feat: [Experimental] Introduce VLM pipeline using HF AutoModelForVision2Seq, featuring SmolDocling model (#1054)
* Skeleton for SmolDocling model and VLM Pipeline Signed-off-by: Christoph Auer <cau@zurich.ibm.com> Signed-off-by: Maksym Lysak <mly@zurich.ibm.com> * wip smolDocling inference and vlm pipeline Signed-off-by: Maksym Lysak <mly@zurich.ibm.com> * WIP, first working code for inference of SmolDocling, and vlm pipeline assembly code, example included. Signed-off-by: Maksym Lysak <mly@zurich.ibm.com> * Fixes to preserve page image and demo export to html Signed-off-by: Maksym Lysak <mly@zurich.ibm.com> * Enabled figure support in vlm_pipeline Signed-off-by: Maksym Lysak <mly@zurich.ibm.com> * Fix for table span compute in vlm_pipeline Signed-off-by: Maksym Lysak <mly@zurich.ibm.com> * Properly propagating image data per page, together with predicted tags in VLM pipeline. This enables correct figure extraction and page numbers in provenances Signed-off-by: Maksym Lysak <mly@zurich.ibm.com> * Cleaned up logs, added pages to vlm_pipeline, basic timing per page measurement in smol_docling models Signed-off-by: Maksym Lysak <mly@zurich.ibm.com> * Replaced hardcoded otsl tokens with the ones from docling-core tokens.py enum Signed-off-by: Maksym Lysak <mly@zurich.ibm.com> * Added tokens/sec measurement, improved example Signed-off-by: Maksym Lysak <mly@zurich.ibm.com> * Added capability for vlm_pipeline to grab text from preconfigured backend Signed-off-by: Maksym Lysak <mly@zurich.ibm.com> * Exposed "force_backend_text" as pipeline parameter Signed-off-by: Maksym Lysak <mly@zurich.ibm.com> * Flipped keep_backend to True for vlm_pipeline assembly to work Signed-off-by: Maksym Lysak <mly@zurich.ibm.com> * Updated vlm pipeline assembly and smol docling model code to support updated doctags Signed-off-by: Maksym Lysak <mly@zurich.ibm.com> * Fixing doctags starting tag, that broke elements on first line during assembly Signed-off-by: Maksym Lysak <mly@zurich.ibm.com> * Introduced SmolDoclingOptions to configure model parameters (such as query and artifacts path) via client code, see example in minimal_smol_docling. Provisioning for other potential vlm all-in-one models. Signed-off-by: Maksym Lysak <mly@zurich.ibm.com> * Moved artifacts_path for SmolDocling into vlm_options instead of global pipeline option Signed-off-by: Maksym Lysak <mly@zurich.ibm.com> * New assembly code for latest model revision, updated prompt and parsing of doctags, updated logging Signed-off-by: Maksym Lysak <mly@zurich.ibm.com> * Updated example of Smol Docling usage Signed-off-by: Maksym Lysak <mly@zurich.ibm.com> * Added captions for the images for SmolDocling assembly code, improved provenance definition for all elements Signed-off-by: Maksym Lysak <mly@zurich.ibm.com> * Update minimal smoldocling example Signed-off-by: Christoph Auer <cau@zurich.ibm.com> * Fix repo id Signed-off-by: Christoph Auer <cau@zurich.ibm.com> * Cleaned up unnecessary logging Signed-off-by: Maksym Lysak <mly@zurich.ibm.com> * More elegant solution in removing the input prompt Signed-off-by: Maksym Lysak <mly@zurich.ibm.com> * removed minimal_smol_docling example from CI checks Signed-off-by: Maksym Lysak <mly@zurich.ibm.com> * Removed special html code wrapping when exporting to docling document, cleaned up comments Signed-off-by: Maksym Lysak <mly@zurich.ibm.com> * Addressing PR comments, added enabled property to SmolDocling, and related VLM pipeline option, few other minor things Signed-off-by: Maksym Lysak <mly@zurich.ibm.com> * Moved keep_backend = True to vlm pipeline Signed-off-by: Maksym Lysak <mly@zurich.ibm.com> * removed pipeline_options.generate_table_images from vlm_pipeline (deprecated in the pipelines) Signed-off-by: Maksym Lysak <mly@zurich.ibm.com> * Added example on how to get original predicted doctags in minimal_smol_docling Signed-off-by: Maksym Lysak <mly@zurich.ibm.com> * removing changes from base_pipeline Signed-off-by: Maksym Lysak <mly@zurich.ibm.com> * Replaced remaining strings to appropriate enums Signed-off-by: Maksym Lysak <mly@zurich.ibm.com> * Updated poetry.lock Signed-off-by: Maksym Lysak <mly@zurich.ibm.com> * re-built poetry.lock Signed-off-by: Maksym Lysak <mly@zurich.ibm.com> * Generalize and refactor VLM pipeline and models Signed-off-by: Christoph Auer <cau@zurich.ibm.com> * Rename example Signed-off-by: Christoph Auer <cau@zurich.ibm.com> * Move imports Signed-off-by: Christoph Auer <cau@zurich.ibm.com> * Expose control over using flash_attention_2 Signed-off-by: Christoph Auer <cau@zurich.ibm.com> * Fix VLM example exclusion in CI Signed-off-by: Christoph Auer <cau@zurich.ibm.com> * Add back device_map and accelerate Signed-off-by: Christoph Auer <cau@zurich.ibm.com> * Make drawing code resilient against bad bboxes Signed-off-by: Christoph Auer <cau@zurich.ibm.com> * chore: clean up code and comments Signed-off-by: Christoph Auer <cau@zurich.ibm.com> * chore: more cleanup Signed-off-by: Christoph Auer <cau@zurich.ibm.com> * chore: fix leftover .to(device) Signed-off-by: Christoph Auer <cau@zurich.ibm.com> * fix: add proper table provenance Signed-off-by: Christoph Auer <cau@zurich.ibm.com> --------- Signed-off-by: Christoph Auer <cau@zurich.ibm.com> Signed-off-by: Maksym Lysak <mly@zurich.ibm.com> Co-authored-by: Maksym Lysak <mly@zurich.ibm.com>
This commit is contained in:
Christoph Auer
GitHub
parent
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commit
3c9fe76b70
2
.github/workflows/checks.yml
vendored
2
.github/workflows/checks.yml
vendored
@ -28,7 +28,7 @@ jobs:
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run: |
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for file in docs/examples/*.py; do
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# Skip batch_convert.py
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if [[ "$(basename "$file")" =~ ^(batch_convert|minimal|export_multimodal|custom_convert|develop_picture_enrichment|rapidocr_with_custom_models|offline_convert|pictures_description|pictures_description_api).py ]]; then
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if [[ "$(basename "$file")" =~ ^(batch_convert|minimal_vlm_pipeline|minimal|export_multimodal|custom_convert|develop_picture_enrichment|rapidocr_with_custom_models|offline_convert|pictures_description|pictures_description_api).py ]]; then
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echo "Skipping $file"
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continue
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fi
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@ -154,6 +154,10 @@ class LayoutPrediction(BaseModel):
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clusters: List[Cluster] = []
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class VlmPrediction(BaseModel):
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text: str = ""
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class ContainerElement(
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BasePageElement
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): # Used for Form and Key-Value-Regions, only for typing.
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@ -197,6 +201,7 @@ class PagePredictions(BaseModel):
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tablestructure: Optional[TableStructurePrediction] = None
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figures_classification: Optional[FigureClassificationPrediction] = None
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equations_prediction: Optional[EquationPrediction] = None
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vlm_response: Optional[VlmPrediction] = None
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PageElement = Union[TextElement, Table, FigureElement, ContainerElement]
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@ -41,6 +41,7 @@ class AcceleratorOptions(BaseSettings):
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num_threads: int = 4
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device: Union[str, AcceleratorDevice] = "auto"
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cuda_use_flash_attention2: bool = False
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@field_validator("device")
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def validate_device(cls, value):
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@ -254,6 +255,45 @@ granite_picture_description = PictureDescriptionVlmOptions(
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)
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class BaseVlmOptions(BaseModel):
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kind: str
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prompt: str
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class ResponseFormat(str, Enum):
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DOCTAGS = "doctags"
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MARKDOWN = "markdown"
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class HuggingFaceVlmOptions(BaseVlmOptions):
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kind: Literal["hf_model_options"] = "hf_model_options"
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repo_id: str
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load_in_8bit: bool = True
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llm_int8_threshold: float = 6.0
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quantized: bool = False
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response_format: ResponseFormat
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@property
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def repo_cache_folder(self) -> str:
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return self.repo_id.replace("/", "--")
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smoldocling_vlm_conversion_options = HuggingFaceVlmOptions(
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repo_id="ds4sd/SmolDocling-256M-preview",
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prompt="Convert this page to docling.",
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response_format=ResponseFormat.DOCTAGS,
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)
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granite_vision_vlm_conversion_options = HuggingFaceVlmOptions(
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repo_id="ibm-granite/granite-vision-3.1-2b-preview",
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# prompt="OCR the full page to markdown.",
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prompt="OCR this image.",
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response_format=ResponseFormat.MARKDOWN,
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)
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# Define an enum for the backend options
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class PdfBackend(str, Enum):
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"""Enum of valid PDF backends."""
