def main(): # Get all args args = parse_arguments() # Logging setup if args.verbose: set_level(verbose=True) elif args.quiet: set_level(silent=True) else: set_level() logger = get_logger('main') logger.info('Trying to break free - please wait') session = Session() logger.debug('Starting PortQuiz') portquiz = PortQuiz(amount=args.max_ports) portquiz.start() logger.debug('Starting Online checks') try: offset, mintime, register = check() logger.debug('OnlineStatus completed: %04x %04x %08x', offset, mintime, register) online_status_obj = OnlineStatus(offset, mintime, register) session.online_status = online_status_obj.get_dict() except Exception, e: logger.exception('Online status check failed: {}'.format(e))
def run(args): nli_model_path = 'saved_models/xlnet-base-cased/' model_file = os.path.join(nli_model_path, 'pytorch_model.bin') config_file = os.path.join(nli_model_path, 'config.json') log = get_logger('conduct_test') model_name = 'xlnet-base-cased' tokenizer = XLNetTokenizer.from_pretrained(model_name, do_lower_case=True) xlnet_config = XLNetConfig.from_pretrained(config_file) model = XLNetForSequenceClassification.from_pretrained(model_file, config=xlnet_config) dataset_reader = ConductDatasetReader(args, tokenizer, log) file_lines = dataset_reader.get_file_lines('data/dados.tsv') results = [] softmax_fn = torch.nn.Softmax(dim=1) model.eval() with torch.no_grad(): for line in tqdm(file_lines): premise, hypothesys, conflict = dataset_reader.parse_line(line) pair_word_ids, input_mask, pair_segment_ids = dataset_reader.convert_text_to_features( premise, hypothesys) tensor_word_ids = torch.tensor([pair_word_ids], dtype=torch.long, device=args.device) tensor_input_mask = torch.tensor([input_mask], dtype=torch.long, device=args.device) tensor_segment_ids = torch.tensor([pair_segment_ids], dtype=torch.long, device=args.device) model_input = { 'input_ids': tensor_word_ids, # word ids 'attention_mask': tensor_input_mask, # input mask 'token_type_ids': tensor_segment_ids } outputs = model(**model_input) logits = outputs[0] nli_scores, nli_class = get_scores_and_class(logits, softmax_fn) nli_scores = nli_scores.detach().cpu().numpy() results.append({ "conduct": premise, "complaint": hypothesys, "nli_class": nli_class, "nli_contradiction_score": nli_scores[0], "nli_entailment_score": nli_scores[1], "nli_neutral_score": nli_scores[2], "conflict": conflict }) df = pd.DataFrame(results) df.to_csv('results/final_results.tsv', sep='\t', index=False)
def __init__(self, extensions, default_prefix, **kwargs): super().__init__( help_command=EmbedHelpCommand(), activity=Activity(type=ActivityType.playing, name="Booting up..."), command_prefix=util.determine_prefix, case_insensitive=True, allowed_mentions=AllowedMentions(everyone=False), max_messages=20000, heartbeat_timeout=120, owner_id=133311691852218378, client_id=500385855072894982, status=Status.idle, intents= Intents( # https://discordpy.readthedocs.io/en/latest/api.html?highlight=intents#intents guilds=True, members=True, # requires verification bans=True, emojis_and_stickers=True, integrations=False, webhooks=False, invites=False, voice_states=False, presences=True, # requires verification guild_messages=True, dm_messages=True, guild_reactions=True, dm_reactions=True, typing=False, message_content=True, # requires verification guild_scheduled_events=False, auto_moderation_configuration=False, auto_moderation_execution=False, ), **kwargs, ) self.default_prefix = default_prefix self.extensions_to_load = extensions self.logger = log.get_logger("MisoBot") self.start_time = time() self.global_cd = commands.CooldownMapping.from_cooldown( 15, 60, commands.BucketType.member) self.db = maria.MariaDB(self) self.cache = cache.Cache(self) self.version = "5.1" self.extensions_loaded = False self.register_hooks()
