#!/usr/bin/env python

import sys
from os.path import abspath, exists
from optparse import OptionParser

#!/usr/bin/python

# stripped down by Senthil Palanisami (Sen) [spenthil@gmail.com] to
# work on Google App Engine and exist as a single file
# use as described in http://code.google.com/p/lorem-ipsum-generator/wiki/ApiOverview
# pulled from trunk http://code.google.com/p/lorem-ipsum-generator/ on Monday April 2, 2012

# Copyright (c) James Hales and individual contributors.
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without 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 contributors may not be used to endorse or promote products
# derived from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
# ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
# ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

import math
import random
import re

# Delimiters that mark ends of sentences
_DELIMITERS_SENTENCES = ['.', '?', '!']

# Delimiters which do not form parts of words (i.e. "hello," is the word
# "hello" with a comma next to it)
_DELIMITERS_WORDS = [','] + _DELIMITERS_SENTENCES

_NEWLINE = "\n"

_DEFAULT_SAMPLE = """Lorem ipsum dolor sit amet, consectetuer adipiscing elit aenean commodo ligula eget dolor. Cum sociis natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus.

Maecenas tempus tellus eget condimentum rhoncus sem quam semper libero sit amet adipiscing sem neque sed ipsum. Nam quam nunc, blandit vel luctus pulvinar hendrerit id lorem. Maecenas nec odio et ante tincidunt tempus.

Nam pretium turpis et arcu duis arcu tortor suscipit eget imperdiet nec imperdiet iaculis ipsum. Integer ante arcu accumsan a consectetuer eget posuere ut mauris.

Curabitur ligula sapien tincidunt non euismod vitae, posuere imperdiet leo. Praesent congue erat at massa sed cursus turpis vitae tortor. Vestibulum ante ipsum primis in faucibus orci luctus et ultrices posuere cubilia Curae.

Aenean posuere tortor sed cursus feugiat nunc augue blandit nunc eu sollicitudin urna dolor sagittis lacus. Donec elit libero, sodales nec volutpat a suscipit non turpis.

In dui magna, posuere eget vestibulum et tempor auctor justo. In ac felis quis tortor malesuada pretium proin sapien ipsum porta a auctor quis euismod ut mi. Pellentesque habitant morbi tristique senectus et netus et malesuada fames ac turpis egestas.

Curabitur at lacus ac velit ornare lobortis. Curabitur a felis in nunc fringilla tristique. Pellentesque libero tortor tincidunt et, tincidunt eget semper nec quam.

Maecenas ullamcorper dui et placerat feugiat eros pede varius nisi condimentum viverra felis nunc et lorem. Sed magna purus fermentum eu tincidunt eu varius ut felis.

Fusce convallis metus id felis luctus adipiscing. Pellentesque egestas neque sit amet convallis pulvinar justo nulla eleifend augue ac auctor orci leo non est. Etiam ut purus mattis mauris sodales aliquam.

Sed augue ipsum, egestas nec vestibulum et malesuada adipiscing dui. Vestibulum facilisis purus nec pulvinar iaculis ligula mi congue nunc vitae euismod ligula urna in dolor. Vestibulum rutrum mi nec elementum vehicula, eros quam gravida nisl id fringilla neque ante vel mi.

Mauris turpis nunc blandit et volutpat molestie, porta ut ligula. Fusce pharetra convallis urna donec mi odio faucibus at scelerisque quis convallis in nisi.

Ut id nisl quis enim dignissim sagittis. Etiam sollicitudin ipsum eu pulvinar rutrum tellus ipsum laoreet sapien, quis venenatis ante odio sit amet eros.

Pellentesque commodo eros a enim vestibulum turpis sem aliquet eget lobortis pellentesque rutrum eu nisl. Sed mollis eros et ultrices tempus mauris ipsum aliquam libero non adipiscing dolor urna a orci.

Pellentesque dapibus hendrerit tortor donec vitae orci sed dolor rutrum auctor. Suspendisse nisl elit rhoncus eget elementum ac condimentum eget diam.

