async def run(client, message, args, prefix, db):
if utils.is_dev(message.author):
mesaj = message.content[len(prefix) + len("duyuru"):]
basarisiz = list()
for i, u in enumerate(message.guild.members):
if not u.bot:
try:
if u.id == 466306304063832065: continue
if u.id == 302515395552739339: continue
if u.id == 284697871331360778: continue
dm = await u.create_dm()
await dm.send(mesaj.format(u.name))
except Exception as e:
print(message.author.display_name + " : " +
message.content + " >>")
print(e)
basarisiz.append(u)
print(f"Duyuru {i}/{len(message.guild.members)}")
if len(basarisiz) != 0:
basarisiz_string = ""
for u in basarisiz:
basarisiz_string = basarisiz_string + str(u) + " \n"
await message.channel.send("Duyuruyu almayan kişiler:\n```" +
basarisiz_string + "```")
for b in basarisiz:
print(str(b.name), b.name, b.id)
else:
raise Exception(
"Bu komutu kullanmak için yeterli yetkiye sahip değilsin.")
def get_quote(provider, data):
if provider == 'supertext':
if is_dev():
url = 'https://dev.supertext.ch/api/v1/translation/quote'
else:
url = 'https://www.supertext.ch/api/v1/translation/quote'
headers = {'Content-type': 'application/json; charset=UTF-8', 'Accept': '*'}
r = requests.post(url, data=json.dumps(data, ensure_ascii=False).encode('ascii', 'xmlcharrefreplace'),
headers=headers, verify=verify_ssl())
return r.content
else:
raise NotImplementedError()
def get_order(provider, data):
if provider == 'supertext':
user, api_key = get_creds('SUPERTEXT', ['USER', 'API_KEY'])
if is_dev():
url = 'https://dev.supertext.ch/api/v1/translation/order'
else:
url = 'https://www.supertext.ch/api/v1/translation/order'
headers = {'Content-type': 'application/json; charset=UTF-8', 'Accept': '*'}
r = requests.post(url, data=json.dumps(data, ensure_ascii=False).encode('ascii', 'xmlcharrefreplace'),
headers=headers, auth=(user, api_key), verify=verify_ssl())
return r.content
else:
raise NotImplementedError()
async def run(client, message, args, prefix, db):
if args[0] == "durum":
await message.channel.send(f"__Veritabanı Durumu__ 🧪\n" + \
f"Heroku Redis - AmazonAWS EU-West\n\n" + \
f"```Bağlantı: {('Sağlıklı', 'Ölü')[int(db.redis.connection_pool is None)]}\n" + \
f"GiriÅŸ: {int(db.elapsed*1000)}ms\n" + \
f"Ort. Query: {int(db.query_elapsed*1000)}ms```" + \
f"")
elif args[0] == "kur":
if is_dev(message.author):
pass
else:
raise Exception(
"Bu komutu kullanmak için yeterli yetkiye sahip değilsin.")
def get_collections_for_bbox(self, bbox=None):
if self.header is None:
self.read_header()
if bbox is None:
if is_dev() is True:
return self.header["collections"][:1]
else:
return self.header["collections"]
filter_bbox = box(*[float(i) for i in bbox.split(",")])
filtered_collections = []
for collection in self.header["collections"]:
collection_bbox = box(*collection["bbox"])
if filter_bbox.intersects(collection_bbox) is True:
filtered_collections.append(collection)
return filtered_collections
async def run(client, message, args, prefix, db):
if is_dev(message.author):
user = get_user(message.guild, args[0])
if not user:
raise Exception(f"'{args[0]}' bulunamadı")
if args[1].startswith("+"):
db[user] = db[user] + int(args[1][1:])
elif args[1].startswith("-"):
db[user] = db[user] + int(args[1][1:])
else:
db[user] = int(args[1])
else:
raise Exception("Bu komutu kullanabilmek için yeterli yetkiye sahip değilsiniz")
async def on_message(self, message):
"""
don't touch this! keep it abstract
"""
# we don't want interaction in any other channels
if message.channel.id != self.secret_channel.id:
return
# quick content reference
content = message.content
# we also want to skip anything that doesn't start with the prefix
if not content.startswith("!"):
return
# strip prefix
content = content[1:]
# get base command
base_command = content.split(" ")
# the function to call
cmd_funct = None
# parse base commands (not mapped)
if content == 'commands':
await self.commands(message)
elif content == 'rules':
await self.rules(message)
elif content == 'devme':
await self.devme(message)
elif base_command[0] in commands_map:
# user commands
cmd_funct = self._get_command_function(commands_map, base_command)
elif base_command[0] in dev_commands_map and utils.is_dev(message.author):
# dev commands
cmd_funct = self._get_command_function(dev_commands_map, base_command)
elif base_command[0] in admin_commands_map and utils.is_admin(message.author):
# admin commands
cmd_funct = self._get_command_function(admin_commands_map, base_command)
if cmd_funct is not None:
await cmd_funct(message)
def get_quote_view(request, pk):
t = TranslationRequest.objects.get(pk=pk)
check_stage(t.status, 'selected_content')
if request.method == 'POST':
if request.POST.get('opt'):
t.order_choice = request.POST.get('opt') # TODO: possible security issue?
