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import logging
import sys
import warnings
from pathlib import Path
from typing import Optional
from docling_core.types.doc import DocItem, ImageRef, PictureItem, TableItem
from docling.backend.abstract_backend import AbstractDocumentBackend
from docling.backend.pdf_backend import PdfDocumentBackend
from docling.datamodel.base_models import AssembledUnit, Page
from docling.datamodel.document import ConversionResult
from docling.datamodel.pipeline_options import (
EasyOcrOptions,
OcrMacOptions,
PdfPipelineOptions,
PictureDescriptionApiOptions,
PictureDescriptionVlmOptions,
RapidOcrOptions,
TesseractCliOcrOptions,
TesseractOcrOptions,
)
from docling.datamodel.settings import settings
from docling.models.base_ocr_model import BaseOcrModel
from docling.models.code_formula_model import CodeFormulaModel, CodeFormulaModelOptions
from docling.models.document_picture_classifier import (
DocumentPictureClassifier,
DocumentPictureClassifierOptions,
)
from docling.models.ds_glm_model import GlmModel, GlmOptions
from docling.models.easyocr_model import EasyOcrModel
from docling.models.layout_model import LayoutModel
from docling.models.ocr_mac_model import OcrMacModel
from docling.models.page_assemble_model import PageAssembleModel, PageAssembleOptions
from docling.models.page_preprocessing_model import (
PagePreprocessingModel,
PagePreprocessingOptions,
)
from docling.models.picture_description_api_model import PictureDescriptionApiModel
from docling.models.picture_description_base_model import PictureDescriptionBaseModel
from docling.models.picture_description_vlm_model import PictureDescriptionVlmModel
from docling.models.rapid_ocr_model import RapidOcrModel
from docling.models.table_structure_model import TableStructureModel
from docling.models.tesseract_ocr_cli_model import TesseractOcrCliModel
from docling.models.tesseract_ocr_model import TesseractOcrModel
from docling.pipeline.base_pipeline import PaginatedPipeline
from docling.utils.model_downloader import download_models
from docling.utils.profiling import ProfilingScope, TimeRecorder
_log = logging.getLogger(__name__)
class StandardPdfPipeline(PaginatedPipeline):
_layout_model_path = LayoutModel._model_path
_table_model_path = TableStructureModel._model_path
def __init__(self, pipeline_options: PdfPipelineOptions):
super().__init__(pipeline_options)
self.pipeline_options: PdfPipelineOptions
artifacts_path: Optional[Path] = None
if pipeline_options.artifacts_path is not None:
artifacts_path = Path(pipeline_options.artifacts_path).expanduser()
self.keep_images = (
self.pipeline_options.generate_page_images
or self.pipeline_options.generate_picture_images
or self.pipeline_options.generate_table_images
)
self.glm_model = GlmModel(options=GlmOptions())
if (ocr_model := self.get_ocr_model(artifacts_path=artifacts_path)) is None:
raise RuntimeError(
f"The specified OCR kind is not supported: {pipeline_options.ocr_options.kind}."
)
self.build_pipe = [
# Pre-processing
PagePreprocessingModel(
options=PagePreprocessingOptions(
images_scale=pipeline_options.images_scale
)
),
# OCR
ocr_model,
# Layout model
LayoutModel(
artifacts_path=artifacts_path,
accelerator_options=pipeline_options.accelerator_options,
),
# Table structure model
TableStructureModel(
enabled=pipeline_options.do_table_structure,
artifacts_path=artifacts_path,
options=pipeline_options.table_structure_options,
accelerator_options=pipeline_options.accelerator_options,
),
# Page assemble
PageAssembleModel(options=PageAssembleOptions()),
]
# Picture description model
if (
picture_description_model := self.get_picture_description_model(
artifacts_path=artifacts_path
)
) is None:
raise RuntimeError(
f"The specified picture description kind is not supported: {pipeline_options.picture_description_options.kind}."
