The purpose of this PR is to refactor OCR-related modules to reduce
unnecessary module imports to avoid potential issues (most likely due to
a "circular import").
### Summary
- add `inference_utils` module
(unstructured/partition/pdf_image/inference_utils.py) to define
unstructured-inference library related utility functions, which will
reduce importing unstructured-inference library functions in other files
- add `conftest.py` in `test_unstructured/partition/pdf_image/`
directory to define fixtures that are available to all tests in the same
directory and its subdirectories
### Testing
CI should pass
This is nice to natively support both Tesseract and Paddle. However, one
might already use another OCR and might want to keep using it (for
quality reasons, for cost reasons etc...).
This PR adds the ability for the user to specify its own OCR agent
implementation that is then called by unstructured.
I am new to unstructured so don't hesitate to let me know if you would
prefer this being done differently and I will rework the PR.
---------
Co-authored-by: Yao You <theyaoyou@gmail.com>
Co-authored-by: Yao You <yao@unstructured.io>
.heic files are an image filetype we have not supported.
#### Testing
```
from unstructured.partition.image import partition_image
png_filename = "example-docs/DA-1p.png"
heic_filename = "example-docs/DA-1p.heic"
png_elements = partition_image(png_filename, strategy="hi_res")
heic_elements = partition_image(heic_filename, strategy="hi_res")
for i in range(len(heic_elements)):
print(heic_elements[i].text == png_elements[i].text)
```
---------
Co-authored-by: christinestraub <christinemstraub@gmail.com>
This PR is the last in a series of PRs for refactoring and fixing the
language parameters (`languages` and `ocr_languages` so we can address
incorrect input by users. See #2293
It is recommended to go though this PR commit-by-commit and note the
commit message. The most significant commit is "update
check_languages..."
- there are multiple places setting the default `hi_res_model_name` in
both `unstructured` and `unstructured-inference`
- they lead to inconsistency and unexpected behaviors
- this fix removes a helper in `unstructured` that tries to set the
default hi_res layout detection model; instead we rely on the
`unstructured-inference` to provide that default when no explicit model
name is passed in
## test
```bash
UNSTRUCTURED_INCLUDE_DEBUG_METADATA=true ipython
```
```python
from unstructured.partition.auto import partition
# find a pdf file
elements = partition("foo.pdf", strategy="hi_res")
assert elements[0].metadata.detection_origin == "yolox"
```
---------
Co-authored-by: ryannikolaidis <1208590+ryannikolaidis@users.noreply.github.com>
Co-authored-by: badGarnet <badGarnet@users.noreply.github.com>
We have added a new version of chipper (Chipperv3), which needs to allow
unstructured to effective work with all the current Chipper versions.
This implies resizing images with the appropriate resolution and make
sure that Chipper elements are not sorted by unstructured.
In addition, it seems that PDFMiner is being called when calling
Chipper, which adds repeated elements from Chipper and PDFMiner.
To evaluate this PR, you can test the code below with the attached PDF.
The code writes a JSON file with the generated elements. The output can
be examined with `cat out.un.json | python -m json.tool`. There are
three things to check:
1. The size of the image passed to Chipper, which can be identiied in
the layout_height and layout_width attributes, which should have values
3301 and 2550 as shown in the example below:
```
[
{
"element_id": "c0493a7872f227e4172c4192c5f48a06",
"metadata": {
"coordinates": {
"layout_height": 3301,
"layout_width": 2550,
```
2. There should be no repeated elements.
3. Order should be closer to reading order.
The script to run Chipper from unstructured is:
```
from unstructured import __version__
print(__version__.__version__)
import json
from unstructured.partition.auto import partition
from unstructured.staging.base import elements_to_json
elements = json.loads(elements_to_json(partition("Huang_Improving_Table_Structure_Recognition_With_Visual-Alignment_Sequential_Coordinate_Modeling_CVPR_2023_paper-p6.pdf", strategy="hi_res", model_name="chipperv3")))
with open('out.un.json', 'w') as w:
json.dump(elements, w)
```
[Huang_Improving_Table_Structure_Recognition_With_Visual-Alignment_Sequential_Coordinate_Modeling_CVPR_2023_paper-p6.pdf](https://github.com/Unstructured-IO/unstructured/files/13817273/Huang_Improving_Table_Structure_Recognition_With_Visual-Alignment_Sequential_Coordinate_Modeling_CVPR_2023_paper-p6.pdf)
---------
Co-authored-by: Antonio Jimeno Yepes <antonio@unstructured.io>
Closes#2320 .
