* add tests for validating strategy
* refactor into determine_pdf_strategy function
* refactor pdf strategies into strategies
* remove commented out code
* remove unreachable code
* add in handling for image types
* a little more refactoring
* import ocr partioning for images
* catch warnings, partition type for valid strategies
* fallback to ocr_only from fast
* fallback logic for hi_res
* test for fallback to ocr only
* fallback logic ofr ocr_only
* more tests for fallback logic
* update doc strings
* version and changelog
* linting, linting, linting
* update docs to include notes about strategy
* fix typos
* change back patched filename
* switch to using PDF objects
* linting, linting, linting
* couple more tweaks
* added test for chevron-page
* version and changelog
* linting, linting, linting
* now processing 4 files
* function to check if pdf is extractable
* add fallback logic for unextractable pdfs
* tests for docs with copy protection
* add test for unprocessable pdf
* update docs
* changelog and version
* update logic for images; reset file before proceeding
* 3 files for api tests
* docs update
* group broken paragraphs with fast strategy
* changelog and version
* fix broken tests for text.py
* formatting for paragraph pattern re
* fix test
* fix whitespace substitution
* one more test tweak
* blurb to account for short lines
* fix for shorter paragraphs
* update changelog
* remove extra line break from auto
* retrigger ci
* trying skipping azure
* skip azure (test)
* updated github and azure fixtures
* update slack fixture
Adds a "fast" strategy for partitioning PDFs that uses pdfminer. The default strategy is "hi_res" and is the original partitioning logic that uses detectron2. If detectron2 is not available and the "hi_res" strategy is selected, partition_pdf fallsback to using the "fast" strategy. The implementation uses pdfminer because that's already installed as a dependency with the local-inference extra. There are other options for accomplishing this as well, but they would entail adding a new dependency. The "fast" strategy substantially speeds up processing.
* Apply import sorting
ruff . --select I --fix
* Remove unnecessary open mode parameter
ruff . --select UP015 --fix
* Use f-string formatting rather than .format
* Remove extraneous parentheses
Also use "" instead of str()
* Resolve missing trailing commas
ruff . --select COM --fix
* Rewrite list() and dict() calls using literals
ruff . --select C4 --fix
* Add () to pytest.fixture, use tuples for parametrize, etc.
ruff . --select PT --fix
* Simplify code: merge conditionals, context managers
ruff . --select SIM --fix
* Import without unnecessary alias
ruff . --select PLR0402 --fix
* Apply formatting via black
* Rewrite ValueError somewhat
Slightly unrelated to the rest of the PR
* Apply formatting to tests via black
* Update expected exception message to match
0d81564
* Satisfy E501 line too long in test
* Update changelog & version
* Add ruff to make tidy and test deps
* Run 'make tidy'
* Update changelog & version
* Update changelog & version
* Add ruff to 'check' target
Doing so required me to also fix some non-auto-fixable issues. Two of them I fixed with a noqa: SIM115, but especially the one in __init__ may need some attention. That said, that refactor is out of scope of this PR.
* page breaks for pptx
* added page breaks for image/pdf
* tests for images with page breaks
* page breaks for html documents
* linting, linting, linting
* changelog and bump version
* update docs
* fix typo
* refactor reusable code to common.py
* add type back in
Adds partition_image to partition image file types, which is integrated into the partition brick. This relies on the 0.2.2 version of unstructured-inference.
