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# cython: auto_pickle=False
"""
Python Lexical Analyser
Classes for building NFAs and DFAs
"""
from __future__ import absolute_import
import cython
from .Transitions import TransitionMap
maxint = 2**31-1 # sentinel value
if not cython.compiled:
try:
unichr
except NameError:
unichr = chr
LOWEST_PRIORITY = -maxint
class Machine(object):
"""A collection of Nodes representing an NFA or DFA."""
def __init__(self):
self.states = [] # [Node]
self.initial_states = {} # {(name, bol): Node}
self.next_state_number = 1
def __del__(self):
for state in self.states:
state.destroy()
def new_state(self):
"""Add a new state to the machine and return it."""
s = Node()
n = self.next_state_number
self.next_state_number = n + 1
s.number = n
self.states.append(s)
return s
def new_initial_state(self, name):
state = self.new_state()
self.make_initial_state(name, state)
return state
def make_initial_state(self, name, state):
self.initial_states[name] = state
def get_initial_state(self, name):
return self.initial_states[name]
def dump(self, file):
file.write("Plex.Machine:\n")
if self.initial_states is not None:
file.write(" Initial states:\n")
for (name, state) in sorted(self.initial_states.items()):
file.write(" '%s': %d\n" % (name, state.number))
for s in self.states:
s.dump(file)
class Node(object):
"""A state of an NFA or DFA."""
def __init__(self):
# Preinitialise the list of empty transitions, because
# the nfa-to-dfa algorithm needs it
self.transitions = TransitionMap() # TransitionMap
self.action_priority = LOWEST_PRIORITY # integer
self.action = None # Action
self.number = 0 # for debug output
self.epsilon_closure = None # used by nfa_to_dfa()
def destroy(self):
self.transitions = None
self.action = None
self.epsilon_closure = None
def add_transition(self, event, new_state):
self.transitions.add(event, new_state)
def link_to(self, state):
"""Add an epsilon-move from this state to another state."""
self.add_transition('', state)
def set_action(self, action, priority):
"""Make this an accepting state with the given action. If
there is already an action, choose the action with highest
priority."""
if priority > self.action_priority:
self.action = action
self.action_priority = priority
def get_action(self):
return self.action
def get_action_priority(self):
return self.action_priority
def is_accepting(self):
return self.action is not None
def __str__(self):
return "State %d" % self.number
def dump(self, file):
# Header
file.write(" State %d:\n" % self.number)
# Transitions
# self.dump_transitions(file)
self.transitions.dump(file)
# Action
action = self.action
priority = self.action_priority
if action is not None:
file.write(" %s [priority %d]\n" % (action, priority))
def __lt__(self, other):
return self.number < other.number
def __hash__(self):
# Prevent overflowing hash values due to arbitrarily large unsigned addresses.
return id(self) & maxint
class FastMachine(object):
"""
FastMachine is a deterministic machine represented in a way that
allows fast scanning.
"""
def __init__(self):
self.initial_states = {} # {state_name:state}
self.states = [] # [state] where state = {event:state, 'else':state, 'action':Action}
self.next_number = 1 # for debugging
self.new_state_template = {
'': None, 'bol': None, 'eol': None, 'eof': None, 'else': None
}
def __del__(self):
for state in self.states:
state.clear()
def new_state(self, action=None):
number = self.next_number
self.next_number = number + 1
result = self.new_state_template.copy()
result['number'] = number
result['action'] = action
self.states.append(result)
return result
def make_initial_state(self, name, state):
self.initial_states[name] = state
@cython.locals(code0=cython.long, code1=cython.long, maxint=cython.long, state=dict)
def add_transitions(self, state, event, new_state, maxint=maxint):
if type(event) is tuple:
code0, code1 = event
if code0 == -maxint:
state['else'] = new_state
elif code1 != maxint:
while code0 < code1:
state[unichr(code0)] = new_state
code0 += 1
else:
state[event] = new_state
def get_initial_state(self, name):
return self.initial_states[name]
def dump(self, file):
file.write("Plex.FastMachine:\n")
file.write(" Initial states:\n")
for name, state in sorted(self.initial_states.items()):
file.write(" %s: %s\n" % (repr(name), state['number']))
for state in self.states:
self.dump_state(state, file)
def dump_state(self, state, file):
# Header
file.write(" State %d:\n" % state['number'])
# Transitions
self.dump_transitions(state, file)
# Action
action = state['action']
if action is not None:
file.write(" %s\n" % action)
def dump_transitions(self, state, file):
chars_leading_to_state = {}
special_to_state = {}
for (c, s) in state.items():
if len(c) == 1:
chars = chars_leading_to_state.get(id(s), None)
if chars is None:
chars = []
chars_leading_to_state[id(s)] = chars
chars.append(c)
elif len(c) <= 4:
special_to_state[c] = s
ranges_to_state = {}
for state in self.states:
char_list = chars_leading_to_state.get(id(state), None)
if char_list:
ranges = self.chars_to_ranges(char_list)
ranges_to_state[ranges] = state
for ranges in sorted(ranges_to_state):
key = self.ranges_to_string(ranges)
state = ranges_to_state[ranges]
file.write(" %s --> State %d\n" % (key, state['number']))
for key in ('bol', 'eol', 'eof', 'else'):
state = special_to_state.get(key, None)
if state:
file.write(" %s --> State %d\n" % (key, state['number']))
@cython.locals(char_list=list, i=cython.Py_ssize_t, n=cython.Py_ssize_t, c1=cython.long, c2=cython.long)
def chars_to_ranges(self, char_list):
char_list.sort()
i = 0
n = len(char_list)
result = []
while i < n:
c1 = ord(char_list[i])
c2 = c1
i += 1
while i < n and ord(char_list[i]) == c2 + 1:
i += 1
c2 += 1
result.append((chr(c1), chr(c2)))
return tuple(result)
def ranges_to_string(self, range_list):
return ','.join(map(self.range_to_string, range_list))
def range_to_string(self, range_tuple):
(c1, c2) = range_tuple
if c1 == c2:
return repr(c1)
else:
return "%s..%s" % (repr(c1), repr(c2))
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