### Summary
In certain circumstances, adjusting the image block crop padding can
improve image block extraction by preventing extracted image blocks from
being clipped.
### Testing
- PDF:
[LM339-D_2-2.pdf](https://github.com/Unstructured-IO/unstructured/files/13968952/LM339-D_2-2.pdf)
- Set two environment variables
`EXTRACT_IMAGE_BLOCK_CROP_HORIZONTAL_PAD` and
`EXTRACT_IMAGE_BLOCK_CROP_VERTICAL_PAD`
(e.g. `EXTRACT_IMAGE_BLOCK_CROP_HORIZONTAL_PAD = 40`,
`EXTRACT_IMAGE_BLOCK_CROP_VERTICAL_PAD = 20`
```
elements = partition_pdf(
filename="LM339-D_2-2.pdf",
extract_image_block_types=["image"],
)
```
### Summary
Adds support for bitmap images (`.bmp`) in both file detection and
partitioning. Bitmap images will be processed with `partition_image`
just like JPGs and PNGs.
### Testing
```python
from unstructured.file_utils.filetype import detect_filetype
from unstructured.partition.auto import partition
from PIL import Image
filename = "example-docs/layout-parser-paper-with-table.jpg"
bmp_filename = "~/tmp/ayout-parser-paper-with-table.bmp"
img = Image.open(filename)
img.save(bmp_filename)
detect_filetype(filename=bmp_filename) # Should be FileType.BMP
elements = partition(filename=bmp_filename)
```
### Summary
The goal of this PR is to keep all image elements when using "hi_res"
strategy. Previously, `Image` elements with small chunks of text were
ignored unless the image block extraction parameters
(`extract_images_in_pdf` or `extract_image_block_types`) were specified.
Now, all image elements are kept regardless of whether the image block
extraction parameters are specified.
### Testing
- on `main` branch,
```
elements = partition_pdf(
filename="example-docs/embedded-images.pdf",
strategy="hi_res",
)
image_elements = [el for el in elements if el.category == ElementType.IMAGE]
print("number of image elements: ", len(image_elements))
```
The above code will display `number of image elements: 0`.
- on this `feature` branch,
The same code will display `number of image elements: 3`
---------
Co-authored-by: ryannikolaidis <1208590+ryannikolaidis@users.noreply.github.com>
Co-authored-by: christinestraub <christinestraub@users.noreply.github.com>
Currently, we're using different kwarg names in partition() and
partition_pdf(), which has implications for the API since it goes
through partition().
### Summary
- rename `extract_element_types` -> `extract_image_block_types`
- rename `image_output_dir_path` to `extract_image_block_output_dir`
- rename `extract_to_payload` -> `extract_image_block_to_payload`
- rename `pdf_extract_images` -> `extract_images_in_pdf` in
`partition.auto`
- add unit tests to test element extraction for `pdf/image` via
`partition.auto`
### Testing
CI should pass.
Closes#2323.
### Summary
- update logic to return "hi_res" if either `extract_images_in_pdf` or
`extract_element_types` is set
- refactor: remove unused `file` parameter from
`determine_pdf_or_image_strategy()`
### Testing
```
from unstructured.partition.pdf import partition_pdf
elements = partition_pdf(
filename="example-docs/embedded-images-tables.pdf",
extract_element_types=["Image"],
extract_to_payload=True,
)
image_elements = [el for el in elements if el.category == ElementType.IMAGE]
print(image_elements)
```
Closes#2302.