Proin viverra, ligula sit amet ultrices semper ligula arcu tristique sapien a accumsan nisi mauris ac eros. Suspendisse faucibus nunc et pellentesque egestas lacus ante convallis tellus vitae iaculis lacus elit id tortor.
"""

_DEFAULT_DICT = """a
ac
accumsan
ad
adipiscing
aenean
aliquam
aliquet
amet
ante
aptent
arcu
at
auctor
augue
bibendum
blandit
class
commodo
condimentum
congue
consectetuer
consequat
conubia
convallis
cras
cubilia
cum
curabitur
curae
cursus
dapibus
diam
dictum
dictumst
dignissim
dis
dolor
donec
dui
duis
egestas
eget
eleifend
elementum
elit
eni
enim
erat
eros
est
et
etiam
eu
euismod
facilisi
facilisis
fames
faucibus
felis
fermentum
feugiat
fringilla
fusce
gravida
habitant
habitasse
hac
hendrerit
hymenaeos
iaculis
id
imperdiet
in
inceptos
integer
interdum
ipsum
justo
lacinia
lacus
laoreet
lectus
leo
libero
ligula
litora
lobortis
lorem
luctus
maecenas
magna
magnis
malesuada
massa
mattis
mauris
metus
mi
molestie
mollis
montes
morbi
mus
nam
nascetur
natoque
nec
neque
netus
nibh
nisi
nisl
non
nonummy
nostra
nulla
nullam
nunc
odio
orci
ornare
parturient
pede
pellentesque
penatibus
per
pharetra
phasellus
placerat
platea
porta
porttitor
posuere
potenti
praesent
pretium
primis
proin
pulvinar
purus
quam
quis
quisque
rhoncus
ridiculus
risus
rutrum
sagittis
sapien
scelerisque
sed
sem
semper
senectus
sit
sociis
sociosqu
sodales
sollicitudin
suscipit
suspendisse
taciti
tellus
tempor
tempus
tincidunt
torquent
tortor
tristique
turpis
ullamcorper
ultrices
ultricies
urna
ut
varius
ve
vehicula
vel
velit
venenatis
vestibulum
vitae
vivamus
viverra
volutpat
vulputate""".split("\n")

def _split_paragraphs(text):
    """
    Splits a piece of text into paragraphs, separated by empty lines.

    """
    lines = text.splitlines()
    paragraphs = [[]]
    for line in lines:
        if len(line.strip()) > 0:
            paragraphs[-1] += [line]
        elif len(paragraphs[-1]) > 0:
            paragraphs.append([])
    paragraphs = map(' '.join, paragraphs)
    paragraphs = map(str.strip, paragraphs)
    paragraphs = filter(lambda s : s != '', paragraphs)
    return paragraphs

def _split_sentences(text):
    """
    Splits a piece of text into sentences, separated by periods, question
    marks and exclamation marks.

    """
    sentence_split = ''
    for delimiter in _DELIMITERS_SENTENCES:
        sentence_split += '\\' + delimiter
    sentence_split = '[' + sentence_split + ']'
    sentences = re.split(sentence_split, text.strip())
    sentences = map(str.strip, sentences)
    sentences = filter(lambda s : s != '', sentences)
    return sentences

def _split_words(text):
    """
    Splits a piece of text into words, separated by whitespace.

    """
    return text.split()

def _mean(values):
    return sum(values) / float(max(len(values), 1))

def _variance(values):
    squared = list(map(lambda x : x**2, values))
    return _mean(squared) - _mean(values)**2

def _sigma(values):
    return math.sqrt(_variance(values))

def _choose_closest(values, target):
    """
    Find the number in the list of values that is closest to the target.
    Prefer the first in the list.
    """
    closest = list(values)[0]
    for value in values:
        if abs(target - value) < abs(target - closest):
            closest = value

    return closest

def _get_word_info(word):
    longest = (word, "")

    for delimiter in _DELIMITERS_WORDS:
        if len(delimiter) > len(longest[1]) and word.endswith(delimiter):
            word = word.rpartition(delimiter)
            longest = (word[0], word[1])

    return (len(longest[0]), longest[1])


class InvalidDictionaryError(Exception):
    def __str__(self):
        return ('The dictionary must be a list of one or more words.')

class InvalidSampleError(Exception):
    def __str__(self):
        return ('The sample text must contain one or more empty-line '
            'delimited paragraphs, and each paragraph must contain one or '
            'more period, question mark, or exclamation mark delimited '
            'sentences.')

class Generator(object):
    """
    Generates random strings of "lorem ipsum" text.

    Markov chains are used to generate the random text based on the analysis
    of a sample text. In the analysis, only paragraph, sentence and word
    lengths, and some basic punctuation matter -- the actual words are
    ignored. A provided list of words is then used to generate the random text,
    so that it will have a similar distribution of paragraph, sentence and word
    lengths.

