def create_one_code_res(input_seq_name, vocabulary):
from c_parser.ast_parser import parse_ast_code_graph
code_graph = parse_ast_code_graph(input_seq_name)
input_length = code_graph.graph_length + 2
in_seq, graph = code_graph.graph
begin_id = vocabulary.word_to_id(vocabulary.begin_tokens[0])
end_id = vocabulary.word_to_id(vocabulary.end_tokens[0])
input_seq = [begin_id] + [vocabulary.word_to_id(t)
for t in in_seq] + [end_id]
adj = [[a + 1, b + 1] for a, b, _ in graph] + [[b + 1, a + 1]
for a, b, _ in graph]
return input_seq, input_length, adj
def parse_ast_node(input_seq_name):
# input_seq_name = [vocabulary.id_to_word(token_id) for token_id in one_final_output]
# print(' '.join(input_seq_name))
from c_parser.ast_parser import parse_ast_code_graph
code_graph = parse_ast_code_graph(input_seq_name)
input_length = code_graph.graph_length + 2
in_seq, graph = code_graph.graph
# begin_id = vocabulary.word_to_id(vocabulary.begin_tokens[0])
# end_id = vocabulary.word_to_id(vocabulary.end_tokens[0])
input_seq = [begin_id] + [vocabulary.word_to_id(t)
for t in in_seq] + [end_id]
adj = [[a + 1, b + 1] for a, b, _ in graph] + [[b + 1, a + 1]
for a, b, _ in graph]
return input_seq_name, input_seq, adj, input_length
def generate_one_graph_input(input_token_names, vocabulary, tokenize_fn):
begin_token = vocabulary.begin_tokens[0]
end_token = vocabulary.end_tokens[0]
if tokenize_fn(' '.join(input_token_names), print_info=False) is None:
input_seq = [begin_token
] + input_token_names + ['<Delimiter>', end_token]
input_length = len(input_seq)
adj = [[0, 1]]
is_effect = False
else:
code_graph, is_effect = parse_ast_code_graph(input_token_names)
input_length = code_graph.graph_length + 2
in_seq, graph = code_graph.graph
input_seq = [begin_token] + in_seq + [end_token]
adj = [[a + 1, b + 1] for a, b, _ in graph] + [[b + 1, a + 1]
for a, b, _ in graph]
return input_seq, input_length, adj, is_effect
def _get_raw_sample(self, row):
# sample = dict(row)
row.select_random_i(only_first=self.only_first)
sample = {}
sample['id'] = row['id']
sample['includes'] = row['includes']
# if not self.is_flatten and self.do_multi_step_sample:
# sample['input_seq'] = row['error_token_id_list'][0]
# sample['input_seq_name'] = row['error_token_name_list'][0][1:-1]
# sample['input_length'] = len(sample['input_seq'])
# elif not self.is_flatten and not self.do_multi_step_sample:
# sample['input_seq'] = row['error_token_id_list']
# sample['input_seq_name'] = [r[1:-1] for r in row['error_token_name_list']]
# sample['input_length'] = [len(ids) for ids in sample['input_seq']]
# else:
sample['input_seq'] = row['error_token_id_list']
sample['input_seq_name'] = row['error_token_name_list'][1:-1]
sample['input_length'] = len(sample['input_seq'])
sample['copy_length'] = sample['input_length']
sample['adj'] = 0
inner_begin_id = self.vocabulary.word_to_id(
self.vocabulary.begin_tokens[1])
inner_end_id = self.vocabulary.word_to_id(
self.vocabulary.end_tokens[1])
if not self.do_multi_step_sample:
sample['target'] = [inner_begin_id
] + row['sample_ac_id_list'] + [inner_end_id]
sample['is_copy_target'] = row['is_copy_list'] + [0]
sample['copy_target'] = row['copy_pos_list'] + [-1]
sample_mask = sorted(row['sample_mask_list'] + [inner_end_id])
sample_mask_dict = {v: i for i, v in enumerate(sample_mask)}
sample['compatible_tokens'] = [
sample_mask for i in range(len(sample['is_copy_target']))
]
sample['compatible_tokens_length'] = [
len(one) for one in sample['compatible_tokens']
]
sample['sample_target'] = row['sample_ac_id_list'] + [inner_end_id]
sample['sample_target'] = [
t if c == 0 else -1 for c, t in zip(sample['is_copy_target'],
sample['sample_target'])
]
sample['sample_small_target'] = [
sample_mask_dict[t] if c == 0 else -1 for c, t in zip(
sample['is_copy_target'], sample['sample_target'])
]
sample['sample_outputs_length'] = len(sample['sample_target'])
sample['full_output_target'] = row['target_ac_token_id_list'][1:-1]
sample['final_output'] = row['ac_code_ids']
sample['final_output_name'] = row['ac_code_name_with_labels'][1:-1]
sample['p1_target'] = row['error_pos_list'][0]
sample['p2_target'] = row['error_pos_list'][1]
sample['error_pos_list'] = row['error_pos_list']
sample['distance'] = row['distance']
sample['includes'] = row['includes']
else:
pass
if self.use_ast:
code_graph = parse_ast_code_graph(sample['input_seq_name'])
sample['input_length'] = code_graph.graph_length + 2
in_seq, graph = code_graph.graph
begin_id = self.vocabulary.word_to_id(
self.vocabulary.begin_tokens[0])
end_id = self.vocabulary.word_to_id(self.vocabulary.end_tokens[0])
sample['input_seq'] = [begin_id] + [
self.vocabulary.word_to_id(t) for t in in_seq
] + [end_id]
sample['adj'] = [[a + 1, b + 1]