### Summary
- add functionality to get a Base64 encoded string from a PIL image
- store base64 encoded image data in two metadata fields: `image_base64`
and `image_mime_type`
- update the "image element filter" logic to keep all image elements in
the output if a user specifies image extraction
### Testing
```
from unstructured.partition.pdf import partition_pdf
elements = partition_pdf(
filename="example-docs/embedded-images-tables.pdf",
strategy="hi_res",
extract_element_types=["Image", "Table"],
extract_to_payload=True,
)
```
or
```
from unstructured.partition.auto import partition
elements = partition(
filename="example-docs/embedded-images-tables.pdf",
strategy="hi_res",
pdf_extract_element_types=["Image", "Table"],
pdf_extract_to_payload=True,
)
```
Closes#2160
Explicitly adds `hi_res_model_name` as kwarg to relevant functions and
notes that `model_name` is to be deprecated.
Testing:
```
from unstructured.partition.auto import partition
filename = "example-docs/DA-1p.pdf"
elements = partition(filename, strategy="hi_res", hi_res_model_name="yolox")
```
---------
Co-authored-by: cragwolfe <crag@unstructured.io>
Co-authored-by: Steve Canny <stcanny@gmail.com>
Co-authored-by: Christine Straub <christinemstraub@gmail.com>
Co-authored-by: Yao You <yao@unstructured.io>
Co-authored-by: Yao You <theyaoyou@gmail.com>
This PR addresses
[CORE-2969](https://unstructured-ai.atlassian.net/browse/CORE-2969)
- pdfminer sometimes fail to decode text in an pdf file and returns cid
codes as text
- now those text will be considered invalid and be replaced with ocr
results in `hi_res` mode
## test
This PR adds unit test for the utility functions. In addition the file
below would return elements with text in cid code on main but proper
ascii text with this PR:
[005-CISA-AA22-076-Strengthening-Cybersecurity-p1-p4.pdf](https://github.com/Unstructured-IO/unstructured/files/13662984/005-CISA-AA22-076-Strengthening-Cybersecurity-p1-p4.pdf)
This change improves both cct accuracy and %missing scores:
**before:**
```
metric average sample_sd population_sd count
--------------------------------------------------
cct-accuracy 0.681 0.267 0.266 105
cct-%missing 0.086 0.159 0.159 105
```
**after:**
```
metric average sample_sd population_sd count
--------------------------------------------------
cct-accuracy 0.697 0.251 0.250 105
cct-%missing 0.071 0.123 0.122 105
```
[CORE-2969]:
https://unstructured-ai.atlassian.net/browse/CORE-2969?atlOrigin=eyJpIjoiNWRkNTljNzYxNjVmNDY3MDlhMDU5Y2ZhYzA5YTRkZjUiLCJwIjoiZ2l0aHViLWNvbS1KU1cifQ
---------
Co-authored-by: ryannikolaidis <1208590+ryannikolaidis@users.noreply.github.com>
Co-authored-by: badGarnet <badGarnet@users.noreply.github.com>
Co-authored-by: christinestraub <christinemstraub@gmail.com>
closes#2222.
### Summary
The "table" elements are saved as `table-<pageN>-<tableN>.jpg`. This
filename is presented in the `image_path` metadata field for the Table
element. The default would be to not do this.
### Testing
PDF: [124_PDFsam_Basel III - Finalising post-crisis
reforms.pdf](https://github.com/Unstructured-IO/unstructured/files/13591714/124_PDFsam_Basel.III.-.Finalising.post-crisis.reforms.pdf)
```
elements = partition_pdf(
filename="124_PDFsam_Basel III - Finalising post-crisis reforms.pdf",
strategy="hi_res",
infer_table_structure=True,
extract_element_types=['Table'],
)
```
### Summary
This PR is the second part of the "image extraction" refactor to move it
from unstructured-inference repo to unstructured repo, the first part is
done in
https://github.com/Unstructured-IO/unstructured-inference/pull/299. This
PR adds logic to support extracting images.
