def interactive_mode(args):
"""Interactive mode"""
print('Start interactive mode', file=sys.stderr)
parser = StandaloneParser(args.parser,
args.load_model,
args.example_preprocessor,
beam_size=args.beam_size,
cuda=args.cuda)
while True:
utterance = input('Query:').strip()
hypotheses = parser.parse(utterance, debug=True)
for hyp_id, hyp in enumerate(hypotheses):
print('------------------ Hypothesis %d ------------------' % hyp_id)
print(hyp.code)
class TranX_Prertrained_Translator():
def __init__(self, dataset="django"):
arg_parser = init_arg_parser()
if dataset == "django":
args = init_arg_parser().parse_args("--mode test \
--load_model src/external_repos/tranX/data/pretrained_models/django.bin \
--beam_size 15 \
--test_file src/external_repos/tranX/data/django/test.bin \
--save_decode_to 0.test.decode \
--decode_max_time_step 100 \
--example_preprocessor django_example_processor"
.split())
elif dataset == "conala":
args = init_arg_parser().parse_args("--mode test \
--load_model src/external_repos/tranX/data/pretrained_models/conala.bin \
--beam_size 15 \
--test_file src/external_repos/tranX/data/conala/test.bin \
--save_decode_to 0.test.decode \
--decode_max_time_step 100 \
--example_preprocessor conala_example_processor"
.split())
self.parser = StandaloneParser(args.parser,
args.load_model,
args.example_preprocessor,
beam_size=args.beam_size,
cuda=args.cuda)
def raw_predict(self, src_str):
# try:
utterance = src_str.strip()
hypotheses = self.parser.parse(utterance, debug=False)
pred_code_list = [hyp.code for hyp in hypotheses]
return pred_code_list[0]
# except:
# return ""
def raw_batch_predict(self, batch_src_strs):
return [self.raw_predict(src_str) for src_str in batch_src_strs]
class CoNaLa_SOTA_Transform():
def __init__(self, cuda=True, fields={'input_field':'input_text', 'output_field':'pred_text'},
model_file='src/external_repos/external-knowledge-codegen/best_pretrained_models/finetune.mined.retapi.distsmpl.dr0.3.lr0.001.lr_de0.5.lr_da15.beam15.seed0.mined_100000.intent_count100k_topk1_temp5.bin'):
'''
This uses the model from Frank Xu preseented in:
Incorporating External Knowledge through Pre-training for Natural Language to Code Generation
It translates English to Python code
'''
self.fields = fields
parser = 'default_parser'
processor = 'conala_example_processor'
beam_size = 15
reranker_file = 'src/external_repos/external-knowledge-codegen/best_pretrained_models/reranker.conala.vocab.src_freq3.code_freq3.mined_100000.intent_count100k_topk1_temp5.bin'
self.parser = StandaloneParser(parser,
model_file,
processor,
beam_size=beam_size,
cuda=cuda,
reranker_path=reranker_file)
def __call__(self, samples):
'''
samples: [dict]: [{'input_text': "`foo` is an empty list"},...]
returns: [dict]: [{'input_text': "`foo` is an empty list", 'pred_text': "foo = []"}]
'''
for sample_obj in tqdm(samples, desc='Tranx:'):
input_text = sample_obj[self.fields['input_field']]
input_text = input_text.strip()
try:
hypotheses = self.parser.parse(input_text, debug=False)
sample_obj[self.fields['output_field']] = hypotheses[0].code
except (IndexError, SyntaxError):
sample_obj[self.fields['output_field']] = ''
print("### ERROR ### input text:", input_text)
print(sys.exc_info()[0])
return samples