bclavie

from transformers import (
    AutoTokenizer,
    DataCollatorWithPadding,
    TrainingArguments,
    Trainer,
    AutoModelForSequenceClassification,
)
from datasets import load_dataset, ClassLabel
import numpy as np
import evaluate

bclavie

# Fetch some text content in two different categories
from wikipediaapi import Wikipedia
wiki = Wikipedia('RAGBot/0.0', 'en')
docs = [{"text": x,
         "category": "person"}
        for x in wiki.page('Hayao_Miyazaki').text.split('\n\n')]
docs += [{"text": x,
         "category": "film"}
         for x in wiki.page('Spirited_Away').text.split('\n\n')]

bclavie

from collections import Counter

# Part 1
inputs = [x.strip() for x in open('inputs.txt', 'r').readlines()]

gamma_str = ''
epsilon_str = ''
for i in range(len(inputs[0])):
    col_count = Counter([x[i] for x in inputs])
    if col_count['1'] > col_count['0']:

bclavie

# Please use the following encoding: utf-8, thanks!
import networkx as nx

''' Part 1'''
graph = nx.read_edgelist('./data/day6_input.txt', delimiter=')', create_using=nx.DiGraph)
orbit_count = sum(len(nx.ancestors(G=graph, source=node)) for node in graph.nodes)
print(orbit_count)
''' Part 2 '''
undir_graph = nx.read_edgelist('./data/day6_input.txt', delimiter=')', create_using=nx.Graph)
shortest = len(nx.shortest_path(undir_graph, source="YOU", target="SAN")) - 3

bclavie

import tweepy

"""Consumer key&secret key from http://dev.twitter.com"""
CONSUMER_KEY = ''
CONSUMER_SECRET = ''

def login(consumer_key, consumer_secret):   
    logon = tweepy.OAuthHandler(consumer_key, consumer_secret)
    logonurl = logon.get_authorization_url()
    """Authenticate tweet deleter"""