t.status = 'selected_quote'
t.save()
return HttpResponseRedirect(reverse('admin:order', kwargs={'pk': pk}))
else:
data = prepare_data(t, t.from_lang, t.to_lang)
quote = get_quote(t.provider, data=data)
if log_to_file_enabled():
log_to_file(data)
if t.provider == 'supertext':
res = json.loads(quote)
return render_to_response(
'aldryn_translator/quote.html', {'res': res, 'dev': is_dev()},
context_instance=RequestContext(request))
else:
raise NotImplementedError()
def check(self, ctx):
return is_dev(ctx)
#!/usr/bin/env python
import os
import sys
from {{ project_name }} import utils
if __name__ == "__main__":
if utils.is_dev():
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "{{ project_name }}.settings.dev")
else:
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "{{ project_name }}.settings.prod")
from django.core.management import execute_from_command_line
execute_from_command_line(sys.argv)
def decode(model_file, target_file, gpu=-1, save_to=None):
context = utils.load_context(model_file)
if context.seed is not None:
utils.set_random_seed(context.seed, gpu)
Log.i("random seed: {}".format(context.seed))
framework_utils.set_debug(App.debug)
loader = context.loader
Log.i('load test dataset from {}'.format(target_file))
test_dataset = loader.load(target_file,
train=False,
size=16 if utils.is_dev() else None)
Log.i('#samples {}'.format(len(test_dataset)))
Log.v('')
Log.v("initialize ...")
Log.v('--------------------------------')
Log.i('# gpu: {}'.format(gpu))
Log.i('# tagset size: {}'.format(len(loader.tag_map)))
Log.i('# model layers: {}'.format(context.models))
Log.i('# context: {}'.format(context))
Log.v('--------------------------------')
Log.v('')
models.USE_ORTHONORMAL = False
# Set up a neural network model
layers = [
models.Input(
word_embeddings=loader.get_embeddings('word'),
char_embeddings=loader.get_embeddings('char'),
char_feature_size=50,
dropout=0.5,
),
models.Recurrent(n_layers=2,
in_size=loader.get_embeddings('word').shape[1] + 50,
out_size=400,
dropout=0.5),
models.Tagger(in_size=400 * 2,
out_size=len(loader.tag_map),
units=100,
dropout=0.5) if context.models[2] is models.Tagger else
models.GoldTagger(out_size=len(loader.tag_map)),
]
if models.Parser in context.models:
layers.extend([
models.Connection(in_size=400 * 2,
out_size=800,
tagset_size=len(loader.tag_map),
tag_embed_size=50,
dropout=0.5),
models.Parser(in_size=850,
n_deprels=len(loader.rel_map),
n_blstm_layers=1,
lstm_hidden_size=400,
parser_mlp_units=800,
dropout=0.50),
])
model = models.MTL(*layers)
chainer.serializers.load_npz(model_file, model)
if gpu >= 0:
framework_utils.set_model_to_device(model, device_id=gpu)
# Setup an evaluator
evaluator = models.Evaluator(loader, target_file, save_to)
evaluator.add_target(model)
# Start decoding
framework_utils.chainer_train_off()
evaluator.on_epoch_validate_begin({'epoch': 0})
for batch_index, batch in enumerate(
test_dataset.batch(context.batch_size, colwise=True,
shuffle=False)):
xs, ts = batch[:-1], batch[-1]
evaluator.on_batch_begin({'train': False, 'xs': xs, 'ts': ts})
model(*xs)
evaluator.on_batch_end({'train': False, 'xs': xs, 'ts': ts})
evaluator.on_epoch_validate_end({'epoch': 0})
def train(
train_file,
test_file=None,
embed_file=None,
embed_size=100,
n_epoch=20,
batch_size=32,
lr=0.001,
l2_lambda=0.0,
grad_clip=5.0,
tasks='tp',
gpu=-1,
save_to=None,
seed=None):
if seed is not None:
utils.set_random_seed(seed, gpu)
Log.i("random seed: {}".format(seed))
framework_utils.set_debug(App.debug)
# Select Task
with_tagging_task = False
with_parsing_task = False
for char in tasks:
if char == 't':
