Update RepoPipeline.py
Browse files- RepoPipeline.py +29 -155
RepoPipeline.py
CHANGED
@@ -2,20 +2,14 @@ from typing import Dict, Any, List
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import ast
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import tarfile
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import torch
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import requests
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import numpy as np
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from ast import AsyncFunctionDef, ClassDef, FunctionDef, Module
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from transformers import Pipeline
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from tqdm.auto import tqdm
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def extract_code_and_docs(text: str):
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"""
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The method for extracting codes and docs in text.
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:param text: python file.
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:return: codes and docs set.
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"""
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code_set = set()
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docs_set = set()
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root = ast.parse(text)
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@@ -34,33 +28,7 @@ def extract_code_and_docs(text: str):
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return code_set, docs_set
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def extract_requirements(lines):
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"""
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The method for extracting requirements.
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:param lines: requirements.
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:return: requirement libraries.
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"""
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requirements_set = set()
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for line in lines:
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try:
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if line != "\n":
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if " == " in line:
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splitLine = line.split(" == ")
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else:
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splitLine = line.split("==")
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requirements_set.add(splitLine[0])
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except:
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pass
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return requirements_set
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def get_metadata(repo_name, headers=None):
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"""
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The method for getting metadata of repository from github_api.
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:param repo_name: repository name.
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:param headers: request headers.
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:return: response json.
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"""
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api_url = f"https://api.github.com/repos/{repo_name}"
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tqdm.write(f"[+] Getting metadata for {repo_name}")
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try:
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@@ -73,15 +41,9 @@ def get_metadata(repo_name, headers=None):
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def extract_information(repos, headers=None):
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"""
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The method for extracting repositories information.
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:param repos: repositories.
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:param headers: request header.
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:return: a list for representing the information of each repository.
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"""
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extracted_infos = []
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for repo_name in tqdm(repos, disable=len(repos) <= 1):
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#
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metadata = get_metadata(repo_name, headers=headers)
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repo_info = {
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"name": repo_name,
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@@ -98,7 +60,7 @@ def extract_information(repos, headers=None):
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if metadata.get("license"):
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repo_info["license"] = metadata["license"]["spdx_id"]
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# Download repo tarball bytes
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download_url = f"https://api.github.com/repos/{repo_name}/tarball"
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tqdm.write(f"[+] Downloading {repo_name}")
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try:
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@@ -108,51 +70,24 @@ def extract_information(repos, headers=None):
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tqdm.write(f"[-] Failed to download {repo_name}: {e}")
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continue
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# Extract
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tqdm.write(f"[+] Extracting {repo_name} info")
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with tarfile.open(fileobj=response.raw, mode="r|gz") as tar:
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for member in tar:
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# 3. Extracting readme.
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elif (member.name == "README.md" or member.name == "README.rst") and member.isfile():
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try:
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file_content = tar.extractfile(member).read().decode("utf-8")
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# extract readme
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readmes_set = set()
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readmes_set.add(file_content)
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repo_info["readmes"].update(readmes_set)
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except UnicodeDecodeError as e:
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tqdm.write(
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f"[-] UnicodeDecodeError in {member.name}, skipping: \n{e}"
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)
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except SyntaxError as e:
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tqdm.write(f"[-] SyntaxError in {member.name}, skipping: \n{e}")
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# 4. Extracting requirements.
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elif member.name == "requirements.txt" and member.isfile():
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try:
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lines = tar.extractfile(member).readlines().decode("utf-8")
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# extract readme
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requirements_set = extract_requirements(lines)
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repo_info["requirements"].update(requirements_set)
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except UnicodeDecodeError as e:
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tqdm.write(
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f"[-] UnicodeDecodeError in {member.name}, skipping: \n{e}"
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)
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except SyntaxError as e:
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tqdm.write(f"[-] SyntaxError in {member.name}, skipping: \n{e}")
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extracted_infos.append(repo_info)
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@@ -160,20 +95,11 @@ def extract_information(repos, headers=None):
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class RepoPipeline(Pipeline):
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"""
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A custom pipeline for generating series of embeddings of a repository.
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"""
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def __init__(self, github_token=None, *args, **kwargs):
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"""
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The initial method for pipeline.
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:param github_token: github_token
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:param args: args
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:param kwargs: kwargs
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"""
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super().__init__(*args, **kwargs)
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#
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self.github_token = github_token
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if self.github_token:
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print("[+] GitHub token set!")
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)
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def _sanitize_parameters(self, **pipeline_parameters):
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"""
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The method for splitting parameters.
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:param pipeline_parameters: parameters
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:return: different parameters of different periods.