### Testing
`git clone -b refactor/remove_image_extraction_code --single-branch
https://github.com/Unstructured-IO/unstructured-inference.git && cd
unstructured-inference && pip install -e . && cd ../`
```
elements = partition_pdf(
filename="example-docs/embedded-images.pdf",
strategy="hi_res",
extract_images_in_pdf=True,
)
print("\n\n".join([str(el) for el in elements]))
```
### Summary
This PR is the second part of `pdfminer` refactor to move it from
`unstructured-inference` repo to `unstructured` repo, the first part is
done in
https://github.com/Unstructured-IO/unstructured-inference/pull/294. This
PR adds logic to merge the extracted layout with the inferred layout.
The updated workflow for the `hi_res` strategy:
* pass the document (as data/filename) to the `inference` repo to get
`inferred_layout` (DocumentLayout)
* pass the `inferred_layout` returned from the `inference` repo and the
document (as data/filename) to the `pdfminer_processing` module, which
first opens the document (create temp file/dir as needed), and splits
the document by pages
* if is_image is `True`, return the passed
inferred_layout(DocumentLayout)
* if is_image is `False`:
* get extracted_layout (TextRegions) from the passed
document(data/filename) by pdfminer
* merge `extracted_layout` (TextRegions) with the passed
`inferred_layout` (DocumentLayout)
* return the `inferred_layout `(DocumentLayout) with updated elements
(all merged LayoutElements) as merged_layout (DocumentLayout)
* pass merged_layout and the document (as data/filename) to the `OCR`
module, which first opens the document (create temp file/dir as needed),
and splits the document by pages (convert PDF pages to image pages for
PDF file)
### Note
This PR also fixes issue #2164 by using functionality similar to the one
implemented in the `fast` strategy workflow when extracting elements by
`pdfminer`.
### TODO
* image extraction refactor to move it from `unstructured-inference`
repo to `unstructured` repo
* improving natural reading order by applying the current default
`xycut` sorting to the elements extracted by `pdfminer`
### Summary
Add a procedure to repair PDF when the PDF structure is invalid for
`PDFminer` to process.
This PR handles two cases of `PSSyntaxError Invalid dictionary
construct: ...`:
* PDFminer open entire document and create pages generator on
`PDFPage.get_pages(fp)`: [sentry log
example](https://unstructuredio.sentry.io/issues/4655715023/?alert_rule_id=14681339&alert_type=issue¬ification_uuid=d8db4cf4-686f-4504-8a22-74a79a8e966f&project=4505909127086080&referrer=slack)
* PDFminer's interpreter process a single page on
`interpreter.process_page(page)`: [sentry log
example](https://unstructuredio.sentry.io/issues/4655898781/?referrer=slack¬ification_uuid=0d929d48-f490-4db8-8dad-5d431c8460bc&alert_rule_id=14681339&alert_type=issue)
**Additional tech details:**
* Add new dependency `pikepdf` in `requirements/extra-pdf-image.in`,
which is used for repairing PDF.
* Add new denpendenct `pypdf` in `requirements/extra-pdf-image.in`,
which is used to find the error page from entire document by reading the
PDF file again (can't find a way to split pdf in PDFminer).
* Refactor the `is null` check for `get_uris_from_annots`, since the
root cause is that `get_uris` passed a None `annots` to
`get_uris_from_annots`, so the Null check should happen in `get_uris`.
* Add more type protection in `get_uris_from_annots` when using any
`PDFObjRef.resolve()` as `dict` (it could still be a `PDFObjRef`). This
should fix :
* https://github.com/Unstructured-IO/unstructured/issues/1922 where
`annotation_dict` is a `PDFObjRef`
* https://github.com/Unstructured-IO/unstructured/issues/1921 where
`rect` is a `PDFObjRef`
### Test
Added three test files (both are larger than 500 KB) for unittests to
test:
* Repair entire doc
* Repair one page
* Reprocess failure after repairing one page (just return the elements
before error page in this case).
