
* add coverage calculation and push Signed-off-by: Michele Dolfi <dol@zurich.ibm.com> * new codecov version and usage of token Signed-off-by: Michele Dolfi <dol@zurich.ibm.com> * enable ruff formatter instead of black and isort Signed-off-by: Michele Dolfi <dol@zurich.ibm.com> * apply ruff lint fixes Signed-off-by: Michele Dolfi <dol@zurich.ibm.com> * apply ruff unsafe fixes Signed-off-by: Michele Dolfi <dol@zurich.ibm.com> * add removed imports Signed-off-by: Michele Dolfi <dol@zurich.ibm.com> * runs 1 on linter issues Signed-off-by: Michele Dolfi <dol@zurich.ibm.com> * finalize linter fixes Signed-off-by: Michele Dolfi <dol@zurich.ibm.com> * Update pyproject.toml Co-authored-by: Cesar Berrospi Ramis <75900930+ceberam@users.noreply.github.com> Signed-off-by: Michele Dolfi <97102151+dolfim-ibm@users.noreply.github.com> --------- Signed-off-by: Michele Dolfi <dol@zurich.ibm.com> Signed-off-by: Michele Dolfi <97102151+dolfim-ibm@users.noreply.github.com> Co-authored-by: Cesar Berrospi Ramis <75900930+ceberam@users.noreply.github.com>
227 lines
8.5 KiB
Python
227 lines
8.5 KiB
Python
import functools
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import logging
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import time
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import traceback
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from abc import ABC, abstractmethod
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from collections.abc import Iterable
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from typing import Any, Callable, List
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from docling_core.types.doc import NodeItem
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from docling.backend.abstract_backend import AbstractDocumentBackend
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from docling.backend.pdf_backend import PdfDocumentBackend
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from docling.datamodel.base_models import (
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ConversionStatus,
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DoclingComponentType,
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ErrorItem,
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Page,
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)
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from docling.datamodel.document import ConversionResult, InputDocument
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from docling.datamodel.pipeline_options import PipelineOptions
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from docling.datamodel.settings import settings
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from docling.models.base_model import GenericEnrichmentModel
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from docling.utils.profiling import ProfilingScope, TimeRecorder
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from docling.utils.utils import chunkify
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_log = logging.getLogger(__name__)
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class BasePipeline(ABC):
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def __init__(self, pipeline_options: PipelineOptions):
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self.pipeline_options = pipeline_options
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self.keep_images = False
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self.build_pipe: List[Callable] = []
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self.enrichment_pipe: List[GenericEnrichmentModel[Any]] = []
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def execute(self, in_doc: InputDocument, raises_on_error: bool) -> ConversionResult:
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conv_res = ConversionResult(input=in_doc)
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_log.info(f"Processing document {in_doc.file.name}")
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try:
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with TimeRecorder(
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conv_res, "pipeline_total", scope=ProfilingScope.DOCUMENT
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):
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# These steps are building and assembling the structure of the
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# output DoclingDocument.
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conv_res = self._build_document(conv_res)
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conv_res = self._assemble_document(conv_res)
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# From this stage, all operations should rely only on conv_res.output
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conv_res = self._enrich_document(conv_res)
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conv_res.status = self._determine_status(conv_res)
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except Exception as e:
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conv_res.status = ConversionStatus.FAILURE
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if raises_on_error:
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raise e
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finally:
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self._unload(conv_res)
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return conv_res
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@abstractmethod
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def _build_document(self, conv_res: ConversionResult) -> ConversionResult:
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pass
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def _assemble_document(self, conv_res: ConversionResult) -> ConversionResult:
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return conv_res
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def _enrich_document(self, conv_res: ConversionResult) -> ConversionResult:
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def _prepare_elements(
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conv_res: ConversionResult, model: GenericEnrichmentModel[Any]
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) -> Iterable[NodeItem]:
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for doc_element, _level in conv_res.document.iterate_items():
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prepared_element = model.prepare_element(
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conv_res=conv_res, element=doc_element
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)
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if prepared_element is not None:
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yield prepared_element
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with TimeRecorder(conv_res, "doc_enrich", scope=ProfilingScope.DOCUMENT):
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for model in self.enrichment_pipe:
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for element_batch in chunkify(
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_prepare_elements(conv_res, model),
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model.elements_batch_size,
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):
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for element in model(
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doc=conv_res.document, element_batch=element_batch
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): # Must exhaust!
