictionContext} instances.
*
* Different tops, different parents.
*
*
* Shared top, same parents.
*
*
* Shared top, different parents.
*
*
* Shared top, all shared parents.
*
*
* Equal tops, merge parents and reduce top to
* {@link SingletonPredictionContext}.
*
*/
public static PredictionContext mergeArrays(
ArrayPredictionContext a,
ArrayPredictionContext b,
boolean rootIsWildcard,
DoubleKeyMap mergeCache)
{
if ( mergeCache!=null ) {
PredictionContext previous = mergeCache.get(a,b);
if ( previous!=null ) return previous;
previous = mergeCache.get(b,a);
if ( previous!=null ) return previous;
}
// merge sorted payloads a + b => M
int i = 0; // walks a
int j = 0; // walks b
int k = 0; // walks target M array
int[] mergedReturnStates =
new int[a.returnStates.length + b.returnStates.length];
PredictionContext[] mergedParents =
new PredictionContext[a.returnStates.length + b.returnStates.length];
// walk and merge to yield mergedParents, mergedReturnStates
while ( i ax
if ( both$ || ax_ax ) {
mergedParents[k] = a_parent; // choose left
mergedReturnStates[k] = payload;
}
else { // ax+ay -> a'[x,y]
PredictionContext mergedParent =
merge(a_parent, b_parent, rootIsWildcard, mergeCache);
mergedParents[k] = mergedParent;
mergedReturnStates[k] = payload;
}
i++; // hop over left one as usual
j++; // but also skip one in right side since we merge
}
else if ( a.returnStates[i] a, copy b[j] to M
mergedParents[k] = b_parent;
mergedReturnStates[k] = b.returnStates[j];
j++;
}
k++;
}
// copy over any payloads remaining in either array
if (i < a.returnStates.length) {
for (int p = i; p < a.returnStates.length; p++) {
mergedParents[k] = a.parents[p];
mergedReturnStates[k] = a.returnStates[p];
k++;
}
}
else {
for (int p = j; p < b.returnStates.length; p++) {
mergedParents[k] = b.parents[p];
mergedReturnStates[k] = b.returnStates[p];
k++;
}
}
// trim merged if we combined a few that had same stack tops
if ( k < mergedParents.length ) { // write index < last position; trim
if ( k == 1 ) { // for just one merged element, return singleton top
PredictionContext a_ =
SingletonPredictionContext.create(mergedParents[0],
mergedReturnStates[0]);
if ( mergeCache!=null ) mergeCache.put(a,b,a_);
return a_;
}
mergedParents = Arrays.copyOf(mergedParents, k);
mergedReturnStates = Arrays.copyOf(mergedReturnStates, k);
}
PredictionContext M =
new ArrayPredictionContext(mergedParents, mergedReturnStates);
// if we created same array as a or b, return that instead
// TODO: track whether this is possible above during merge sort for speed
if ( M.equals(a) ) {
if ( mergeCache!=null ) mergeCache.put(a,b,a);
return a;
}
if ( M.equals(b) ) {
if ( mergeCache!=null ) mergeCache.put(a,b,b);
return b;
}
combineCommonParents(mergedParents);
if ( mergeCache!=null ) mergeCache.put(a,b,M);
return M;
}
/**
* Make pass over all M {@code parents}; merge any {@code equals()}
* ones.
*/
protected static void combineCommonParents(PredictionContext[] parents) {
Map uniqueParents =
new HashMap();
for (int p = 0; p < parents.length; p++) {
PredictionContext parent = parents[p];
if ( !uniqueParents.containsKey(parent) ) { // don't replace
uniqueParents.put(parent, parent);
}
}
for (int p = 0; p < parents.length; p++) {
parents[p] = uniqueParents.get(parents[p]);
}
}
public static String toDOTString(PredictionContext context) {
if ( context==null ) return "";
StringBuilder buf = new StringBuilder();
buf.append("digraph G {\n");
buf.append("rankdir=LR;\n");
List nodes = getAllContextNodes(context);
Collections.sort(nodes, new Comparator() {
@Override
public int compare(PredictionContext o1, PredictionContext o2) {
return o1.id - o2.id;
}
});
for (PredictionContext current : nodes) {
if ( current instanceof SingletonPredictionContext ) {
String s = String.valueOf(current.id);
buf.append(" s").append(s);
String returnState = String.valueOf(current.getReturnState(0));
if ( current instanceof EmptyPredictionContext ) returnState = "$";
buf.append(" [label=\"").append(returnState).append("\"];\n");
continue;
}
ArrayPredictionContext arr = (ArrayPredictionContext)current;
buf.append(" s").append(arr.id);
buf.append(" [shape=box, label=\"");
buf.append("[");
boolean first = true;
for (int inv : arr.returnStates) {
if ( !first ) buf.append(", ");
if ( inv == EMPTY_RETURN_STATE ) buf.append("$");
else buf.append(inv);
first = false;
}
buf.append("]");
buf.append("\"];\n");
}
for (PredictionContext current : nodes) {
if ( current==EMPTY ) continue;
for (int i = 0; i < current.size(); i++) {
if ( current.getParent(i)==null ) continue;
