#
# 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.
#
#
# This enumeration defines the prediction modes available in ANTLR 4 along with
# utility methods for analyzing configuration sets for conflicts and/or
# ambiguities.


from antlr4.atn.ATN import ATN
from antlr4.atn.ATNConfig import ATNConfig
from antlr4.atn.ATNConfigSet import ATNConfigSet
from antlr4.atn.ATNState import RuleStopState
from antlr4.atn.SemanticContext import SemanticContext

class PredictionMode(object):
    #
    # The SLL(*) prediction mode. This prediction mode ignores the current
    # parser context when making predictions. This is the fastest prediction
    # mode, and provides correct results for many grammars. This prediction
    # mode is more powerful than the prediction mode provided by ANTLR 3, but
    # may result in syntax errors for grammar and input combinations which are
    # not SLL.
    #
    # <p>
    # 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.</p>
    #
    # <p>
    # This prediction mode does not provide any guarantees for prediction
    # behavior for syntactically-incorrect inputs.</p>
    #
    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.
    #
    # <p>
    # 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 <em>exactly which</em> alternatives are
    # ambiguous.</p>
    #
    # <p>
    # This prediction mode does not provide any guarantees for prediction
    # behavior for syntactically-incorrect inputs.</p>
    #
    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.
    #
    # <p>
    # 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.</p>
    #
    # <p>
    # This prediction mode does not provide any guarantees for prediction
    # behavior for syntactically-incorrect inputs.</p>
    #
    LL_EXACT_AMBIG_DETECTION = 2


    #
    # Computes the SLL prediction termination condition.
    #
    # <p>
    # This method computes the SLL prediction termination condition for both of
    # the following cases.</p>
    #
    # <ul>
    # <li>The usual SLL+LL fallback upon SLL conflict</li>
    # <li>Pure SLL without LL fallback</li>
    # </ul>
    #
    # <p><strong>COMBINED SLL+LL PARSING</strong></p>
    #
    # <p>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.</p>
    #
    # <p>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.</p>
    #
    # <p>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.</p>
    #
    # <p><strong>HEURISTIC</strong></p>
    #
    # <p>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):</p>
    #
    # <p>{@code [12|1|[], 6|2|[], 12|2|[]]. s : (ID | ID ID?) ';' ;}</p>
    #
    # <p>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|[]]}.</p>
    #
    # <p>It also let's us continue for this rule:</p>
    #
    # <p>{@code [1|1|[], 1|2|[], 8|3|[]] a : A | A | A B ;}</p>
    #
    # <p>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.</p>
    #
    # <p><strong>PURE SLL PARSING</strong></p>
    #
    # <p>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.</p>
    #
    # <p><strong>PREDICATES IN SLL+LL PARSING</strong></p>
    #
    # <p>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.</p>
    #
    # <p>Implementation-wise, {@link ATNConfigSet} combines stack contexts but not
    # semantic predicate contexts so we might see two configurations like the
    # following.</p>
    #
    # <p>{@code (s, 1, x, {}), (s, 1, x', {p})}</p>
    #
    # <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.</p>
    #
    # <p>{@code (s, 1, x, {}), (s, 1, x', {p}), (s, 2, x'', {})}</p>
    #
    # <p>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.</p>
    #
    @classmethod
    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)

    # 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):
        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):
        return all(isinstance(cfg.state, RuleStopState) for cfg in configs)