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pass
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return conv_res
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@abstractmethod
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def _determine_status(self, conv_res: ConversionResult) -> ConversionStatus:
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pass
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def _unload(self, conv_res: ConversionResult):
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pass
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@classmethod
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@abstractmethod
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def get_default_options(cls) -> PipelineOptions:
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pass
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@classmethod
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@abstractmethod
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def is_backend_supported(cls, backend: AbstractDocumentBackend):
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pass
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# def _apply_on_elements(self, element_batch: Iterable[NodeItem]) -> Iterable[Any]:
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# for model in self.build_pipe:
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# element_batch = model(element_batch)
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#
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# yield from element_batch
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class PaginatedPipeline(BasePipeline): # TODO this is a bad name.
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def __init__(self, pipeline_options: PipelineOptions):
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super().__init__(pipeline_options)
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self.keep_backend = False
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def _apply_on_pages(
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self, conv_res: ConversionResult, page_batch: Iterable[Page]
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) -> Iterable[Page]:
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for model in self.build_pipe:
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page_batch = model(conv_res, page_batch)
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yield from page_batch
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def _build_document(self, conv_res: ConversionResult) -> ConversionResult:
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if not isinstance(conv_res.input._backend, PdfDocumentBackend):
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raise RuntimeError(
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f"The selected backend {type(conv_res.input._backend).__name__} for {conv_res.input.file} is not a PDF backend. "
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f"Can not convert this with a PDF pipeline. "
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f"Please check your format configuration on DocumentConverter."
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)
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# conv_res.status = ConversionStatus.FAILURE
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# return conv_res
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total_elapsed_time = 0.0
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with TimeRecorder(conv_res, "doc_build", scope=ProfilingScope.DOCUMENT):
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for i in range(conv_res.input.page_count):
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start_page, end_page = conv_res.input.limits.page_range
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if (start_page - 1) <= i <= (end_page - 1):
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conv_res.pages.append(Page(page_no=i))
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try:
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# Iterate batches of pages (page_batch_size) in the doc
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for page_batch in chunkify(
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conv_res.pages, settings.perf.page_batch_size
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):
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start_batch_time = time.monotonic()
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# 1. Initialise the page resources
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init_pages = map(
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functools.partial(self.initialize_page, conv_res), page_batch
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)
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# 2. Run pipeline stages
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pipeline_pages = self._apply_on_pages(conv_res, init_pages)
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for p in pipeline_pages: # Must exhaust!
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# Cleanup cached images
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if not self.keep_images:
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p._image_cache = {}
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# Cleanup page backends
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if not self.keep_backend and p._backend is not None:
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p._backend.unload()
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end_batch_time = time.monotonic()
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total_elapsed_time += end_batch_time - start_batch_time
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if (
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self.pipeline_options.document_timeout is not None
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and total_elapsed_time > self.pipeline_options.document_timeout
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):
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_log.warning(
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f"Document processing time ({total_elapsed_time:.3f} seconds) exceeded the specified timeout of {self.pipeline_options.document_timeout:.3f} seconds"
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)
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conv_res.status = ConversionStatus.PARTIAL_SUCCESS
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break
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_log.debug(
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f"Finished converting page batch time={end_batch_time:.3f}"
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)
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except Exception as e:
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conv_res.status = ConversionStatus.FAILURE
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trace = "\n".join(
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traceback.format_exception(type(e), e, e.__traceback__)
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)
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_log.warning(
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f"Encountered an error during conversion of document {conv_res.input.document_hash}:\n"
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f"{trace}"
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)
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raise e
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return conv_res
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def _unload(self, conv_res: ConversionResult) -> ConversionResult:
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for page in conv_res.pages:
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if page._backend is not None:
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page._backend.unload()
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if conv_res.input._backend:
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conv_res.input._backend.unload()
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return conv_res
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def _determine_status(self, conv_res: ConversionResult) -> ConversionStatus:
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status = ConversionStatus.SUCCESS
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for page in conv_res.pages:
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if page._backend is None or not page._backend.is_valid():
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conv_res.errors.append(
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ErrorItem(
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component_type=DoclingComponentType.DOCUMENT_BACKEND,
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module_name=type(page._backend).__name__,
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error_message=f"Page {page.page_no} failed to parse.",
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)
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)
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status = ConversionStatus.PARTIAL_SUCCESS
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return status
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# Initialise and load resources for a page
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@abstractmethod
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def initialize_page(self, conv_res: ConversionResult, page: Page) -> Page:
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pass
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