Faster text detection + layout

Switched model architecture for the text detection and layout models:

• 30% faster on GPU
• 4x faster on CPU
• 12x faster on MPS (M series macs)

Accuracy should be about the same, or slightly better, from my benchmarks.

v0.4.14: Merge pull request #141 from VikParuchuri/dev

New transformers version added a new kwarg to donut embeddings. This now handles and ignores that kwarg, and also slightly future-proofs in case this happens again.

Minor bugfixes

• Fix rotation and copy bugs

Fix image bugs

• Fix bugs with RGBA images
• Fix assert bug
• Add back in thumbnail method for resizing
• Slightly optimize segformer code

Change image resize

• Image resize from cv2 to PIL - cv2 caused benchmark regressions

OCR speedups

• Speed up base OCR model ~15-20%, and reduce memory usage by ~25% (can do higher batch sizes)
• Add static cache for compilation - torch.compile will result in another 15% speedup
• Other optimizations, like faster image resizing
• Bugfixes, like enabling different length language inputs for OCR (batching different docs with different languages together)

Processor improvements

• Remove unneeded format conversions
• Fix bug in OCR, where only one color channel was used for OCR - results should be better now
• Speed up layout/text detection a bit

OCR speedup

Cut OCR time in half. Combined with the previous release, OCR should now take about 40% as much time as it did before.

Significant speedup for layout, line detection

• Improve CPU postprocessing for line detection and layout - cut postprocessing time to 1/3 of original
• Unpin transformers version after investigating model performance

This should result in an ~2x speedup for layout and text detection. The effect will be most noticeable on GPU. I haven't fully benchmarked, though.

Bug fixes

• Fix memory leak with layout and text detection models and large batch sizes
• Improve ordering model generation slightly