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@ -285,7 +325,24 @@ class PipelineOptions(BaseModel):
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enable_remote_services: bool = False
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class PdfPipelineOptions(PipelineOptions):
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class PaginatedPipelineOptions(PipelineOptions):
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images_scale: float = 1.0
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generate_page_images: bool = False
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generate_picture_images: bool = False
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class VlmPipelineOptions(PaginatedPipelineOptions):
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artifacts_path: Optional[Union[Path, str]] = None
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generate_page_images: bool = True
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force_backend_text: bool = (
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False # (To be used with vlms, or other generative models)
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)
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# If True, text from backend will be used instead of generated text
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vlm_options: Union[HuggingFaceVlmOptions] = smoldocling_vlm_conversion_options
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class PdfPipelineOptions(PaginatedPipelineOptions):
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"""Options for the PDF pipeline."""
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artifacts_path: Optional[Union[Path, str]] = None
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@ -295,6 +352,10 @@ class PdfPipelineOptions(PipelineOptions):
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do_formula_enrichment: bool = False # True: perform formula OCR, return Latex code
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do_picture_classification: bool = False # True: classify pictures in documents
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do_picture_description: bool = False # True: run describe pictures in documents
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force_backend_text: bool = (
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False # (To be used with vlms, or other generative models)
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)
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# If True, text from backend will be used instead of generated text
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table_structure_options: TableStructureOptions = TableStructureOptions()
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ocr_options: Union[
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180
docling/models/hf_vlm_model.py
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180
docling/models/hf_vlm_model.py
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@ -0,0 +1,180 @@
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import logging
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import time
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from pathlib import Path
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from typing import Iterable, List, Optional
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from docling.datamodel.base_models import Page, VlmPrediction
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from docling.datamodel.document import ConversionResult
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from docling.datamodel.pipeline_options import (
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AcceleratorDevice,
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AcceleratorOptions,
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HuggingFaceVlmOptions,
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)
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from docling.datamodel.settings import settings
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from docling.models.base_model import BasePageModel
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from docling.utils.accelerator_utils import decide_device
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from docling.utils.profiling import TimeRecorder
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_log = logging.getLogger(__name__)
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class HuggingFaceVlmModel(BasePageModel):
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def __init__(
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self,
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enabled: bool,
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artifacts_path: Optional[Path],
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accelerator_options: AcceleratorOptions,
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vlm_options: HuggingFaceVlmOptions,
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):
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self.enabled = enabled
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self.vlm_options = vlm_options
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if self.enabled:
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import torch
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from transformers import ( # type: ignore
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AutoModelForVision2Seq,
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AutoProcessor,
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BitsAndBytesConfig,
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)
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device = decide_device(accelerator_options.device)
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self.device = device
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_log.debug("Available device for HuggingFace VLM: {}".format(device))
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repo_cache_folder = vlm_options.repo_id.replace("/", "--")
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# PARAMETERS:
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if artifacts_path is None:
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artifacts_path = self.download_models(self.vlm_options.repo_id)
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elif (artifacts_path / repo_cache_folder).exists():
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artifacts_path = artifacts_path / repo_cache_folder
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self.param_question = vlm_options.prompt # "Perform Layout Analysis."
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self.param_quantization_config = BitsAndBytesConfig(
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load_in_8bit=vlm_options.load_in_8bit, # True,
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llm_int8_threshold=vlm_options.llm_int8_threshold, # 6.0
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)
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self.param_quantized = vlm_options.quantized # False
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self.processor = AutoProcessor.from_pretrained(artifacts_path)
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if not self.param_quantized:
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self.vlm_model = AutoModelForVision2Seq.from_pretrained(
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artifacts_path,
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device_map=device,
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torch_dtype=torch.bfloat16,
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_attn_implementation=(
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"flash_attention_2"
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if self.device.startswith("cuda")
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and accelerator_options.cuda_use_flash_attention2
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else "eager"
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),
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) # .to(self.device)
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else:
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self.vlm_model = AutoModelForVision2Seq.from_pretrained(
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artifacts_path,
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device_map=device,
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torch_dtype="auto",
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quantization_config=self.param_quantization_config,
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_attn_implementation=(
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"flash_attention_2"
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if self.device.startswith("cuda")
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and accelerator_options.cuda_use_flash_attention2
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else "eager"
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),
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) # .to(self.device)
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@staticmethod
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def download_models(
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repo_id: str,
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local_dir: Optional[Path] = None,
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force: bool = False,
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progress: bool = False,
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) -> Path:
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from huggingface_hub import snapshot_download
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from huggingface_hub.utils import disable_progress_bars
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if not progress:
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disable_progress_bars()
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download_path = snapshot_download(
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repo_id=repo_id,
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force_download=force,
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local_dir=local_dir,
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# revision="v0.0.1",
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)
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return Path(download_path)
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def __call__(
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self, conv_res: ConversionResult, page_batch: Iterable[Page]
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) -> Iterable[Page]:
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for page in page_batch:
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assert page._backend is not None
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if not page._backend.is_valid():
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yield page
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else:
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with TimeRecorder(conv_res, "vlm"):
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assert page.size is not None
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hi_res_image = page.get_image(scale=2.0) # 144dpi
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# hi_res_image = page.get_image(scale=1.0) # 72dpi
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if hi_res_image is not None:
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im_width, im_height = hi_res_image.size
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# populate page_tags with predicted doc tags
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page_tags = ""
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if hi_res_image:
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if hi_res_image.mode != "RGB":
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hi_res_image = hi_res_image.convert("RGB")
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messages = [
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{
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"role": "user",
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"content": [
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{
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"type": "text",
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"text": "This is a page from a document.",
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},
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{"type": "image"},
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{"type": "text", "text": self.param_question},
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],
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}
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]
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prompt = self.processor.apply_chat_template(
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messages, add_generation_prompt=False
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)
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inputs = self.processor(
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text=prompt, images=[hi_res_image], return_tensors="pt"
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)
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inputs = {k: v.to(self.device) for k, v in inputs.items()}
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start_time = time.time()
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# Call model to generate:
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generated_ids = self.vlm_model.generate(
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**inputs, max_new_tokens=4096, use_cache=True
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)
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generation_time = time.time() - start_time
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generated_texts = self.processor.batch_decode(
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generated_ids[:, inputs["input_ids"].shape[1] :],
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skip_special_tokens=False,
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)[0]
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num_tokens = len(generated_ids[0])
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page_tags = generated_texts
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# inference_time = time.time() - start_time
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# tokens_per_second = num_tokens / generation_time
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# print("")
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# print(f"Page Inference Time: {inference_time:.2f} seconds")
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# print(f"Total tokens on page: {num_tokens:.2f}")
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# print(f"Tokens/sec: {tokens_per_second:.2f}")
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# print("")
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page.predictions.vlm_response = VlmPrediction(text=page_tags)
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yield page
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534
docling/pipeline/vlm_pipeline.py
Normal file
534
docling/pipeline/vlm_pipeline.py
Normal file
@ -0,0 +1,534 @@
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import itertools
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import logging
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import re
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import warnings
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from io import BytesIO
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# from io import BytesIO
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from pathlib import Path
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from typing import Optional
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from docling_core.types import DoclingDocument
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from docling_core.types.doc import (
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BoundingBox,
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DocItem,
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DocItemLabel,
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DoclingDocument,
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GroupLabel,
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ImageRef,
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ImageRefMode,
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PictureItem,
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ProvenanceItem,
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Size,
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TableCell,
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TableData,
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TableItem,
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)
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from docling_core.types.doc.tokens import DocumentToken, TableToken
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from docling.backend.abstract_backend import AbstractDocumentBackend
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from docling.backend.md_backend import MarkdownDocumentBackend
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from docling.backend.pdf_backend import PdfDocumentBackend
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from docling.datamodel.base_models import InputFormat, Page
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from docling.datamodel.document import ConversionResult, InputDocument
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from docling.datamodel.pipeline_options import (
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PdfPipelineOptions,
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ResponseFormat,
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VlmPipelineOptions,
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)
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from docling.datamodel.settings import settings
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from docling.models.hf_vlm_model import HuggingFaceVlmModel
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from docling.pipeline.base_pipeline import PaginatedPipeline
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from docling.utils.profiling import ProfilingScope, TimeRecorder
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_log = logging.getLogger(__name__)
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class VlmPipeline(PaginatedPipeline):
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def __init__(self, pipeline_options: VlmPipelineOptions):
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super().__init__(pipeline_options)
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self.keep_backend = True
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warnings.warn(
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"The VlmPipeline is currently experimental and may change in upcoming versions without notice.",
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category=UserWarning,
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stacklevel=2,
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)
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self.pipeline_options: VlmPipelineOptions
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artifacts_path: Optional[Path] = None
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if pipeline_options.artifacts_path is not None:
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artifacts_path = Path(pipeline_options.artifacts_path).expanduser()
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elif settings.artifacts_path is not None:
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artifacts_path = Path(settings.artifacts_path).expanduser()
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if artifacts_path is not None and not artifacts_path.is_dir():
|
||||
raise RuntimeError(
|
||||
f"The value of {artifacts_path=} is not valid. "
|
||||
"When defined, it must point to a folder containing all models required by the pipeline."
