# When using this prediction mode, the parser will either return a correct # parse tree (i.e. the same parse tree that would be returned with the # {@link #LL} prediction mode), or it will report a syntax error. If a # syntax error is encountered when using the {@link #SLL} prediction mode, # it may be due to either an actual syntax error in the input or indicate # that the particular combination of grammar and input requires the more # powerful {@link #LL} prediction abilities to complete successfully.
# ## This prediction mode does not provide any guarantees for prediction # behavior for syntactically-incorrect inputs.
# SLL = 0 # # The LL(*) prediction mode. This prediction mode allows the current parser # context to be used for resolving SLL conflicts that occur during # prediction. This is the fastest prediction mode that guarantees correct # parse results for all combinations of grammars with syntactically correct # inputs. # ## When using this prediction mode, the parser will make correct decisions # for all syntactically-correct grammar and input combinations. However, in # cases where the grammar is truly ambiguous this prediction mode might not # report a precise answer for exactly which alternatives are # ambiguous.
# ## This prediction mode does not provide any guarantees for prediction # behavior for syntactically-incorrect inputs.
# LL = 1 # # The LL(*) prediction mode with exact ambiguity detection. In addition to # the correctness guarantees provided by the {@link #LL} prediction mode, # this prediction mode instructs the prediction algorithm to determine the # complete and exact set of ambiguous alternatives for every ambiguous # decision encountered while parsing. # ## This prediction mode may be used for diagnosing ambiguities during # grammar development. Due to the performance overhead of calculating sets # of ambiguous alternatives, this prediction mode should be avoided when # the exact results are not necessary.
# ## This prediction mode does not provide any guarantees for prediction # behavior for syntactically-incorrect inputs.
# LL_EXACT_AMBIG_DETECTION = 2 # # Computes the SLL prediction termination condition. # ## This method computes the SLL prediction termination condition for both of # the following cases.
# #COMBINED SLL+LL PARSING
# #When LL-fallback is enabled upon SLL conflict, correct predictions are # ensured regardless of how the termination condition is computed by this # method. Due to the substantially higher cost of LL prediction, the # prediction should only fall back to LL when the additional lookahead # cannot lead to a unique SLL prediction.
# #Assuming combined SLL+LL parsing, an SLL configuration set with only # conflicting subsets should fall back to full LL, even if the # configuration sets don't resolve to the same alternative (e.g. # {@code {1,2}} and {@code {3,4}}. If there is at least one non-conflicting # configuration, SLL could continue with the hopes that more lookahead will # resolve via one of those non-conflicting configurations.
# #Here's the prediction termination rule them: SLL (for SLL+LL parsing) # stops when it sees only conflicting configuration subsets. In contrast, # full LL keeps going when there is uncertainty.
# #HEURISTIC
# #As a heuristic, we stop prediction when we see any conflicting subset # unless we see a state that only has one alternative associated with it. # The single-alt-state thing lets prediction continue upon rules like # (otherwise, it would admit defeat too soon):
# #{@code [12|1|[], 6|2|[], 12|2|[]]. s : (ID | ID ID?) ';' ;}
# #When the ATN simulation reaches the state before {@code ';'}, it has a # DFA state that looks like: {@code [12|1|[], 6|2|[], 12|2|[]]}. Naturally # {@code 12|1|[]} and {@code 12|2|[]} conflict, but we cannot stop # processing this node because alternative to has another way to continue, # via {@code [6|2|[]]}.
# #It also let's us continue for this rule:
# #{@code [1|1|[], 1|2|[], 8|3|[]] a : A | A | A B ;}
# #After matching input A, we reach the stop state for rule A, state 1. # State 8 is the state right before B. Clearly alternatives 1 and 2 # conflict and no amount of further lookahead will separate the two. # However, alternative 3 will be able to continue and so we do not stop # working on this state. In the previous example, we're concerned with # states associated with the conflicting alternatives. Here alt 3 is not # associated with the conflicting configs, but since we can continue # looking for input reasonably, don't declare the state done.