    """

    # Words that can be used in the generated output
    # Maps a word-length to a list of words of that length
    __words = {}

    # Chains of three words that appear in the sample text
    # Maps a pair of word-lengths to a third word-length and an optional
    # piece of trailing punctuation (for example, a period, comma, etc.)
    __chains = {}

    # Pairs of word-lengths that can appear at the beginning of sentences
    __starts = []

    # Sample that the generated text is based on
    __sample = ""

    # Statistics for sentence and paragraph generation
    __sentence_mean = 0
    __sentence_sigma = 0
    __paragraph_mean = 0
    __paragraph_sigma = 0

    # Last calculated statistics, in case they are overwritten by user
    __generated_sentence_mean = 0
    __generated_sentence_sigma = 0
    __generated_paragraph_mean = 0
    __generated_paragraph_sigma = 0

    def __init__(self, sample=_DEFAULT_SAMPLE, dictionary=_DEFAULT_DICT):
        """
        Initialises a generator that will use the provided sample text and
        dictionary to produce sentences.

        """
        self.sample = sample
        self.dictionary = dictionary

    def __set_sentence_mean(self, mean):
        if mean < 0:
            raise ValueError('Mean sentence length must be non-negative.')
        self.__sentence_mean = mean

    def __set_sentence_sigma(self, sigma):
        if sigma < 0:
            raise ValueError('Standard deviation of sentence length must be '
                'non-negative.')
        self.__sentence_sigma = sigma

    def __set_paragraph_mean(self, mean):
        if mean < 0:
            raise ValueError('Mean paragraph length must be non-negative.')
        self.__paragraph_mean = mean

    def __set_paragraph_sigma(self, sigma):
        if sigma < 0:
            raise ValueError('Standard deviation of paragraph length must be '
                'non-negative.')
        self.__paragraph_sigma = sigma

    def __get_sentence_mean(self):
        """
        A non-negative value determining the mean sentence length (in words)
        of generated sentences. Is changed to match the sample text when the
        sample text is updated.
        """
        return self.__sentence_mean

    def __get_sentence_sigma(self):
        """
        A non-negative value determining the standard deviation of sentence
        lengths (in words) of generated sentences. Is changed to match the
        sample text when the sample text is updated.
        """
        return self.__sentence_sigma

    def __get_paragraph_mean(self):
        """
        A non-negative value determining the mean paragraph length (in
        sentences) of generated sentences. Is changed to match the sample text
        when the sample text is updated.
        """
        return self.__paragraph_mean

    def __get_paragraph_sigma(self):
        """
        A non-negative value determining the standard deviation of paragraph
        lengths (in sentences) of generated sentences. Is changed to match the
        sample text when the sample text is updated.
        """
        return self.__paragraph_sigma

    sentence_mean = property(__get_sentence_mean, __set_sentence_mean)
    sentence_sigma = property(__get_sentence_sigma, __set_sentence_sigma)
    paragraph_mean = property(__get_paragraph_mean, __set_paragraph_mean)
    paragraph_sigma = property(__get_paragraph_sigma, __set_paragraph_sigma)

    def __generate_chains(self, sample):
        """
        Generates the __chains and __starts values required for sentence generation.
        """
        words = _split_words(sample)
        if len(words) <= 0:
            raise InvalidSampleError

        word_info = map(_get_word_info, words)

        previous = (0, 0)
        chains = {}
        starts = [previous]

        for pair in word_info:
            if pair[0] == 0:
                continue
            chains.setdefault(previous, []).append(pair)
            if pair[1] in _DELIMITERS_SENTENCES:
                starts.append(previous)
            previous = (previous[1], pair[0])

        if len(chains) > 0:
            self.__chains = chains
            self.__starts = starts
        else:
            raise InvalidSampleError

    def __generate_statistics(self, sample):
        """
        Calculates the mean and standard deviation of sentence and paragraph lengths.
        """
        self.__generate_sentence_statistics(sample)
        self.__generate_paragraph_statistics(sample)
        self.reset_statistics()

    def __generate_sentence_statistics(self, sample):
        """
        Calculates the mean and standard deviation of the lengths of sentences
        (in words) in a sample text.
        """
        sentences = filter(lambda s : len(s.strip()) > 0, _split_sentences(sample))
        sentence_lengths = list(map(lambda a: len(list(a)), map(_split_words, sentences)))
        self.__generated_sentence_mean = _mean(sentence_lengths)
        self.__generated_sentence_sigma = _sigma(sentence_lengths)