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"""
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# The parameters of "preprocess" period.
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preprocess_parameters = {}
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if "github_token" in pipeline_parameters:
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preprocess_parameters["github_token"] = pipeline_parameters["github_token"]
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# The parameters of "forward" period.
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forward_parameters = {}
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if "max_length" in pipeline_parameters:
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forward_parameters["max_length"] = pipeline_parameters["max_length"]
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# The parameters of "postprocess" period.
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postprocess_parameters = {}
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return preprocess_parameters, forward_parameters, postprocess_parameters
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def preprocess(self, input_: Any, github_token=None) -> List:
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The method for "preprocess" period.
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:param input_: the input.
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:param github_token: github_token.
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:return: a list about repository information.
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"""
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# Making input to list format.
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if isinstance(input_, str):
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input_ = [input_]
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# Building
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headers = {"Accept": "application/vnd.github+json"}
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token = github_token or self.github_token
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if token:
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headers["Authorization"] = f"Bearer {token}"
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# Getting repositories' information: input_ means series of repositories
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extracted_infos = extract_information(input_, headers=headers)
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return extracted_infos
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def encode(self, text, max_length):
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"""
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The method for encoding the text to embedding by using UniXcoder.
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:param text: text.
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:param max_length: the max length.
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:return: the embedding of text.
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"""
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assert max_length < 1024
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# Getting the tokenizer.
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tokenizer = self.tokenizer
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tokens = (
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[tokenizer.cls_token, "<encoder-only>", tokenizer.sep_token]
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)
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tokens_id = tokenizer.convert_tokens_to_ids(tokens)
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source_ids = torch.tensor([tokens_id]).to(self.device)
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token_embeddings = self.model(source_ids)[0]
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sentence_embeddings = token_embeddings.mean(dim=1)
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return sentence_embeddings
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def generate_embeddings(self, text_sets, max_length):
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"""
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The method for generating embeddings of a text set.
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:param text_sets: text set.
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:param max_length: max length.
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:return: the embeddings of text set.
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"""
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assert max_length < 1024
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# Concat the embeddings of each sentence/text in vertical dimension.
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return torch.zeros((1, 768), device=self.device) \
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if
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else torch.cat([self.encode(text, max_length) for text in text_sets], dim=0)
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def _forward(self, extracted_infos: List, max_length=512) -> List:
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"""
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The method for "forward" period.
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:param extracted_infos: the information of repositories.
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:param max_length: max length.
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:return: the output of this pipeline.
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"""
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model_outputs = []
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# The number of repository.
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num_repos = len(extracted_infos)
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with tqdm(total=num_repos) as progress_bar:
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# For each repository
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info["requirement_embeddings"] = requirement_embeddings.cpu().numpy()
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info["mean_requirement_embedding"] = torch.mean(requirement_embeddings, dim=0).cpu().numpy()
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#
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tqdm.write(f"[*] Generating readme embeddings for {repo_name}")
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readme_embeddings = self.generate_embeddings(repo_info["readmes"], max_length)
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info["readme_embeddings"] = readme_embeddings.cpu().numpy()
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info["mean_readme_embedding"] = torch.mean(readme_embeddings, dim=0).cpu().numpy()
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# Repo-level mean embedding
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info["mean_repo_embedding"] = np.concatenate([
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info["mean_code_embedding"],
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info["mean_doc_embedding"],
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info["mean_requirement_embedding"],
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info["mean_readme_embedding"]
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], axis=0)
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# TODO Remove test
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info["code_embeddings_shape"] = info["code_embeddings"].shape
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info["doc_embeddings_shape"] = info["
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info["requirement_embeddings_shape"] = info["requirement_embeddings"].shape
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info["readme_embeddings_shape"] = info["readme_embeddings"].shape
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info["mean_repo_embedding_shape"] = info["mean_repo_embedding"].shape
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progress_bar.update(1)
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model_outputs.append(info)
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return model_outputs
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def postprocess(self, model_outputs: List, **postprocess_parameters: Dict) -> List:
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"""
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The method for "postprocess" period.
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:param model_outputs: the output of this pipeline.
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:param postprocess_parameters: the parameters of "postprocess" period.
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:return: model output.