# #PURE SLL PARSING
# #To handle pure SLL parsing, all we have to do is make sure that we # combine stack contexts for configurations that differ only by semantic # predicate. From there, we can do the usual SLL termination heuristic.
# #PREDICATES IN SLL+LL PARSING
# #SLL decisions don't evaluate predicates until after they reach DFA stop # states because they need to create the DFA cache that works in all # semantic situations. In contrast, full LL evaluates predicates collected # during start state computation so it can ignore predicates thereafter. # This means that SLL termination detection can totally ignore semantic # predicates.
# #Implementation-wise, {@link ATNConfigSet} combines stack contexts but not # semantic predicate contexts so we might see two configurations like the # following.
# #{@code (s, 1, x, {}), (s, 1, x', {p})}
# #Before testing these configurations against others, we have to merge # {@code x} and {@code x'} (without modifying the existing configurations). # For example, we test {@code (x+x')==x''} when looking for conflicts in # the following configurations.
# #{@code (s, 1, x, {}), (s, 1, x', {p}), (s, 2, x'', {})}
# #If the configuration set has predicates (as indicated by # {@link ATNConfigSet#hasSemanticContext}), this algorithm makes a copy of # the configurations to strip out all of the predicates so that a standard # {@link ATNConfigSet} will merge everything ignoring predicates.
# @classmethod def hasSLLConflictTerminatingPrediction(cls, mode:PredictionMode, configs:ATNConfigSet): # Configs in rule stop states indicate reaching the end of the decision # rule (local context) or end of start rule (full context). If all # configs meet this condition, then none of the configurations is able # to match additional input so we terminate prediction. # if cls.allConfigsInRuleStopStates(configs): return True # pure SLL mode parsing if mode == PredictionMode.SLL: # Don't bother with combining configs from different semantic # contexts if we can fail over to full LL; costs more time # since we'll often fail over anyway. if configs.hasSemanticContext: # dup configs, tossing out semantic predicates dup = ATNConfigSet() for c in configs: c = ATNConfig(config=c, semantic=SemanticContext.NONE) dup.add(c) configs = dup # now we have combined contexts for configs with dissimilar preds # pure SLL or combined SLL+LL mode parsing altsets = cls.getConflictingAltSubsets(configs) return cls.hasConflictingAltSet(altsets) and not cls.hasStateAssociatedWithOneAlt(configs) # Checks if any configuration in {@code configs} is in a # {@link RuleStopState}. Configurations meeting this condition have reached # the end of the decision rule (local context) or end of start rule (full # context). # # @param configs the configuration set to test # @return {@code true} if any configuration in {@code configs} is in a # {@link RuleStopState}, otherwise {@code false} @classmethod def hasConfigInRuleStopState(cls, configs:ATNConfigSet): return any(isinstance(cfg.state, RuleStopState) for cfg in configs) # Checks if all configurations in {@code configs} are in a # {@link RuleStopState}. Configurations meeting this condition have reached # the end of the decision rule (local context) or end of start rule (full # context). # # @param configs the configuration set to test # @return {@code true} if all configurations in {@code configs} are in a # {@link RuleStopState}, otherwise {@code false} @classmethod def allConfigsInRuleStopStates(cls, configs:ATNConfigSet): return all(isinstance(cfg.state, RuleStopState) for cfg in configs) # # Full LL prediction termination. # #Can we stop looking ahead during ATN simulation or is there some # uncertainty as to which alternative we will ultimately pick, after # consuming more input? Even if there are partial conflicts, we might know # that everything is going to resolve to the same minimum alternative. That # means we can stop since no more lookahead will change that fact. On the # other hand, there might be multiple conflicts that resolve to different # minimums. That means we need more look ahead to decide which of those # alternatives we should predict.