    def __generate_paragraph_statistics(self, sample):
        """
        Calculates the mean and standard deviation of the lengths of paragraphs
        (in sentences) in a sample text.
        """
        paragraphs = filter(lambda s : len(s.strip()) > 0, _split_paragraphs(sample))
        paragraph_lengths = list(map(lambda a: len(list(a)), map(_split_sentences, paragraphs)))
        self.__generated_paragraph_mean = _mean(paragraph_lengths)
        self.__generated_paragraph_sigma = _sigma(paragraph_lengths)

    def reset_statistics(self):
        """
        Returns the values of sentence_mean, sentence_sigma, paragraph_mean,
        and paragraph_sigma to their values as calculated from the sample
        text.
        """
        self.sentence_mean = self.__generated_sentence_mean
        self.sentence_sigma = self.__generated_sentence_sigma
        self.paragraph_mean = self.__generated_paragraph_mean
        self.paragraph_sigma = self.__generated_paragraph_sigma

    def __get_sample(self):
        """
        The sample text that generated sentences are based on.

        Sentences are generated so that they will have a similar distribution
        of word, sentence and paragraph lengths and punctuation.

        Sample text should be a string consisting of a number of paragraphs,
        each separated by empty lines. Each paragraph should consist of a
        number of sentences, separated by periods, exclamation marks and/or
        question marks. Sentences consist of words, separated by white space.
        """
        return self.__sample

    def __set_sample(self, sample):
        self.__sample = sample
        self.__generate_chains(sample)
        self.__generate_statistics(sample)

    def __get_dictionary(self):
        """
        The list of words that generated sentences are made of.

        The dictionary should be a list of one or more words, as strings.
        """
        dictionary = []
        map(dictionary.extend, self.__words.values())
        return dictionary

    def __set_dictionary(self, dictionary):
        words = {}

        for word in dictionary:
            try:
                word = str(word)
                words.setdefault(len(word), set()).add(word)
            except TypeError:
                continue

        if len(words) > 0:
            self.__words = words
        else:
            raise InvalidDictionaryError

    sample = property(__get_sample, __set_sample)
    dictionary = property(__get_dictionary, __set_dictionary)

    def __choose_random_start(self):
        starts = set(self.__starts)
        chains = set(self.__chains.keys())
        valid_starts = list(chains.intersection(starts))
        return random.choice(valid_starts)

    def generate_sentence(self, start_with_lorem=False):
        """
        Generates a single sentence, of random length.

        If start_with_lorem=True, then the sentence will begin with the
        standard "Lorem ipsum..." first sentence.
        """
        if len(self.__chains) == 0 or len(self.__starts) == 0:
            raise InvalidSampleError

        if len(self.__words) == 0:
            raise InvalidDictionaryError

        # The length of the sentence is a normally distributed random variable.
        sentence_length = random.normalvariate(self.sentence_mean,
            self.sentence_sigma)
        sentence_length = max(int(round(sentence_length)), 1)

        sentence = []
        previous = ()

        word_delimiter = '' # Defined here in case while loop doesn't run

        # Start the sentence with "Lorem ipsum...", if desired
        if start_with_lorem:
            lorem = "lorem ipsum dolor sit amet, consecteteur adipiscing elit"
            lorem = lorem.split()
            sentence += lorem[:sentence_length]
            last_char = sentence[-1][-1]
            if last_char in _DELIMITERS_WORDS:
                word_delimiter = last_char

        # Generate a sentence from the "chains"
        while len(sentence) < sentence_length:
            # If the current starting point is invalid, choose another randomly
            if (previous not in self.__chains):
                previous = self.__choose_random_start()

            # Choose the next "chain" to go to. This determines the next word
            # length we'll use, and whether there is e.g. a comma at the end of
            # the word.
            chain = random.choice(self.__chains[previous])
            word_length = chain[0]

            # If the word delimiter contained in the chain is also a sentence
            # delimiter, then we don't include it because we don't want the
            # sentence to end prematurely (we want the length to match the
            # sentence_length value).
            if chain[1] in _DELIMITERS_SENTENCES:
                word_delimiter = ''
            else:
                word_delimiter = chain[1]

            # Choose a word randomly that matches (or closely matches) the
            # length we're after.
            closest_length = _choose_closest(
                    self.__words.keys(),
                    word_length)
            word = random.choice(list(self.__words[closest_length]))

            sentence += [word + word_delimiter]
            previous = (previous[1], word_length)

        # Finish the sentence off with capitalisation, a period and
        # form it into a string
        sentence = ' '.join(sentence)
        sentence = sentence.capitalize()
        sentence = sentence.rstrip(word_delimiter) + '.'

        return sentence

    def generate_paragraph(self, start_with_lorem=False):
        """
        Generates a single lorem ipsum paragraph, of random length.

