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121 lines
4.3 KiB
Python
121 lines
4.3 KiB
Python
"""Test the client module of the scraper app."""
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from typing import Any
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import pytest
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from dirty_equals import HasLen, IsDatetime, IsInstance, IsPositiveInt
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from pydantic import Json, TypeAdapter
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from app.scraper.models import SourceGraphRepoData
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@pytest.fixture()
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def source_graph_matched_repos_data() -> Json[Any]:
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"""Return the sample data of the matched repositories."""
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return [
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{
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"type": "repo",
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"repositoryID": 55636527,
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"repository": "github.com/tiangolo/sqlmodel",
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"repoStars": 10277,
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"repoLastFetched": "2023-07-31T18:47:22.875731Z",
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"description": (
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"SQL databases in Python, designed "
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"for simplicity, compatibility, "
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"and robustness."
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),
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"metadata": {
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"fastapi": "null",
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"json": "null",
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"json-schema": "null",
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"pydantic": "null",
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"python": "null",
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"sql": "null",
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"sqlalchemy": "null",
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},
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},
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{
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"type": "repo",
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"repositoryID": 59434622,
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"repository": "github.com/reflex-dev/reflex",
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"repoStars": 10061,
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"repoLastFetched": "2023-07-31T08:58:42.692906Z",
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"description": "(Previously Pynecone) 🕸 Web apps in pure Python 🐍",
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},
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{
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"type": "repo",
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"repositoryID": 42982149,
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"repository": "github.com/PaddlePaddle/PaddleNLP",
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"repoStars": 9804,
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"repoLastFetched": "2023-07-31T16:48:08.839209Z",
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"description": (
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"👑 Easy-to-use and powerful NLP library with 🤗 "
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"Awesome model zoo, supporting wide-range of NLP tasks "
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"from research to industrial applications, including"
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" 🗂Text Classification, 🔍 Neural Search, ❓ Question "
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"Answering, ℹ️ Information Extraction, "
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"📄 Document Intelligence, 💌 Sentiment Analysis etc."
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),
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"metadata": {
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"bert": "null",
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"embedding": "null",
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"ernie": "null",
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"information-extraction": "null",
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"neural-search": "null",
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"nlp": "null",
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"paddlenlp": "null",
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"pretrained-models": "null",
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"question-answering": "null",
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"search-engine": "null",
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"semantic-analysis": "null",
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"sentiment-analysis": "null",
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"seq2seq": "null",
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"transformer": "null",
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"transformers": "null",
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"uie": "null",
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},
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},
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{
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"type": "repo",
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"repositoryID": 36246068,
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"repository": "github.com/realpython/materials",
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"repoStars": 4359,
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"repoLastFetched": "2023-07-31T05:15:16.993896Z",
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},
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]
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def test_source_graph_repo_data(source_graph_matched_repos_data: Json[Any]) -> None:
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"""Test the SourceGraphRepoData deserialization."""
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assert source_graph_matched_repos_data == HasLen(4)
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_SourceGraphRepoDataListValidator = TypeAdapter(list[SourceGraphRepoData])
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repos_parsed = _SourceGraphRepoDataListValidator.validate_python(
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source_graph_matched_repos_data
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)
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assert repos_parsed == HasLen(4)
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assert all(repo == IsInstance[SourceGraphRepoData] for repo in repos_parsed)
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assert all(
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repo.repo_id == repo_data["repositoryID"]
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for repo, repo_data in zip(
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repos_parsed, source_graph_matched_repos_data, strict=True
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)
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)
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assert all(
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repo.repo_handle == repo_data["repository"]
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for repo, repo_data in zip(
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repos_parsed, source_graph_matched_repos_data, strict=True
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)
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)
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assert all(
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repo.stars == IsPositiveInt and repo.stars == repo_data["repoStars"]
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for repo, repo_data in zip(
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repos_parsed, source_graph_matched_repos_data, strict=True
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)
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)
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assert all(
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str(repo.repo_url) == f"https://{repo_data['repository']}"
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for repo, repo_data in zip(
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repos_parsed, source_graph_matched_repos_data, strict=True
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)
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)
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assert all(repo.last_fetched_at == IsDatetime for repo in repos_parsed)
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