def validate_on_test_set(args, device): log = get_logger(f"test-results") SEED = 42 random.seed(SEED) np.random.seed(SEED) torch.manual_seed(SEED) log.info(f'Using device {device}') model_name = 'xlnet-base-cased' tokenizer = XLNetTokenizer.from_pretrained(model_name, do_lower_case=True) xlnet_config = XLNetConfig.from_pretrained(args.config_file) data_reader = KaggleMNLIDatasetReader(args, tokenizer, log) model = XLNetForSequenceClassification.from_pretrained(args.model_file, config=xlnet_config) model.to(device) if args.n_gpu > 1: model = torch.nn.DataParallel(model) log.info(f'Running on {args.n_gpu} GPUS') test_executor = KaggleTest(tokenizer, log, data_reader) write_kaggle_results("matched", args.test_matched_file, test_executor, device, model) write_kaggle_results("mismatched", args.test_mismatched_file, test_executor, device, model)
from time import time import psutil from discord.ext import commands, tasks from prometheus_client import Counter, Gauge, Histogram, Summary from modules import log logger = log.get_logger(__name__) class Prometheus(commands.Cog): """Collects prometheus metrics""" def __init__(self, bot): self.bot = bot self.ram_gauge = Gauge( "miso_memory_usage_bytes", "Memory usage of the bot process in bytes.", ) self.cpu_gauge = Gauge( "system_cpu_usage_percent", "CPU usage of the system in percent.", ["core"], ) self.event_counter = Counter( "miso_gateway_events_total", "Total number of gateway events.", ["event_type"], ) self.command_histogram = Histogram( "miso_command_response_time_seconds",
import traceback import discord import asyncio from discord.ext import commands, flags from modules import queries, exceptions, log, util, emojis logger = log.get_logger(__name__) command_logger = log.get_logger("commands") class ErrorHander(commands.Cog): """Any errors during command invocation will propagate here""" def __init__(self, bot): self.bot = bot self.message_levels = { "info": { "description_prefix": ":information_source:", "color": int("3b88c3", 16), "help_footer": False, }, "warning": { "description_prefix": ":warning:", "color": int("ffcc4d", 16), "help_footer": False, }, "error": { "description_prefix": ":no_entry:", "color": int("be1931", 16), "help_footer": False, }, "cooldown": {
import argparse from pathlib import Path from modules import log from modules import utils from modules import config config = config.get_config('config') log_path = Path(config['global']['log_path']) script_name = utils.get_script_name(__file__) logger = log.get_logger(script_name, log_path=log_path) def main(args): print('this is a template') def get_parser(): parser = argparse.ArgumentParser( description='Template script description', formatter_class=argparse.ArgumentDefaultsHelpFormatter ) parser.add_argument( '-t', '--test', action='store_true', help='Test run. No files will be modified' ) return parser
def get_train_logger(args): logger_name = f'{args.model_name}-batch{args.batch_size}-seq{args.max_seq_len}' \ f'-warmup{args.warmup_steps}-ep{args.epochs}-{args.dataset_name}' return get_logger(logger_name)
import asyncio import os import aiomysql from modules import exceptions, log logger = log.get_logger(__name__) log.get_logger("aiomysql") class MariaDB: def __init__(self, bot): self.bot = bot self.pool = None async def wait_for_pool(self): i = 0 while self.pool is None and i < 10: logger.warning("Pool not initialized yet. waiting...") await asyncio.sleep(1) i += 1 if self.pool is None: logger.error("Pool wait timeout! ABORTING") return False return True async def initialize_pool(self): cred = { "db": os.environ["DB_NAME"],
import argparse from pathlib import Path from modules import log from modules import config from bookmark import Bookmark, BookmarkCollection config = config.get_config('config') log_path = Path(config['global']['log_path']) logger = log.get_logger('bkm-org', log_path=log_path) def main(args): if args['bookmarkfile']: collection_fpath = Path(args['bookmarkfile']) if not collection_fpath.exists(): collection_fpath.touch() else: collection_fpath = Path(config['bkm-org']['collection_fpath']) if args['sync']: utype = args['sync'] bc = BookmarkCollection(collection_fpath) sync_bookmarks(bc, utype) return if args['import']: itype = args['import'][0] fpath = args['import'][1] bc = BookmarkCollection()