# #The basic idea is to split the set of configurations {@code C}, into # conflicting subsets {@code (s, _, ctx, _)} and singleton subsets with # non-conflicting configurations. Two configurations conflict if they have # identical {@link ATNConfig#state} and {@link ATNConfig#context} values # but different {@link ATNConfig#alt} value, e.g. {@code (s, i, ctx, _)} # and {@code (s, j, ctx, _)} for {@code i!=j}.
# #Reduce these configuration subsets to the set of possible alternatives. # You can compute the alternative subsets in one pass as follows:
# #{@code A_s,ctx = {i | (s, i, ctx, _)}} for each configuration in # {@code C} holding {@code s} and {@code ctx} fixed.
# #Or in pseudo-code, for each configuration {@code c} in {@code C}:
# #
# map[c] U= c.{@link ATNConfig#alt alt} # map hash/equals uses s and x, not
# alt and not pred
#
#
# The values in {@code map} are the set of {@code A_s,ctx} sets.
# #If {@code |A_s,ctx|=1} then there is no conflict associated with # {@code s} and {@code ctx}.
# #Reduce the subsets to singletons by choosing a minimum of each subset. If # the union of these alternative subsets is a singleton, then no amount of # more lookahead will help us. We will always pick that alternative. If, # however, there is more than one alternative, then we are uncertain which # alternative to predict and must continue looking for resolution. We may # or may not discover an ambiguity in the future, even if there are no # conflicting subsets this round.
# #The biggest sin is to terminate early because it means we've made a # decision but were uncertain as to the eventual outcome. We haven't used # enough lookahead. On the other hand, announcing a conflict too late is no # big deal; you will still have the conflict. It's just inefficient. It # might even look until the end of file.
# #No special consideration for semantic predicates is required because # predicates are evaluated on-the-fly for full LL prediction, ensuring that # no configuration contains a semantic context during the termination # check.
# #CONFLICTING CONFIGS
# #Two configurations {@code (s, i, x)} and {@code (s, j, x')}, conflict # when {@code i!=j} but {@code x=x'}. Because we merge all # {@code (s, i, _)} configurations together, that means that there are at # most {@code n} configurations associated with state {@code s} for # {@code n} possible alternatives in the decision. The merged stacks # complicate the comparison of configuration contexts {@code x} and # {@code x'}. Sam checks to see if one is a subset of the other by calling # merge and checking to see if the merged result is either {@code x} or # {@code x'}. If the {@code x} associated with lowest alternative {@code i} # is the superset, then {@code i} is the only possible prediction since the # others resolve to {@code min(i)} as well. However, if {@code x} is # associated with {@code j>i} then at least one stack configuration for # {@code j} is not in conflict with alternative {@code i}. The algorithm # should keep going, looking for more lookahead due to the uncertainty.
# #For simplicity, I'm doing a equality check between {@code x} and # {@code x'} that lets the algorithm continue to consume lookahead longer # than necessary. The reason I like the equality is of course the # simplicity but also because that is the test you need to detect the # alternatives that are actually in conflict.
# #CONTINUE/STOP RULE
# #Continue if union of resolved alternative sets from non-conflicting and # conflicting alternative subsets has more than one alternative. We are # uncertain about which alternative to predict.
# #The complete set of alternatives, {@code [i for (_,i,_)]}, tells us which # alternatives are still in the running for the amount of input we've # consumed at this point. The conflicting sets let us to strip away # configurations that won't lead to more states because we resolve # conflicts to the configuration with a minimum alternate for the # conflicting set.
# #CASES
# #EXACT AMBIGUITY DETECTION
# #If all states report the same conflicting set of alternatives, then we # know we have the exact ambiguity set.
# #|A_i|>1 and
# A_i = A_j for all i, j.
In other words, we continue examining lookahead until all {@code A_i} # have more than one alternative and all {@code A_i} are the same. If # {@code A={{1,2}, {1,3}}}, then regular LL prediction would terminate # because the resolved set is {@code {1}}. To determine what the real # ambiguity is, we have to know whether the ambiguity is between one and # two or one and three so we keep going. We can only stop prediction when # we need exact ambiguity detection when the sets look like # {@code A={{1,2}}} or {@code {{1,2},{1,2}}}, etc...
