diff --git a/docling_eval/benchmarks/constants.py b/docling_eval/benchmarks/constants.py index 5bc86550..cb65d1cd 100644 --- a/docling_eval/benchmarks/constants.py +++ b/docling_eval/benchmarks/constants.py @@ -40,6 +40,7 @@ class EvaluationModality(str, Enum): MARKDOWN_TEXT = "markdown_text" # to compute the text accuracy CAPTIONING = "captioning" # to compute the accuracy of captions to table/figure BBOXES_TEXT = "bboxes_text" + OCR = "ocr" KEY_VALUE = "key_value" @@ -67,7 +68,7 @@ class BenchMarkNames(str, Enum): # ??? # OCR - # ??? + PIXPARSEIDL = "PixparseIdl" class ConverterTypes(str, Enum): diff --git a/docling_eval/evaluators/ocr/__init__.py b/docling_eval/evaluators/ocr/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/docling_eval/evaluators/ocr/ocr_evaluator.py b/docling_eval/evaluators/ocr/ocr_evaluator.py new file mode 100644 index 00000000..bfc2facf --- /dev/null +++ b/docling_eval/evaluators/ocr/ocr_evaluator.py @@ -0,0 +1,314 @@ +import glob +import json +import logging +import os +import statistics +from pathlib import Path +from typing import Any, Dict, List + +import evaluate +import pandas as pd +from docling_core.types.doc.document import DoclingDocument +from pydantic import BaseModel +from tqdm import tqdm # type: ignore + +from docling_eval.benchmarks.constants import BenchMarkColumns # type: ignore + +logging.basicConfig( + level=logging.DEBUG, # Set the logging level + format="%(asctime)s - %(levelname)s - %(message)s", +) + +log = logging.getLogger(__name__) + + +class DatasetStatistics(BaseModel): + mean: float + median: float + min: float + max: float + std: float + count: int + + +class PageEvaluation(BaseModel): + doc_id: str + gd_text: str + extracted_text: str + cer: float + source_file: str + engine: str + + +class FileEvaluation(BaseModel): + file_name: str + engine_name: str + evaluations: List[PageEvaluation] + cer_stats: DatasetStatistics + + +class DatasetTextractEvaluation(BaseModel): + file_evaluations: List[FileEvaluation] + + +def compute_stats(cers_list: List[float]) -> DatasetStatistics: + """Compute statistics for CER scores""" + result = { + "mean": statistics.mean(cers_list) if cers_list else 0.0, + "median": statistics.median(cers_list) if cers_list else 0.0, + "min": min(cers_list) if cers_list else 0.0, + "max": max(cers_list) if cers_list else 0.0, + "std": statistics.stdev(cers_list) if len(cers_list) > 1 else 0.0, + "count": len(cers_list), + } + + return DatasetStatistics(**result) + + +def load_jsonl_dataset(jsonl_path: str) -> Dict[str, List[Dict]]: + """Load dataset from JSONL file(s) and organize by source file""" + data_by_file: Dict[str, List[Dict[str, Any]]] = {} + for file_path in glob.glob(jsonl_path): + file_name = os.path.basename(file_path) + data_by_file[file_name] = [] + + with open(file_path, "r") as f: + for line in f: + if line.strip(): + item = json.loads(line) + item["_source_file"] = file_path + data_by_file[file_name].append(item) + + return data_by_file + + +class OCREvaluator: + def __init__(self): + # https://huggingface.co/spaces/evaluate-metric/cer + self._cer_eval = evaluate.load("cer") + + def __call__( + self, input_ds_path: Path, output_path: Path, split: str = "test" + ) -> DatasetTextractEvaluation: + search_pattern = os.path.join(input_ds_path, "*", split, "shard_*.parquet") + parquet_files = glob.glob(search_pattern) + logging.info(f"Loading Parquet files from pattern: {search_pattern}") + logging.info(f"Found {len(parquet_files)} matching files") + + dataset_by_file = {} + engine_mapping = {} + + for file_path in parquet_files: + file_name = Path(file_path).name + path_parts = Path(file_path).parts + engine_name = path_parts[-3] + composite_key = f"{engine_name}/{file_name}" + engine_mapping[composite_key] = engine_name + + try: + df = pd.read_parquet(file_path) + dataset_by_file[composite_key] = df.to_dict("records") + logging.info(f"Successfully loaded {file_path} with {len(df)} records") + except Exception as e: + logging.error(f"Error loading {file_path}: {str(e)}") + + if not dataset_by_file: + raise ValueError(f"Failed to load dataset from {search_pattern}") + + file_count = len(dataset_by_file) + total_records = sum(len(records) for records in dataset_by_file.values()) + logging.info(f"Loaded {total_records} records from {file_count} Parquet files") + + file_evaluations = [] + + for composite_key, dataset in dataset_by_file.items(): + engine_name = engine_mapping[composite_key] + file_name = composite_key.split("/", 1)[1] # Remove engine prefix + + logging.info( + f"Processing file: {file_name} (Engine: {engine_name}) with {len(dataset)} records" + ) + + text_evaluations_list = [] + file_cers_list = [] + + for i, data in tqdm( + enumerate(dataset), + desc=f"Processing {engine_name}/{file_name}", + ncols=120, + total=len(dataset), + ): + doc_id = data[BenchMarkColumns.DOC_ID] + true_doc_dict = data[BenchMarkColumns.GROUNDTRUTH] + pred_doc_dict = data[BenchMarkColumns.PREDICTION] + + true_doc = DoclingDocument.model_validate_json(true_doc_dict) + pred_doc = DoclingDocument.model_validate_json(pred_doc_dict) + + # Extract text from documents + gd_text = self._extract_text(true_doc.export_to_dict()) + extracted_text = self._extract_text(pred_doc.export_to_dict()) + + if gd_text and extracted_text: + cer = self._compute_cer_score(gd_text, extracted_text) + else: + cer = 1.0 # max error when text is missing + + file_cers_list.append(cer) + + text_evaluation = PageEvaluation( + doc_id=doc_id, + gd_text=gd_text, + extracted_text=extracted_text, + cer=cer, + source_file=file_name, + engine=engine_name, + ) + + text_evaluations_list.append(text_evaluation) + logging.debug( + f"Engine: {engine_name}, File: {file_name}, Document {doc_id} CER: {cer:.4f}" + ) + + file_cer_stats = compute_stats(file_cers_list) + + file_evaluation = FileEvaluation( + file_name=file_name, + engine_name=engine_name, + evaluations=text_evaluations_list, + cer_stats=file_cer_stats, + ) + + file_evaluations.append(file_evaluation) + + logging.info(f"\n{engine_name}/{file_name} CER Statistics:") + logging.debug(f" - Mean CER: {file_cer_stats.mean:.4f}") + logging.debug(f" - Median CER: {file_cer_stats.median:.4f}") + logging.debug(f" - Min CER: {file_cer_stats.min:.4f}") + logging.debug(f" - Max CER: {file_cer_stats.max:.4f}") + logging.debug(f" - Std Dev: {file_cer_stats.std:.4f}") + logging.debug(f" - Count: {file_cer_stats.count}") + + # Only create output after processing all files + output_path.mkdir(parents=True, exist_ok=True) + + results_path = output_path / "evaluation_results.json" + with open(results_path, "w") as f: + results_data = { + "file_evaluations": [ + { + "file_name": eval_item.file_name, + "engine_name": eval_item.engine_name, # Include engine name in output + "statistics": eval_item.cer_stats.model_dump(), + "evaluations": [e.model_dump() for e in eval_item.evaluations], + } + for eval_item in file_evaluations + ] + } + json.dump(results_data, f, indent=2) + + logging.info(f"Saved evaluation results to {results_path}") + + chart_path = output_path / "ocr_comparison_chart.png" + self._create_comparison_chart(file_evaluations, chart_path) + + return DatasetTextractEvaluation(file_evaluations=file_evaluations) + + def _compute_cer_score(self, true_txt: str, pred_txt: str): + """Compute CER score with the HF evaluate and the default Tokenizer""" + result = self._cer_eval.compute(predictions=[pred_txt], references=[true_txt]) + return result + + def _extract_text(self, json_data: Dict[str, Any]) -> str: + """Extract text from document JSON structure""" + extracted_text = "" + if "texts" in json_data: + for text_item in json_data["texts"]: + if "text" in text_item: + extracted_text += text_item["text"] + " " + return extracted_text.strip() + + def _create_comparison_chart( + self, file_evaluations: List[FileEvaluation], output_path: Path + ): + """Create a bar chart comparing CER across different OCR engines""" + try: + import matplotlib.pyplot as plt + import numpy as np + + engine_data = {} # type: ignore + for eval_item in file_evaluations: + engine_name = eval_item.engine_name + + if engine_name not in engine_data: + engine_data[engine_name] = { + "mean_cers": [], + "median_cers": [], + "counts": [], + } + + engine_data[engine_name]["mean_cers"].append(eval_item.cer_stats.mean) + engine_data[engine_name]["median_cers"].append( + eval_item.cer_stats.median + ) + engine_data[engine_name]["counts"].append(eval_item.cer_stats.count) + + engines = [] + mean_cers = [] + median_cers = [] + + for engine, data in engine_data.items(): + engines.append(engine) + + if len(data["mean_cers"]) > 1: + total_count = sum(data["counts"]) + weighted_mean = ( + sum(m * c for m, c in zip(data["mean_cers"], data["counts"])) + / total_count + ) + weighted_median = ( + sum(m * c for m, c in zip(data["median_cers"], data["counts"])) + / total_count + ) + mean_cers.append(weighted_mean) + median_cers.append(weighted_median) + else: + mean_cers.append(data["mean_cers"][0]) + median_cers.append(data["median_cers"][0]) + + x = np.arange(len(engines)) + width = 0.35 + + fig, ax = plt.subplots(figsize=(10, 6)) + ax.bar(x - width / 2, mean_cers, width, label="Mean CER") + ax.bar(x + width / 2, median_cers, width, label="Median CER") + + ax.set_xlabel("OCR Engine") + ax.set_ylabel("Character Error Rate (CER)") + ax.set_title("OCR Engine Comparison - Lower is Better") + ax.set_xticks(x) + ax.set_xticklabels(engines) + ax.legend() + + for i, v in enumerate(mean_cers): + ax.text(i - width / 2, v + 0.01, f"{v:.3f}", ha="center") + + for i, v in enumerate(median_cers): + ax.text(i + width / 2, v + 0.01, f"{v:.3f}", ha="center") + + plt.tight_layout() + plt.savefig(output_path) + logging.info(f"Saved comparison chart to {output_path}") + + except ImportError: + logging.warning( + "Could not create comparison chart: matplotlib is not installed" + ) + + +if __name__ == "__main__": + evaluator = OCREvaluator() + dataset_path = Path("docling-eval/custom-dataset/ground-truth") + output_path = Path("docling-eval/custom-dataset/ground-truth") + evaluation_results = evaluator(dataset_path, output_path) + print(f"Completed evaluation. Results saved to {output_path}") diff --git a/docling_eval_next/dataset_builders/dataset_builder.py b/docling_eval_next/dataset_builders/dataset_builder.py index 29b141cd..8b9e0978 100644 --- a/docling_eval_next/dataset_builders/dataset_builder.py +++ b/docling_eval_next/dataset_builders/dataset_builder.py @@ -79,7 +79,7 @@ def retrieve_input_dataset(self) -> Path: def iterate(self) -> Iterable[DatasetRecord]: pass - def update_prediction(self, record: DatasetRecord): + def update_prediction(self, record: DatasetRecord, **extra_args): # This might need customization depending on the input the dataset has. # The default implementation assumes that there is an original file in binary format which is accepted. input_data = record.original @@ -91,7 +91,7 @@ def update_prediction(self, record: DatasetRecord): ) pred_doc = self.prediction_provider.predict( - record.ground_truth_doc, stream=input_data + record.ground_truth_doc, stream=input_data, **extra_args ) record.predicted_doc = pred_doc diff --git a/docling_eval_next/dataset_builders/pixparse_builder.py b/docling_eval_next/dataset_builders/pixparse_builder.py new file mode 100644 index 00000000..5c7ce5aa --- /dev/null +++ b/docling_eval_next/dataset_builders/pixparse_builder.py @@ -0,0 +1,196 @@ +import json +import logging +import os +from io import BytesIO +from pathlib import Path +from typing import Dict, Iterable + +from docling_core.types import DoclingDocument +from docling_core.types.doc import ( + BoundingBox, + CoordOrigin, + DocItemLabel, + PageItem, + ProvenanceItem, + Size, +) +from docling_core.types.io import DocumentStream +from PIL import Image +from tqdm import tqdm + +from docling_eval.benchmarks.utils import get_binary, get_binhash +from docling_eval.visualisation.visualisations import save_comparison_html_with_clusters +from docling_eval_next.datamodels.dataset_record import DatasetRecord +from docling_eval_next.dataset_builders.dataset_builder import ( + BaseEvaluationDatasetBuilder, +) +from docling_eval_next.prediction_providers.prediction_provider import ( + BasePredictionProvider, +) + +TRUE_HTML_EXPORT_LABELS = { + DocItemLabel.TITLE, + DocItemLabel.DOCUMENT_INDEX, + DocItemLabel.SECTION_HEADER, + DocItemLabel.PARAGRAPH, + DocItemLabel.TABLE, + DocItemLabel.PICTURE, + DocItemLabel.FORMULA, + DocItemLabel.CHECKBOX_UNSELECTED, + DocItemLabel.CHECKBOX_SELECTED, + DocItemLabel.TEXT, + DocItemLabel.LIST_ITEM, + DocItemLabel.CODE, + DocItemLabel.REFERENCE, + DocItemLabel.CAPTION, + DocItemLabel.PAGE_HEADER, + DocItemLabel.PAGE_FOOTER, + DocItemLabel.FOOTNOTE, +} + +PRED_HTML_EXPORT_LABELS = { + DocItemLabel.TITLE, + DocItemLabel.DOCUMENT_INDEX, + DocItemLabel.SECTION_HEADER, + DocItemLabel.PARAGRAPH, + DocItemLabel.TABLE, + DocItemLabel.PICTURE, + DocItemLabel.FORMULA, + DocItemLabel.CHECKBOX_UNSELECTED, + DocItemLabel.CHECKBOX_SELECTED, + DocItemLabel.TEXT, + DocItemLabel.LIST_ITEM, + DocItemLabel.CODE, + DocItemLabel.REFERENCE, + DocItemLabel.PAGE_HEADER, + DocItemLabel.PAGE_FOOTER, + DocItemLabel.FOOTNOTE, +} + + +class PixparseDatasetBuilder(BaseEvaluationDatasetBuilder): + def __init__( + self, + dataset_source: Path, + prediction_provider: BasePredictionProvider, + target: Path, + do_visualization: bool = True, + split: str = "test", + max_items: int = -1, + ): + super().__init__( + name="pixparse", + dataset_source=dataset_source, + prediction_provider=prediction_provider, + target=target, + ) + self.dataset_local_path = dataset_source + self.do_visualization = do_visualization + self.split = split + self.max_items = max_items + + def _create_ground_truth_doc( + self, doc_id: str, gt_data: Dict, image_file: Path + ) -> DoclingDocument: + """Create a DoclingDocument from ground truth data and image file.""" + w, h = Image.open(image_file).size + + true_doc = DoclingDocument(name=doc_id) + true_doc.pages[1] = PageItem( + size=Size(width=float(w), height=float(h)), page_no=1 + ) + + for page_idx, page in enumerate(gt_data["pages"], 1): + for text, bbox, _ in zip(page["text"], page["bbox"], page["score"]): + bbox_obj = BoundingBox.from_tuple( + ( + float(bbox[0]), + float(bbox[1]), + float(bbox[0] + bbox[2]), + float(bbox[1] + bbox[3]), + ), + CoordOrigin.TOPLEFT, + ) + prov = ProvenanceItem( + page_no=page_idx, bbox=bbox_obj, charspan=(0, len(text)) + ) + true_doc.add_text(label=DocItemLabel.TEXT, text=text, prov=prov) + + return true_doc + + def iterate(self) -> Iterable[DatasetRecord]: + if not self.retrieved: + raise RuntimeError( + "You must first retrieve the source dataset. Call retrieve_input_dataset()." + ) + + assert self.dataset_local_path is not None + + output_dir = self.target + output_dir.mkdir(parents=True, exist_ok=True) + + viz_dir = output_dir / "visualizations" + if self.do_visualization: + viz_dir.mkdir(parents=True, exist_ok=True) + + ground_truth_files = list(self.dataset_local_path.rglob("ground_truth.json")) + if self.max_items > 0: + ground_truth_files = ground_truth_files[: self.max_items] + + for gt_file in tqdm( + ground_truth_files, + desc="Processing files for OCR Benchmark", + total=len(ground_truth_files), + ncols=128, + ): + try: + image_file = gt_file.parent / "original.tif" + if not image_file.exists(): + logging.info(f"Warning: No image file found for {gt_file}") + continue + + doc_id = gt_file.parent.name + input_dir = self.dataset_local_path + + with open(gt_file, "r") as f: + gt_data = json.load(f) + + true_doc = self._create_ground_truth_doc(doc_id, gt_data, image_file) + + image_bytes = get_binary(image_file) + image_stream = DocumentStream( + name=image_file.name, stream=BytesIO(image_bytes) + ) + record = DatasetRecord( + predictor_info=self.prediction_provider.info(), + doc_id=doc_id, + doc_hash=get_binhash(image_bytes), + ground_truth_doc=true_doc, + original=image_stream, + mime_type="image/tiff", + ) + + self.update_prediction(record=record) + + # Generate visualization if requested + if self.do_visualization and record.predicted_doc is not