)
self.enrichment_pipe = [
# Code Formula Enrichment Model
CodeFormulaModel(
enabled=pipeline_options.do_code_enrichment
or pipeline_options.do_formula_enrichment,
artifacts_path=artifacts_path,
options=CodeFormulaModelOptions(
do_code_enrichment=pipeline_options.do_code_enrichment,
do_formula_enrichment=pipeline_options.do_formula_enrichment,
),
accelerator_options=pipeline_options.accelerator_options,
),
# Document Picture Classifier
DocumentPictureClassifier(
enabled=pipeline_options.do_picture_classification,
artifacts_path=artifacts_path,
options=DocumentPictureClassifierOptions(),
accelerator_options=pipeline_options.accelerator_options,
),
# Document Picture description
picture_description_model,
]
if (
self.pipeline_options.do_formula_enrichment
or self.pipeline_options.do_code_enrichment
or self.pipeline_options.do_picture_description
):
self.keep_backend = True
@staticmethod
def download_models_hf(
local_dir: Optional[Path] = None, force: bool = False
) -> Path:
warnings.warn(
"The usage of StandardPdfPipeline.download_models_hf() is deprecated "
"use instead the utility `docling-tools models download`, or "
"the upstream method docling.utils.models_downloader.download_all()",
DeprecationWarning,
stacklevel=3,
)
output_dir = download_models(output_dir=local_dir, force=force, progress=False)
return output_dir
def get_ocr_model(
self, artifacts_path: Optional[Path] = None
) -> Optional[BaseOcrModel]:
if isinstance(self.pipeline_options.ocr_options, EasyOcrOptions):
return EasyOcrModel(
enabled=self.pipeline_options.do_ocr,
artifacts_path=artifacts_path,
options=self.pipeline_options.ocr_options,
accelerator_options=self.pipeline_options.accelerator_options,
)
elif isinstance(self.pipeline_options.ocr_options, TesseractCliOcrOptions):
return TesseractOcrCliModel(
enabled=self.pipeline_options.do_ocr,
options=self.pipeline_options.ocr_options,
)
elif isinstance(self.pipeline_options.ocr_options, TesseractOcrOptions):
return TesseractOcrModel(
enabled=self.pipeline_options.do_ocr,
options=self.pipeline_options.ocr_options,
)
elif isinstance(self.pipeline_options.ocr_options, RapidOcrOptions):
return RapidOcrModel(
enabled=self.pipeline_options.do_ocr,
options=self.pipeline_options.ocr_options,
accelerator_options=self.pipeline_options.accelerator_options,
)
elif isinstance(self.pipeline_options.ocr_options, OcrMacOptions):
if "darwin" != sys.platform:
raise RuntimeError(
f"The specified OCR type is only supported on Mac: {self.pipeline_options.ocr_options.kind}."
)
return OcrMacModel(
enabled=self.pipeline_options.do_ocr,
options=self.pipeline_options.ocr_options,
)
return None
def get_picture_description_model(
self, artifacts_path: Optional[Path] = None
) -> Optional[PictureDescriptionBaseModel]:
if isinstance(
self.pipeline_options.picture_description_options,
PictureDescriptionApiOptions,
):
return PictureDescriptionApiModel(
enabled=self.pipeline_options.do_picture_description,
options=self.pipeline_options.picture_description_options,
)
elif isinstance(
self.pipeline_options.picture_description_options,
PictureDescriptionVlmOptions,
):
return PictureDescriptionVlmModel(
enabled=self.pipeline_options.do_picture_description,
artifacts_path=artifacts_path,
options=self.pipeline_options.picture_description_options,
accelerator_options=self.pipeline_options.accelerator_options,
)
return None
def initialize_page(self, conv_res: ConversionResult, page: Page) -> Page:
with TimeRecorder(conv_res, "page_init"):
page._backend = conv_res.input._backend.load_page(page.page_no) # type: ignore
if page._backend is not None and page._backend.is_valid():
page.size = page._backend.get_size()
return page
def _assemble_document(self, conv_res: ConversionResult) -> ConversionResult:
all_elements = []
all_headers = []
all_body = []
with TimeRecorder(conv_res, "doc_assemble", scope=ProfilingScope.DOCUMENT):
for p in conv_res.pages:
if p.assembled is not None:
for el in p.assembled.body:
all_body.append(el)
for el in p.assembled.headers:
all_headers.append(el)
for el in p.assembled.elements:
all_elements.append(el)
conv_res.assembled = AssembledUnit(
elements=all_elements, headers=all_headers, body=all_body
)
conv_res.document = self.glm_model(conv_res)
# Generate page images in the output
if self.pipeline_options.generate_page_images:
for page in conv_res.pages:
assert page.image is not None
page_no = page.page_no + 1
conv_res.document.pages[page_no].image = ImageRef.from_pil(
page.image, dpi=int(72 * self.pipeline_options.images_scale)
)
# Generate images of the requested element types
if (
self.pipeline_options.generate_picture_images
or self.pipeline_options.generate_table_images
):
scale = self.pipeline_options.images_scale
for element, _level in conv_res.document.iterate_items():
if not isinstance(element, DocItem) or len(element.prov) == 0:
continue
if (
isinstance(element, PictureItem)
and self.pipeline_options.generate_picture_images
) or (
isinstance(element, TableItem)
and self.pipeline_options.generate_table_images
):
page_ix = element.prov[0].page_no - 1
page = conv_res.pages[page_ix]
assert page.size is not None
assert page.image is not None
crop_bbox = (
element.prov[0]
.bbox.scaled(scale=scale)
.to_top_left_origin(page_height=page.size.height * scale)
)
cropped_im = page.image.crop(crop_bbox.as_tuple())
element.image = ImageRef.from_pil(
cropped_im, dpi=int(72 * scale)
)
return conv_res
@classmethod
def get_default_options(cls) -> PdfPipelineOptions:
return PdfPipelineOptions()
@classmethod
def is_backend_supported(cls, backend: AbstractDocumentBackend):
return isinstance(backend, PdfDocumentBackend)
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