|
||||
)
|
||||
|
||||
# force_backend_text = False - use text that is coming from VLM response
|
||||
# force_backend_text = True - get text from backend using bounding boxes predicted by SmolDocling doctags
|
||||
self.force_backend_text = (
|
||||
pipeline_options.force_backend_text
|
||||
and pipeline_options.vlm_options.response_format == ResponseFormat.DOCTAGS
|
||||
)
|
||||
|
||||
self.keep_images = self.pipeline_options.generate_page_images
|
||||
|
||||
self.build_pipe = [
|
||||
HuggingFaceVlmModel(
|
||||
enabled=True, # must be always enabled for this pipeline to make sense.
|
||||
artifacts_path=artifacts_path,
|
||||
accelerator_options=pipeline_options.accelerator_options,
|
||||
vlm_options=self.pipeline_options.vlm_options,
|
||||
),
|
||||
]
|
||||
|
||||
self.enrichment_pipe = [
|
||||
# Other models working on `NodeItem` elements in the DoclingDocument
|
||||
]
|
||||
|
||||
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:
|
||||
with TimeRecorder(conv_res, "doc_assemble", scope=ProfilingScope.DOCUMENT):
|
||||
|
||||
if (
|
||||
self.pipeline_options.vlm_options.response_format
|
||||
== ResponseFormat.DOCTAGS
|
||||
):
|
||||
conv_res.document = self._turn_tags_into_doc(conv_res.pages)
|
||||
elif (
|
||||
self.pipeline_options.vlm_options.response_format
|
||||
== ResponseFormat.MARKDOWN
|
||||
):
|
||||
conv_res.document = self._turn_md_into_doc(conv_res)
|
||||
|
||||
else:
|
||||
raise RuntimeError(
|
||||
f"Unsupported VLM response format {self.pipeline_options.vlm_options.response_format}"
|
||||
)
|
||||
|
||||
# Generate images of the requested element types
|
||||
if self.pipeline_options.generate_picture_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
|
||||
):
|
||||
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
|
||||
|
||||
def _turn_md_into_doc(self, conv_res):
|
||||
predicted_text = ""
|
||||
for pg_idx, page in enumerate(conv_res.pages):
|
||||
if page.predictions.vlm_response:
|
||||
predicted_text += page.predictions.vlm_response.text + "\n\n"
|
||||
response_bytes = BytesIO(predicted_text.encode("utf8"))
|
||||
out_doc = InputDocument(
|
||||
path_or_stream=response_bytes,
|
||||
filename=conv_res.input.file.name,
|
||||
format=InputFormat.MD,
|
||||
backend=MarkdownDocumentBackend,
|
||||
)
|
||||
backend = MarkdownDocumentBackend(
|
||||
in_doc=out_doc,
|
||||
path_or_stream=response_bytes,
|
||||
)
|
||||
return backend.convert()
|
||||
|
||||
def _turn_tags_into_doc(self, pages: list[Page]) -> DoclingDocument:
|
||||
###############################################
|
||||
# Tag definitions and color mappings
|
||||
###############################################
|
||||
|
||||
# Maps the recognized tag to a Docling label.
|
||||
# Code items will be given DocItemLabel.CODE
|
||||
tag_to_doclabel = {
|
||||
"title": DocItemLabel.TITLE,
|
||||
"document_index": DocItemLabel.DOCUMENT_INDEX,
|
||||
"otsl": DocItemLabel.TABLE,
|
||||
"section_header_level_1": DocItemLabel.SECTION_HEADER,
|
||||
"checkbox_selected": DocItemLabel.CHECKBOX_SELECTED,
|
||||
"checkbox_unselected": DocItemLabel.CHECKBOX_UNSELECTED,
|
||||
"text": DocItemLabel.TEXT,
|
||||
"page_header": DocItemLabel.PAGE_HEADER,
|
||||
"page_footer": DocItemLabel.PAGE_FOOTER,
|
||||
"formula": DocItemLabel.FORMULA,
|
||||
"caption": DocItemLabel.CAPTION,
|
||||
"picture": DocItemLabel.PICTURE,
|
||||
"list_item": DocItemLabel.LIST_ITEM,
|
||||
"footnote": DocItemLabel.FOOTNOTE,
|
||||
"code": DocItemLabel.CODE,
|
||||
}
|
||||
|
||||
# Maps each tag to an associated bounding box color.
|
||||
tag_to_color = {
|
||||
"title": "blue",
|
||||
"document_index": "darkblue",
|
||||
"otsl": "green",
|
||||
"section_header_level_1": "purple",
|
||||
"checkbox_selected": "black",
|
||||
"checkbox_unselected": "gray",
|
||||
"text": "red",
|
||||
"page_header": "orange",
|
||||
"page_footer": "cyan",
|
||||
"formula": "pink",
|
||||
"caption": "magenta",
|
||||
"picture": "yellow",
|
||||
"list_item": "brown",
|
||||
"footnote": "darkred",
|
||||
"code": "lightblue",
|
||||
}
|
||||
|
||||
def extract_bounding_box(text_chunk: str) -> Optional[BoundingBox]:
|
||||
"""Extracts <loc_...> bounding box coords from the chunk, normalized by / 500."""
|
||||
coords = re.findall(r"<loc_(\d+)>", text_chunk)
|
||||
if len(coords) == 4:
|
||||
l, t, r, b = map(float, coords)
|
||||
return BoundingBox(l=l / 500, t=t / 500, r=r / 500, b=b / 500)
|
||||
return None
|
||||
|
||||
def extract_inner_text(text_chunk: str) -> str:
|
||||
"""Strips all <...> tags inside the chunk to get the raw text content."""