# @classmethod def resolvesToJustOneViableAlt(cls, altsets:list): return cls.getSingleViableAlt(altsets) # # Determines if every alternative subset in {@code altsets} contains more # than one alternative. # # @param altsets a collection of alternative subsets # @return {@code true} if every {@link BitSet} in {@code altsets} has # {@link BitSet#cardinality cardinality} > 1, otherwise {@code false} # @classmethod def allSubsetsConflict(cls, altsets:list): return not cls.hasNonConflictingAltSet(altsets) # # Determines if any single alternative subset in {@code altsets} contains # exactly one alternative. # # @param altsets a collection of alternative subsets # @return {@code true} if {@code altsets} contains a {@link BitSet} with # {@link BitSet#cardinality cardinality} 1, otherwise {@code false} # @classmethod def hasNonConflictingAltSet(cls, altsets:list): return any(len(alts) == 1 for alts in altsets) # # Determines if any single alternative subset in {@code altsets} contains # more than one alternative. # # @param altsets a collection of alternative subsets # @return {@code true} if {@code altsets} contains a {@link BitSet} with # {@link BitSet#cardinality cardinality} > 1, otherwise {@code false} # @classmethod def hasConflictingAltSet(cls, altsets:list): return any(len(alts) > 1 for alts in altsets) # # Determines if every alternative subset in {@code altsets} is equivalent. # # @param altsets a collection of alternative subsets # @return {@code true} if every member of {@code altsets} is equal to the # others, otherwise {@code false} # @classmethod def allSubsetsEqual(cls, altsets:list): if not altsets: return True first = next(iter(altsets)) return all(alts == first for alts in iter(altsets)) # # Returns the unique alternative predicted by all alternative subsets in # {@code altsets}. If no such alternative exists, this method returns # {@link ATN#INVALID_ALT_NUMBER}. # # @param altsets a collection of alternative subsets # @classmethod def getUniqueAlt(cls, altsets:list): all = cls.getAlts(altsets) if len(all)==1: return next(iter(all)) return ATN.INVALID_ALT_NUMBER # Gets the complete set of represented alternatives for a collection of # alternative subsets. This method returns the union of each {@link BitSet} # in {@code altsets}. # # @param altsets a collection of alternative subsets # @return the set of represented alternatives in {@code altsets} # @classmethod def getAlts(cls, altsets:list): return set.union(*altsets) # # This function gets the conflicting alt subsets from a configuration set. # For each configuration {@code c} in {@code configs}: # #
# map[c] U= c.{@link ATNConfig#alt alt} # map hash/equals uses s and x, not
# alt and not pred
#
#
@classmethod
def getConflictingAltSubsets(cls, configs:ATNConfigSet):
configToAlts = dict()
for c in configs:
h = hash((c.state.stateNumber, c.context))
alts = configToAlts.get(h, None)
if alts is None:
alts = set()
configToAlts[h] = alts
alts.add(c.alt)
return configToAlts.values()
#
# Get a map from state to alt subset from a configuration set. For each
# configuration {@code c} in {@code configs}:
#
#
# map[c.{@link ATNConfig#state state}] U= c.{@link ATNConfig#alt alt}
#
#
@classmethod
def getStateToAltMap(cls, configs:ATNConfigSet):
m = dict()
for c in configs:
alts = m.get(c.state, None)
if alts is None:
alts = set()
m[c.state] = alts
alts.add(c.alt)
return m
@classmethod
def hasStateAssociatedWithOneAlt(cls, configs:ATNConfigSet):
return any(len(alts) == 1 for alts in cls.getStateToAltMap(configs).values())
@classmethod
def getSingleViableAlt(cls, altsets:list):
viableAlts = set()
for alts in altsets:
minAlt = min(alts)
viableAlts.add(minAlt)
if len(viableAlts)>1 : # more than 1 viable alt
return ATN.INVALID_ALT_NUMBER
return min(viableAlts)