wehub-resource-sync
e4dcfc49aa
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76 lines
2.6 KiB
Python
76 lines
2.6 KiB
Python
from __future__ import annotations
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import importlib.util
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import json
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from pathlib import Path
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import sys
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import types
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def _load_question_extractor_module():
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module_path = (
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Path(__file__).resolve().parents[2]
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/ "deeptutor"
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/ "tools"
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/ "question"
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/ "question_extractor.py"
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)
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stubbed_modules = {
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"deeptutor.services.config": {"get_agent_params": lambda *_args, **_kwargs: {}},
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"deeptutor.services.llm": {"complete": lambda *_args, **_kwargs: None},
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"deeptutor.services.llm.capabilities": {
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"supports_response_format": lambda *_args, **_kwargs: False
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},
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"deeptutor.services.llm.config": {"get_llm_config": lambda: None},
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"deeptutor.utils.json_parser": {"parse_json_response": lambda *_args, **_kwargs: {}},
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}
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original_modules: dict[str, types.ModuleType | None] = {}
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for module_name, attributes in stubbed_modules.items():
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original_modules[module_name] = sys.modules.get(module_name)
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module = types.ModuleType(module_name)
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for attr_name, value in attributes.items():
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setattr(module, attr_name, value)
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sys.modules[module_name] = module
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try:
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spec = importlib.util.spec_from_file_location("question_extractor_under_test", module_path)
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assert spec and spec.loader
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module = importlib.util.module_from_spec(spec)
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spec.loader.exec_module(module)
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return module
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finally:
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for module_name, original_module in original_modules.items():
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if original_module is None:
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sys.modules.pop(module_name, None)
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else:
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sys.modules[module_name] = original_module
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def test_load_parsed_paper_supports_nested_hybrid_auto_output(tmp_path: Path) -> None:
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question_extractor = _load_question_extractor_module()
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paper_dir = tmp_path / "mimic_exam"
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parsed_dir = paper_dir / "hybrid_auto"
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images_dir = parsed_dir / "images"
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images_dir.mkdir(parents=True)
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markdown_path = parsed_dir / "exam.md"
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markdown_path.write_text("# Exam content", encoding="utf-8")
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content_list_path = parsed_dir / "exam_content_list.json"
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content_list_path.write_text(
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json.dumps([{"type": "text", "text": "Question 1"}], ensure_ascii=False),
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encoding="utf-8",
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)
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(images_dir / "figure.png").write_text("image-bytes", encoding="utf-8")
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markdown_content, content_list, discovered_images_dir = question_extractor.load_parsed_paper(
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paper_dir
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)
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assert markdown_content == "# Exam content"
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assert content_list == [{"type": "text", "text": "Question 1"}]
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assert discovered_images_dir == images_dir
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