def hasSLLConflictTerminatingPrediction(cls, mode, configs):
# 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)
def __init__(self,
stateNumber: int = -1,
configs: ATNConfigSet = ATNConfigSet()):
self.stateNumber = stateNumber
self.configs = configs
# {@code edges[symbol]} points to target of symbol. Shift up by 1 so (-1)
# {@link Token#EOF} maps to {@code edges[0]}.
self.edges = None
self.isAcceptState = False
# if accept state, what ttype do we match or alt do we predict?
# This is set to {@link ATN#INVALID_ALT_NUMBER} when {@link #predicates}{@code !=null} or
# {@link #requiresFullContext}.
self.prediction = 0
self.lexerActionExecutor = None
# Indicates that this state was created during SLL prediction that
# discovered a conflict between the configurations in the state. Future
# {@link ParserATNSimulator#execATN} invocations immediately jumped doing
# full context prediction if this field is true.
self.requiresFullContext = False
# During SLL parsing, this is a list of predicates associated with the
# ATN configurations of the DFA state. When we have predicates,
# {@link #requiresFullContext} is {@code false} since full context prediction evaluates predicates
# on-the-fly. If this is not null, then {@link #prediction} is
# {@link ATN#INVALID_ALT_NUMBER}.
#
# <p>We only use these for non-{@link #requiresFullContext} but conflicting states. That
# means we know from the context (it's $ or we don't dip into outer
# context) that it's an ambiguity not a conflict.</p>
#
# <p>This list is computed by {@link ParserATNSimulator#predicateDFAState}.</p>
self.predicates = None
class ATNSimulator(object):
# Must distinguish between missing edge and edge we know leads nowhere#/
ERROR = DFAState(0x7FFFFFFF, ATNConfigSet())
# The context cache maps all PredictionContext objects that are ==
# to a single cached copy. This cache is shared across all contexts
# in all ATNConfigs in all DFA states. We rebuild each ATNConfigSet
# to use only cached nodes/graphs in addDFAState(). We don't want to
# fill this during closure() since there are lots of contexts that
# pop up but are not used ever again. It also greatly slows down closure().
#
# <p>This cache makes a huge difference in memory and a little bit in speed.
# For the Java grammar on java.*, it dropped the memory requirements
# at the end from 25M to 16M. We don't store any of the full context
# graphs in the DFA because they are limited to local context only,
# but apparently there's a lot of repetition there as well. We optimize
# the config contexts before storing the config set in the DFA states
# by literally rebuilding them with cached subgraphs only.</p>
#
# <p>I tried a cache for use during closure operations, that was
# whacked after each adaptivePredict(). It cost a little bit
# more time I think and doesn't save on the overall footprint
# so it's not worth the complexity.</p>
#/
def __init__(self, atn, sharedContextCache):
self.atn = atn
self.sharedContextCache = sharedContextCache
def getCachedContext(self, context):
if self.sharedContextCache is None:
return context
visited = dict()
return getCachedPredictionContext(context, self.sharedContextCache,
visited)
def setPrecedenceDfa(self, precedenceDfa):
if self.precedenceDfa != precedenceDfa:
self._states = dict()
if precedenceDfa:
precedenceState = DFAState(configs=ATNConfigSet())
precedenceState.edges = []
precedenceState.isAcceptState = False
precedenceState.requiresFullContext = False
self.s0 = precedenceState
else:
self.s0 = None
self.precedenceDfa = precedenceDfa
def __init__(self, atnStartState, decision=0):
# From which ATN state did we create this DFA?
self.atnStartState = atnStartState
self.decision = decision
# A set of all DFA states. Use {@link Map} so we can get old state back
# ({@link Set} only allows you to see if it's there).
self._states = dict()
self.s0 = None
# {@code true} if this DFA is for a precedence decision; otherwise,
# {@code false}. This is the backing field for {@link #isPrecedenceDfa},
# {@link #setPrecedenceDfa}.
self.precedenceDfa = False
if isinstance(atnStartState, StarLoopEntryState):
if atnStartState.isPrecedenceDecision:
self.precedenceDfa = True
precedenceState = DFAState(configs=ATNConfigSet())
precedenceState.edges = []
precedenceState.isAcceptState = False
precedenceState.requiresFullContext = False
self.s0 = precedenceState
newState.configs = configs
dfa.states[newState] = newState
return newState
def getDFA(self, mode: int):
return self.decisionToDFA[mode]
# Get the text matched so far for the current token.
def getText(self, input: InputStream):
# index is first lookahead char, don't include.
return input.getText(self.startIndex, input.index - 1)
def consume(self, input: InputStream):
curChar = input.LA(1)
if curChar == ord('\n'):
self.line += 1
self.column = 0
else:
self.column += 1
input.consume()
def getTokenName(self, t: int):
if t == -1:
return "EOF"
else:
return "'" + chr(t) + "'"
LexerATNSimulator.ERROR = DFAState(0x7FFFFFFF, ATNConfigSet())
del Lexer
