#!/usr/bin/env python

import argparse
import marshal
import random
import regex

class Lipsum:
 clean = regex.compile ('\\s+$')

 """
 Create a new lipsum instance by either training a model from given input
 text file, or loading a previously saved model. Behavior actually depends
 on whether the 3rd parameter (length) is provided since it's only used when
 training a new model (but yeah, that's still an ugly hack).
 """
 def __init__ (self, path, length = None):
  # If length is none we assume input is a trained model
  if length is None:
   with open (args.model, 'rb') as file:
    (self.model, self.length) = marshal.load (file)

  # Otherwise start training
  else:
   self.length = length
   self.model = {}

   # Tokenize input string into (prefix -> (suffix, count)) tree
   tokenize = regex.compile ('((?:[-\\w]+|[^\\n.])\\s*)*(?:[\\n.]\\s*)?')
   tree = {}

   with open (path, 'r') as file:
    for match in tokenize.finditer (file.read ()):
     lexems = map (self.clean_lexem, match.captures (1))

     # Ignore empty sequences
     if len (lexems) < 1:
      continue

     # Register suffixes, including special "end of line" marker
     prefix = (None, ) * length

     for lexem in lexems + [None]:
      suffixes = tree.setdefault (prefix, {})
      suffixes[lexem] = suffixes.get (lexem, 0) + 1

      prefix = prefix[1:] + (lexem, )

   # Convert to (prefix -> (suffix, probability)) model
   for (key, suffixes) in tree.iteritems ():
    occurrences = float (sum ((count for (suffix, count) in suffixes.iteritems ())))
    thresholds = []
    total = 0

    for (lexem, count) in suffixes.iteritems ():
     total += count / occurrences

     thresholds.append ((lexem, total))

    self.model[key] = thresholds

 """
 Cleanup input lexem by squashing all "dirty" characters (see the "clean"
 regular expression above) into a single space character.
 """
 def clean_lexem (self, lexem):
  return self.clean.sub (' ', lexem).lower ()

 """
 Find first suffix above given value (used for random suffix selection).
 This method could/should be replaced by some functional call like:
 bisect (suffixes, lambda suffix: suffix[1], value)
 """
 def first_above (self, suffixes, value):
  i = 0

  while i < len (suffixes) and suffixes[i][1] <= value:
   i += 1

  return i < len (suffixes) and suffixes[i][0] or None

 """
 Generate a random lexems sequence using currently loaded model.
 """
 def generate (self):
  buffer = ''
  prefix = tuple ([None] * self.length)

  while prefix in self.model:
   lexem = self.first_above (self.model[prefix], random.random ())

   if lexem is None:
    break

   buffer += lexem
   prefix = prefix[1:] + (lexem, )

  return buffer

 """
 Save current model to file.
 """
 def save (self, path):
  with open (path, 'wb') as file:
   marshal.dump ((self.model, self.length), file)

parser = argparse.ArgumentParser (description = 'Lipsum blabla')
parser.add_argument ('-g', '--generate', type = int, default = 1, help = 'Generate N lines', metavar = 'N')
parser.add_argument ('-l', '--length', type = int, default = 3, help = 'Set prefix length', metavar = 'LEN')
parser.add_argument ('-m', '--model', action = 'store', help = 'Specify path to model', metavar = 'FILE')
parser.add_argument ('-t', '--train', action = 'store', help = 'Train from given file (and save if -m is specified)', metavar = 'FILE')

args = parser.parse_args ()

if args.train is not None:
 lipsum = Lipsum (args.train, args.length)

 if args.model is not None:
  lipsum.save (args.model)

elif args.model is not None:
 lipsum = Lipsum (args.model)

else:
 raise Exception ('please specify either model or train argument')

for i in range (args.generate):
 print lipsum.generate ()