|
||||
return re.sub(r"<.*?>", "", text_chunk, flags=re.DOTALL).strip()
|
||||
|
||||
def extract_text_from_backend(page: Page, bbox: BoundingBox | None) -> str:
|
||||
# Convert bounding box normalized to 0-100 into page coordinates for cropping
|
||||
text = ""
|
||||
if bbox:
|
||||
if page.size:
|
||||
bbox.l = bbox.l * page.size.width
|
||||
bbox.t = bbox.t * page.size.height
|
||||
bbox.r = bbox.r * page.size.width
|
||||
bbox.b = bbox.b * page.size.height
|
||||
if page._backend:
|
||||
text = page._backend.get_text_in_rect(bbox)
|
||||
return text
|
||||
|
||||
def otsl_parse_texts(texts, tokens):
|
||||
split_word = TableToken.OTSL_NL.value
|
||||
split_row_tokens = [
|
||||
list(y)
|
||||
for x, y in itertools.groupby(tokens, lambda z: z == split_word)
|
||||
if not x
|
||||
]
|
||||
table_cells = []
|
||||
r_idx = 0
|
||||
c_idx = 0
|
||||
|
||||
def count_right(tokens, c_idx, r_idx, which_tokens):
|
||||
span = 0
|
||||
c_idx_iter = c_idx
|
||||
while tokens[r_idx][c_idx_iter] in which_tokens:
|
||||
c_idx_iter += 1
|
||||
span += 1
|
||||
if c_idx_iter >= len(tokens[r_idx]):
|
||||
return span
|
||||
return span
|
||||
|
||||
def count_down(tokens, c_idx, r_idx, which_tokens):
|
||||
span = 0
|
||||
r_idx_iter = r_idx
|
||||
while tokens[r_idx_iter][c_idx] in which_tokens:
|
||||
r_idx_iter += 1
|
||||
span += 1
|
||||
if r_idx_iter >= len(tokens):
|
||||
return span
|
||||
return span
|
||||
|
||||
for i, text in enumerate(texts):
|
||||
cell_text = ""
|
||||
if text in [
|
||||
TableToken.OTSL_FCEL.value,
|
||||
TableToken.OTSL_ECEL.value,
|
||||
TableToken.OTSL_CHED.value,
|
||||
TableToken.OTSL_RHED.value,
|
||||
TableToken.OTSL_SROW.value,
|
||||
]:
|
||||
row_span = 1
|
||||
col_span = 1
|
||||
right_offset = 1
|
||||
if text != TableToken.OTSL_ECEL.value:
|
||||
cell_text = texts[i + 1]
|
||||
right_offset = 2
|
||||
|
||||
# Check next element(s) for lcel / ucel / xcel, set properly row_span, col_span
|
||||
next_right_cell = ""
|
||||
if i + right_offset < len(texts):
|
||||
next_right_cell = texts[i + right_offset]
|
||||
|
||||
next_bottom_cell = ""
|
||||
if r_idx + 1 < len(split_row_tokens):
|
||||
if c_idx < len(split_row_tokens[r_idx + 1]):
|
||||
next_bottom_cell = split_row_tokens[r_idx + 1][c_idx]
|
||||
|
||||
if next_right_cell in [
|
||||
TableToken.OTSL_LCEL.value,
|
||||
TableToken.OTSL_XCEL.value,
|
||||
]:
|
||||
# we have horisontal spanning cell or 2d spanning cell
|
||||
col_span += count_right(
|
||||
split_row_tokens,
|
||||
c_idx + 1,
|
||||
r_idx,
|
||||
[TableToken.OTSL_LCEL.value, TableToken.OTSL_XCEL.value],
|
||||
)
|
||||
if next_bottom_cell in [
|
||||
TableToken.OTSL_UCEL.value,
|
||||
TableToken.OTSL_XCEL.value,
|
||||
]:
|
||||
# we have a vertical spanning cell or 2d spanning cell
|
||||
row_span += count_down(
|
||||
split_row_tokens,
|
||||
c_idx,
|
||||
r_idx + 1,
|
||||
[TableToken.OTSL_UCEL.value, TableToken.OTSL_XCEL.value],
|
||||
)
|
||||
|
||||
table_cells.append(
|
||||
TableCell(
|
||||
text=cell_text.strip(),
|
||||
row_span=row_span,
|
||||
col_span=col_span,
|
||||
start_row_offset_idx=r_idx,
|
||||
end_row_offset_idx=r_idx + row_span,
|
||||
start_col_offset_idx=c_idx,
|
||||
end_col_offset_idx=c_idx + col_span,
|
||||
)
|
||||
)
|
||||
if text in [
|
||||
TableToken.OTSL_FCEL.value,
|
||||
TableToken.OTSL_ECEL.value,
|
||||
TableToken.OTSL_CHED.value,
|
||||
TableToken.OTSL_RHED.value,
|
||||
TableToken.OTSL_SROW.value,
|
||||
TableToken.OTSL_LCEL.value,
|
||||
TableToken.OTSL_UCEL.value,
|
||||
TableToken.OTSL_XCEL.value,
|
||||
]:
|
||||
c_idx += 1
|
||||
if text == TableToken.OTSL_NL.value:
|
||||
r_idx += 1
|
||||
c_idx = 0
|
||||
return table_cells, split_row_tokens
|
||||
|
||||
def otsl_extract_tokens_and_text(s: str):
|
||||
# Pattern to match anything enclosed by < > (including the angle brackets themselves)
|
||||
pattern = r"(<[^>]+>)"
|
||||
# Find all tokens (e.g. "<otsl>", "<loc_140>", etc.)
|
||||
tokens = re.findall(pattern, s)
|
||||
# Remove any tokens that start with "<loc_"
|
||||
tokens = [
|
||||
token
|
||||
for token in tokens
|
||||
if not (
|
||||
token.startswith(rf"<{DocumentToken.LOC.value}")
|
||||
or token
|
||||
in [
|
||||
rf"<{DocumentToken.OTSL.value}>",
|
||||
rf"</{DocumentToken.OTSL.value}>",
|
||||
]
|
||||
)
|
||||
]
|
||||
# Split the string by those tokens to get the in-between text
|
||||
text_parts = re.split(pattern, s)
|
||||
text_parts = [
|
||||
token
|
||||
for token in text_parts
|
||||
if not (
|
||||
token.startswith(rf"<{DocumentToken.LOC.value}")
|
||||
or token
|
||||
in [
|
||||
rf"<{DocumentToken.OTSL.value}>",
|
||||
rf"</{DocumentToken.OTSL.value}>",
|
||||
]
|
||||
)
|
||||
]
|
||||
# Remove any empty or purely whitespace strings from text_parts
|
||||
text_parts = [part for part in text_parts if part.strip()]
|
||||
|
||||
return tokens, text_parts
|
||||
|
||||
def parse_table_content(otsl_content: str) -> TableData:
|
||||
tokens, mixed_texts = otsl_extract_tokens_and_text(otsl_content)
|
||||
table_cells, split_row_tokens = otsl_parse_texts(mixed_texts, tokens)
|
||||
|
||||
return TableData(
|
||||
num_rows=len(split_row_tokens),
|
||||
num_cols=(
|
||||
max(len(row) for row in split_row_tokens) if split_row_tokens else 0
|
||||
),
|
||||
table_cells=table_cells,
|
||||
)
|
||||
|
||||
doc = DoclingDocument(name="Document")
|
||||
for pg_idx, page in enumerate(pages):
|
||||
xml_content = ""
|
||||
predicted_text = ""
|
||||
if page.predictions.vlm_response:
|
||||
predicted_text = page.predictions.vlm_response.text
|
||||
image = page.image
|
||||
|
||||
page_no = pg_idx + 1
|
||||
bounding_boxes = []
|
||||
|
||||
if page.size:
|
||||
pg_width = page.size.width
|
||||
pg_height = page.size.height
|
||||
size = Size(width=pg_width, height=pg_height)
|
||||
parent_page = doc.add_page(page_no=page_no, size=size)
|
||||
|
||||
"""