with_tagging_task = True
elif char == 'p':
with_parsing_task = True
else:
raise ValueError("Invalid task specified: {}".format(char))
if not any([with_tagging_task, with_parsing_task]):
raise RuntimeError("No valid task specified")
Log.i('Task: tagging={}, parsing={}'
.format(with_tagging_task, with_parsing_task))
# Transition System
transition_system = transition.ArcStandard
if with_parsing_task:
Log.i('Transition System: {}'.format(transition_system))
# Load files
Log.i('initialize DataLoader with embed_file={} and embed_size={}'
.format(embed_file, embed_size))
loader = dataset.DataLoader(word_embed_file=embed_file,
word_embed_size=embed_size,
char_embed_size=10,
transition_system=transition_system)
Log.i('load train dataset from {}'.format(train_file))
train_dataset = loader.load(train_file, train=True,
size=120 if utils.is_dev() else None)
if test_file:
Log.i('load test dataset from {}'.format(test_file))
test_dataset = loader.load(test_file, train=False,
size=16 if utils.is_dev() else None)
else:
test_dataset = None
Log.v('')
Log.v("initialize ...")
Log.v('--------------------------------')
Log.i('# Minibatch-size: {}'.format(batch_size))
Log.i('# epoch: {}'.format(n_epoch))
Log.i('# gpu: {}'.format(gpu))
Log.i('# tagset size: {}'.format(len(loader.tag_map)))
Log.v('--------------------------------')
Log.v('')
# Set up a neural network model
layers = [
models.Input(
word_embeddings=loader.get_embeddings('word'),
char_embeddings=loader.get_embeddings('char'),
char_feature_size=50,
dropout=0.5,
),
models.Recurrent(
n_layers=2,
in_size=loader.get_embeddings('word').shape[1] + 50,
out_size=400,
dropout=0.5),
models.Tagger(
in_size=400 * 2,
out_size=len(loader.tag_map),
units=100,
dropout=0.5) if with_tagging_task else
models.GoldTagger(out_size=len(loader.tag_map)),
]
if with_parsing_task:
layers.extend([
models.Connection(
tagset_size=len(loader.tag_map),
tag_embed_size=50,
dropout=0.5),
models.Parser(
in_size=850,
n_deprels=len(loader.rel_map),
n_blstm_layers=2,
lstm_hidden_size=400,
parser_mlp_units=800,
dropout=0.50,
transition_system=transition_system),
])
model = models.MTL(*layers)
if gpu >= 0:
framework_utils.set_model_to_device(model, device_id=gpu)
# Setup an optimizer
optimizer = chainer.optimizers.Adam(
alpha=lr, beta1=0.9, beta2=0.999, eps=1e-08)
optimizer.setup(model)
if l2_lambda > 0.0:
optimizer.add_hook(chainer.optimizer.WeightDecay(l2_lambda))
else:
l2_lambda = False
if grad_clip > 0.0:
optimizer.add_hook(chainer.optimizer.GradientClipping(grad_clip))
else:
grad_clip = False
# optimizer.add_hook(
# framework_utils.optimizers.ExponentialDecayAnnealing(
# initial_lr=lr, decay_rate=0.75, decay_step=5000, lr_key='alpha'))
Log.i('optimizer: Adam(alpha={}, beta1=0.9, '
'beta2=0.999, eps=1e-08), grad_clip={}, '
'regularization: WeightDecay(lambda={})'
.format(lr, grad_clip, l2_lambda))
# Setup a trainer
trainer = Trainer(optimizer, model,
loss_func=model.compute_loss,
accuracy_func=model.compute_accuracy)
trainer.configure(framework_utils.config)
if test_dataset:
evaluator = models.Evaluator(loader, test_file, save_to)
evaluator.add_target(model)
trainer.attach_callback(evaluator)
if save_to is not None:
accessid = Log.getLogger().accessid
date = Log.getLogger().accesstime.strftime('%Y%m%d')
trainer.attach_callback(
framework_utils.callbacks.Saver(
model,
basename="{}-{}".format(date, accessid),
directory=save_to,
context=dict(App.context,
models=[type(layer) for layer in layers],
loader=loader)))
# Start training
trainer.fit(train_dataset, None,
batch_size=batch_size,
epochs=n_epoch,
validation_data=test_dataset,
verbose=App.verbose)