Shofel2_T124_python/venv/lib/python3.10/site-packages/antlr4/dfa/DFAState.py

#
# 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.
#/

# Map a predicate to a predicted alternative.#/
from io import StringIO
from antlr4.atn.ATNConfigSet import ATNConfigSet
from antlr4.atn.SemanticContext import SemanticContext


class PredPrediction(object):
    def __init__(self, pred:SemanticContext, alt:int):
        self.alt = alt
        self.pred = pred

    def __str__(self):
        return "(" + str(self.pred) + ", " + str(self.alt) +  ")"

# A DFA state represents a set of possible ATN configurations.
#  As Aho, Sethi, Ullman p. 117 says "The DFA uses its state
#  to keep track of all possible states the ATN can be in after
#  reading each input symbol.  That is to say, after reading
#  input a1a2..an, the DFA is in a state that represents the
#  subset T of the states of the ATN that are reachable from the
#  ATN's start state along some path labeled a1a2..an."
#  In conventional NFA→DFA conversion, therefore, the subset T
#  would be a bitset representing the set of states the
#  ATN could be in.  We need to track the alt predicted by each
#  state as well, however.  More importantly, we need to maintain
#  a stack of states, tracking the closure operations as they
#  jump from rule to rule, emulating rule invocations (method calls).
#  I have to add a stack to simulate the proper lookahead sequences for
#  the underlying LL grammar from which the ATN was derived.
#
#  <p>I use a set of ATNConfig objects not simple states.  An ATNConfig
#  is both a state (ala normal conversion) and a RuleContext describing
#  the chain of rules (if any) followed to arrive at that state.</p>
#
#  <p>A DFA state may have multiple references to a particular state,
#  but with different ATN contexts (with same or different alts)
#  meaning that state was reached via a different set of rule invocations.</p>
#/
class DFAState(object):

    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



    # Get the set of all alts mentioned by all ATN configurations in this
    #  DFA state.
    def getAltSet(self):
        if self.configs is not None:
            return set(cfg.alt for cfg in self.configs) or None
        return None

    def __hash__(self):
        return hash(self.configs)

    # Two {@link DFAState} instances are equal if their ATN configuration sets
    # are the same. This method is used to see if a state already exists.
    #
    # <p>Because the number of alternatives and number of ATN configurations are
    # finite, there is a finite number of DFA states that can be processed.
    # This is necessary to show that the algorithm terminates.</p>
    #
    # <p>Cannot test the DFA state numbers here because in
    # {@link ParserATNSimulator#addDFAState} we need to know if any other state
    # exists that has this exact set of ATN configurations. The
    # {@link #stateNumber} is irrelevant.</p>
    def __eq__(self, other):
        # compare set of ATN configurations in this set with other
        if self is other:
            return True
        elif not isinstance(other, DFAState):
            return False
        else:
            return self.configs==other.configs

    def __str__(self):
        with StringIO() as buf:
            buf.write(str(self.stateNumber))
            buf.write(":")
            buf.write(str(self.configs))
            if self.isAcceptState:
                buf.write("=>")
                if self.predicates is not None:
                    buf.write(str(self.predicates))
                else:
                    buf.write(str(self.prediction))
            return buf.getvalue()