String s = String.valueOf(current.id);
buf.append(" s").append(s);
buf.append("->");
buf.append("s");
buf.append(current.getParent(i).id);
if ( current.size()>1 ) buf.append(" [label=\"parent["+i+"]\"];\n");
else buf.append(";\n");
}
}
buf.append("}\n");
return buf.toString();
}
// From Sam
public static PredictionContext getCachedContext(
PredictionContext context,
PredictionContextCache contextCache,
IdentityHashMap visited)
{
if (context.isEmpty()) {
return context;
}
PredictionContext existing = visited.get(context);
if (existing != null) {
return existing;
}
existing = contextCache.get(context);
if (existing != null) {
visited.put(context, existing);
return existing;
}
boolean changed = false;
PredictionContext[] parents = new PredictionContext[context.size()];
for (int i = 0; i < parents.length; i++) {
PredictionContext parent = getCachedContext(context.getParent(i), contextCache, visited);
if (changed || parent != context.getParent(i)) {
if (!changed) {
parents = new PredictionContext[context.size()];
for (int j = 0; j < context.size(); j++) {
parents[j] = context.getParent(j);
}
changed = true;
}
parents[i] = parent;
}
}
if (!changed) {
contextCache.add(context);
visited.put(context, context);
return context;
}
PredictionContext updated;
if (parents.length == 0) {
updated = EMPTY;
}
else if (parents.length == 1) {
updated = SingletonPredictionContext.create(parents[0], context.getReturnState(0));
}
else {
ArrayPredictionContext arrayPredictionContext = (ArrayPredictionContext)context;
updated = new ArrayPredictionContext(parents, arrayPredictionContext.returnStates);
}
contextCache.add(updated);
visited.put(updated, updated);
visited.put(context, updated);
return updated;
}
// // extra structures, but cut/paste/morphed works, so leave it.
// // seems to do a breadth-first walk
// public static List getAllNodes(PredictionContext context) {
// Map visited =
// new IdentityHashMap();
// Deque workList = new ArrayDeque();
// workList.add(context);
// visited.put(context, context);
// List nodes = new ArrayList();
// while (!workList.isEmpty()) {
// PredictionContext current = workList.pop();
// nodes.add(current);
// for (int i = 0; i < current.size(); i++) {
// PredictionContext parent = current.getParent(i);
// if ( parent!=null && visited.put(parent, parent) == null) {
// workList.push(parent);
// }
// }
// }
// return nodes;
// }
// ter's recursive version of Sam's getAllNodes()
public static List getAllContextNodes(PredictionContext context) {
List nodes = new ArrayList();
Map visited =
new IdentityHashMap();
getAllContextNodes_(context, nodes, visited);
return nodes;
}
public static void getAllContextNodes_(PredictionContext context,
List nodes,
Map visited)
{
if ( context==null || visited.containsKey(context) ) return;
visited.put(context, context);
nodes.add(context);
for (int i = 0; i < context.size(); i++) {
getAllContextNodes_(context.getParent(i), nodes, visited);
}
}
public String toString(Recognizer recog) {
return toString();
// return toString(recog, ParserRuleContext.EMPTY);
}
public String[] toStrings(Recognizer recognizer, int currentState) {
return toStrings(recognizer, EMPTY, currentState);
}
// FROM SAM
public String[] toStrings(Recognizer recognizer, PredictionContext stop, int currentState) {
List result = new ArrayList();
outer:
for (int perm = 0; ; perm++) {
int offset = 0;
boolean last = true;
PredictionContext p = this;
int stateNumber = currentState;
StringBuilder localBuffer = new StringBuilder();
localBuffer.append("[");
while ( !p.isEmpty() && p != stop ) {
int index = 0;
if (p.size() > 0) {
int bits = 1;
while ((1 << bits) < p.size()) {
bits++;
}
int mask = (1 << bits) - 1;
index = (perm >> offset) & mask;
last &= index >= p.size() - 1;
if (index >= p.size()) {
continue outer;
}
offset += bits;
}
if ( recognizer!=null ) {
if (localBuffer.length() > 1) {
// first char is '[', if more than that this isn't the first rule
localBuffer.append(' ');
}
ATN atn = recognizer.getATN();
ATNState s = atn.states.get(stateNumber);
String ruleName = recognizer.getRuleNames()[s.ruleIndex];
localBuffer.append(ruleName);
}
else if ( p.getReturnState(index)!= EMPTY_RETURN_STATE) {
if ( !p.isEmpty() ) {
if (localBuffer.length() > 1) {
// first char is '[', if more than that this isn't the first rule
localBuffer.append(' ');
}
localBuffer.append(p.getReturnState(index));
}
}
stateNumber = p.getReturnState(index);
p = p.getParent(index);
}
localBuffer.append("]");
result.add(localBuffer.toString());
if (last) {
break;
}
}
return result.toArray(new String[result.size()]);
}
}