        If start_with_lorem=True, then the paragraph will begin with the
        standard "Lorem ipsum..." first sentence.
        """
        paragraph = []

        # The length of the paragraph is a normally distributed random variable.
        paragraph_length = random.normalvariate(self.paragraph_mean,
            self.paragraph_sigma)
        paragraph_length = max(int(round(paragraph_length)), 1)

        # Construct a paragraph from a number of sentences.
        while len(paragraph) < paragraph_length:
            sentence = self.generate_sentence(
                start_with_lorem = (start_with_lorem and len(paragraph) == 0)
                )
            paragraph += [sentence]

        # Form the paragraph into a string.
        paragraph = ' '.join(paragraph)

        return paragraph


class MarkupGenerator(Generator):
    """
    Provides a number of methods for producing "lorem ipsum" text with
    varying formats.

    """
    def __generate_markup(self, begin, end, between, quantity,
        start_with_lorem, function):
        """
        Generates multiple pieces of text, with begin before each piece, end
        after each piece, and between between each piece. Accepts a function
        that returns a string.
        """
        text = []

        while len(text) < quantity:
            part = function(
                    start_with_lorem = (start_with_lorem and len(text) == 0)
                    )
            part = begin + part + end
            text += [part]

        text = between.join(text)
        return text

    def __generate_markup_paragraphs(self, begin_paragraph, end_paragraph,
        between_paragraphs, quantity, start_with_lorem=False):
        return self.__generate_markup(
                begin_paragraph,
                end_paragraph,
                between_paragraphs,
                quantity,
                start_with_lorem,
                self.generate_paragraph)

    def __generate_markup_sentences(self, begin_sentence, end_sentence,
        between_sentences, quantity, start_with_lorem=False):
        return self.__generate_markup(
                begin_sentence,
                end_sentence,
                between_sentences,
                quantity,
                start_with_lorem,
                self.generate_sentence)

    def generate_paragraphs_plain(self, quantity, start_with_lorem=False):
        """Generates a number of paragraphs, separated by empty lines."""
        return self.__generate_markup_paragraphs(
                begin_paragraph='',
                end_paragraph='',
                between_paragraphs=_NEWLINE * 2,
                quantity=quantity,
                start_with_lorem=start_with_lorem
                )

    def generate_sentences_plain(self, quantity, start_with_lorem=False):
        """Generates a number of sentences."""
        return self.__generate_markup_sentences(
                begin_sentence='',
                end_sentence='',
                between_sentences=' ',
                quantity=quantity,
                start_with_lorem=start_with_lorem
                )

    def generate_paragraphs_html_p(self, quantity, start_with_lorem=False):
        """
        Generates a number of paragraphs, with each paragraph
        surrounded by HTML pararaph tags.
        """
        return self.__generate_markup_paragraphs(
                begin_paragraph='<p>' + _NEWLINE + '\t',
                end_paragraph=_NEWLINE + '</p>',
                between_paragraphs=_NEWLINE,
                quantity=quantity,
                start_with_lorem=start_with_lorem
                )

    def generate_sentences_html_p(self, quantity, start_with_lorem=False):
        """
        Generates a number of sentences, with each sentence
        surrounded by HTML pararaph tags.
        """
        return self.__generate_markup_sentences(
                begin_sentence='<p>' + _NEWLINE + '\t',
                end_sentence=_NEWLINE + '</p>',
                between_sentences=_NEWLINE,
                quantity=quantity,
                start_with_lorem=start_with_lorem
                )

    def generate_paragraphs_html_li(self, quantity, start_with_lorem=False):
        """Generates a number of paragraphs, separated by empty lines."""
        output = self.__generate_markup_paragraphs(
                begin_paragraph='\t<li>\n\t\t',
                end_paragraph='\n\t</li>',
                between_paragraphs=_NEWLINE,
                quantity=quantity,
                start_with_lorem=start_with_lorem
                )
        return ('<ul>' + _NEWLINE + output + _NEWLINE + '</ul>')

