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"""ANTLR3 runtime package"""
# begin[licence]
#
# [The "BSD licence"]
# Copyright (c) 2005-2012 Terence Parr
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# modification, are permitted provided that the following conditions
# are met:
# 1. Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# 2. Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
# 3. The name of the author may not be used to endorse or promote products
# derived from this software without specific prior written permission.
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# end[licence]
from .constants import EOF
from .exceptions import NoViableAltException, BacktrackingFailed
class DFA(object):
"""@brief A DFA implemented as a set of transition tables.
Any state that has a semantic predicate edge is special; those states
are generated with if-then-else structures in a specialStateTransition()
which is generated by cyclicDFA template.
"""
def __init__(
self,
recognizer, decisionNumber,
eot, eof, min, max, accept, special, transition
):
## Which recognizer encloses this DFA? Needed to check backtracking
self.recognizer = recognizer
self.decisionNumber = decisionNumber
self.eot = eot
self.eof = eof
self.min = min
self.max = max
self.accept = accept
self.special = special
self.transition = transition
def predict(self, input):
"""
From the input stream, predict what alternative will succeed
using this DFA (representing the covering regular approximation
to the underlying CFL). Return an alternative number 1..n. Throw
an exception upon error.
"""
mark = input.mark()
s = 0 # we always start at s0
try:
for _ in range(50000):
specialState = self.special[s]
if specialState >= 0:
s = self.specialStateTransition(specialState, input)
if s == -1:
self.noViableAlt(s, input)
return 0
input.consume()
continue
if self.accept[s] >= 1:
return self.accept[s]
# look for a normal char transition
c = input.LA(1)
if c >= self.min[s] and c <= self.max[s]:
# move to next state
snext = self.transition[s][c-self.min[s]]
if snext < 0:
# was in range but not a normal transition
# must check EOT, which is like the else clause.
# eot[s]>=0 indicates that an EOT edge goes to another
# state.
if self.eot[s] >= 0: # EOT Transition to accept state?
s = self.eot[s]
input.consume()
# TODO: I had this as return accept[eot[s]]
# which assumed here that the EOT edge always
# went to an accept...faster to do this, but
# what about predicated edges coming from EOT
# target?
continue
self.noViableAlt(s, input)
return 0
s = snext
input.consume()
continue
if self.eot[s] >= 0:
s = self.eot[s]
input.consume()
continue
# EOF Transition to accept state?
if c == EOF and self.eof[s] >= 0:
return self.accept[self.eof[s]]
# not in range and not EOF/EOT, must be invalid symbol
self.noViableAlt(s, input)
return 0
else:
raise RuntimeError("DFA bang!")
finally:
input.rewind(mark)
def noViableAlt(self, s, input):
if self.recognizer._state.backtracking > 0:
raise BacktrackingFailed
nvae = NoViableAltException(
self.getDescription(),
self.decisionNumber,
s,
input
)
self.error(nvae)
raise nvae
def error(self, nvae):
"""A hook for debugging interface"""
pass
def specialStateTransition(self, s, input):
return -1
def getDescription(self):
return "n/a"
## def specialTransition(self, state, symbol):
## return 0
@classmethod
def unpack(cls, string):
"""@brief Unpack the runlength encoded table data.
Terence implemented packed table initializers, because Java has a
size restriction on .class files and the lookup tables can grow
pretty large. The generated JavaLexer.java of the Java.g example
would be about 15MB with uncompressed array initializers.
Python does not have any size restrictions, but the compilation of
such large source files seems to be pretty memory hungry. The memory
consumption of the python process grew to >1.5GB when importing a
15MB lexer, eating all my swap space and I was to impacient to see,
if it could finish at all. With packed initializers that are unpacked
at import time of the lexer module, everything works like a charm.
"""
ret = []
for i in range(0, len(string) - 1, 2):
(n, v) = ord(string[i]), ord(string[i + 1])
if v == 0xFFFF:
v = -1
ret += [v] * n
return ret