* Also seems like splitting the document into smaller pages could fix
this problem, but not sure why. For example, I saw error from reprocess
in the whole
[cancer.pdf](https://github.com/Unstructured-IO/unstructured/files/13461616/cancer.pdf)
doc, but no error when i split the pdf by error page....
* tested if i can repair the entire doc again in this case, saw other
error which means repairing is not helping imo
* PDFminer can process the whole doc after pikepdf only repaired the
entire doc in the first place, but we can't repair by pages in this way
---------
Co-authored-by: cragwolfe <crag@unstructured.io>
Closes#2059.
We've found some pdfs that throw an error in pdfminer. These files use a
ICCBased color profile but do not include an expected value `N`. As a
workaround, we can wrap pdfminer and drop any colorspace info, since we
don't need to render the document.
To verify, try to partition the document in the linked issue.
```
elements = partition(filename="google-2023-environmental-report_condensed.pdf", strategy="fast")
```
---------
Co-authored-by: cragwolfe <crag@unstructured.io>
Closes#2038.
### Summary
The `fast` strategy should not fall back to a more expensive strategy.
### Testing
For
[9493801-p17.pdf](https://github.com/Unstructured-IO/unstructured/files/13292884/9493801-p17.pdf),
the following code should return an empty list.
```
elements = partition(filename=filename, strategy="fast")
```
---------
Co-authored-by: ryannikolaidis <1208590+ryannikolaidis@users.noreply.github.com>
Co-authored-by: christinestraub <christinestraub@users.noreply.github.com>
### Summary
Closes#2011
`languages` was missing from the metadata when partitioning pdfs via
`hi_res` and `fast` strategies and missing from image partitions via
`hi_res`. This PR adds `languages` to the relevant function calls so it
is included in the resulting elements.
### Testing
On the main branch, `partition_image` will include `languages` when
`strategy='ocr_only'`, but not when `strategy='hi_res'`:
```
filename = "example-docs/english-and-korean.png"
from unstructured.partition.image import partition_image
elements = partition_image(filename, strategy="ocr_only", languages=['eng', 'kor'])
elements[0].metadata.languages
elements = partition_image(filename, strategy="hi_res", languages=['eng', 'kor'])
elements[0].metadata.languages
```
For `partition_pdf`, `'ocr_only'` will include `languages` in the
metadata, but `'fast'` and `'hi_res'` will not.
```
filename = "example-docs/korean-text-with-tables.pdf"
from unstructured.partition.pdf import partition_pdf
elements = partition_pdf(filename, strategy="ocr_only", languages=['kor'])
elements[0].metadata.languages
elements = partition_pdf(filename, strategy="fast", languages=['kor'])
elements[0].metadata.languages
elements = partition_pdf(filename, strategy="hi_res", languages=['kor'])
elements[0].metadata.languages
```
On this branch, `languages` is included in the metadata regardless of
strategy
---------
Co-authored-by: ryannikolaidis <1208590+ryannikolaidis@users.noreply.github.com>
Co-authored-by: Coniferish <Coniferish@users.noreply.github.com>
Closes#2027
Tables or pages that contain only numbers are returned as floats in a
pandas.DataFrame when the image or page is converted from
`.image_to_data()`. An AttributeError was raised downstream when trying
to `.strip()` the floats. This update converts those floats if needed
and otherwise strips the text.
Testing (note: the document used for testing is new, so you will have to
copy it to the main branch in order to see that this snippet raises an
AttributeError on the main branch, but works on this branch)
```
from unstructured.partition.pdf import partition_pdf
filename = "example-docs/all-number-table.pdf"
partition_pdf(filename, strategy="ocr_only")
```
---------
Co-authored-by: cragwolfe <crag@unstructured.io>
### Summary
- add constants for element type
- replace the `TYPE_TO_TEXT_ELEMENT_MAP` dictionary using the
`ElementType` constants
- replace element type strings using the constants
### Testing
CI should pass.
Fix TypeError: string indices must be integers. The `annotation_dict`
variable is conditioned to be `None` if instance type is not dict. Then
we add logic to skip the attempt if the value is `None`.