None: + image: Image.Image = Image.open(BytesIO(image_bytes)) + if image.mode not in ( + "RGB", + "RGBA", + "L", + ): + image = image.convert("RGB") + save_comparison_html_with_clusters( + filename=viz_dir / f"{doc_id}-clusters.html", + true_doc=true_doc, + pred_doc=record.predicted_doc, + page_image=image, + true_labels=TRUE_HTML_EXPORT_LABELS, + pred_labels=PRED_HTML_EXPORT_LABELS, + ) + yield record + + except Exception as e: + logging.error(f"Error processing {gt_file}: {str(e)}") + raise diff --git a/docling_eval_next/prediction_providers/aws_prediction_provider.py b/docling_eval_next/prediction_providers/aws_prediction_provider.py new file mode 100644 index 00000000..fcb9505c --- /dev/null +++ b/docling_eval_next/prediction_providers/aws_prediction_provider.py @@ -0,0 +1,274 @@ +import json +import os +from io import BytesIO +from pathlib import Path +from typing import Dict, List, Optional + +import boto3 +from docling_core.types import DoclingDocument +from docling_core.types.doc.base import BoundingBox, CoordOrigin, Size +from docling_core.types.doc.document import ( + DoclingDocument, + PageItem, + ProvenanceItem, + TableCell, + TableData, +) +from docling_core.types.doc.labels import DocItemLabel +from docling_core.types.io import DocumentStream +from PIL import Image + +from docling_eval_next.prediction_providers.prediction_provider import ( + BasePredictionProvider, +) + + +class AWSTextractPredictionProvider(BasePredictionProvider): + """Provider that calls the AWS Textract API for predicting the tables in document.""" + + def __init__(self, **kwargs): + super().__init__(**kwargs) + + aws_access_key_id = os.getenv("AWS_ACCESS_KEY_ID") + aws_secret_access_key = os.getenv("AWS_SECRET_ACCESS_KEY") + + if not aws_access_key_id: + raise ValueError( + "Environment variable AWS_ACCESS_KEY_ID is not set. This is required to authenticate with AWS." + ) + if not aws_secret_access_key: + raise ValueError( + "Environment variable AWS_SECRET_ACCESS_KEY is not set. This is required to authenticate with AWS." + ) + + region_name = os.getenv("AWS_REGION", "us-east-1") + + self.textract_client = boto3.client("textract", region_name=region_name) + + self.skip_api_if_prediction_is_present = bool( + kwargs.get("skip_api_if_prediction_is_present", False) is True + ) + self.predictions_dir = kwargs.get("predictions_dir") + os.makedirs(self.predictions_dir, exist_ok=True) + + def extract_bbox_from_geometry(self, geometry): + """Helper function to extract bbox coordinates from AWS Textract geometry data.""" + if not geometry or not geometry.get("BoundingBox"): + return {"l": 0, "t": 0, "r": 0, "b": 0} + + bbox = geometry.get("BoundingBox") + + left = bbox.get("Left", 0) + top = bbox.get("Top", 0) + width = bbox.get("Width", 0) + height = bbox.get("Height", 0) + + return {"l": left, "t": top, "r": left + width, "b": top + height} + + def get_cell_content(self, cell, blocks_map): + """Extract text content from a table cell.""" + text = "" + if "Relationships" in cell: + for relationship in cell.get("Relationships", []): + if relationship["Type"] == "CHILD": + for child_id in relationship["Ids"]: + child = blocks_map[child_id] + if child["BlockType"] == "WORD": + text += child["Text"] + " " + return text.strip() + + def process_table(self, table, blocks_map, page_no): + """Process a single table from AWS Textract output.""" + table_cells = [] + + table_bbox = self.extract_bbox_from_geometry(table.get("Geometry", {})) + table_bbox_obj = BoundingBox( + l=table_bbox["l"], + t=table_bbox["t"], + r=table_bbox["r"], + b=table_bbox["b"], + coord_origin=CoordOrigin.TOPLEFT, + ) + + table_prov = ProvenanceItem( + page_no=page_no, bbox=table_bbox_obj, charspan=(0, 0) + ) + + cells = [] + if "Relationships" in table: + for relationship in table.get("Relationships", []): + if relationship["Type"] == "CHILD": + for cell_id in relationship["Ids"]: + cell = blocks_map[cell_id] + if cell["BlockType"] == "CELL": + cells.append(cell) + + max_row = 0 + max_col = 0 + for cell in cells: + row = cell.get("RowIndex", 0) + col = cell.get("ColumnIndex", 0) + max_row = max(max_row, row) + max_col = max(max_col, col) + + for cell in cells: + row_index = cell.get("RowIndex", 1) - 1 + col_index = cell.get("ColumnIndex", 1) - 1 + row_span = cell.get("RowSpan", 1) + col_span = cell.get("ColumnSpan", 1) + + # Get cell content + cell_text = self.get_cell_content(cell, blocks_map) + + # Get cell bbox + cell_bbox = self.extract_bbox_from_geometry(cell.get("Geometry", {})) + + # Create TableCell object + table_cell = TableCell( + bbox=BoundingBox( + l=cell_bbox["l"], + t=cell_bbox["t"], + r=cell_bbox["r"], + b=cell_bbox["b"], + coord_origin=CoordOrigin.TOPLEFT, + ), + row_span=row_span, + col_span=col_span, + start_row_offset_idx=row_index, + end_row_offset_idx=row_index + row_span, + start_col_offset_idx=col_index, + end_col_offset_idx=col_index + col_span, + text=cell_text, + # AWS doesn't directly tell us which cells are headers, so we're using heuristics + # Setting first row as column header and first column as row header + column_header=(row_index == 0), + row_header=(col_index == 0), + row_section=False, + ) + + table_cells.append(table_cell) + + table_data = TableData( + table_cells=table_cells, num_rows=max_row, num_cols=max_col + ) + + return table_prov, table_data + + def convert_aws_output_to_docling( + self, analyze_result, filename, file_bytes + ) -> DoclingDocument: + """Converts AWS Textract output to DoclingDocument format.""" + doc_id = Path(filename).stem + doc = DoclingDocument(name=filename) + + blocks_map = {block["Id"]: block for block in analyze_result.get("Blocks", [])} + + processed_pages = set() + + # Get page dimensions from page block + # AWS provides normalized coordinates, so we need to multiply by a typical page size + # width = 8.5 * 72 # Standard US Letter width in points + # height = 11 * 72 # Standard US Letter height in points + im = Image.open(BytesIO(file_bytes)) + width, height = im.size + + for block in analyze_result.get("Blocks", []): + if block["BlockType"] == "PAGE": + page_no = int(block.get("Page", 1)) + processed_pages.add(page_no) + + doc.pages[page_no] = PageItem( + size=Size(width=float(width), height=float(height)), + page_no=page_no, + ) + + if block["BlockType"] == "WORD" and block.get("Page", 1) == page_no: + text_content = block.get("Text", "") + bbox = self.extract_bbox_from_geometry(block.get("Geometry", {})) + + # Scale normalized coordinates to the page dimensions + bbox_obj = BoundingBox( + l=bbox["l"] * width, + t=bbox["t"] * height, + r=bbox["r"] * width, + b=bbox["b"] * height, + coord_origin=CoordOrigin.TOPLEFT, + ) + + prov = ProvenanceItem( + page_no=page_no, + bbox=bbox_obj, + charspan=(0, len(text_content)), + ) + + doc.add_text(label=DocItemLabel.TEXT, text=text_content, prov=prov) + + if block["BlockType"] == "TABLE": + page_no = int(block.get("Page", 1)) + table_prov, table_data = self.process_table(block, blocks_map, page_no) + doc.add_table(prov=table_prov, data=table_data, caption=None) + + return doc + + def predict( + self, + gt_doc: DoclingDocument, + stream: Optional[DocumentStream] = None, + **extra_args, + ) -> DoclingDocument: + """For the given document stream (single document), run the API and create the doclingDocument.""" + + if stream is None or stream.name is None: + raise ValueError("The 'stream' parameter or its 'name' attribute is None.") + + print(f"Creating prediction for file - {stream.name}..") + stream_file_basename = Path(stream.name).stem + + raw_prediction_file_name = os.path.join( + self.predictions_dir, f"{gt_doc.name}.raw.json" + ) + docling_document_file_name = os.path.join( + self.predictions_dir, f"{gt_doc.name}.docling.json" + ) + + # Check if the prediction exists, if so - reuse it + if self.skip_api_if_prediction_is_present and os.path.exists( + raw_prediction_file_name + ): + print( + f"Skipping AWS API call and re-using existing prediction from [{raw_prediction_file_name}]." + ) + with open(raw_prediction_file_name, "r", encoding="utf-8") as f: + result_json = json.load(f) + else: + + file_bytes = stream.stream.read() + stream.stream.seek(0) + response = self.textract_client.analyze_document( + Document={"Bytes": file_bytes}, FeatureTypes=["TABLES", "FORMS"] + ) + + try: + with open(raw_prediction_file_name, "w") as outfile: + json.dump(response, outfile, indent=4) + print(f"JSON response saved to '{raw_prediction_file_name}'") + except IOError as e: + print(f"Error saving JSON to file: {e}") + + result_json = json.loads(json.dumps(response, default=str)) + print(f"Successfully processed [{stream.name}] using AWS Textract API!") + + pred_docling_doc = self.convert_aws_output_to_docling( + result_json, raw_prediction_file_name, stream.stream.read() + ) + + with open(docling_document_file_name, "w", encoding="utf-8") as f: + json.dump(pred_docling_doc.export_to_dict(), f, indent=2) + print( + f"Saved Docling Document output of prediction to - {docling_document_file_name}" + ) + + return pred_docling_doc + + def info(self) -> Dict: + return {"asset": "AWS Textract", "version": "1.0.0"} diff --git a/docling_eval_next/prediction_providers/azure_prediction_provider.py b/docling_eval_next/prediction_providers/azure_prediction_provider.py new file mode 100644 index 00000000..e9836401 --- /dev/null +++ b/docling_eval_next/prediction_providers/azure_prediction_provider.py @@ -0,0 +1,239 @@ +import json +import os +from pathlib import Path +from typing import Dict, Optional + +from docling_core.types import DoclingDocument +from docling_core.types.doc.base import BoundingBox, CoordOrigin, Size +from docling_core.types.doc.document import ( + DoclingDocument, + PageItem, + ProvenanceItem, + TableCell, + TableData, +) +from docling_core.types.doc.labels import DocItemLabel +from docling_core.types.io import DocumentStream + +from docling_eval_next.prediction_providers.prediction_provider import ( + BasePredictionProvider, +) + + +class AzureDocIntelligencePredictionProvider(BasePredictionProvider): + """Provider that calls the Microsoft Azure Document Intelligence API for predicting the tables in document.""" + + def __init__( + self, **kwargs + ): # could be the docling converter options, the remote credentials for MS/Google, etc. + super().__init__(**kwargs) + + # TODO - Need a temp directory to save Azure outputs + # Validate the required library + try: + from azure.ai.formrecognizer import AnalysisFeature, DocumentAnalysisClient + from azure.core.credentials import AzureKeyCredential + except ImportError: + raise ImportError("azure-ai-formrecognizer library is not installed..") + + # Validate the required endpoints to call the API + endpoint = os.getenv("AZURE_DOCUMENT_INTELLIGENCE_ENDPOINT") + key = os.getenv("AZURE_DOCUMENT_INTELLIGENCE_KEY") + + if not endpoint or not key: + raise ValueError( + "AZURE_DOCUMENT_INTELLIGENCE_ENDPOINT and AZURE_DOCUMENT_INTELLIGENCE_KEY must be set in environment variables." + ) + + self.doc_intelligence_client = DocumentAnalysisClient( + endpoint, AzureKeyCredential(key) + ) + + # Save the flags for intermediate results and processing + self.skip_api_if_prediction_is_present = bool( + kwargs.get("skip_api_if_prediction_is_present", False) is True + ) + self.predictions_dir = kwargs.get("predictions_dir") + os.makedirs(self.predictions_dir, exist_ok=True) + + def extract_bbox_from_polygon(self, polygon): + """Helper function to extract bbox coordinates from polygon data.""" + if not polygon or not isinstance(polygon, list): + return {"l": 0, "t": 0, "r": 0, "b": 0} + + # Handle flat array format: [x1, y1, x2, y2, x3, y3, x4, y4] + if len(polygon) >= 8 and all(isinstance(p, (int, float)) for p in polygon): + return {"l": polygon[0], "t": polygon[1], "r": polygon[4], "b": polygon[5]} + # Handle array of point objects: [{x, y}, {x, y}, ...] + elif len(polygon) >= 4 and all( + isinstance(p, dict) and "x" in p and "y" in p for p in polygon + ): + return { + "l": polygon[0]["x"], + "t": polygon[0]["y"], + "r": polygon[2]["x"], + "b": polygon[2]["y"], + } + else: + return {"l": 0, "t": 0, "r": 0, "b": 0} + + def convert_azure_output_to_docling( + self, analyze_result, filename + ) -> DoclingDocument: + """Converts Azure Document Intelligence output to DoclingDocument format.""" + doc = DoclingDocument(name=filename) + + for page in analyze_result.get("pages", []): + page_no = page.get("page_number", 1) + + page_width = page.get("width") + page_height = page.get("height") + doc.pages[page_no] = PageItem( + size=Size(width=float(page_width), height=float(page_height)), + page_no=page_no, + ) + + for word in page.get("words", []): + polygon = word.get("polygon", []) + bbox = self.extract_bbox_from_polygon(polygon) + + text_content = word.get("content", "") + + bbox_obj = BoundingBox( + l=bbox["l"], + t=bbox["t"], + r=bbox["r"], + b=bbox["b"], + coord_origin=CoordOrigin.TOPLEFT, + ) + + prov = ProvenanceItem( + page_no=page_no, bbox=bbox_obj, charspan=(0, len(text_content)) + ) + + doc.add_text(label=DocItemLabel.TEXT, text=text_content, prov=prov) + + for table in analyze_result.get("tables", []): + page_no = table.get("page_range", {}).get("first_page_number", 1) + row_count = table.get("row_count", 0) + col_count = table.get("column_count", 0) + + table_polygon = table.get("bounding_regions", [{}])[0].get("polygon", []) + table_bbox = self.extract_bbox_from_polygon(table_polygon) + + table_bbox_obj = BoundingBox( + l=table_bbox["l"], + t=table_bbox["t"], + r=table_bbox["r"], + b=table_bbox["b"], + coord_origin=CoordOrigin.TOPLEFT, + ) + + table_prov = ProvenanceItem( + page_no=page_no, bbox=table_bbox_obj, charspan=(0, 0) + ) + + table_cells = [] + + for cell in table.get("cells", []): + + cell_text = cell.get("content", "").strip() + row_index = cell.get("row_index", 0) + col_index = cell.get("column_index", 0) + row_span = cell.get("row_span", 1) + col_span = cell.get("column_span", 1) + + cell_polygon = cell.get("bounding_regions", [{}])[0].get("polygon", []) + cell_bbox = self.extract_bbox_from_polygon(cell_polygon) + + table_cell = TableCell( + bbox=BoundingBox( + l=cell_bbox["l"], + t=cell_bbox["t"], + r=cell_bbox["r"], + b=cell_bbox["b"], + coord_origin=CoordOrigin.TOPLEFT, + ), + row_span=row_span, + col_span=col_span, + start_row_offset_idx=row_index, + end_row_offset_idx=row_index + row_span, + start_col_offset_idx=col_index, + end_col_offset_idx=col_index + col_span, + text=cell_text, + column_header=False, + row_header=False, + row_section=False, + ) + + table_cells.append(table_cell) + + table_data = TableData( + table_cells=table_cells, num_rows=row_count, num_cols=col_count + ) + + doc.add_table(prov=table_prov, data=table_data, caption=None) + + return doc + + def predict( + self, + gt_doc: DoclingDocument, + stream: Optional[DocumentStream] = None, + **extra_args, + ) -> DoclingDocument: + """For the given document stream (single document), run the API and create the doclingDocument.""" + + if stream is None or stream.name is None: + raise ValueError("The 'stream' parameter or its 'name' attribute is None.") + + print(f"Creating prediction for file - {stream.name}..") + stream_file_basename = Path(stream.name).stem + + raw_prediction_file_name = os.path.join( + self.predictions_dir, f"{gt_doc.name}.raw.json" + ) + docling_document_file_name = os.path.join( + self.predictions_dir, f"{gt_doc.name}.docling.json" + ) + # Check if the prediction exists, if so - reuse it + if self.skip_api_if_prediction_is_present and os.path.exists( + raw_prediction_file_name + ): + print( + f"Skipping Azure API call and re-using existing prediction from [{raw_prediction_file_name}]." + ) + with open(raw_prediction_file_name, "r", encoding="utf-8") as f: + result_json = json.load(f) + + else: + # Call the Azure API by passing in the image for prediction + poller = self.doc_intelligence_client.begin_analyze_document( + "prebuilt-layout", stream.stream, features=[] + ) + result = poller.result() + result_json = result.to_dict() + print(f"Successfully processed [{stream.name}] using Azure API..!!") + try: + + with open(raw_prediction_file_name, "w") as