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140 lines
5.0 KiB
Python
140 lines
5.0 KiB
Python
"""Golden-output tests for the PDF scraper (Phase 2 port).
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The golden trees under tests/golden/phase2/pdf*/ were captured from the
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PRE-DocumentSkillBuilder code; these tests prove the port is byte-identical.
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The fixture exercises every build path PDF supports: page_number-keyed pages,
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headings list (no top-level heading), multi-language code samples (incl.
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>500 chars + quality ordering), the code_blocks compat key, extracted_images
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(pre-saved) and legacy raw-bytes images, pattern keywords, language stats,
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quality statistics, and all three categorization paths (single-source,
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chapters, keywords incl. the empty-category and "other" buckets).
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"""
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import copy
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from tests.phase2_golden_utils import assert_matches_golden, build_snapshot
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LONG_CODE = "def long_example():\n" + "\n".join(f" x{i} = {i}" for i in range(60))
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PAGES = [
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{
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"page_number": 1,
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"text": "Welcome to the project. This page explains setup and installation.",
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"headings": [
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{"level": "h1", "text": "Getting Started Guide"},
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{"level": "h2", "text": "Installation Steps"},
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],
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"code_samples": [
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{"language": "python", "code": "print('hello')", "quality_score": 8.5},
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{"language": "bash", "code": "pip install thing", "quality_score": 6.0},
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],
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# pdf_extractor_poc format: already saved to disk, only linked
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"extracted_images": [
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{
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"filename": "manual_page1_img1.png",
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"page_number": 1,
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"width": 100,
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"height": 80,
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}
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],
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},
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{
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"page_number": 2,
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"text": "All endpoints are documented here.",
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"headings": [{"level": "h2", "text": "API Usage"}],
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"code_samples": [
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{"language": "python", "code": LONG_CODE, "quality_score": 9.5},
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],
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# Legacy format: raw bytes saved to assets/ during the build
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"images": [{"index": 0, "data": b"\x89PNG-fake-bytes"}],
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},
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{
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"page_number": 3,
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"text": "Common errors and how to fix them.",
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"headings": [{"level": "h1", "text": "Troubleshooting"}],
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# code_blocks (not code_samples) exercises the compat fallback
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"code_blocks": [{"language": "text", "code": "ERROR 42: retry the request"}],
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},
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]
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def _extracted_data() -> dict:
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return {
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"pages": copy.deepcopy(PAGES),
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"total_pages": 3,
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"total_code_blocks": 4,
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"total_images": 2,
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"metadata": {"title": "The Manual", "author": "Jane Doe"},
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"languages_detected": {"python": 2, "bash": 1, "text": 1},
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"quality_statistics": {"average_quality": 7.4, "valid_code_blocks": 4},
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}
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def test_pdf_build_matches_golden(tmp_path):
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"""pdf_path set → single-category fast path."""
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from skill_seekers.cli.pdf_scraper import PDFToSkillConverter
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converter = PDFToSkillConverter(
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{
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"name": "golden_pdf",
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"description": "Use when testing the pdf golden build",
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"pdf_path": "fixtures/manual.pdf",
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"output_dir": str(tmp_path / "skill"),
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}
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)
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converter.extracted_data = _extracted_data()
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assert_matches_golden(build_snapshot(converter), "pdf")
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def test_pdf_keyword_categorization_matches_golden(tmp_path):
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"""No pdf_path, no chapters → keyword categorization (multi-source).
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Covers the scored-match, "other"-bucket, and empty-category paths.
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"""
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from skill_seekers.cli.pdf_scraper import PDFToSkillConverter
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converter = PDFToSkillConverter(
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{
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"name": "golden_pdf_kw",
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"description": "Use when testing keyword categorization",
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"output_dir": str(tmp_path / "skill"),
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"categories": {
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"setup": ["setup", "installation"],
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"api": ["endpoints"],
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"deployment": ["kubernetes"], # stays empty
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},
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}
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)
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converter.extracted_data = _extracted_data()
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assert_matches_golden(build_snapshot(converter), "pdf_kw")
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def test_pdf_chapter_categorization_matches_golden(tmp_path):
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"""No pdf_path, chapters present → chapter-range categorization,
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incl. the uncategorized ("Additional Content") bucket."""
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from skill_seekers.cli.pdf_scraper import PDFToSkillConverter
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converter = PDFToSkillConverter(
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{
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"name": "golden_pdf_ch",
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"description": "Use when testing chapter categorization",
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"output_dir": str(tmp_path / "skill"),
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}
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)
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data = _extracted_data()
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# Page 4 falls outside every chapter range → "uncategorized" bucket.
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data["pages"].append(
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{
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"page_number": 4,
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"text": "Appendix material outside any chapter.",
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"headings": [],
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}
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)
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data["total_pages"] = 4
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data["chapters"] = [
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{"title": "Chapter 1: Basics", "start_page": 1, "end_page": 2},
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{"title": "Chapter 2: Advanced", "start_page": 3, "end_page": 3},
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]
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converter.extracted_data = data
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assert_matches_golden(build_snapshot(converter), "pdf_chapters")
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