### Summary
Update `ocr_only` strategy in `partition_pdf()`. This PR adds the
functionality to get accurate coordinate data when partitioning PDFs and
Images with the `ocr_only` strategy.
- Add functionality to perform OCR region grouping based on the OCR text
taken from `pytesseract.image_to_string()`
- Add functionality to get layout elements from OCR regions (ocr_layout)
for both `tesseract` and `paddle`
- Add functionality to determine the `source` of merged text regions
when merging text regions in `merge_text_regions()`
- Merge multiple test functions related to "ocr_only" strategy into
`test_partition_pdf_with_ocr_only_strategy()`
- This PR also fixes [issue
#1792](https://github.com/Unstructured-IO/unstructured/issues/1792)
### Evaluation
```
# Image
PYTHONPATH=. python examples/custom-layout-order/evaluate_natural_reading_order.py example-docs/double-column-A.jpg ocr_only xy-cut image
# PDF
PYTHONPATH=. python examples/custom-layout-order/evaluate_natural_reading_order.py example-docs/multi-column-2p.pdf ocr_only xy-cut pdf
```
### Test
- **Before update**
All elements have the same coordinate data
- **After update**
All elements have accurate coordinate data
---------
Co-authored-by: ryannikolaidis <1208590+ryannikolaidis@users.noreply.github.com>
Co-authored-by: christinestraub <christinestraub@users.noreply.github.com>
This PR introduces `clean_pdfminer_inner_elements` , which deletes
pdfminer elements inside other detection origins such as YoloX or
detectron.
This function returns the clean document.
Also, the ingest-test fixtures were updated to reflect the new standard
output.
The best way to check that this function is working properly is check
the new test `test_clean_pdfminer_inner_elements` in
`test_unstructured/partition/utils/test_processing_elements.py`
---------
Co-authored-by: Roman Isecke <roman@unstructured.io>
Co-authored-by: ryannikolaidis <1208590+ryannikolaidis@users.noreply.github.com>
Co-authored-by: rbiseck3 <rbiseck3@users.noreply.github.com>
Co-authored-by: Roman Isecke <136338424+rbiseck3@users.noreply.github.com>
- yolox has better recall than yolox_quantized, the current default
model, for table detection
- update logic so that when `infer_table_structure=True` the default
model is `yolox` instead of `yolox_quantized`
- user can still override the default by passing in a `model_name` or
set the env variable `UNSTRUCTURED_HI_RES_MODEL_NAME`
## Test:
Partition the attached file with
```python
from unstructured.partition.pdf import partition_pdf
yolox_elements = partition_pdf(filename, strategy="hi_re", infer_table_structure=True)
yolox_quantized_elements = partition_pdf(filename, strategy="hi_re", infer_table_structure=True, model_name="yolox_quantized")
```
Compare the table elements between those two and yolox (default)
elements should have more complete table.
[AK_AK-PERS_CAFR_2008_3.pdf](https://github.com/Unstructured-IO/unstructured/files/13191198/AK_AK-PERS_CAFR_2008_3.pdf)
Closes
[#1859](https://github.com/Unstructured-IO/unstructured/issues/1859).
* **Fixes elements partitioned from an image file missing certain
metadata** Metadata for image files, like file type, was being handled
differently from other file types. This caused a bug where other
metadata, like the file name, was being missed. This change brought
metadata handling for image files to be more in line with the handling
for other file types so that file name and other metadata fields are
being captured.
