import random

import pytest

import nlp

from nlp4e import Rules, Lexicon, Grammar, ProbRules, ProbLexicon, ProbGrammar, E0

from nlp4e import Chart, CYK_parse, subspan, astar_search_parsing, beam_search_parsing

# Clumsy imports because we want to access certain nlp.py globals explicitly, because

# they are accessed by functions within nlp.py

random.seed("aima-python")

def test_rules():

check = {'A': [['B', 'C'], ['D', 'E']], 'B': [['E'], ['a'], ['b', 'c']]}

assert Rules(A="B C | D E", B="E | a | b c") == check

def test_lexicon():

check = {'Article': ['the', 'a', 'an'], 'Pronoun': ['i', 'you', 'he']}

lexicon = Lexicon(Article="the | a | an", Pronoun="i | you | he")

assert lexicon == check

def test_grammar():

rules = Rules(A="B C | D E", B="E | a | b c")

lexicon = Lexicon(Article="the | a | an", Pronoun="i | you | he")

grammar = Grammar("Simplegram", rules, lexicon)

assert grammar.rewrites_for('A') == [['B', 'C'], ['D', 'E']]

assert grammar.isa('the', 'Article')

grammar = nlp.E_Chomsky

for rule in grammar.cnf_rules():

assert len(rule) == 3

def test_generation():

lexicon = Lexicon(Article="the | a | an",

Pronoun="i | you | he")

rules = Rules(

S="Article | More | Pronoun",

More="Article Pronoun | Pronoun Pronoun"

)

grammar = Grammar("Simplegram", rules, lexicon)

sentence = grammar.generate_random('S')

for token in sentence.split():

found = False

for non_terminal, terminals in grammar.lexicon.items():

if token in terminals:

found = True

assert found

def test_prob_rules():

check = {'A': [(['B', 'C'], 0.3), (['D', 'E'], 0.7)],

'B': [(['E'], 0.1), (['a'], 0.2), (['b', 'c'], 0.7)]}

rules = ProbRules(A="B C [0.3] | D E [0.7]", B="E [0.1] | a [0.2] | b c [0.7]")

assert rules == check

def test_prob_lexicon():

check = {'Article': [('the', 0.5), ('a', 0.25), ('an', 0.25)],

'Pronoun': [('i', 0.4), ('you', 0.3), ('he', 0.3)]}

lexicon = ProbLexicon(Article="the [0.5] | a [0.25] | an [0.25]",

Pronoun="i [0.4] | you [0.3] | he [0.3]")

assert lexicon == check

def test_prob_grammar():

rules = ProbRules(A="B C [0.3] | D E [0.7]", B="E [0.1] | a [0.2] | b c [0.7]")

lexicon = ProbLexicon(Article="the [0.5] | a [0.25] | an [0.25]",

Pronoun="i [0.4] | you [0.3] | he [0.3]")

grammar = ProbGrammar("Simplegram", rules, lexicon)

assert grammar.rewrites_for('A') == [(['B', 'C'], 0.3), (['D', 'E'], 0.7)]

assert grammar.isa('the', 'Article')

grammar = nlp.E_Prob_Chomsky

for rule in grammar.cnf_rules():

assert len(rule) == 4

def test_prob_generation():

lexicon = ProbLexicon(Verb="am [0.5] | are [0.25] | is [0.25]",

Pronoun="i [0.4] | you [0.3] | he [0.3]")

rules = ProbRules(

S="Verb [0.5] | More [0.3] | Pronoun [0.1] | nobody is here [0.1]",

More="Pronoun Verb [0.7] | Pronoun Pronoun [0.3]")

grammar = ProbGrammar("Simplegram", rules, lexicon)

sentence = grammar.generate_random('S')

assert len(sentence) == 2

def test_chart_parsing():

chart = Chart(nlp.E0)

parses = chart.parses('the stench is in 2 2')

assert len(parses) == 1

def test_CYK_parse():

grammar = nlp.E_Prob_Chomsky

words = ['the', 'robot', 'is', 'good']

P = CYK_parse(words, grammar)

assert len(P) == 5

grammar = nlp.E_Prob_Chomsky_

words = ['astronomers', 'saw', 'stars']

P = CYK_parse(words, grammar)

assert len(P) == 3

def test_subspan():

spans = subspan(3)

assert spans.__next__() == (1, 1, 2)

assert spans.__next__() == (2, 2, 3)

assert spans.__next__() == (1, 1, 3)

assert spans.__next__() == (1, 2, 3)

def test_text_parsing():

words = ["the", "wumpus", "is", "dead"]

grammer = E0

assert astar_search_parsing(words, grammer) == 'S'

assert beam_search_parsing(words, grammer) == 'S'

words = ["the", "is", "wupus", "dead"]

assert astar_search_parsing(words, grammer) is False

assert beam_search_parsing(words, grammer) is False

if __name__ == '__main__':

pytest.main()