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import ast
import tarfile
from ast import AsyncFunctionDef, ClassDef, FunctionDef, Module
from io import BytesIO
import numpy as np
import requests
import torch
from transformers import Pipeline
def extract_code_and_docs(text: str):
"""Extract code and documentation from a Python file.
Args:
text (str): Source code of a Python file
Returns:
tuple: A tuple of two sets, the first is the code set, and the second is the docs set,
each set contains unique code string or docstring, respectively.
"""
root = ast.parse(text)
def_nodes = [
node
for node in ast.walk(root)
if isinstance(node, (AsyncFunctionDef, FunctionDef, ClassDef, Module))
]
code_set = set()
docs_set = set()
for node in def_nodes:
docs = ast.get_docstring(node)
node_without_docs = node
if docs is not None:
docs_set.add(docs)
# Remove docstrings from the node
node_without_docs.body = node_without_docs.body[1:]
if isinstance(node, (AsyncFunctionDef, FunctionDef)):
code_set.add(ast.unparse(node_without_docs))
return code_set, docs_set
def get_topics(repo_name, headers=None):
api_url = f"https://api.github.com/repos/{repo_name}"
print(f"[+] Getting topics for {repo_name}")
try:
response = requests.get(api_url, headers=headers)
response.raise_for_status()
except requests.exceptions.HTTPError as e:
print(f"[-] Failed to get topics for {repo_name}: {e}")
return []
metadata = response.json()
topics = metadata.get("topics", [])
if topics:
print(f"[+] Topics found for {repo_name}: {topics}")
return topics
def download_and_extract(repos, headers=None):
extracted_info = {}
for repo_name in repos:
extracted_info[repo_name] = {
"funcs": set(),
"docs": set(),
"topics": get_topics(repo_name, headers=headers),
}
download_url = f"https://api.github.com/repos/{repo_name}/tarball"
print(f"[+] Extracting functions and docstrings from {repo_name}")
try:
response = requests.get(download_url, headers=headers, stream=True)
response.raise_for_status()
except requests.exceptions.HTTPError as e:
print(f"[-] Failed to download {repo_name}: {e}")
continue
repo_bytes = BytesIO(response.raw.read())
print(f"[+] Extracting {repo_name} info")
with tarfile.open(fileobj=repo_bytes) as tar:
for member in tar.getmembers():
if member.isfile() and member.name.endswith(".py"):
file_content = tar.extractfile(member).read().decode("utf-8")
try:
code_set, docs_set = extract_code_and_docs(file_content)
except SyntaxError as e:
print(f"[-] SyntaxError in {member.name}: {e}, skipping")
continue
extracted_info[repo_name]["funcs"].update(code_set)
extracted_info[repo_name]["docs"].update(docs_set)
return extracted_info
class RepoEmbeddingPipeline(Pipeline):
def __init__(self, github_token=None, *args, **kwargs):
super().__init__(*args, **kwargs)
self.API_HEADERS = {"Accept": "application/vnd.github+json"}
if not github_token:
print(
"[!] Consider setting GITHUB_TOKEN to avoid hitting rate limits\n"
"For more info, see:"
"https://docs.github.com/authentication/keeping-your-account-and-data-secure/creating-a-personal-access-token"
)
else:
self.API_HEADERS["Authorization"] = f"Bearer {github_token}"
print("[+] Using GITHUB_TOKEN for authentication")
def _sanitize_parameters(self, **kwargs):
_forward_kwargs = {}
if "max_length" in kwargs:
_forward_kwargs["max_length"] = kwargs["max_length"]
return {}, _forward_kwargs, {}
def preprocess(self, inputs):
if isinstance(inputs, str):
inputs = (inputs,)
extracted_infos = download_and_extract(inputs, headers=self.API_HEADERS)
return extracted_infos
def encode(self, text, max_length):
"""
Generates an embedding for a input string.
Parameters:
* `text`- The input string to be embedded.
* `max_length`- The maximum total source sequence length after tokenization.
"""
assert max_length < 1024
tokenizer = self.tokenizer
tokens = (
[tokenizer.cls_token, "<encoder-only>", tokenizer.sep_token]
+ tokenizer.tokenize(text)[: max_length - 4]
+ [tokenizer.sep_token]
)
tokens_id = tokenizer.convert_tokens_to_ids(tokens)
source_ids = torch.tensor([tokens_id]).to(self.device)
token_embeddings = self.model(source_ids)[0]
sentence_embeddings = token_embeddings.mean(dim=1)
return sentence_embeddings
def _forward(self, extracted_infos, max_length=512):
repo_dataset = {}
for repo_name, repo_info in extracted_infos.items():
entry = {"topics": repo_info.get("topics")}
print(f"[+] Generating embeddings for {repo_name}")
if entry.get("code_embeddings") is None:
code_embeddings = [
[func, self.encode(func, max_length).squeeze().tolist()]
for func in repo_info["funcs"]
]
entry["code_embeddings"] = code_embeddings
entry["mean_code_embedding"] = (
np.mean([x[1] for x in code_embeddings], axis=0).tolist()
if code_embeddings
else None
)
if entry.get("doc_embeddings") is None:
doc_embeddings = [
[doc, self.encode(doc, max_length).squeeze().tolist()]
for doc in repo_info["docs"]
]
entry["doc_embeddings"] = doc_embeddings
entry["mean_doc_embedding"] = (
np.mean([x[1] for x in doc_embeddings], axis=0).tolist()
if doc_embeddings
else None
)
repo_dataset[repo_name] = entry
return repo_dataset
def postprocess(self, repo_dataset):
return repo_dataset
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