Additionally:
* Added test to verify filename is being captured in metadata
* Cleaned up `CHANGELOG.md` formatting
#### Testing:
The following produces output `None` on `main`, but outputs the filename
`layout-parser-paper-fast.jpg` on this branch:
```python
from unstructured.partition.auto import partition
elements = partition("example-docs/layout-parser-paper-fast.jpg")
print(elements[0].metadata.filename)
```
### Summary
A follow up ticket on
https://github.com/Unstructured-IO/unstructured/pull/1801, I forgot to
remove the lines that pass extract_tables to inference, and noted the
table regression if we only do one OCR for entire doc
**Tech details:**
* stop passing `extract_tables` parameter to inference
* added table extraction ingest test for image, which was skipped
before, and the "text_as_html" field contains the OCR output from the
table OCR refactor PR
* replaced `assert_called_once_with` with `call_args` so that the unit
tests don't need to test additional parameters
* added `error_margin` as ENV when comparing bounding boxes
of`ocr_region` with `table_element`
* added more tests for tables and noted the table regression in test for
partition pdf
### Test
* for stop passing `extract_tables` parameter to inference, run test
`test_partition_pdf_hi_res_ocr_mode_with_table_extraction` before this
branch and you will see warning like `Table OCR from get_tokens method
will be deprecated....`, which means it called the table OCR in
inference repo. This branch removed the warning.
Closes `unstructured-inference` issue
[#265](https://github.com/Unstructured-IO/unstructured-inference/issues/265).
Cleaned up the kwarg handling, taking opportunities to turn instances of
handling kwargs as dicts to just using them as normal in function
signatures.
#### Testing:
Should just pass CI.
This PR resolves#1754
- function wrapper tries to use `cast` to convert kwargs into `str` but
when a value is `None` `cast(str, None)` still returns `None`
- fix replaces the conversion to simply using `str()` function call
### Description
Currently linting only takes place over the base unstructured directory
but we support python files throughout the repo. It makes sense for all
those files to also abide by the same linting rules so the entire repo
was set to be inspected when the linters are run. Along with that
autoflake was added as a linter which has a lot of added benefits such
as removing unused imports for you that would currently break flake and
require manual intervention.
The only real relevant changes in this PR are in the `Makefile`,
`setup.cfg`, and `requirements/test.in`. The rest is the result of
running the linters.
PR to support schema changes introduced from [PR
232](https://github.com/Unstructured-IO/unstructured-inference/pull/232)
in `unstructured-inference`.
Specifically what needs to be supported is:
* Change to the way `LayoutElement` from `unstructured-inference` is
structured, specifically that this class is no longer a subclass of
`Rectangle`, and instead `LayoutElement` has a `bbox` property that
captures the location information and a `from_coords` method that allows
construction of a `LayoutElement` directly from coordinates.
* Removal of `LocationlessLayoutElement` since chipper now exports
bounding boxes, and if we need to support elements without bounding
boxes, we can make the `bbox` property mentioned above optional.
* Getting hierarchy data directly from the inference elements rather
than in post-processing
* Don't try to reorder elements received from chipper v2, as they should
already be ordered.
#### Testing:
The following demonstrates that the new version of chipper is inferring
hierarchy.
```python
from unstructured.partition.pdf import partition_pdf
elements = partition_pdf("example-docs/layout-parser-paper-fast.pdf", strategy="hi_res", model_name="chipper")
children = [el for el in elements if el.metadata.parent_id is not None]
print(children)
```
Also verify that running the traditional `hi_res` gives different
results:
```python
from unstructured.partition.pdf import partition_pdf
elements = partition_pdf("example-docs/layout-parser-paper-fast.pdf", strategy="hi_res")
```
---------
Co-authored-by: Sebastian Laverde Alfonso <lavmlk20201@gmail.com>
Co-authored-by: ryannikolaidis <1208590+ryannikolaidis@users.noreply.github.com>
Co-authored-by: christinestraub <christinemstraub@gmail.com>
Each partitioner has a test like `test_partition_x_with_json()`. What
these do is serialize the elements produced by the partitioner to JSON,
then read them back in from JSON and compare the before and after
elements.
Because our element equality (`Element.__eq__()`) is shallow, this
doesn't tell us a lot, but if we take it one more step, like
`List[Element] -> JSON -> List[Element] -> JSON` and then compare the
JSON, it gives us some confidence that the serialized elements can be
"re-hydrated" without losing any information.
This actually showed up a few problems, all in the
serialization/deserialization (serde) code that all elements share.