    #
    # Full LL prediction termination.
    #
    # <p>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.</p>
    #
    # <p>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}.</p>
    #
    # <p>Reduce these configuration subsets to the set of possible alternatives.
    # You can compute the alternative subsets in one pass as follows:</p>
    #
    # <p>{@code A_s,ctx = {i | (s, i, ctx, _)}} for each configuration in
    # {@code C} holding {@code s} and {@code ctx} fixed.</p>
    #
    # <p>Or in pseudo-code, for each configuration {@code c} in {@code C}:</p>
    #
    # <pre>
    # map[c] U= c.{@link ATNConfig#alt alt} # map hash/equals uses s and x, not
    # alt and not pred
    # </pre>
    #
    # <p>The values in {@code map} are the set of {@code A_s,ctx} sets.</p>
    #
    # <p>If {@code |A_s,ctx|=1} then there is no conflict associated with
    # {@code s} and {@code ctx}.</p>
    #
    # <p>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.</p>
    #
    # <p>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.</p>
    #
    # <p>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.</p>
    #
    # <p><strong>CONFLICTING CONFIGS</strong></p>
    #
    # <p>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.</p>
    #
    # <p>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.</p>
    #
    # <p><strong>CONTINUE/STOP RULE</strong></p>
    #
    # <p>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.</p>
    #
    # <p>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.</p>
    #
    # <p><strong>CASES</strong></p>
    #
    # <ul>
    #
    # <li>no conflicts and more than 1 alternative in set =&gt; continue</li>
    #
    # <li> {@code (s, 1, x)}, {@code (s, 2, x)}, {@code (s, 3, z)},
    # {@code (s', 1, y)}, {@code (s', 2, y)} yields non-conflicting set
    # {@code {3}} U conflicting sets {@code min({1,2})} U {@code min({1,2})} =
    # {@code {1,3}} =&gt; continue
    # </li>
    #
    # <li>{@code (s, 1, x)}, {@code (s, 2, x)}, {@code (s', 1, y)},
    # {@code (s', 2, y)}, {@code (s'', 1, z)} yields non-conflicting set
    # {@code {1}} U conflicting sets {@code min({1,2})} U {@code min({1,2})} =
    # {@code {1}} =&gt; stop and predict 1</li>
    #
    # <li>{@code (s, 1, x)}, {@code (s, 2, x)}, {@code (s', 1, y)},
    # {@code (s', 2, y)} yields conflicting, reduced sets {@code {1}} U
    # {@code {1}} = {@code {1}} =&gt; stop and predict 1, can announce
    # ambiguity {@code {1,2}}</li>
    #
    # <li>{@code (s, 1, x)}, {@code (s, 2, x)}, {@code (s', 2, y)},
    # {@code (s', 3, y)} yields conflicting, reduced sets {@code {1}} U
    # {@code {2}} = {@code {1,2}} =&gt; continue</li>
    #
    # <li>{@code (s, 1, x)}, {@code (s, 2, x)}, {@code (s', 3, y)},
    # {@code (s', 4, y)} yields conflicting, reduced sets {@code {1}} U
    # {@code {3}} = {@code {1,3}} =&gt; continue</li>
    #
    # </ul>
    #
    # <p><strong>EXACT AMBIGUITY DETECTION</strong></p>
    #
    # <p>If all states report the same conflicting set of alternatives, then we
    # know we have the exact ambiguity set.</p>
    #
    # <p><code>|A_<em>i</em>|&gt;1</code> and
    # <code>A_<em>i</em> = A_<em>j</em></code> for all <em>i</em>, <em>j</em>.</p>
    #
    # <p>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...</p>
    #
    @classmethod
    def resolvesToJustOneViableAlt(cls, altsets):
        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} &gt; 1, otherwise {@code false}
    #
    @classmethod
    def allSubsetsConflict(cls, altsets):
        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):
        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} &gt; 1, otherwise {@code false}
    #
    @classmethod
    def hasConflictingAltSet(cls, altsets):
        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):
        if not altsets:
            return True
        return all(alts == altsets[0] for alts in altsets[1:])

    #
    # 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):
        all = cls.getAlts(altsets)
        if len(all)==1:
            return all.pop()
        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):
        return set.union(*altsets)

    #
    # This function gets the conflicting alt subsets from a configuration set.
    # For each configuration {@code c} in {@code configs}:
    #
    # <pre>
    # map[c] U= c.{@link ATNConfig#alt alt} # map hash/equals uses s and x, not
    # alt and not pred
    # </pre>
    #
    @classmethod
    def getConflictingAltSubsets(cls, configs):
        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}:
    #
    # <pre>
    # map[c.{@link ATNConfig#state state}] U= c.{@link ATNConfig#alt alt}
    # </pre>
    #
    @classmethod
    def getStateToAltMap(cls, configs):
        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):
        return any(len(alts) == 1 for alts in cls.getStateToAltMap(configs).values())

    @classmethod
    def getSingleViableAlt(cls, altsets):
        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)