|
||||
1. Finds all <tag>...</tag> blocks in the entire string (multi-line friendly) in the order they appear.
|
||||
2. For each chunk, extracts bounding box (if any) and inner text.
|
||||
3. Adds the item to a DoclingDocument structure with the right label.
|
||||
4. Tracks bounding boxes + color in a separate list for later visualization.
|
||||
"""
|
||||
|
||||
# Regex for all recognized tags
|
||||
tag_pattern = (
|
||||
rf"<(?P<tag>{DocItemLabel.TITLE}|{DocItemLabel.DOCUMENT_INDEX}|"
|
||||
rf"{DocItemLabel.CHECKBOX_UNSELECTED}|{DocItemLabel.CHECKBOX_SELECTED}|"
|
||||
rf"{DocItemLabel.TEXT}|{DocItemLabel.PAGE_HEADER}|"
|
||||
rf"{DocItemLabel.PAGE_FOOTER}|{DocItemLabel.FORMULA}|"
|
||||
rf"{DocItemLabel.CAPTION}|{DocItemLabel.PICTURE}|"
|
||||
rf"{DocItemLabel.LIST_ITEM}|{DocItemLabel.FOOTNOTE}|{DocItemLabel.CODE}|"
|
||||
rf"{DocItemLabel.SECTION_HEADER}_level_1|{DocumentToken.OTSL.value})>.*?</(?P=tag)>"
|
||||
)
|
||||
|
||||
# DocumentToken.OTSL
|
||||
pattern = re.compile(tag_pattern, re.DOTALL)
|
||||
|
||||
# Go through each match in order
|
||||
for match in pattern.finditer(predicted_text):
|
||||
full_chunk = match.group(0)
|
||||
tag_name = match.group("tag")
|
||||
|
||||
bbox = extract_bounding_box(full_chunk)
|
||||
doc_label = tag_to_doclabel.get(tag_name, DocItemLabel.PARAGRAPH)
|
||||
color = tag_to_color.get(tag_name, "white")
|
||||
|
||||
# Store bounding box + color
|
||||
if bbox:
|
||||
bounding_boxes.append((bbox, color))
|
||||
|
||||
if tag_name == DocumentToken.OTSL.value:
|
||||
table_data = parse_table_content(full_chunk)
|
||||
bbox = extract_bounding_box(full_chunk)
|
||||
|
||||
if bbox:
|
||||
prov = ProvenanceItem(
|
||||
bbox=bbox.resize_by_scale(pg_width, pg_height),
|
||||
charspan=(0, 0),
|
||||
page_no=page_no,
|
||||
)
|
||||
doc.add_table(data=table_data, prov=prov)
|
||||
else:
|
||||
doc.add_table(data=table_data)
|
||||
|
||||
elif tag_name == DocItemLabel.PICTURE:
|
||||
text_caption_content = extract_inner_text(full_chunk)
|
||||
if image:
|
||||
if bbox:
|
||||
im_width, im_height = image.size
|
||||
|
||||
crop_box = (
|
||||
int(bbox.l * im_width),
|
||||
int(bbox.t * im_height),
|
||||
int(bbox.r * im_width),
|
||||
int(bbox.b * im_height),
|
||||
)
|
||||
cropped_image = image.crop(crop_box)
|
||||
pic = doc.add_picture(
|
||||
parent=None,
|
||||
image=ImageRef.from_pil(image=cropped_image, dpi=72),
|
||||
prov=(
|
||||
ProvenanceItem(
|
||||
bbox=bbox.resize_by_scale(pg_width, pg_height),
|
||||
charspan=(0, 0),
|
||||
page_no=page_no,
|
||||
)
|
||||
),
|
||||
)
|
||||
# If there is a caption to an image, add it as well
|
||||
if len(text_caption_content) > 0:
|
||||
caption_item = doc.add_text(
|
||||
label=DocItemLabel.CAPTION,
|
||||
text=text_caption_content,
|
||||
parent=None,
|
||||
)
|
||||
pic.captions.append(caption_item.get_ref())
|
||||
else:
|
||||
if bbox:
|
||||
# In case we don't have access to an binary of an image
|
||||
doc.add_picture(
|
||||
parent=None,
|
||||
prov=ProvenanceItem(
|
||||
bbox=bbox, charspan=(0, 0), page_no=page_no
|
||||
),
|
||||
)
|
||||
# If there is a caption to an image, add it as well
|
||||
if len(text_caption_content) > 0:
|
||||
caption_item = doc.add_text(
|
||||
label=DocItemLabel.CAPTION,
|
||||
text=text_caption_content,
|
||||
parent=None,
|
||||
)
|
||||
pic.captions.append(caption_item.get_ref())
|
||||
else:
|
||||
# For everything else, treat as text
|
||||
if self.force_backend_text:
|
||||
text_content = extract_text_from_backend(page, bbox)
|
||||
else:
|
||||
text_content = extract_inner_text(full_chunk)
|
||||
doc.add_text(
|
||||
label=doc_label,
|
||||
text=text_content,
|
||||
prov=(
|
||||
ProvenanceItem(
|
||||
bbox=bbox.resize_by_scale(pg_width, pg_height),
|
||||
charspan=(0, len(text_content)),
|
||||
page_no=page_no,
|
||||
)
|
||||
if bbox
|
||||
else None
|
||||
),
|
||||
)
|
||||
return doc
|
||||
|
||||
@classmethod
|
||||
def get_default_options(cls) -> VlmPipelineOptions:
|
||||
return VlmPipelineOptions()
|
||||
|
||||
@classmethod
|
||||
def is_backend_supported(cls, backend: AbstractDocumentBackend):
|
||||
return isinstance(backend, PdfDocumentBackend)
|
||||
@ -43,6 +43,11 @@ def draw_clusters(
|
||||
y0 *= scale_x
|
||||
y1 *= scale_y
|
||||
|
||||
if y1 <= y0:
|
||||
y1, y0 = y0, y1
|
||||
if x1 <= x0:
|
||||
x1, x0 = x0, x1
|
||||
|
||||
cluster_fill_color = (*list(DocItemLabel.get_color(c.label)), 70)
|
||||
cluster_outline_color = (
|
||||
*list(DocItemLabel.get_color(c.label)),
|
||||
|
||||
96
docs/examples/minimal_vlm_pipeline.py
Normal file
96
docs/examples/minimal_vlm_pipeline.py
Normal file
@ -0,0 +1,96 @@
|
||||
import json
|
||||
import time
|
||||
from pathlib import Path
|
||||
|
||||
import yaml
|
||||
|
||||
from docling.datamodel.base_models import InputFormat
|
||||
from docling.datamodel.pipeline_options import (
|
||||
AcceleratorDevice,
|
||||
VlmPipelineOptions,
|
||||
granite_vision_vlm_conversion_options,
|
||||
smoldocling_vlm_conversion_options,
|
||||
)
|
||||
from docling.datamodel.settings import settings
|
||||
from docling.document_converter import DocumentConverter, PdfFormatOption
|
||||
from docling.pipeline.vlm_pipeline import VlmPipeline
|
||||
|
||||
sources = [
|
||||
"tests/data/2305.03393v1-pg9-img.png",
|
||||
]
|
||||
|
||||
## Use experimental VlmPipeline
|
||||
pipeline_options = VlmPipelineOptions()
|
||||
# If force_backend_text = True, text from backend will be used instead of generated text
|
||||
pipeline_options.force_backend_text = False
|
||||
|
||||
## On GPU systems, enable flash_attention_2 with CUDA:
|
||||
# pipeline_options.accelerator_options.device = AcceleratorDevice.CUDA
|
||||
# pipeline_options.accelerator_options.cuda_use_flash_attention2 = True
|
||||
|
||||
## Pick a VLM model. We choose SmolDocling-256M by default
|
||||
pipeline_options.vlm_options = smoldocling_vlm_conversion_options
|
||||
|
||||
## Alternative VLM models:
|
||||
# pipeline_options.vlm_options = granite_vision_vlm_conversion_options
|
||||
|
||||
from docling_core.types.doc import DocItemLabel, ImageRefMode
|
||||
from docling_core.types.doc.document import DEFAULT_EXPORT_LABELS
|
||||
|
||||
## Set up pipeline for PDF or image inputs
|
||||
converter = DocumentConverter(
|
||||
format_options={
|
||||
InputFormat.PDF: PdfFormatOption(
|
||||
pipeline_cls=VlmPipeline,
|
||||
pipeline_options=pipeline_options,
|
||||
),
|
||||
InputFormat.IMAGE: PdfFormatOption(
|
||||
pipeline_cls=VlmPipeline,
|
||||
pipeline_options=pipeline_options,
|
||||
),
|
||||
}
|
||||
)
|
||||
|
||||
out_path = Path("scratch")
|
||||
out_path.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
for source in sources:
|
||||
start_time = time.time()
|
||||
print("================================================")
|
||||
print("Processing... {}".format(source))
|
||||
print("================================================")
|
||||
print("")
|
||||
|
||||
res = converter.convert(source)
|
||||
|
||||
print("------------------------------------------------")
|
||||
print("MD:")
|
||||
print("------------------------------------------------")
|
||||
print("")
|
||||
print(res.document.export_to_markdown())
|
||||
|
||||
for page in res.pages:
|
||||
print("")
|
||||
print("Predicted page in DOCTAGS:")
|
||||
print(page.predictions.vlm_response.text)
|
||||
|
||||
res.document.save_as_html(
|
||||
filename=Path("{}/{}.html".format(out_path, res.input.file.stem)),
|
||||
image_mode=ImageRefMode.REFERENCED,
|
||||
labels=[*DEFAULT_EXPORT_LABELS, DocItemLabel.FOOTNOTE],
|
||||
)
|
||||
|
||||
with (out_path / f"{res.input.file.stem}.json").open("w") as fp:
|
||||
fp.write(json.dumps(res.document.export_to_dict()))
|
||||
|
||||
pg_num = res.document.num_pages()
|
||||
|
||||
print("")
|
||||
inference_time = time.time() - start_time
|
||||
print(
|
||||
f"Total document prediction time: {inference_time:.2f} seconds, pages: {pg_num}"
|
||||
)
|
||||
|
||||
print("================================================")
|
||||
print("done!")
