def Expects(self, State):
keys = self.StateTokenMap.keys()
Tokens = kjSet.NewSet([])
for (state1, token) in keys:
if State == state1:
kjSet.addMember(token, Tokens)
return kjSet.get_elts(Tokens)
def Expects(self, State):
keys = self.StateTokenMap.keys()
Tokens = kjSet.NewSet( [] )
for (state1,token) in keys:
if State == state1:
kjSet.addMember(token,Tokens)
return kjSet.get_elts(Tokens)
def SLRFixDFA(self):
DFA = self.DFA
NFA = self.SLRNFA
# look through the states (except 0=success) of the DFA
# initially don't add any new states, just record
# actions to be done
# uses convention that 0 is successful final state
# ToDo is a dictionary which maps
# (State, Token) to a item to reduce
ToDo = {}
Error = None
for State in range(1, len(DFA.States) ):
# look for a final item for a rule in this state
fromNFAindices = kjSet.get_elts(DFA.States[State][1])
for NFAindex in fromNFAindices:
item = NFA.States[NFAindex][1]
# if the item is final remember to do the reductions...
if self.SLRItemIsFinal(item):
(ruleindex, position) = item
Rule = self.Rules[ruleindex]
Head = Rule.Nonterm
Following = kjSet.Neighbors( self.Follow, Head )
for Token in Following:
key = (State, Token)
if not ToDo.has_key(key):
ToDo[ key ] = item
else:
# it might be okay if the items are identical?
item2 = ToDo[key]
if item != item2:
print "reduce/reduce conflict on ",key
self.ItemDump(item)
self.ItemDump(item2)
Error = " apparent reduce/reduce conflict"
#endif
#endfor
#endif
#endfor NFAindex
#endfor State
# for each (State,Token) pair which indicates a reduction
# record the reduction UNLESS the map is already set for the pair
for key in ToDo.keys():
(State,Token) = key
item = ToDo[key]
(rulenum, dotpos) = item
ExistingMap = DFA.map( State, Token )
if ExistingMap[0] == NOMATCHFLAG:
DFA.SetReduction( State, Token, rulenum )
else:
print "apparent shift/reduce conflict"
print "reduction: ", key, ": "
self.ItemDump(item)
print "existing map ", ExistingMap
Error = " apparent shift/reduce conflict"
#endfor
if Error and ABORTONERROR:
raise NotSLRError, Error
def SLRFixDFA(self):
DFA = self.DFA
NFA = self.SLRNFA
# look through the states (except 0=success) of the DFA
# initially don't add any new states, just record
# actions to be done
# uses convention that 0 is successful final state
# ToDo is a dictionary which maps
# (State, Token) to a item to reduce
ToDo = {}
Error = None
for State in range(1, len(DFA.States) ):
# look for a final item for a rule in this state
fromNFAindices = kjSet.get_elts(DFA.States[State][1])
for NFAindex in fromNFAindices:
item = NFA.States[NFAindex][1]
# if the item is final remember to do the reductions...
if self.SLRItemIsFinal(item):
(ruleindex, position) = item
Rule = self.Rules[ruleindex]
Head = Rule.Nonterm
Following = kjSet.Neighbors( self.Follow, Head )
for Token in Following:
key = (State, Token)
if not ToDo.has_key(key):
ToDo[ key ] = item
else:
# it might be okay if the items are identical?
item2 = ToDo[key]
if item != item2:
print "reduce/reduce conflict on ",key
self.ItemDump(item)
self.ItemDump(item2)
Error = " apparent reduce/reduce conflict"
#endif
#endfor
#endif
#endfor NFAindex
#endfor State
# for each (State,Token) pair which indicates a reduction
# record the reduction UNLESS the map is already set for the pair
for key in ToDo.keys():
(State,Token) = key
item = ToDo[key]
(rulenum, dotpos) = item
ExistingMap = DFA.map( State, Token )
if ExistingMap[0] == NOMATCHFLAG:
DFA.SetReduction( State, Token, rulenum )
else:
print "apparent shift/reduce conflict"
print "reduction: ", key, ": "
self.ItemDump(item)
print "existing map ", ExistingMap
Error = " apparent shift/reduce conflict"
#endfor
if Error and ABORTONERROR:
raise NotSLRError, Error
def DumpItemSet(self,State):
DFA = self.DFA
NFA = self.SLRNFA
print
print "STATE ", State, " *******"
fromNFAindices = kjSet.get_elts(DFA.States[State][1])
for NFAindex in fromNFAindices:
item = NFA.States[NFAindex][1]
print " ", NFAindex, ": ",
self.ItemDump(item)
def MakeTokenArchives(self):
# make a list of all tokens and
# initialize token > int dictionary
keys = self.Gram.DFA.StateTokenMap.keys()
tokenToInt = {}
tokenSet = kjSet.NewSet([])
for k in keys:
kjSet.addMember(k[1], tokenSet)
tokens = kjSet.get_elts(tokenSet)
for i in range(0,len(tokens)):
tokenToInt[ tokens[i] ] = i
self.keys = keys
self.tokens = tokens # global sub
self.tokInt = tokenToInt # global sub
def Eclosure(self, Epsilon, DoNullMaps=0):
''' return the epsilon closure of the FSM as a new FSM
DoNullMap, if set, will map unexpected tokens to
the "empty" state (usually creating a really big fsm)
'''
Closure = CFSMachine( self.root_nonTerminal )
# compute the Epsilon Graph between states
EGraph = kjSet.NewDG([])
for State in range(0,self.maxState+1):
# every state is E-connected to self
kjSet.AddArc( EGraph, State, State )
# add possible transition on epsilon (ONLY ONE SUPPORTED!)
key = (State, Epsilon)
if self.StateTokenMap.has_key(key):
keymap = self.StateTokenMap[key]
if keymap[0][0] != MOVETOFLAG:
raise TypeError, "unexpected map type in StateTokenMap"
for (Flag,ToState) in keymap:
kjSet.AddArc( EGraph, State, ToState )
#endfor
# transitively close EGraph
kjSet.TransClose( EGraph )
# Translate EGraph into a dictionary of lists
EMap = {}
for State in range(0,self.maxState+1):
EMap[State] = kjSet.Neighbors( EGraph, State )
# make each e-closure of each self.state a state of the closure FSM.
# here closure states assumed transient -- reset elsewhere.
# first do the initial state
Closure.States[ Closure.initial_state ] = \
[TRANSFLAG, kjSet.NewSet(EMap[self.initial_state]) ]
# do all other states (save initial and successful final states)
#for State in range(0,self.maxState+1):
# if State != self.initial_state \
# and State != self.successful_final_state:
# Closure.NewSetState(TRANSFLAG, kjSet.NewSet(EMap[State]) )
##endfor
# compute set of all known tokens EXCEPT EPSILON
Tokens = kjSet.NewSet( [] )
for (State, Token) in self.StateTokenMap.keys():
if Token != Epsilon:
kjSet.addMember(Token, Tokens)
# tranform it into a list
Tokens = kjSet.get_elts(Tokens)
# for each state of the the closure FSM (past final) add transitions
# and add new states as needed until all states are processed
# (uses convention that states are allocated sequentially)
ThisClosureState = 1
while ThisClosureState <= Closure.maxState:
MemberStates = kjSet.get_elts(Closure.States[ThisClosureState][1])
# for each possible Token, compute the union UTrans of all
# e-closures for all transitions for all member states,
# on the Token, make UTrans a new state (if needed),
# and transition ThisClosureState to UTrans on Token
for Token in Tokens:
UTrans = kjSet.NewSet( [] )
for MState in MemberStates:
# if MState has a transition on Token, include
# EMap for the destination state
key = (MState, Token)
if self.StateTokenMap.has_key(key):
DStateTup = self.StateTokenMap[key]
if DStateTup[0][0] != MOVETOFLAG:
raise TypeError, "unknown map type"
for (DFlag, DState) in DStateTup:
for EDState in EMap[DState]:
kjSet.addMember(EDState, UTrans)
#endif
#endfor MState
# register UTrans as a new state if needed
UTState = Closure.NewSetState(TRANSFLAG, UTrans)
# record transition from
# ThisClosureState to UTState on Token
if DoNullMaps:
Closure.SetMap( ThisClosureState, Token, UTState)
else:
if not kjSet.Empty(UTrans):
Closure.SetMap( ThisClosureState, Token, UTState)
#endfor Token
ThisClosureState = ThisClosureState +1
#endwhile
return Closure
