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from typing import Dict, Any, List
import ast
import tarfile
from ast import AsyncFunctionDef, ClassDef, FunctionDef, Module
import torch
import requests
from transformers import Pipeline
from tqdm.auto import tqdm
def extract_code_and_docs(text: str):
code_set = set()
docs_set = set()
root = ast.parse(text)
for node in ast.walk(root):
if not isinstance(node, (AsyncFunctionDef, FunctionDef, ClassDef, Module)):
continue
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_metadata(repo_name, headers=None):
api_url = f"https://api.github.com/repos/{repo_name}"
tqdm.write(f"[+] Getting metadata for {repo_name}")
try:
response = requests.get(api_url, headers=headers)
response.raise_for_status()
return response.json()
except requests.exceptions.HTTPError as e:
tqdm.write(f"[-] Failed to retrieve metadata from {repo_name}: {e}")
return {}
def extract_information(repos, headers=None):
extracted_infos = []
for repo_name in tqdm(repos, disable=len(repos) <= 1):
# Get metadata
metadata = get_metadata(repo_name, headers=headers)
repo_info = {
"name": repo_name,
"codes": set(),
"docs": set(),
"requirements": set(),
"readmes": set(),
"topics": [],
"license": "",
"stars": metadata.get("stargazers_count"),
}
if metadata.get("topics"):
repo_info["topics"] = metadata["topics"]
if metadata.get("license"):
repo_info["license"] = metadata["license"]["spdx_id"]
# Download repo tarball bytes
download_url = f"https://api.github.com/repos/{repo_name}/tarball"
tqdm.write(f"[+] Downloading {repo_name}")
try:
response = requests.get(download_url, headers=headers, stream=True)
response.raise_for_status()
except requests.exceptions.HTTPError as e:
tqdm.write(f"[-] Failed to download {repo_name}: {e}")
continue
# Extract python files and parse them
tqdm.write(f"[+] Extracting {repo_name} info")
with tarfile.open(fileobj=response.raw, mode="r|gz") as tar:
for member in tar:
if (member.name.endswith(".py") and member.isfile()) is False:
continue
try:
file_content = tar.extractfile(member).read().decode("utf-8")
code_set, docs_set = extract_code_and_docs(file_content)
repo_info["codes"].update(code_set)
repo_info["docs"].update(docs_set)
except UnicodeDecodeError as e:
tqdm.write(
f"[-] UnicodeDecodeError in {member.name}, skipping: \n{e}"
)
except SyntaxError as e:
tqdm.write(f"[-] SyntaxError in {member.name}, skipping: \n{e}")
extracted_infos.append(repo_info)
return extracted_infos
class RepoPipeline(Pipeline):
def __init__(self, github_token=None, *args, **kwargs):
super().__init__(*args, **kwargs)
# Github token
self.github_token = github_token
if self.github_token:
print("[+] GitHub token set!")
else:
print(
"[*] Please set GitHub token to avoid unexpected errors. \n"
"For more info, see: "
"https://docs.github.com/authentication/keeping-your-account-and-data-secure/creating-a-personal-access-token"
)
def _sanitize_parameters(self, **pipeline_parameters):
preprocess_parameters = {}
if "github_token" in pipeline_parameters:
preprocess_parameters["github_token"] = pipeline_parameters["github_token"]
forward_parameters = {}
if "max_length" in pipeline_parameters:
forward_parameters["max_length"] = pipeline_parameters["max_length"]
postprocess_parameters = {}
return preprocess_parameters, forward_parameters, postprocess_parameters
def preprocess(self, input_: Any, **preprocess_parameters: Dict) -> List:
# Making input to list format
if isinstance(input_, str):
input_ = [input_]
# Building token
github_token = preprocess_parameters["preprocess_parameters"]
headers = {"Accept": "application/vnd.github+json"}
token = github_token or self.github_token
if token:
headers["Authorization"] = f"Bearer {token}"
# Getting repositories' information: input_ means series of repositories
extracted_infos = extract_information(input_, headers=headers)
return extracted_infos
def encode(self, text, max_length):
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 generate_embeddings(self, text_sets, max_length):
assert max_length < 1024
return torch.concat([self.encode(text, max_length) for text in text_sets], dim=0) \
if text_sets is None or len(text_sets) == 0 \
else torch.zeros((1, 768), device=self.device)
def _forward(self, extracted_infos: List, **forward_parameters: Dict) -> List:
max_length = 512 if forward_parameters["max_length"] is None else forward_parameters["max_length"]
model_outputs = []
num_repos = len(extracted_infos)
with tqdm(total=num_repos) as progress_bar:
# For each repository
for repo_info in extracted_infos:
repo_name = repo_info["name"]
info = {
"name": repo_name,
"topics": repo_info["topics"],
"license": repo_info["license"],
"stars": repo_info["stars"],
}
progress_bar.set_description(f"Processing {repo_name}")
# Code embeddings
tqdm.write(f"[*] Generating code embeddings for {repo_name}")
code_embeddings = self.generate_embeddings(repo_info["codes"], max_length)
info["code_embeddings"] = code_embeddings.item()
info["mean_code_embedding"] = torch.mean(code_embeddings, dim=0).item()
# Doc embeddings
tqdm.write(f"[*] Generating doc embeddings for {repo_name}")
doc_embeddings = self.generate_embeddings(repo_info["docs"], max_length)
info["doc_embeddings"] = doc_embeddings.item()
info["mean_doc_embedding"] = torch.mean(doc_embeddings, dim=0).item()
# Requirement embeddings
tqdm.write(f"[*] Generating requirement embeddings for {repo_name}")
requirement_embeddings = self.generate_embeddings(repo_info["requirements"], max_length)
info["requirement_embeddings"] = requirement_embeddings.item()
info["mean_requirement_embedding"] = torch.mean(requirement_embeddings, dim=0).item()
# Requirement embeddings
tqdm.write(f"[*] Generating readme embeddings for {repo_name}")
readme_embeddings = self.generate_embeddings(repo_info["readmes"], max_length)
info["readme_embeddings"] = readme_embeddings.item()
info["mean_readme_embedding"] = torch.mean(readme_embeddings, dim=0).item()
progress_bar.update(1)
model_outputs.append(info)
return model_outputs
def postprocess(self, model_outputs: List, **postprocess_parameters: Dict) -> List:
return model_outputs
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