#!/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 ()