|
||||
print("================================================")
|
||||
673
poetry.lock
generated
673
poetry.lock
generated
@ -1,4 +1,35 @@
|
||||
# This file is automatically @generated by Poetry 1.8.5 and should not be changed by hand.
|
||||
# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand.
|
||||
|
||||
[[package]]
|
||||
name = "accelerate"
|
||||
version = "1.4.0"
|
||||
description = "Accelerate"
|
||||
optional = true
|
||||
python-versions = ">=3.9.0"
|
||||
files = [
|
||||
{file = "accelerate-1.4.0-py3-none-any.whl", hash = "sha256:f6e1e7dfaf9d799a20a1dc45efbf4b1546163eac133faa5acd0d89177c896e55"},
|
||||
{file = "accelerate-1.4.0.tar.gz", hash = "sha256:37d413e1b64cb8681ccd2908ae211cf73e13e6e636a2f598a96eccaa538773a5"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
huggingface-hub = ">=0.21.0"
|
||||
numpy = ">=1.17,<3.0.0"
|
||||
packaging = ">=20.0"
|
||||
psutil = "*"
|
||||
pyyaml = "*"
|
||||
safetensors = ">=0.4.3"
|
||||
torch = ">=2.0.0"
|
||||
|
||||
[package.extras]
|
||||
deepspeed = ["deepspeed"]
|
||||
dev = ["bitsandbytes", "black (>=23.1,<24.0)", "datasets", "diffusers", "evaluate", "hf-doc-builder (>=0.3.0)", "parameterized", "pytest (>=7.2.0,<=8.0.0)", "pytest-subtests", "pytest-xdist", "rich", "ruff (>=0.6.4,<0.7.0)", "scikit-learn", "scipy", "timm", "torchdata (>=0.8.0)", "torchpippy (>=0.2.0)", "tqdm", "transformers"]
|
||||
quality = ["black (>=23.1,<24.0)", "hf-doc-builder (>=0.3.0)", "ruff (>=0.6.4,<0.7.0)"]
|
||||
rich = ["rich"]
|
||||
sagemaker = ["sagemaker"]
|
||||
test-dev = ["bitsandbytes", "datasets", "diffusers", "evaluate", "scikit-learn", "scipy", "timm", "torchdata (>=0.8.0)", "torchpippy (>=0.2.0)", "tqdm", "transformers"]
|
||||
test-prod = ["parameterized", "pytest (>=7.2.0,<=8.0.0)", "pytest-subtests", "pytest-xdist"]
|
||||
test-trackers = ["comet-ml", "dvclive", "tensorboard", "wandb"]
|
||||
testing = ["bitsandbytes", "datasets", "diffusers", "evaluate", "parameterized", "pytest (>=7.2.0,<=8.0.0)", "pytest-subtests", "pytest-xdist", "scikit-learn", "scipy", "timm", "torchdata (>=0.8.0)", "torchpippy (>=0.2.0)", "tqdm", "transformers"]
|
||||
|
||||
[[package]]
|
||||
name = "aiohappyeyeballs"
|
||||
@ -13,92 +44,92 @@ files = [
|
||||
|
||||
[[package]]
|
||||
name = "aiohttp"
|
||||
version = "3.11.12"
|
||||
version = "3.11.13"
|
||||
description = "Async http client/server framework (asyncio)"
|
||||
optional = false
|
||||
python-versions = ">=3.9"
|
||||
files = [
|
||||
{file = "aiohttp-3.11.12-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:aa8a8caca81c0a3e765f19c6953416c58e2f4cc1b84829af01dd1c771bb2f91f"},
|
||||
{file = "aiohttp-3.11.12-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:84ede78acde96ca57f6cf8ccb8a13fbaf569f6011b9a52f870c662d4dc8cd854"},
|
||||
{file = "aiohttp-3.11.12-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:584096938a001378484aa4ee54e05dc79c7b9dd933e271c744a97b3b6f644957"},
|
||||
{file = "aiohttp-3.11.12-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:392432a2dde22b86f70dd4a0e9671a349446c93965f261dbaecfaf28813e5c42"},
|
||||
{file = "aiohttp-3.11.12-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:88d385b8e7f3a870146bf5ea31786ef7463e99eb59e31db56e2315535d811f55"},
|
||||
{file = "aiohttp-3.11.12-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:b10a47e5390c4b30a0d58ee12581003be52eedd506862ab7f97da7a66805befb"},
|
||||
{file = "aiohttp-3.11.12-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0b5263dcede17b6b0c41ef0c3ccce847d82a7da98709e75cf7efde3e9e3b5cae"},
|
||||
{file = "aiohttp-3.11.12-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:50c5c7b8aa5443304c55c262c5693b108c35a3b61ef961f1e782dd52a2f559c7"},
|
||||
{file = "aiohttp-3.11.12-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:d1c031a7572f62f66f1257db37ddab4cb98bfaf9b9434a3b4840bf3560f5e788"},
|
||||
{file = "aiohttp-3.11.12-cp310-cp310-musllinux_1_2_armv7l.whl", hash = "sha256:7e44eba534381dd2687be50cbd5f2daded21575242ecfdaf86bbeecbc38dae8e"},
|
||||
{file = "aiohttp-3.11.12-cp310-cp310-musllinux_1_2_i686.whl", hash = "sha256:145a73850926018ec1681e734cedcf2716d6a8697d90da11284043b745c286d5"},
|
||||
{file = "aiohttp-3.11.12-cp310-cp310-musllinux_1_2_ppc64le.whl", hash = "sha256:2c311e2f63e42c1bf86361d11e2c4a59f25d9e7aabdbdf53dc38b885c5435cdb"},
|
||||
{file = "aiohttp-3.11.12-cp310-cp310-musllinux_1_2_s390x.whl", hash = "sha256:ea756b5a7bac046d202a9a3889b9a92219f885481d78cd318db85b15cc0b7bcf"},
|
||||
{file = "aiohttp-3.11.12-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:526c900397f3bbc2db9cb360ce9c35134c908961cdd0ac25b1ae6ffcaa2507ff"},
|
||||
{file = "aiohttp-3.11.12-cp310-cp310-win32.whl", hash = "sha256:b8d3bb96c147b39c02d3db086899679f31958c5d81c494ef0fc9ef5bb1359b3d"},
|
||||
{file = "aiohttp-3.11.12-cp310-cp310-win_amd64.whl", hash = "sha256:7fe3d65279bfbee8de0fb4f8c17fc4e893eed2dba21b2f680e930cc2b09075c5"},
|
||||
{file = "aiohttp-3.11.12-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:87a2e00bf17da098d90d4145375f1d985a81605267e7f9377ff94e55c5d769eb"},
|
||||
{file = "aiohttp-3.11.12-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:b34508f1cd928ce915ed09682d11307ba4b37d0708d1f28e5774c07a7674cac9"},
|
||||
{file = "aiohttp-3.11.12-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:936d8a4f0f7081327014742cd51d320296b56aa6d324461a13724ab05f4b2933"},
|
||||
{file = "aiohttp-3.11.12-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2de1378f72def7dfb5dbd73d86c19eda0ea7b0a6873910cc37d57e80f10d64e1"},
|
||||
{file = "aiohttp-3.11.12-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:b9d45dbb3aaec05cf01525ee1a7ac72de46a8c425cb75c003acd29f76b1ffe94"},
|
||||
{file = "aiohttp-3.11.12-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:930ffa1925393381e1e0a9b82137fa7b34c92a019b521cf9f41263976666a0d6"},
|
||||
{file = "aiohttp-3.11.12-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8340def6737118f5429a5df4e88f440746b791f8f1c4ce4ad8a595f42c980bd5"},
|
||||
{file = "aiohttp-3.11.12-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:4016e383f91f2814e48ed61e6bda7d24c4d7f2402c75dd28f7e1027ae44ea204"},
|
||||
{file = "aiohttp-3.11.12-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:3c0600bcc1adfaaac321422d615939ef300df81e165f6522ad096b73439c0f58"},
|
||||
{file = "aiohttp-3.11.12-cp311-cp311-musllinux_1_2_armv7l.whl", hash = "sha256:0450ada317a65383b7cce9576096150fdb97396dcfe559109b403c7242faffef"},
|
||||
{file = "aiohttp-3.11.12-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:850ff6155371fd802a280f8d369d4e15d69434651b844bde566ce97ee2277420"},
