# Path: deepanimebot/classifiers.py
# def fetch_cvimage_from_url(url, maxsize=10 * 1024 * 1024):
#     def classify(self, *args, **kwargs):
#     def __init__(self, dataset_path, input_shape, model_name='model'):
#     def classify(self, cvimage):
#     def __init__(self, image_classifier):
#     def classify(self, url=None):
#     def __init__(self, base_url):
#     def classify(self, **params):
# class MockClassifier(object):
# class ImageClassifier(object):
# class URLClassifier(object):
# class RemoteClassifier(object):
# 
# Path: deepanimebot/exceptions.py
# class NotImage(Exception): pass
# class RemoteError(Exception): pass
# 
# Path: deepanimebot/messages.py
# class Messages(object):
# class StatusMessages(Messages):
# class DMMessages(Messages):
#     def took_too_long():
#     def something_went_wrong():
#     def not_an_image():
#     def unknown_image():
#     def my_guess(cls, y, top_n=3, max_length=None, preface="Probable Anime:"):
#     def give_me_an_image():
#     def give_me_an_image():


import functools
import logging
import os
import random
import time
import tweepy
import deploy
import gceutil
    import configargparse
from deepanimebot import classifiers
from deepanimebot import exceptions as exc
from deepanimebot import messages
    return retweeted_status.author.screen_name == screen_name


def status_mentions(status, screen_name):
    for mention in status.entities.get('user_mentions', []):
        if mention['screen_name'] == screen_name:
            return True
    return False


def url_from_entities(entities):
    for media in entities.get('media', []):
        if media['type'] == 'photo':
            return media['media_url']
    for url in entities.get('urls', []):
        return url['expanded_url']


def main(args):
    if args.debug:
        logger.setLevel(logging.DEBUG)

    auth = tweepy.OAuthHandler(args.consumer_key, args.consumer_secret)
    auth.set_access_token(args.access_token, args.access_token_secret)
    api = tweepy.API(auth, wait_on_rate_limit=True)
    screen_name = api.me().screen_name

    if args.classifier == 'mock':
        classifier = classifiers.MockClassifier()
    elif args.classifier == 'local':

# Path: deepanimebot/classifiers.py
# def fetch_cvimage_from_url(url, maxsize=10 * 1024 * 1024):
#     def classify(self, *args, **kwargs):
#     def __init__(self, dataset_path, input_shape, model_name='model'):
#     def classify(self, cvimage):
#     def __init__(self, image_classifier):
#     def classify(self, url=None):
#     def __init__(self, base_url):
#     def classify(self, **params):
# class MockClassifier(object):
# class ImageClassifier(object):
# class URLClassifier(object):
# class RemoteClassifier(object):
# 
# Path: deepanimebot/exceptions.py
# class NotImage(Exception): pass
# class RemoteError(Exception): pass
# 
# Path: deepanimebot/messages.py
# class Messages(object):
# class StatusMessages(Messages):
# class DMMessages(Messages):
#     def took_too_long():
#     def something_went_wrong():
#     def not_an_image():
#     def unknown_image():
#     def my_guess(cls, y, top_n=3, max_length=None, preface="Probable Anime:"):
#     def give_me_an_image():
#     def give_me_an_image():


# Path: deepanimebot/bot.py
import functools
import logging
import os
import random
import time
import tweepy
import deploy
import gceutil
    import configargparse
from deepanimebot import classifiers
from deepanimebot import exceptions as exc
from deepanimebot import messages

    return retweeted_status.author.screen_name == screen_name


def status_mentions(status, screen_name):
    for mention in status.entities.get('user_mentions', []):
        if mention['screen_name'] == screen_name:
            return True
    return False


def url_from_entities(entities):
    for media in entities.get('media', []):
        if media['type'] == 'photo':
            return media['media_url']
    for url in entities.get('urls', []):
        return url['expanded_url']


def main(args):
    if args.debug:
        logger.setLevel(logging.DEBUG)

    auth = tweepy.OAuthHandler(args.consumer_key, args.consumer_secret)
    auth.set_access_token(args.access_token, args.access_token_secret)
    api = tweepy.API(auth, wait_on_rate_limit=True)
    screen_name = api.me().screen_name

    if args.classifier == 'mock':
        classifier = classifiers.MockClassifier()
    elif args.classifier == 'local':

        classifier = classifiers.URLClassifier(classifiers.ImageClassifier(args.dataset_path, INPUT_SHAPE))