outfile: + json.dump(result_json, outfile, indent=4) + print(f"JSON response saved to '{raw_prediction_file_name}'") + except IOError as e: + print(f"Error saving JSON to file: {e}") + + # Convert the prediction to doclingDocument + pred_docling_doc = self.convert_azure_output_to_docling( + result_json, stream.name + ) + + with open(docling_document_file_name, "w", encoding="utf-8") as f: + json.dump(pred_docling_doc.export_to_dict(), f, indent=2) + print( + f"Saved Docling Document output of prediction to - {docling_document_file_name}" + ) + + return pred_docling_doc + + def info(self) -> Dict: + return {"asset": "Azure AI Document Intelligence", "version": "1.0.0"} diff --git a/docling_eval_next/prediction_providers/google_prediction_provider.py b/docling_eval_next/prediction_providers/google_prediction_provider.py new file mode 100644 index 00000000..1a0408c3 --- /dev/null +++ b/docling_eval_next/prediction_providers/google_prediction_provider.py @@ -0,0 +1,280 @@ +import json +import logging +import os +from pathlib import Path +from typing import Dict, Optional + +from docling_core.types import DoclingDocument +from docling_core.types.doc.base import BoundingBox, CoordOrigin, Size +from docling_core.types.doc.document import ( + DoclingDocument, + PageItem, + ProvenanceItem, + TableCell, + TableData, +) +from docling_core.types.doc.labels import DocItemLabel +from docling_core.types.io import DocumentStream +from google.cloud import documentai +from google.protobuf.json_format import MessageToDict # Convert to JSON for storage + +from docling_eval_next.prediction_providers.prediction_provider import ( + BasePredictionProvider, +) + + +class GoogleDocAIPredictionProvider(BasePredictionProvider): + """Provider that calls the Google Document AI API for predicting the tables in document.""" + + def __init__(self, **kwargs): + super().__init__(**kwargs) + + if not hasattr(documentai, "DocumentProcessorServiceClient"): + raise ValueError( + "Error: google-cloud-documentai library not installed. Google Doc AI functionality will be disabled." + ) + google_project_id = os.getenv("GOOGLE_PROJECT_ID") + google_location = os.getenv("GOOGLE_LOCATION", "us") + google_processor_id = os.getenv("GOOGLE_PROCESSOR_ID") + + if not os.getenv("GOOGLE_APPLICATION_CREDENTIALS"): + raise ValueError( + "GOOGLE_APPLICATION_CREDENTIALS must be set in environment variables." + ) + + if not google_project_id or not google_processor_id: + raise ValueError( + "GOOGLE_CLOUD_PROJECT_ID and GOOGLE_DOCAI_PROCESSOR_ID must be set in environment variables." + ) + + self.doc_ai_client = documentai.DocumentProcessorServiceClient() + self.google_processor_name = f"projects/{google_project_id}/locations/{google_location}/processors/{google_processor_id}" + + self.skip_api_if_prediction_is_present = bool( + kwargs.get("skip_api_if_prediction_is_present", False) is True + ) + self.predictions_dir = kwargs.get("predictions_dir") + self.mime_type = kwargs.get("mime_type") + os.makedirs(self.predictions_dir, exist_ok=True) + + def extract_bbox_from_vertices(self, vertices): + """Helper function to extract bbox coordinates from vertices.""" + if len(vertices) >= 4: + return { + "l": vertices[0].get("x", 0), + "t": vertices[0].get("y", 0), + "r": vertices[2].get("x", 0), + "b": vertices[2].get("y", 0), + } + return {"l": 0, "t": 0, "r": 0, "b": 0} + + def process_table_row(self, row, row_index, document, table_data, is_header=False): + """Process a table row and add cells to table_data.""" + for cell_index, cell in enumerate(row.get("cells", [])): + cell_text_content = "" + if "layout" in cell and "textAnchor" in cell["layout"]: + for text_segment in cell["layout"]["textAnchor"].get( + "textSegments", [] + ): + start_index = int(text_segment.get("startIndex", 0)) + end_index = int(text_segment.get("endIndex", 0)) + if document.get("text") and start_index < len(document["text"]): + cell_text_content += document["text"][start_index:end_index] + cell_bbox = self.extract_bbox_from_vertices( + cell.get("layout", {}).get("boundingPoly", {}).get("vertices", []) + ) + row_span = cell.get("rowSpan", 1) + col_span = cell.get("colSpan", 1) + + table_cell = TableCell( + bbox=BoundingBox( + l=cell_bbox["l"], + t=cell_bbox["t"], + r=cell_bbox["r"], + b=cell_bbox["b"], + coord_origin=CoordOrigin.TOPLEFT, + ), + row_span=row_span, + col_span=col_span, + start_row_offset_idx=row_index, + end_row_offset_idx=row_index + row_span - 1, + start_col_offset_idx=cell_index, + end_col_offset_idx=cell_index + col_span - 1, + text=cell_text_content.strip(), + column_header=is_header, + row_header=not is_header + and cell_index == 0, # First column might be row header + row_section=False, + ) + + table_data.table_cells.append(table_cell) + + def convert_google_output_to_docling(self, document, filename): + """Converts Google Document AI output to DoclingDocument format.""" + doc_id = Path(filename).stem + doc = DoclingDocument(name=doc_id) + + for page in document.get("pages", []): + page_no = page.get("pageNumber", 1) + page_width = page.get("dimension", {}).get("width", 0) + page_height = page.get("dimension", {}).get("height", 0) + doc.pages[page_no] = PageItem( + size=Size(width=float(page_width), height=float(page_height)), + page_no=page_no, + ) + + for paragraph in page.get("paragraphs", []): + # Extract text content from text_anchor and text_segments + text_content = "" + if "layout" in paragraph and "textAnchor" in paragraph["layout"]: + for text_segment in paragraph["layout"]["textAnchor"].get( + "textSegments", [] + ): + if "endIndex" in text_segment: + start_index = int(text_segment.get("startIndex", 0)) + end_index = int(text_segment.get("endIndex", 0)) + if document.get("text") and start_index < len( + document["text"] + ): + text_content += document["text"][start_index:end_index] + + # Extract paragraph bounding box + para_bbox = self.extract_bbox_from_vertices( + paragraph.get("layout", {}) + .get("boundingPoly", {}) + .get("vertices", []) + ) + + bbox_obj = BoundingBox( + l=para_bbox["l"], + t=para_bbox["t"], + r=para_bbox["r"], + b=para_bbox["b"], + coord_origin=CoordOrigin.TOPLEFT, + ) + + prov = ProvenanceItem( + page_no=page_no, bbox=bbox_obj, charspan=(0, len(text_content)) + ) + + doc.add_text(label=DocItemLabel.PARAGRAPH, text=text_content, prov=prov) + + for table in page.get("tables", []): + table_bbox = self.extract_bbox_from_vertices( + table.get("layout", {}).get("boundingPoly", {}).get("vertices", []) + ) + + num_rows = len(table.get("headerRows", [])) + len( + table.get("bodyRows", []) + ) + num_cols = 0 # Will be calculated based on cells + table_bbox_obj = BoundingBox( + l=table_bbox["l"], + t=table_bbox["t"], + r=table_bbox["r"], + b=table_bbox["b"], + coord_origin=CoordOrigin.TOPLEFT, + ) + + table_prov = ProvenanceItem( + page_no=page_no, bbox=table_bbox_obj, charspan=[] + ) + + table_data = TableData( + table_cells=[], + num_rows=num_rows, + num_cols=0, # Will update as we process cells + grid=[], + ) + + for row_index, row in enumerate(table.get("headerRows", [])): + num_cols = max(table_data.num_cols, len(row.get("cells", []))) + table_data.num_cols = num_cols + + self.process_table_row( + row, row_index, document, table_data, is_header=True + ) + + header_row_count = len(table.get("headerRows", [])) + for row_index, row in enumerate(table.get("bodyRows", [])): + actual_row_index = header_row_count + row_index + num_cols = max(table_data.num_cols, len(row.get("cells", []))) + table_data.num_cols = num_cols + + self.process_table_row( + row, actual_row_index, document, table_data, is_header=False + ) + + doc.add_table(table_data=table_data, prov=table_prov) + + return doc + + def predict( + self, + gt_doc: DoclingDocument, + stream: Optional[DocumentStream] = None, + **extra_args, + ) -> DoclingDocument: + """For the given document stream (single document), run the API and create the doclingDocument.""" + + if stream is None or stream.name is None: + raise ValueError("The 'stream' parameter or its 'name' attribute is None.") + + print(f"Creating