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Statistical sampling library for use in virtdata libraries, based on apache commons math 4

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/*
 * 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()]); } }




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