Datasets:
jblitzar
/
github-python-corpus

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# bb does not end with JCC, so ignore
if len(bb.outgoing_edges) != 2:
continue
# evaluate both branches
# (does not differentiate between True/False branch)
# but for our purpose, we don't need to
if bb in seen_bbs:
continue
total_conds_seen += 1
for branch in bb.outgoing_edges:
# bb has multiple incoming edges, so ignore
if len(branch.target.incoming_edges) != 1:
continue
# ignore authentic bb
if branch.target.start in metadata.good_bbs:
continue
# core analysis
alerted_rules_in_bb = list() # reset to empty
last_instr_addr = get_last_bb_instr(bb) # if_addr
for analysis in analyses:
analysis_result = analysis(bv, branch.target,
branch.target.start, metadata)
if analysis_result:
# add list of rules alerted in current basic block
alerted_rules_in_bb.extend(analysis_result)
if alerted_rules_in_bb: # list not empty there are alerted
# rules in current basic block
# format: (OP addr, binja branch object, rule list)
cur_pass_patch_locations.append(
OpaquePredicateInfo(last_instr_addr, branch,
alerted_rules_in_bb)
)
seen_bbs.add(bb)
return (cur_pass_patch_locations, total_conds_seen)
# <FILESEP>
from enum import Enum
import argparse
import dataclasses
from dataclasses import dataclass, field
from typing import Optional
from transformers import HfArgumentParser, TrainingArguments
from tasks.utils import *
@dataclass
class DataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
Using `HfArgumentParser` we can turn this class
into argparse arguments to be able to specify them on
the command line.training_args
"""
task_name: str = field(
metadata={
"help": "The name of the task to train on: " + ", ".join(TASKS),
"choices": TASKS
},
)
dataset_name: str = field(
metadata={
"help": "The name of the dataset to use: " + ", ".join(DATASETS),
"choices": DATASETS
}
)
dataset_config_name: Optional[str] = field(
default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
)
max_seq_length: int = field(
default=128,
metadata={
"help": "The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded."
},
)
overwrite_cache: bool = field(
default=False, metadata={"help": "Overwrite the cached preprocessed datasets or not."}
)
pad_to_max_length: bool = field(
default=True,
metadata={
"help": "Whether to pad all samples to `max_seq_length`. "
"If False, will pad the samples dynamically when batching to the maximum length in the batch."
},
)
max_train_samples: Optional[int] = field(
default=None,
metadata={
"help": "For debugging purposes or quicker training, truncate the number of training examples to this "