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||||
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||||
{file = "aiohttp-3.11.12-cp311-cp311-musllinux_1_2_s390x.whl", hash = "sha256:76719dd521c20a58a6c256d058547b3a9595d1d885b830013366e27011ffe804"},
|
||||
{file = "aiohttp-3.11.12-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:97fe431f2ed646a3b56142fc81d238abcbaff08548d6912acb0b19a0cadc146b"},
|
||||
{file = "aiohttp-3.11.12-cp311-cp311-win32.whl", hash = "sha256:e10c440d142fa8b32cfdb194caf60ceeceb3e49807072e0dc3a8887ea80e8c16"},
|
||||
{file = "aiohttp-3.11.12-cp311-cp311-win_amd64.whl", hash = "sha256:246067ba0cf5560cf42e775069c5d80a8989d14a7ded21af529a4e10e3e0f0e6"},
|
||||
{file = "aiohttp-3.11.12-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:e392804a38353900c3fd8b7cacbea5132888f7129f8e241915e90b85f00e3250"},
|
||||
{file = "aiohttp-3.11.12-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:8fa1510b96c08aaad49303ab11f8803787c99222288f310a62f493faf883ede1"},
|
||||
{file = "aiohttp-3.11.12-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:dc065a4285307607df3f3686363e7f8bdd0d8ab35f12226362a847731516e42c"},
|
||||
{file = "aiohttp-3.11.12-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:cddb31f8474695cd61fc9455c644fc1606c164b93bff2490390d90464b4655df"},
|
||||
{file = "aiohttp-3.11.12-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:9dec0000d2d8621d8015c293e24589d46fa218637d820894cb7356c77eca3259"},
|
||||
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|
||||
{file = "aiohttp-3.11.12-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6dfe7f984f28a8ae94ff3a7953cd9678550dbd2a1f9bda5dd9c5ae627744c78e"},
|
||||
{file = "aiohttp-3.11.12-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:a481a574af914b6e84624412666cbfbe531a05667ca197804ecc19c97b8ab1b0"},
|
||||
{file = "aiohttp-3.11.12-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:1987770fb4887560363b0e1a9b75aa303e447433c41284d3af2840a2f226d6e0"},
|
||||
{file = "aiohttp-3.11.12-cp312-cp312-musllinux_1_2_armv7l.whl", hash = "sha256:a4ac6a0f0f6402854adca4e3259a623f5c82ec3f0c049374133bcb243132baf9"},
|
||||
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||||
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||||
{file = "aiohttp-3.11.12-cp312-cp312-musllinux_1_2_s390x.whl", hash = "sha256:68d54234c8d76d8ef74744f9f9fc6324f1508129e23da8883771cdbb5818cbef"},
|
||||
{file = "aiohttp-3.11.12-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:c9fd9dcf9c91affe71654ef77426f5cf8489305e1c66ed4816f5a21874b094b9"},
|
||||
{file = "aiohttp-3.11.12-cp312-cp312-win32.whl", hash = "sha256:0ed49efcd0dc1611378beadbd97beb5d9ca8fe48579fc04a6ed0844072261b6a"},
|
||||
{file = "aiohttp-3.11.12-cp312-cp312-win_amd64.whl", hash = "sha256:54775858c7f2f214476773ce785a19ee81d1294a6bedc5cc17225355aab74802"},
|
||||
{file = "aiohttp-3.11.12-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:413ad794dccb19453e2b97c2375f2ca3cdf34dc50d18cc2693bd5aed7d16f4b9"},
|
||||
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|
||||
{file = "aiohttp-3.11.12-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:d589264dbba3b16e8951b6f145d1e6b883094075283dafcab4cdd564a9e353a0"},
|
||||
{file = "aiohttp-3.11.12-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e5148ca8955affdfeb864aca158ecae11030e952b25b3ae15d4e2b5ba299bad2"},
|
||||
{file = "aiohttp-3.11.12-cp313-cp313-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:525410e0790aab036492eeea913858989c4cb070ff373ec3bc322d700bdf47c1"},
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||||
{file = "aiohttp-3.11.13-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b27961d65639128336b7a7c3f0046dcc62a9443d5ef962e3c84170ac620cec47"},
|
||||
{file = "aiohttp-3.11.13-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:a01fe9f1e05025eacdd97590895e2737b9f851d0eb2e017ae9574d9a4f0b6252"},
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||||
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||||
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||||
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|
||||
{file = "aiohttp-3.11.13-cp39-cp39-musllinux_1_2_s390x.whl", hash = "sha256:85fa0b18558eb1427090912bd456a01f71edab0872f4e0f9e4285571941e4090"},
|
||||
{file = "aiohttp-3.11.13-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:a86dc177eb4c286c19d1823ac296299f59ed8106c9536d2b559f65836e0fb2c6"},
|
||||
{file = "aiohttp-3.11.13-cp39-cp39-win32.whl", hash = "sha256:684eea71ab6e8ade86b9021bb62af4bf0881f6be4e926b6b5455de74e420783a"},
|
||||
{file = "aiohttp-3.11.13-cp39-cp39-win_amd64.whl", hash = "sha256:82c249f2bfa5ecbe4a1a7902c81c0fba52ed9ebd0176ab3047395d02ad96cfcb"},
|
||||
{file = "aiohttp-3.11.13.tar.gz", hash = "sha256:8ce789231404ca8fff7f693cdce398abf6d90fd5dae2b1847477196c243b1fbb"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
@ -773,13 +804,13 @@ files = [
|
||||
|
||||
[[package]]
|
||||
name = "decorator"
|
||||
version = "5.1.1"
|
||||
version = "5.2.1"
|
||||
description = "Decorators for Humans"
|
||||
optional = false
|
||||
python-versions = ">=3.5"
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "decorator-5.1.1-py3-none-any.whl", hash = "sha256:b8c3f85900b9dc423225913c5aace94729fe1fa9763b38939a95226f02d37186"},
|
||||
{file = "decorator-5.1.1.tar.gz", hash = "sha256:637996211036b6385ef91435e4fae22989472f9d571faba8927ba8253acbc330"},
|
||||
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|
||||
{file = "decorator-5.2.1.tar.gz", hash = "sha256:65f266143752f734b0a7cc83c46f4618af75b8c5911b00ccb61d0ac9b6da0360"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
@ -1499,13 +1530,13 @@ pyreadline3 = {version = "*", markers = "sys_platform == \"win32\" and python_ve
|
||||
|
||||
[[package]]
|
||||
name = "identify"
|
||||
version = "2.6.7"
|
||||
version = "2.6.8"
|
||||
description = "File identification library for Python"
|
||||
optional = false
|
||||
python-versions = ">=3.9"
|
||||
files = [
|
||||
{file = "identify-2.6.7-py2.py3-none-any.whl", hash = "sha256:155931cb617a401807b09ecec6635d6c692d180090a1cedca8ef7d58ba5b6aa0"},
|
||||
{file = "identify-2.6.7.tar.gz", hash = "sha256:3fa266b42eba321ee0b2bb0936a6a6b9e36a1351cbb69055b3082f4193035684"},
|
||||
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|
||||
]
|
||||
|
||||
[package.extras]
|
||||
@ -2617,13 +2648,13 @@ min-versions = ["babel (==2.9.0)", "click (==7.0)", "colorama (==0.4)", "ghp-imp
|
||||
|
||||
[[package]]
|
||||
name = "mkdocs-autorefs"
|
||||
version = "1.3.1"
|
||||
version = "1.4.0"
|
||||
description = "Automatically link across pages in MkDocs."