for a, b, _ in graph] + [[b + 1, a + 1]
for a, b, _ in graph]
return sample
def test_parse_ast_code_graph(seq_name):
try:
return parse_ast_code_graph(seq_name[1:-1]).graph_length
except Exception as e:
return 710
def _get_raw_sample(self, row):
# error_tokens = self.vocabulary.parse_text_without_pad([[k.value for k in self.data_df.iloc[index]["tokens"]]],
# use_position_label=True)[0]
# ac_tokens = self.vocabulary.parse_text_without_pad([[k.value for k in self.data_df.iloc[index]["ac_tokens"]]],
# use_position_label=True)[0]
# sample = dict(row)
sample = {}
sample['id'] = row['id']
sample['includes'] = row['includes']
if not self.is_flatten and self.do_multi_step_sample:
sample['input_seq'] = row['error_token_id_list'][0]
sample['input_seq_name'] = row['error_token_name_list'][0][1:-1]
sample['input_length'] = len(sample['input_seq'])
elif not self.is_flatten and not self.do_multi_step_sample:
sample['input_seq'] = row['error_token_id_list']
sample['input_seq_name'] = [
r[1:-1] for r in row['error_token_name_list']
]
sample['input_length'] = [len(ids) for ids in sample['input_seq']]
else:
sample['input_seq'] = row['error_token_id_list']
sample['input_seq_name'] = row['error_token_name_list'][1:-1]
sample['input_length'] = len(sample['input_seq'])
sample['copy_length'] = sample['input_length']
sample['last_input_seq_name'] = sample['input_seq_name']
inner_begin_id = self.vocabulary.word_to_id(
self.vocabulary.begin_tokens[1])
inner_end_id = self.vocabulary.word_to_id(
self.vocabulary.end_tokens[1])
if not self.do_multi_step_sample:
# sample['sample_inputs'] = [[inner_begin_id]+one for one in row['sample_ac_id_list']]
# sample['sample_inputs_length'] = [len(ids) for ids in sample['sample_inputs']]
if not self.is_flatten:
sample['is_copy_target'] = [
one + [0] for one in row['is_copy_list']
]
sample['copy_target'] = [
one + [-1] for one in row['copy_pos_list']
]
sample['sample_target'] = [
one + [inner_end_id] for one in row['sample_ac_id_list']
]
sample['sample_outputs_length'] = [
len(ids) for ids in sample['sample_target']
]
sample['target'] = [
one + [inner_end_id] for one in row['sample_ac_id_list']
]
error_start_pos_list, error_end_pos_list = list(
*row['error_pos_list'])
sample['p1_target'] = error_start_pos_list
sample['p2_target'] = error_end_pos_list
sample['error_pos_list'] = row['error_pos_list']
sample['compatible_tokens'] = [
row['sample_mask_list'] + [inner_end_id]
for i in range(len(sample['sample_target']))
]
sample['compatible_tokens_length'] = [
len(one) for one in sample['compatible_tokens']
]
sample['distance'] = row['distance']
sample['adj'] = 0
else:
sample['target'] = [
inner_begin_id
] + row['sample_ac_id_list'] + [inner_end_id]
sample['is_copy_target'] = row['is_copy_list'] + [0]
sample['copy_target'] = row['copy_pos_list'] + [-1]
sample_mask = sorted(row['sample_mask_list'] + [inner_end_id])
sample_mask_dict = {v: i for i, v in enumerate(sample_mask)}
sample['compatible_tokens'] = [
sample_mask for i in range(len(sample['is_copy_target']))
]
sample['compatible_tokens_length'] = [
len(one) for one in sample['compatible_tokens']
]
sample['sample_target'] = row['sample_ac_id_list'] + [
inner_end_id
]
sample['sample_target'] = [
t if c == 0 else -1 for c, t in zip(
sample['is_copy_target'], sample['sample_target'])
]
if self.only_sample:
sample['sample_target'] = [
st if c == 0 else sample['input_seq'][t] for c, t, st
in zip(sample['is_copy_target'], sample['copy_target'],
sample['sample_target'])
]
sample['is_copy_target'] = [0] * len(
sample['is_copy_target'])
sample['sample_small_target'] = [
sample_mask_dict[t] if c == 0 else -1 for c, t in zip(
sample['is_copy_target'], sample['sample_target'])
]
sample['sample_outputs_length'] = len(sample['sample_target'])
sample['full_output_target'] = row['target_ac_token_id_list'][
1:-1]
sample['final_output'] = row['ac_code_ids']
sample['p1_target'] = row['error_pos_list'][0]
sample['p2_target'] = row['error_pos_list'][1]
sample['error_pos_list'] = row['error_pos_list']
sample['distance'] = row['distance']
sample['includes'] = row['includes']
sample['adj'] = 0
else:
sample['copy_length'] = sample['input_length']
sample['error_count'] = row['distance']
sample['adj'] = 0
if self.use_ast:
code_graph = parse_ast_code_graph(sample['input_seq_name'])
sample['input_length'] = code_graph.graph_length + 2
in_seq, graph = code_graph.graph
begin_id = self.vocabulary.word_to_id(
self.vocabulary.begin_tokens[0])
end_id = self.vocabulary.word_to_id(self.vocabulary.end_tokens[0])
sample['input_seq'] = [begin_id] + [
self.vocabulary.word_to_id(t) for t in in_seq
] + [end_id]
sample['adj'] = [[a + 1, b + 1]
for a, b, _ in graph] + [[b + 1, a + 1]
for a, b, _ in graph]
return sample
