def bot_func(bot, update, args):
text = " ".join(args)
words = utils.tokenize(text)
seq_1 = data.encode_words(words, emb_dict)
input_seq = model.pack_input(seq_1, net.emb)
enc = net.encode(input_seq)
if prog_args.sample:
_, tokens = net.decode_chain_sampling(enc,
input_seq.data[0:1],
seq_len=data.MAX_TOKENS,
stop_at_token=end_token)
else:
_, tokens = net.decode_chain_argmax(enc,
input_seq.data[0:1],
seq_len=data.MAX_TOKENS,
stop_at_token=end_token)
if tokens[-1] == end_token:
tokens = tokens[:-1]
reply = data.decode_words(tokens, rev_emb_dict)
if reply:
reply_text = utils.untokenize(reply)
bot.send_message(chat_id=update.message.chat_id, text=reply_text)
def words_to_words(words, emb_dict, rev_emb_dict, net, use_sampling=False):
tokens = data.encode_words(words, emb_dict)
input_seq = model.pack_input(tokens, net.emb)
enc = net.encode(input_seq)
end_token = emb_dict[data.END_TOKEN]
if use_sampling:
_, out_tokens = net.decode_chain_sampling(enc, input_seq.data[0:1], seq_len=data.MAX_TOKENS,
stop_at_token=end_token)
else:
_, out_tokens = net.decode_chain_argmax(enc, input_seq.data[0:1], seq_len=data.MAX_TOKENS,
stop_at_token=end_token)
if out_tokens[-1] == end_token:
out_tokens = out_tokens[:-1]
out_words = data.decode_words(out_tokens, rev_emb_dict)
return out_words
def bot_func(bot, update, args):
text = " ".join(args)
words = utils.tokenize(text)
seq_1 = data.encode_words(words, emb_dict)
input_seq = model.pack_input(seq_1, net.emb)
enc = net.encode(input_seq)
if prog_args.sample:
_, tokens = net.decode_chain_sampling(enc, input_seq.data[0:1], seq_len=data.MAX_TOKENS,
stop_at_token=end_token)
else:
_, tokens = net.decode_chain_argmax(enc, input_seq.data[0:1], seq_len=data.MAX_TOKENS,
stop_at_token=end_token)
if tokens[-1] == end_token:
tokens = tokens[:-1]
reply = data.decode_words(tokens, rev_emb_dict)
if reply:
reply_text = utils.untokenize(reply)
bot.send_message(chat_id=update.message.chat_id, text=reply_text)
def words_to_words(words, emb_dict, rev_emb_dict, net, use_sampling=False):
tokens = data.encode_words(words, emb_dict)
input_seq = model.pack_input(tokens, net.emb)
enc = net.encode(input_seq)
end_token = emb_dict[data.END_TOKEN]
if use_sampling:
_, out_tokens = net.decode_chain_sampling(enc,
input_seq.data[0:1],
seq_len=data.MAX_TOKENS,
stop_at_token=end_token)
else:
_, out_tokens = net.decode_chain_argmax(enc,
input_seq.data[0:1],
seq_len=data.MAX_TOKENS,
stop_at_token=end_token)
if out_tokens[-1] == end_token:
out_tokens = out_tokens[:-1]
out_words = data.decode_words(out_tokens, rev_emb_dict)
return out_words
def test_encode_words(self):
res = data.encode_words(['a', 'b', 'c'], self.emb_dict)
self.assertEqual(res, [0, 3, 4, 2, 1])
def test_encode_words(self):
res = data.encode_words(["a", "b", "c"], self.emb_dict)
self.assertEqual(res, [0, 3, 4, 2, 1])