# -*- coding: utf-8 -*-

import pytest
import numpy

@pytest.mark.models
class TestModelSanity:
	"""
	This is to make sure the model works as expected. The tests make sure that values are properly set.
	Tests are not meant to evaluate the content of the output, only make sure the output is formally okay.
	"""

	@pytest.fixture(scope='class', params=['en','de'])
	def example(self, request, EN, DE):
		if request.param == 'en':
			return EN(u'There was a stranger standing at the big street talking to herself.')
		elif request.param == 'de':
			return DE(u'An der großen Straße stand eine merkwürdige Gestalt und führte Selbstgespräche.')

	def test_tokenization(self, example):
		# tokenization should split the document into tokens
		assert len(example) > 1

	def test_tagging(self, example):
		# if tagging was done properly, pos tags shouldn't be empty
		assert example.is_tagged
		assert all( t.pos != 0 for t in example )
		assert all( t.tag != 0 for t in example )

	def test_parsing(self, example):
		# if parsing was done properly
		# - dependency labels shouldn't be empty
		# - the head of some tokens should not be root
		assert example.is_parsed
		assert all( t.dep != 0 for t in example )
		assert any( t.dep != i for i,t in enumerate(example) )

	def test_ner(self, example):
		# if ner was done properly, ent_iob shouldn't be empty
		assert all( t.ent_iob != 0 for t in example )

	def test_vectors(self, example):
		# if vectors are available, they should differ on different words
		# this isn't a perfect test since this could in principle fail in a sane model as well,
		# but that's very unlikely and a good indicator if something is wrong
		vector0 = example[0].vector
		vector1 = example[1].vector
		vector2 = example[2].vector
		assert not numpy.array_equal(vector0,vector1)
		assert not numpy.array_equal(vector0,vector2)
		assert not numpy.array_equal(vector1,vector2)

	def test_probs(self, example):
		# if frequencies/probabilities are okay, they should differ for different words
		# this isn't a perfect test since this could in principle fail in a sane model as well,
		# but that's very unlikely and a good indicator if something is wrong
		prob0 = example[0].prob
		prob1 = example[1].prob
		prob2 = example[2].prob
		assert not prob0 == prob1
		assert not prob0 == prob2
		assert not prob1 == prob2