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"""
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return model_outputs
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import ast
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import tarfile
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from ast import AsyncFunctionDef, ClassDef, FunctionDef, Module
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import torch
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import requests
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from transformers import Pipeline
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from tqdm.auto import tqdm
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def extract_code_and_docs(text: str):
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code_set = set()
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docs_set = set()
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root = ast.parse(text)
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return code_set, docs_set
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def get_metadata(repo_name, headers=None):
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api_url = f"https://api.github.com/repos/{repo_name}"
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tqdm.write(f"[+] Getting metadata for {repo_name}")
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try:
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def extract_information(repos, headers=None):
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extracted_infos = []
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for repo_name in tqdm(repos, disable=len(repos) <= 1):
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# Get metadata
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metadata = get_metadata(repo_name, headers=headers)
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repo_info = {
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"name": repo_name,
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if metadata.get("license"):
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repo_info["license"] = metadata["license"]["spdx_id"]
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# Download repo tarball bytes
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download_url = f"https://api.github.com/repos/{repo_name}/tarball"
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tqdm.write(f"[+] Downloading {repo_name}")
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try:
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tqdm.write(f"[-] Failed to download {repo_name}: {e}")
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continue
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# Extract python files and parse them
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tqdm.write(f"[+] Extracting {repo_name} info")
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with tarfile.open(fileobj=response.raw, mode="r|gz") as tar:
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for member in tar:
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if (member.name.endswith(".py") and member.isfile()) is False:
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continue
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try:
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file_content = tar.extractfile(member).read().decode("utf-8")
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code_set, docs_set = extract_code_and_docs(file_content)
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repo_info["codes"].update(code_set)
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repo_info["docs"].update(docs_set)
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except UnicodeDecodeError as e:
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tqdm.write(
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f"[-] UnicodeDecodeError in {member.name}, skipping: \n{e}"
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)
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except SyntaxError as e:
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tqdm.write(f"[-] SyntaxError in {member.name}, skipping: \n{e}")
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extracted_infos.append(repo_info)
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class RepoPipeline(Pipeline):
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def __init__(self, github_token=None, *args, **kwargs):
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super().__init__(*args, **kwargs)
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# Github token
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self.github_token = github_token
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if self.github_token:
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print("[+] GitHub token set!")
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)
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def _sanitize_parameters(self, **pipeline_parameters):
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preprocess_parameters = {}
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if "github_token" in pipeline_parameters:
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preprocess_parameters["github_token"] = pipeline_parameters["github_token"]
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forward_parameters = {}
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if "max_length" in pipeline_parameters:
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forward_parameters["max_length"] = pipeline_parameters["max_length"]
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postprocess_parameters = {}
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return preprocess_parameters, forward_parameters, postprocess_parameters
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def preprocess(self, input_: Any, github_token=None) -> List:
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# Making input to list format
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if isinstance(input_, str):
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input_ = [input_]
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# Building token
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headers = {"Accept": "application/vnd.github+json"}
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token = github_token or self.github_token
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if token:
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headers["Authorization"] = f"Bearer {token}"
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# Getting repositories' information: input_ means series of repositories
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extracted_infos = extract_information(input_, headers=headers)
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return extracted_infos
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def encode(self, text, max_length):
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assert max_length < 1024
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tokenizer = self.tokenizer
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tokens = (
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[tokenizer.cls_token, "<encoder-only>", tokenizer.sep_token]
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)
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tokens_id = tokenizer.convert_tokens_to_ids(tokens)
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source_ids = torch.tensor([tokens_id]).to(self.device)
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token_embeddings = self.model(source_ids)[0]
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sentence_embeddings = token_embeddings.mean(dim=1)
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return sentence_embeddings
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def generate_embeddings(self, text_sets, max_length):
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assert max_length < 1024
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return torch.zeros((1, 768), device=self.device) \
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if text_sets is None or len(text_sets) == 0 \
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else torch.cat([self.encode(text, max_length) for text in text_sets], dim=0)
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def _forward(self, extracted_infos: List, max_length=512) -> List:
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model_outputs = []
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num_repos = len(extracted_infos)
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with tqdm(total=num_repos) as progress_bar:
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# For each repository
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info["requirement_embeddings"] = requirement_embeddings.cpu().numpy()
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info["mean_requirement_embedding"] = torch.mean(requirement_embeddings, dim=0).cpu().numpy()
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+
# Requirement embeddings
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tqdm.write(f"[*] Generating readme embeddings for {repo_name}")
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readme_embeddings = self.generate_embeddings(repo_info["readmes"], max_length)
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info["readme_embeddings"] = readme_embeddings.cpu().numpy()
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info["mean_readme_embedding"] = torch.mean(readme_embeddings, dim=0).cpu().numpy()
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info["code_embeddings_shape"] = info["code_embeddings"].shape
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+
info["doc_embeddings_shape"] = info["doc_embeddings_shape"].shape
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progress_bar.update(1)
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model_outputs.append(info)
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return model_outputs
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def postprocess(self, model_outputs: List, **postprocess_parameters: Dict) -> List:
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return model_outputs
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+
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+
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