|
||||
optional = false
|
||||
python-versions = ">=3.9"
|
||||
files = [
|
||||
{file = "mkdocs_autorefs-1.3.1-py3-none-any.whl", hash = "sha256:18c504ae4d3ee7f344369bb26cb31d4105569ee252aab7d75ec2734c2c8b0474"},
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||||
{file = "mkdocs_autorefs-1.3.1.tar.gz", hash = "sha256:a6d30cbcccae336d622a66c2418a3c92a8196b69782774529ad441abb23c0902"},
|
||||
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||||
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|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
@ -3144,35 +3175,35 @@ test = ["pytest (>=7.2)", "pytest-cov (>=4.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "nh3"
|
||||
version = "0.2.20"
|
||||
version = "0.2.21"
|
||||
description = "Python binding to Ammonia HTML sanitizer Rust crate"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
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{file = "nh3-0.2.20-cp38-abi3-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:10317cd96fe4bbd4eb6b95f3920b71c902157ad44fed103fdcde43e3b8ee8be6"},
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{file = "nh3-0.2.20-cp38-abi3-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:8698db4c04b140800d1a1cd3067fda399e36e1e2b8fc1fe04292a907350a3e9b"},
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{file = "nh3-0.2.20-cp38-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:813f1c8012dd64c990514b795508abb90789334f76a561fa0fd4ca32d2275330"},
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{file = "nh3-0.2.20-cp38-abi3-win_amd64.whl", hash = "sha256:da87573f03084edae8eb87cfe811ec338606288f81d333c07d2a9a0b9b976c0b"},
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{file = "nh3-0.2.21-cp313-cp313t-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:d426d7be1a2f3d896950fe263332ed1662f6c78525b4520c8e9861f8d7f0d243"},
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{file = "nh3-0.2.21-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:9d67709bc0d7d1f5797b21db26e7a8b3d15d21c9c5f58ccfe48b5328483b685b"},
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{file = "nh3-0.2.21-cp313-cp313t-musllinux_1_2_armv7l.whl", hash = "sha256:55823c5ea1f6b267a4fad5de39bc0524d49a47783e1fe094bcf9c537a37df251"},
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{file = "nh3-0.2.21-cp313-cp313t-musllinux_1_2_i686.whl", hash = "sha256:818f2b6df3763e058efa9e69677b5a92f9bc0acff3295af5ed013da544250d5b"},
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{file = "nh3-0.2.21-cp313-cp313t-win32.whl", hash = "sha256:637d4a10c834e1b7d9548592c7aad760611415fcd5bd346f77fd8a064309ae6d"},
|
||||
{file = "nh3-0.2.21-cp313-cp313t-win_amd64.whl", hash = "sha256:713d16686596e556b65e7f8c58328c2df63f1a7abe1277d87625dcbbc012ef82"},
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{file = "nh3-0.2.21-cp38-abi3-macosx_10_12_x86_64.macosx_11_0_arm64.macosx_10_12_universal2.whl", hash = "sha256:a772dec5b7b7325780922dd904709f0f5f3a79fbf756de5291c01370f6df0967"},
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{file = "nh3-0.2.21-cp38-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d002b648592bf3033adfd875a48f09b8ecc000abd7f6a8769ed86b6ccc70c759"},
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{file = "nh3-0.2.21-cp38-abi3-manylinux_2_17_ppc64.manylinux2014_ppc64.whl", hash = "sha256:b8d55ea1fc7ae3633d758a92aafa3505cd3cc5a6e40470c9164d54dff6f96d42"},
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{file = "nh3-0.2.21-cp38-abi3-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:6ae319f17cd8960d0612f0f0ddff5a90700fa71926ca800e9028e7851ce44a6f"},
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{file = "nh3-0.2.21-cp38-abi3-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:63ca02ac6f27fc80f9894409eb61de2cb20ef0a23740c7e29f9ec827139fa578"},
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{file = "nh3-0.2.21-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a5f77e62aed5c4acad635239ac1290404c7e940c81abe561fd2af011ff59f585"},
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{file = "nh3-0.2.21-cp38-abi3-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:087ffadfdcd497658c3adc797258ce0f06be8a537786a7217649fc1c0c60c293"},
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{file = "nh3-0.2.21-cp38-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:ac7006c3abd097790e611fe4646ecb19a8d7f2184b882f6093293b8d9b887431"},
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{file = "nh3-0.2.21-cp38-abi3-musllinux_1_2_armv7l.whl", hash = "sha256:6141caabe00bbddc869665b35fc56a478eb774a8c1dfd6fba9fe1dfdf29e6efa"},
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{file = "nh3-0.2.21-cp38-abi3-musllinux_1_2_i686.whl", hash = "sha256:20979783526641c81d2f5bfa6ca5ccca3d1e4472474b162c6256745fbfe31cd1"},
|
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{file = "nh3-0.2.21-cp38-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:a7ea28cd49293749d67e4fcf326c554c83ec912cd09cd94aa7ec3ab1921c8283"},
|
||||
{file = "nh3-0.2.21-cp38-abi3-win32.whl", hash = "sha256:6c9c30b8b0d291a7c5ab0967ab200598ba33208f754f2f4920e9343bdd88f79a"},
|
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{file = "nh3-0.2.21-cp38-abi3-win_amd64.whl", hash = "sha256:bb0014948f04d7976aabae43fcd4cb7f551f9f8ce785a4c9ef66e6c2590f8629"},
|
||||
{file = "nh3-0.2.21.tar.gz", hash = "sha256:4990e7ee6a55490dbf00d61a6f476c9a3258e31e711e13713b2ea7d6616f670e"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
@ -4282,93 +4313,109 @@ wcwidth = "*"
|
||||
|
||||
[[package]]
|
||||
name = "propcache"
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{file = "rpds_py-0.23.1-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:a1e17d8dc8e57d8e0fd21f8f0f0a5211b3fa258b2e444c2053471ef93fe25a00"},
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{file = "rpds_py-0.23.1.tar.gz", hash = "sha256:7f3240dcfa14d198dba24b8b9cb3b108c06b68d45b7babd9eefc1038fdf7e707"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
@ -6166,13 +6213,13 @@ train = ["accelerate (>=0.20.3)", "datasets"]
|
||||
|
||||
[[package]]
|
||||
name = "setuptools"
|
||||
version = "75.8.0"
|
||||
version = "75.8.1"
|
||||
description = "Easily download, build, install, upgrade, and uninstall Python packages"
|
||||
optional = false
|
||||
python-versions = ">=3.9"
|
||||
files = [
|
||||
{file = "setuptools-75.8.0-py3-none-any.whl", hash = "sha256:e3982f444617239225d675215d51f6ba05f845d4eec313da4418fdbb56fb27e3"},
|
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{file = "setuptools-75.8.0.tar.gz", hash = "sha256:c5afc8f407c626b8313a86e10311dd3f661c6cd9c09d4bf8c15c0e11f9f2b0e6"},
|
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{file = "setuptools-75.8.1-py3-none-any.whl", hash = "sha256:3bc32c0b84c643299ca94e77f834730f126efd621de0cc1de64119e0e17dab1f"},
|
||||
{file = "setuptools-75.8.1.tar.gz", hash = "sha256:65fb779a8f28895242923582eadca2337285f0891c2c9e160754df917c3d2530"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
@ -7781,9 +7828,9 @@ type = ["pytest-mypy"]
|
||||
ocrmac = ["ocrmac"]
|
||||
rapidocr = ["onnxruntime", "onnxruntime", "rapidocr-onnxruntime"]
|
||||
tesserocr = ["tesserocr"]
|
||||
vlm = ["transformers", "transformers"]
|
||||
vlm = ["accelerate", "transformers", "transformers"]
|
||||
|
||||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = "^3.9"
|
||||
content-hash = "21c25b86d88aa138f7faa68fcba95af1d9a5edaa22550bd325997aed6eef44fe"
|
||||
content-hash = "1d4718b694098b0676f1ad1606d769887e51fc29f604e5f4c83dd5e1c90557e7"
|
||||
|
||||
@ -58,10 +58,14 @@ onnxruntime = [
|
||||
{ version = ">=1.7.0,<1.20.0", optional = true, markers = "python_version < '3.10'" },
|
||||
{ version = "^1.7.0", optional = true, markers = "python_version >= '3.10'" }
|
||||
]
|
||||
|
||||
transformers = [
|
||||
{markers = "sys_platform != 'darwin' or platform_machine != 'x86_64'", version = "^4.46.0", optional = true },
|
||||
{markers = "sys_platform == 'darwin' and platform_machine == 'x86_64'", version = "~4.42.0", optional = true }
|
||||
]
|
||||
accelerate = [
|
||||
{markers = "sys_platform != 'darwin' or platform_machine != 'x86_64'", version = "^1.2.1", optional = true },
|
||||
]
|
||||
pillow = ">=10.0.0,<12.0.0"
|
||||
tqdm = "^4.65.0"
|
||||
|
||||
@ -124,7 +128,7 @@ torchvision = [
|
||||
[tool.poetry.extras]
|
||||
tesserocr = ["tesserocr"]
|
||||
ocrmac = ["ocrmac"]
|
||||
vlm = ["transformers"]
|
||||
vlm = ["transformers", "accelerate"]
|
||||
rapidocr = ["rapidocr-onnxruntime", "onnxruntime"]
|
||||
|
||||
[tool.poetry.scripts]
|
||||
|
||||
Loading…
Reference in New Issue
Block a user