nitter/tests/test_card.py

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from base import BaseTestCase, Card, Conversation
from parameterized import parameterized
card = [
['Thom_Wolf/status/1122466524860702729',
'pytorch/fairseq',
'Facebook AI Research Sequence-to-Sequence Toolkit written in Python. - pytorch/fairseq',
'github.com', True],
['nim_lang/status/1136652293510717440',
'Version 0.20.0 released',
'We are very proud to announce Nim version 0.20. This is a massive release, both literally and figuratively. It contains more than 1,000 commits and it marks our release candidate for version 1.0!',
'nim-lang.org', True],
['voidtarget/status/1094632512926605312',
'Basic OBS Studio plugin, written in nim, supporting C++ (C fine too)',
'Basic OBS Studio plugin, written in nim, supporting C++ (C fine too) - obsplugin.nim',
'gist.github.com', True],
['FluentAI/status/1116417904831029248',
'Amazons Alexa isnt just AI — thousands of humans are listening',
'One of the only ways to improve Alexa is to have human beings check it for errors',
'theverge.com', True]
]
no_thumb = [
['Bountysource/status/1141879700639215617',
'Post a bounty on kivy/plyer!',
'Automation and Screen Reader Support',
'bountysource.com'],
['brent_p/status/1088857328680488961',
'Hts Nim Sugar',
'hts-nim is a library that allows one to use htslib via the nim programming language. Nim is a garbage-collected language that compiles to C and often has similar performance. I have become very...',
'brentp.github.io'],
['voidtarget/status/1133028231672582145',
'sinkingsugar/nimqt-example',
'A sample of a Qt app written using mostly nim. Contribute to sinkingsugar/nimqt-example development by creating an account on GitHub.',
'github.com'],
['mobile_test/status/490378953744318464',
'Nantasket Beach',
'Explore this photo titled Nantasket Beach by Ben Sandofsky (@sandofsky) on 500px',
'500px.com'],
['nim_lang/status/1082989146040340480',
'Nim in 2018: A short recap',
'Posted in r/programming by u/miran1',
'reddit.com'],
['lorenlugosch/status/1115440394148487168',
'Fluent Speech Commands: A dataset for spoken language understanding research',
'In recent years, with the advent of deep neural networks, the accuracy of speech recognition models have been notably improved which have made possible the production of speech-to-text systems that...',
'fluent.ai']
]
playable = [
['nim_lang/status/1118234460904919042',
'Nim development blog 2019-03',
'Arne (aka Krux02)* debugging: * improved nim-gdb, $ works, framefilter * alias for --debugger:native: -g* bugs: * forwarding of .pure. * sizeof union* fe...',
'youtube.com'],
['nim_lang/status/1121090879823986688',
'Nim - First natively compiled language w/ hot code-reloading at...',
'#nim #c++ #ACCUConfNim is a statically typed systems and applications programming language which offers perhaps some of the most powerful metaprogramming cap...',
'youtube.com']
]
# promo = [
# ['BangOlufsen/status/1145698701517754368',
# 'Upgrade your journey', '',
# 'www.bang-olufsen.com'],
# ['BangOlufsen/status/1154934429900406784',
# 'Learn more about Beosound Shape', '',
# 'www.bang-olufsen.com']
# ]
class CardTest(BaseTestCase):
@parameterized.expand(card)
def test_card(self, tweet, title, description, destination, large):
self.open_nitter(tweet)
c = Card(Conversation.main + " ")
self.assert_text(title, c.title)
self.assert_text(destination, c.destination)
self.assertIn('_img', self.get_image_url(c.image + ' img'))
if len(description) > 0:
self.assert_text(description, c.description)
if large:
self.assert_element_visible('.card.large')
else:
self.assert_element_not_visible('.card.large')
@parameterized.expand(no_thumb)
def test_card_no_thumb(self, tweet, title, description, destination):
self.open_nitter(tweet)
c = Card(Conversation.main + " ")
self.assert_text(title, c.title)
self.assert_text(destination, c.destination)
if len(description) > 0:
self.assert_text(description, c.description)
@parameterized.expand(playable)
def test_card_playable(self, tweet, title, description, destination):
self.open_nitter(tweet)
c = Card(Conversation.main + " ")
self.assert_text(title, c.title)
self.assert_text(destination, c.destination)
self.assertIn('_img', self.get_image_url(c.image + ' img'))
self.assert_element_visible('.card-overlay')
if len(description) > 0:
self.assert_text(description, c.description)
# @parameterized.expand(promo)
# def test_card_promo(self, tweet, title, description, destination):
# self.open_nitter(tweet)
# c = Card(Conversation.main + " ")
# self.assert_text(title, c.title)
# self.assert_text(destination, c.destination)
# self.assert_element_visible('.video-overlay')
# if len(description) > 0:
# self.assert_text(description, c.description)