    def generate_sentences_html_li(self, quantity, start_with_lorem=False):
        """Generates a number of sentences surrounded by HTML 'li' tags."""
        output = self.__generate_markup_sentences(
                begin_sentence='\t<li>' + _NEWLINE + '\t\t',
                end_sentence=_NEWLINE + '\t</li>',
                between_sentences=_NEWLINE,
                quantity=quantity,
                start_with_lorem=start_with_lorem
                )
        return ('<ul>' + _NEWLINE + output + _NEWLINE + '</ul>')



####################

def load_contents(file):
    file = open(abspath(file), 'r')
    contents = file.read()
    file.close()
    return contents

def main():
    (options, args) = parse_args()
    generator = init_generator(options)
    start_cli(options, generator)

def parse_args():
    parser = OptionParser()
    parser.add_option("-p", "--paragraphs", dest="paragraphs", help="generate NUM paragraphs", metavar="NUM", type="int")
    parser.add_option("-s", "--sentences", dest="sentences", help="generate NUM sentences", metavar="NUM", type="int")
    parser.add_option("--sample", dest="sample_path", help="use FILE as the sample text", metavar="FILE")
    parser.add_option("--dictionary", dest="dictionary_path", help="use FILE as the dictionary text", metavar="FILE")
    parser.add_option("--sentence-mean", dest="sentence_mean", help="set the mean sentence length to NUM", metavar="NUM", type="float")
    parser.add_option("--paragraph-mean", dest="paragraph_mean", help="set the mean paragraph length to NUM", metavar="NUM", type="float")
    parser.add_option("--sentence-sigma", dest="sentence_sigma", help="set the standard deviation sentence length to NUM", metavar="NUM", type="float")
    parser.add_option("--paragraph-sigma", dest="paragraph_sigma", help="set the standard deviation paragraph length to NUM", metavar="NUM", type="float")
    parser.add_option("-l", "--lorem", dest="lorem", action="store_true", help="start with \"Lorem ipsum dolor...\"")
    parser.add_option("-f", "--format", metavar="FORMAT", dest="format", action="store", help="produce format in plain, html-p, or html-li format", choices=("plain", "html-p", "html-li"))
    parser.add_option("-g", "--gui", dest="gui", action="store_true", help="force GUI to start")

    return parser.parse_args()

def init_generator(options):
    generator = MarkupGenerator()

    # Set the sample and dictionary texts
    if options.sample_path:
        try:
            generator.sample = load_contents(options.sample_path)
        except IOError:
            error('Unable to load sample file "%s".\n' % options.sample_path)
        except InvalidSampleError:
            error('Invalid sample file "%s".\n' % options.sample_path)

    if options.dictionary_path:
        try:
            generator.dictionary = load_contents(options.dictionary_path).split()
        except IOError:
            error('Unable to load dictionary file "%s".\n' % options.dictionary_path)
        except InvalidDictionaryError:
            error('Invalid sample file "%s".\n' % options.dictionary_path)

    # Set statistics
    try:
        if options.sentence_mean:
            generator.sentence_mean = options.sentence_mean
        if options.paragraph_mean:
            generator.paragraph_mean = options.paragraph_mean
        if options.sentence_sigma:
            generator.sentence_sigma = options.sentence_sigma
        if options.paragraph_sigma:
            generator.paragraph_sigma = options.paragraph_sigma
    except ValueError as e:
        error("%s\n" % e)

    return generator

def error(message):
    sys.stderr.write(message)
    exit()

def start_cli(options, generator):
    output = ""

    if options.paragraphs:
        if options.format == "html-p":
            output = generator.generate_paragraphs_html_p(options.paragraphs, options.lorem)
        elif options.format == "html-li":
            output = generator.generate_paragraphs_html_li(options.paragraphs, options.lorem)
        else:
            output = generator.generate_paragraphs_plain(options.paragraphs, options.lorem)
    elif options.sentences:
        if options.format == "html-p":
            output = generator.generate_sentences_html_p(options.sentences, options.lorem)
        elif options.format == "html-li":
            output = generator.generate_sentences_html_li(options.sentences, options.lorem)
        else:
            output = generator.generate_sentences_plain(options.sentences, options.lorem)

    print(output)

if __name__ == '__main__':
    main()