prediction for file - {stream.name}..") + stream_file_basename = Path(stream.name).stem + + raw_prediction_file_name = os.path.join( + self.predictions_dir, f"{gt_doc.name}.raw.json" + ) + docling_document_file_name = os.path.join( + self.predictions_dir, f"{gt_doc.name}.docling.json" + ) + + if self.skip_api_if_prediction_is_present and os.path.exists( + raw_prediction_file_name + ): + print( + f"Skipping AWS API call and re-using existing prediction from [{raw_prediction_file_name}]." + ) + with open(raw_prediction_file_name, "r", encoding="utf-8") as f: + result_json = json.load(f) + else: + # Get file content and mime type + file_content = stream.stream.read() + + # Reset stream position + stream.stream.seek(0) + + # Process the document + raw_document = documentai.RawDocument( + content=file_content, mime_type=self.mime_type + ) + request = documentai.ProcessRequest( + name=self.google_processor_name, raw_document=raw_document + ) + response = self.doc_ai_client.process_document(request=request) + result_json = MessageToDict(response.document._pb) + logging.info( + f"Successfully processed [{raw_prediction_file_name}] using Google Document AI API!" + ) + try: + with open(raw_prediction_file_name, "w") as outfile: + json.dump(result_json, outfile, indent=4) + print(f"JSON response saved to '{raw_prediction_file_name}'") + except IOError as e: + print(f"Error saving JSON to file: {e}") + + pred_docling_doc = self.convert_google_output_to_docling( + result_json, raw_prediction_file_name + ) + + with open(docling_document_file_name, "w", encoding="utf-8") as f: + json.dump(pred_docling_doc.export_to_dict(), f, indent=2) + print( + f"Saved Docling Document output of prediction to - {docling_document_file_name}" + ) + + return pred_docling_doc + + def info(self) -> Dict: + return {"asset": "Google Document AI", "version": "1.0.0"} diff --git a/docs/examples/README.md b/docs/examples/README.md new file mode 100644 index 00000000..36c01fca --- /dev/null +++ b/docs/examples/README.md @@ -0,0 +1,46 @@ +### OCR PixParse Evaluation + +Prerequisites for evaluating OCR on the PixParse dataset. + +## Setup Instructions + +1. Clone the repository: +```bash +git clone https://huggingface.co/datasets/samiuc/pixparse-idl +``` + + +2. Environment Configuration + +Set the following environment variables for cloud service providers: + +#### AWS +```bash +export AWS_ACCESS_KEY_ID="your_aws_access_key" +export AWS_SECRET_ACCESS_KEY="your_aws_secret_key" +``` + +#### Google Cloud +```bash +export GOOGLE_PROJECT_ID="your_google_project_id" +export GOOGLE_PROCESSOR_ID="your_google_processor_id" +export GOOGLE_APPLICATION_CREDENTIALS="path/to/your/credentials.json" +``` + +#### Azure +```bash +export AZURE_DOCUMENT_INTELLIGENCE_ENDPOINT="your_azure_endpoint" +export AZURE_DOCUMENT_INTELLIGENCE_KEY="your_azure_key" +``` + +3. Running Evaluation + +``` +poetry run python create_pixparse.py +``` + +**Additional Notes** + +- Set `reprocess=False` to use cached results if available +- Other hyperscaler/OCR engines can be selected by changing the `ocr_engine` and `hyperscaler` parameter +- Results will be saved to the specified output directory \ No newline at end of file diff --git a/docs/examples/run_ocr_pixparse_builder_example.py b/docs/examples/run_ocr_pixparse_builder_example.py new file mode 100644 index 00000000..bb5b4169 --- /dev/null +++ b/docs/examples/run_ocr_pixparse_builder_example.py @@ -0,0 +1,76 @@ +import logging +import os +from pathlib import Path + +from docling_eval.evaluators.ocr.ocr_evaluator import OCREvaluator +from docling_eval_next.dataset_builders.pixparse_builder import PixparseDatasetBuilder +from docling_eval_next.prediction_providers.aws_prediction_provider import ( + AWSTextractPredictionProvider, +) +from docling_eval_next.prediction_providers.azure_prediction_provider import ( + AzureDocIntelligencePredictionProvider, +) +from docling_eval_next.prediction_providers.google_prediction_provider import ( + GoogleDocAIPredictionProvider, +) + + +def main(): + idir = Path("/docling-eval/pixparse-idl") + odir = Path("/docling-eval/pixparse-idl/output") + + os.makedirs(odir, exist_ok=True) + + aws_provider = AWSTextractPredictionProvider( + predictions_dir=odir / "aws", skip_api_if_prediction_is_present=True + ) + aws_dataset = PixparseDatasetBuilder( + prediction_provider=aws_provider, + dataset_source=idir, + target=odir / "aws", + ) + aws_downloaded_path = aws_dataset.retrieve_input_dataset() + logging.info(f"Dataset downloaded to {aws_downloaded_path} for AWS") + + aws_dataset.save_to_disk() + + azure_provider = AzureDocIntelligencePredictionProvider( + predictions_dir=odir / "azure", skip_api_if_prediction_is_present=True + ) + azure_dataset = PixparseDatasetBuilder( + prediction_provider=azure_provider, + dataset_source=idir, + target=odir / "azure", + ) + + azure_downloaded_path = azure_dataset.retrieve_input_dataset() + logging.info(f"Dataset downloaded to {azure_downloaded_path} for AZURE") + azure_dataset.save_to_disk() + + provider_args = { + "predictions_dir": odir / "google", + "skip_api_if_prediction_is_present": True, + "mime_type": "image/tiff", + } + google_provider = GoogleDocAIPredictionProvider(**provider_args) + google_dataset = PixparseDatasetBuilder( + prediction_provider=google_provider, + dataset_source=idir, + target=odir / "google", + ) + + google_downloaded_path = google_dataset.retrieve_input_dataset() + logging.info(f"Dataset downloaded to {google_downloaded_path} for Google") + + google_dataset.save_to_disk() + + logging.info("Evaluate the OCR for the Pixparse dataset") + evaluator = OCREvaluator() + dataset_path = Path("/docling-eval/pixparse-idl/output") + output_path = Path("/docling-eval/pixparse-idl") + evaluation_results = evaluator(dataset_path, output_path) + logging.info(f"Completed evaluation. Results saved to {output_path}") + + +if __name__ == "__main__": + main() diff --git a/poetry.lock b/poetry.lock index cc576a53..daad1fdd 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.8.5 and should not be changed by hand. +# This file is automatically @generated by Poetry 2.1.1 and should not be changed by hand. 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optional = false python-versions = ">=3.9" +groups = ["main"] files = [ {file = "aiohttp-3.11.14-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:e2bc827c01f75803de77b134afdbf74fa74b62970eafdf190f3244931d7a5c0d"}, {file = "aiohttp-3.11.14-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:e365034c5cf6cf74f57420b57682ea79e19eb29033399dd3f40de4d0171998fa"}, @@ -143,7 +147,7 @@ propcache = ">=0.2.0" yarl = ">=1.17.0,<2.0" [package.extras] -speedups = ["Brotli", "aiodns (>=3.2.0)", "brotlicffi"] +speedups = ["Brotli ; platform_python_implementation == \"CPython\"", "aiodns (>=3.2.0) ; sys_platform == \"linux\" or sys_platform == \"darwin\"", "brotlicffi ; platform_python_implementation != \"CPython\""] [[package]] name = "aiosignal" @@ -151,6 +155,7 @@ version = "1.3.2" description = "aiosignal: a list of registered asynchronous callbacks" optional = false python-versions = ">=3.9" +groups = ["main"] files = [ {file = "aiosignal-1.3.2-py2.py3-none-any.whl", hash = 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positions" optional = false python-versions = ">=3.8" +groups = ["dev"] files = [ {file = "asttokens-3.0.0-py3-none-any.whl", hash = "sha256:e3078351a059199dd5138cb1c706e6430c05eff2ff136af5eb4790f9d28932e2"}, {file = "asttokens-3.0.0.tar.gz", hash = "sha256:0dcd8baa8d62b0c1d118b399b2ddba3c4aff271d0d7a9e0d4c1681c79035bbc7"}, @@ -232,6 +243,8 @@ version = "5.0.1" description = "Timeout context manager for asyncio programs" optional = false python-versions = ">=3.8" +groups = ["main"] +markers = "python_version == \"3.10\"" files = [ {file = "async_timeout-5.0.1-py3-none-any.whl", hash = "sha256:39e3809566ff85354557ec2398b55e096c8364bacac9405a7a1fa429e77fe76c"}, {file = "async_timeout-5.0.1.tar.gz", hash = "sha256:d9321a7a3d5a6a5e187e824d2fa0793ce379a202935782d555d6e9d2735677d3"}, @@ -243,18 +256,19 @@ version = "25.3.0" description = "Classes Without Boilerplate" optional = false python-versions = ">=3.8" +groups = ["main"] files = [ {file = "attrs-25.3.0-py3-none-any.whl", hash = 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python_version >= \"3.10\"", "pytest-xdist[psutil]"] +cov = ["cloudpickle ; platform_python_implementation == \"CPython\"", "coverage[toml] (>=5.3)", "hypothesis", "mypy (>=1.11.1) ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pytest-xdist[psutil]"] +dev = ["cloudpickle ; platform_python_implementation == \"CPython\"", "hypothesis", "mypy (>=1.11.1) ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pre-commit-uv", "pympler", "pytest (>=4.3.0)", "pytest-mypy-plugins ; platform_python_implementation == \"CPython\" and python_version >= \"3.10\"", "pytest-xdist[psutil]"] docs = ["cogapp", "furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier"] -tests = ["cloudpickle", "hypothesis", "mypy (>=1.11.1)", "pympler", "pytest (>=4.3.0)", 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= [ {file = "distlib-0.3.9-py2.py3-none-any.whl", hash = "sha256:47f8c22fd27c27e25a65601af709b38e4f0a45ea4fc2e710f65755fa8caaaf87"}, {file = "distlib-0.3.9.tar.gz", hash = "sha256:a60f20dea646b8a33f3e7772f74dc0b2d0772d2837ee1342a00645c81edf9403"}, @@ -889,6 +1492,7 @@ version = "2.26.0" description = "SDK and CLI for parsing PDF, DOCX, HTML, and more, to a unified document representation for powering downstream workflows such as gen AI applications." optional = false python-versions = "<4.0,>=3.9" +groups = ["main"] files = [ {file = "docling-2.26.0-py3-none-any.whl", hash = "sha256:0ddebcd4c258c8fa9e4f84801940ae3c4fce59ad8ff81bc10939f686073323a0"}, {file = "docling-2.26.0.tar.gz", hash = "sha256:db7fc425b876023e4ad42a1fdfc0f285f507c38bc8a24ee2ae00e2eb8af0120d"}, @@ -919,26 +1523,27 @@ rtree = ">=1.3.0,<2.0.0" scipy = {version = ">=1.6.0,<2.0.0", markers = "python_version >= \"3.10\""} tqdm = ">=4.65.0,<5.0.0" transformers = [ - {version = ">=4.46.0,<5.0.0", optional = true, markers = 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"transformers (>=4.46.0,<5.0.0)"] +vlm = ["accelerate (>=1.2.1,<2.0.0) ; sys_platform != \"darwin\" or platform_machine != \"x86_64\"", "transformers (>=4.42.0,<4.43.0) ; sys_platform == \"darwin\" and platform_machine == \"x86_64\"", "transformers (>=4.46.0,<5.0.0) ; sys_platform != \"darwin\" or platform_machine != \"x86_64\""] [[package]] name = "docling-core" -version = "2.23.3" +version = "2.24.0" description = "A python library to define and validate data types in Docling." optional = false python-versions = "<4.0,>=3.9" +groups = ["main"] files = [ - {file = "docling_core-2.23.3-py3-none-any.whl", hash = "sha256:a2166ffc41f8fdf6fdb99b33da6c7146eccf6382712ea92e95772604fb5af5e5"}, - {file = "docling_core-2.23.3.tar.gz", hash = "sha256:a64ce41e0881c06962a2b3ec80e0665f84de0809dedf1bf84f3a14b75dd665c4"}, + {file = "docling_core-2.24.0-py3-none-any.whl", hash = "sha256:5c14f8dd8c6add6f5fa8104ff202df1baa7fd2471123bea56137f866e21b8d40"}, + {file = "docling_core-2.24.0.tar.gz", hash = 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"sha256:f9e14a7a0b92526d4dfd3f390f3d7e075f59d14d6b8a0a564fbc26299e56cd47"}, @@ -1039,6 +1646,7 @@ version = "0.21.2" description = "Docutils -- Python Documentation Utilities" optional = false python-versions = ">=3.9" +groups = ["dev"] files = [ {file = "docutils-0.21.2-py3-none-any.whl", hash = "sha256:dafca5b9e384f0e419294eb4d2ff9fa826435bf15f15b7bd45723e8ad76811b2"}, {file = "docutils-0.21.2.tar.gz", hash = "sha256:3a6b18732edf182daa3cd12775bbb338cf5691468f91eeeb109deff6ebfa986f"}, @@ -1050,6 +1658,7 @@ version = "1.3.1" description = "Dictionary wrapper for quick access to deeply nested keys." optional = false python-versions = ">=3.5,<4.0" +groups = ["dev"] files = [ {file = "dotty_dict-1.3.1-py3-none-any.whl", hash = "sha256:5022d234d9922f13aa711b4950372a06a6d64cb6d6db9ba43d0ba133ebfce31f"}, {file = "dotty_dict-1.3.1.tar.gz", hash = "sha256:4b016e03b8ae265539757a53eba24b9bfda506fb94fbce0bee843c6f05541a15"}, @@ -1061,6 +1670,7 @@ version = "1.7.2" description = "End-to-End Multi-Lingual Optical Character Recognition (OCR) Solution" optional = false python-versions = "*" +groups = ["main"] files = [ {file = "easyocr-1.7.2-py3-none-any.whl", hash = "sha256:5be12f9b0e595d443c9c3d10b0542074b50f0ec2d98b141a109cd961fd1c177c"}, ] @@ -1085,6 +1695,7 @@ version = "2.0.0" description = "An implementation of lxml.xmlfile for the standard library" optional = false python-versions = ">=3.8" +groups = ["main"] files = [ {file = "et_xmlfile-2.0.0-py3-none-any.whl", hash = "sha256:7a91720bc756843502c3b7504c77b8fe44217c85c537d85037f0f536151b2caa"}, {file = "et_xmlfile-2.0.0.tar.gz", hash = "sha256:dab3f4764309081ce75662649be815c4c9081e88f0837825f90fd28317d4da54"}, @@ -1096,6 +1707,7 @@ version = "0.4.3" description = "HuggingFace community-driven open-source library of evaluation" optional = false python-versions = ">=3.8.0" +groups = ["main"] files = [ {file = "evaluate-0.4.3-py3-none-any.whl", hash = "sha256:47d8770bdea76e2c2ed0d40189273027d1a41ccea861bcc7ba12d30ec5d1e517"}, {file = "evaluate-0.4.3.tar.gz", hash = "sha256:3a5700cf83aabee9549264e1e5666f116367c61dbd4d38352015e859a5e2098d"}, @@ -1131,6 +1743,8 @@ version = "1.2.2" description = "Backport of PEP 654 (exception groups)" optional = false python-versions = ">=3.7" +groups = ["dev"] +markers = "python_version == \"3.10\"" files = [ {file = "exceptiongroup-1.2.2-py3-none-any.whl", hash = "sha256:3111b9d131c238bec2f8f516e123e14ba243563fb135d3fe885990585aa7795b"}, {file = "exceptiongroup-1.2.2.tar.gz", hash = "sha256:47c2edf7c6738fafb49fd34290706d1a1a2f4d1c6df275526b62cbb4aa5393cc"}, @@ -1145,6 +1759,7 @@ version = "2.1.1" description = "execnet: rapid multi-Python deployment" optional = false python-versions = ">=3.8" +groups = ["dev"] files = [ {file = "execnet-2.1.1-py3-none-any.whl", hash = "sha256:26dee51f1b80cebd6d0ca8e74dd8745419761d3bef34163928cbebbdc4749fdc"}, {file = "execnet-2.1.1.tar.gz", hash = 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(>=2.4)", "pytest-ruff (>=0.2.1)"] +check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1) ; sys_platform != \"cygwin\""] cover = ["pytest-cov"] doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] enabler = ["pytest-enabler (>=2.2)"] @@ -1785,6 +2668,7 @@ version = "0.19.2" description = "An autocompletion tool for Python that can be used for text editors." optional = false python-versions = ">=3.6" +groups = ["dev"] files = [ {file = "jedi-0.19.2-py2.py3-none-any.whl", hash = "sha256:a8ef22bde8490f57fe5c7681a3c83cb58874daf72b4784de3cce5b6ef6edb5b9"}, {file = "jedi-0.19.2.tar.gz", hash = "sha256:4770dc3de41bde3966b02eb84fbcf557fb33cce26ad23da12c742fb50ecb11f0"}, @@ -1804,13 +2688,15 @@ version = "0.9.0" description = "Low-level, pure Python DBus protocol wrapper." optional = false python-versions = ">=3.7" +groups = ["dev"] +markers = "sys_platform == \"linux\"" files = [ {file = "jeepney-0.9.0-py3-none-any.whl", hash = "sha256:97e5714520c16fc0a45695e5365a2e11b81ea79bba796e26f9f1d178cb182683"}, {file = "jeepney-0.9.0.tar.gz", hash = "sha256:cf0e9e845622b81e4a28df94c40345400256ec608d0e55bb8a3feaa9163f5732"}, ] [package.extras] -test = ["async-timeout", "pytest", "pytest-asyncio (>=0.17)", "pytest-trio", "testpath", "trio"] +test = ["async-timeout ; python_version < \"3.11\"", "pytest", "pytest-asyncio (>=0.17)", "pytest-trio", "testpath", "trio"] trio = ["trio"] [[package]] @@ -1819,6 +2705,7 @@ version = "3.1.6" description = "A very fast and expressive template engine." optional = false python-versions = ">=3.7" +groups = ["main"] files = [ {file = "jinja2-3.1.6-py3-none-any.whl", hash = "sha256:85ece4451f492d0c13c5dd7c13a64681a86afae63a5f347908daf103ce6d2f67"}, {file = "jinja2-3.1.6.tar.gz", hash = "sha256:0137fb05990d35f1275a587e9aee6d56da821fc83491a0fb838183be43f66d6d"}, @@ -1830,12 +2717,45 @@ MarkupSafe = ">=2.0" [package.extras] i18n = ["Babel (>=2.7)"] +[[package]] +name = "jiwer" +version = "3.1.0" +description = "Evaluate your speech-to-text system with similarity measures such as word error rate (WER)" +optional = false +python-versions = ">=3.8" +groups = ["main"] +files = [ + {file = "jiwer-3.1.0-py3-none-any.whl", hash = "sha256:5a14b5bba4692e1946ca3c6946435f7d90b1b526076ccb6c12be763e2146237d"}, + {file = "jiwer-3.1.0.tar.gz", hash = "sha256:dc492d09e570f1baba98c76aba09baf8e09c06e6808a4ba412dd4bde67fb79ac"}, +] + +[package.dependencies] +click = ">=8.1.8" +rapidfuzz = ">=3.9.7" + +[package.extras] +dev = ["black (>=24.8.0)", "flake8 (>=5.0.4)", "pytest (>=8.3.4)", "pytest-benchmark (>=4.0.0)"] +docs = ["mkdocs (>=1.6.1)", "mkdocs-gen-files (>=0.5.0)", "mkdocs-literate-nav (>=0.6.1)", "mkdocs-material (>=9.5.50)", "mkdocstrings[python] (>=0.26.1)"] + +[[package]] +name = "jmespath" +version = "1.0.1" +description = "JSON Matching Expressions" +optional = false +python-versions = ">=3.7" +groups = ["main"] +files = [ + {file = "jmespath-1.0.1-py3-none-any.whl", hash = "sha256:02e2e4cc71b5bcab88332eebf907519190dd9e6e82107fa7f83b1003a6252980"}, + {file = "jmespath-1.0.1.tar.gz", hash = "sha256:90261b206d6defd58fdd5e85f478bf633a2901798906be2ad389150c5c60edbe"}, +] + [[package]] name = "joblib" version = "1.4.2" description = "Lightweight pipelining with Python functions" optional = false python-versions = ">=3.8" +groups = ["main"] files = [ {file = "joblib-1.4.2-py3-none-any.whl", hash = "sha256:06d478d5674cbc267e7496a410ee875abd68e4340feff4490bcb7afb88060ae6"}, {file = "joblib-1.4.2.tar.gz", hash = "sha256:2382c5816b2636fbd20a09e0f4e9dad4736765fdfb7dca582943b9c1366b3f0e"}, @@ -1847,6 +2767,7 @@ version = "3.1.0" description = "Library with helpers for the jsonlines file format" optional = false python-versions = ">=3.6" +groups = ["main"] files = [ {file = "jsonlines-3.1.0-py3-none-any.whl", hash = "sha256:632f5e38f93dfcb1ac8c4e09780b92af3a55f38f26e7c47ae85109d420b6ad39"}, {file = "jsonlines-3.1.0.tar.gz", hash = "sha256:2579cb488d96f815b0eb81629e3e6b0332da0962a18fa3532958f7ba14a5c37f"}, @@ -1861,6 +2782,7 @@ version = "1.1.0" description = "jsonref is a library for automatic dereferencing of JSON Reference objects for Python." optional = false python-versions = ">=3.7" +groups = ["main"] files = [ {file = "jsonref-1.1.0-py3-none-any.whl", hash = "sha256:590dc7773df6c21cbf948b5dac07a72a251db28b0238ceecce0a2abfa8ec30a9"}, {file = "jsonref-1.1.0.tar.gz", hash = "sha256:32fe8e1d85af0fdefbebce950af85590b22b60f9e95443176adbde4e1ecea552"}, @@ -1872,6 +2794,7 @@ version = "4.23.0" description = "An implementation of JSON Schema validation for Python" optional = false python-versions = ">=3.8" +groups = ["main"] files = [ {file = "jsonschema-4.23.0-py3-none-any.whl", hash = "sha256:fbadb6f8b144a8f8cf9f0b89ba94501d143e50411a1278633f56a7acf7fd5566"}, {file = "jsonschema-4.23.0.tar.gz", hash = "sha256:d71497fef26351a33265337fa77ffeb82423f3ea21283cd9467bb03999266bc4"}, @@ -1893,6 +2816,7 @@ version = "2024.10.1" description = "The JSON Schema meta-schemas and vocabularies, exposed as a Registry" optional = false python-versions = ">=3.9" +groups = ["main"] files = [ {file = "jsonschema_specifications-2024.10.1-py3-none-any.whl", hash = "sha256:a09a0680616357d9a0ecf05c12ad234479f549239d0f5b55f3deea67475da9bf"}, {file = "jsonschema_specifications-2024.10.1.tar.gz", hash = "sha256:0f38b83639958ce1152d02a7f062902c41c8fd20d558b0c34344292d417ae272"}, @@ -1907,6 +2831,7 @@ version = "8.6.3" description = "Jupyter protocol implementation and client libraries" optional = false python-versions = ">=3.8" +groups = ["dev"] files = [ {file = "jupyter_client-8.6.3-py3-none-any.whl", hash = "sha256:e8a19cc986cc45905ac3362915f410f3af85424b4c0905e94fa5f2cb08e8f23f"}, {file = "jupyter_client-8.6.3.tar.gz", hash = "sha256:35b3a0947c4a6e9d589eb97d7d4cd5e90f910ee73101611f01283732bd6d9419"}, @@ -1921,7 +2846,7 @@ traitlets = ">=5.3" [package.extras] docs = ["ipykernel", "myst-parser", "pydata-sphinx-theme", "sphinx (>=4)", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling"] -test = ["coverage", "ipykernel (>=6.14)", "mypy", "paramiko", "pre-commit", "pytest (<8.2.0)", "pytest-cov", "pytest-jupyter[client] (>=0.4.1)", "pytest-timeout"] +test = ["coverage", "ipykernel (>=6.14)", "mypy", "paramiko ; sys_platform == \"win32\"", "pre-commit", "pytest (<8.2.0)", "pytest-cov", "pytest-jupyter[client] (>=0.4.1)", "pytest-timeout"] [[package]] name = "jupyter-core" @@ -1929,6 +2854,7 @@ version = "5.7.2" description = "Jupyter core package. A base package on which Jupyter projects rely." optional = false python-versions = ">=3.8" +groups = ["dev"] files = [ {file = "jupyter_core-5.7.2-py3-none-any.whl", hash = "sha256:4f7315d2f6b4bcf2e3e7cb6e46772eba760ae459cd1f59d29eb57b0a01bd7409"}, {file = "jupyter_core-5.7.2.tar.gz", hash = "sha256:aa5f8d32bbf6b431ac830496da7392035d6f61b4f54872f15c4bd2a9c3f536d9"}, @@ -1949,6 +2875,7 @@ version = "3.0.13" description = "Jupyter interactive widgets for JupyterLab" optional = false python-versions = ">=3.7" +groups = ["dev"] files = [ {file = "jupyterlab_widgets-3.0.13-py3-none-any.whl", hash = "sha256:e3cda2c233ce144192f1e29914ad522b2f4c40e77214b0cc97377ca3d323db54"}, {file = "jupyterlab_widgets-3.0.13.tar.gz", hash = "sha256:a2966d385328c1942b683a8cd96b89b8dd82c8b8f81dda902bb2bc06d46f5bed"}, @@ -1960,6 +2887,7 @@ version = "25.6.0" description = "Store and access your passwords safely." optional = false python-versions = ">=3.9" +groups = ["dev"] files = [ {file = "keyring-25.6.0-py3-none-any.whl", hash = "sha256:552a3f7af126ece7ed5c89753650eec89c7eaae8617d0aa4d9ad2b75111266bd"}, {file = "keyring-25.6.0.tar.gz", hash = "sha256:0b39998aa941431eb3d9b0d4b2460bc773b9df6fed7621c2dfb291a7e0187a66"}, @@ -1975,7 +2903,7 @@ pywin32-ctypes = {version = ">=0.2.0", markers = "sys_platform == \"win32\""} SecretStorage = {version = ">=3.2", markers = "sys_platform == \"linux\""} [package.extras] -check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)"] +check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1) ; sys_platform != \"cygwin\""] completion = ["shtab (>=1.1.0)"] cover = ["pytest-cov"] doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] @@ -1989,6 +2917,7 @@ version = "1.4.8" description = "A fast implementation of the Cassowary constraint solver" optional = false python-versions = ">=3.10" +groups = ["main"] files = [ {file = "kiwisolver-1.4.8-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:88c6f252f6816a73b1f8c904f7bbe02fd67c09a69f7cb8a0eecdbf5ce78e63db"}, {file = "kiwisolver-1.4.8-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:c72941acb7b67138f35b879bbe85be0f6c6a70cab78fe3ef6db9c024d9223e5b"}, @@ -2078,6 +3007,7 @@ version = "3.77.0" description = "Pure Python library for LaTeX to MathML conversion" optional = false python-versions = ">=3.8.1,<4.0.0" +groups = ["main"] files = [ {file = "latex2mathml-3.77.0-py3-none-any.whl", hash = "sha256:5531e18a2a9eae7c24e257118b6a444cbba253cd27ff3e81f1bd6c41e88e786e"}, {file = "latex2mathml-3.77.0.tar.gz", hash = "sha256:e2f501d1878f2e489c3f6f12786bef74c62f712d2770f7f3c837eb20a55d0a1e"}, @@ -2089,6 +3019,7 @@ version = "0.4" description = "Makes it easy to load subpackages and functions on demand." optional = false python-versions = ">=3.7" +groups = ["main"] files = [ {file = "lazy_loader-0.4-py3-none-any.whl", hash = "sha256:342aa8e14d543a154047afb4ba8ef17f5563baad3fc610d7b15b213b0f119efc"}, {file = "lazy_loader-0.4.tar.gz", hash = "sha256:47c75182589b91a4e1a85a136c074285a5ad4d9f39c63e0d7fb76391c4574cd1"}, @@ -2108,6 +3039,7 @@ version = "1.10.0" description = "A fast and thorough lazy object proxy." optional = false python-versions = ">=3.8" +groups = ["dev"] files = [ {file = "lazy-object-proxy-1.10.0.tar.gz", hash = "sha256:78247b6d45f43a52ef35c25b5581459e85117225408a4128a3daf8bf9648ac69"}, {file = "lazy_object_proxy-1.10.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:855e068b0358ab916454464a884779c7ffa312b8925c6f7401e952dcf3b89977"}, @@ -2150,13 +3082,14 @@ files = [ [[package]] name = "lightning-utilities" -version = "0.14.1" +version = "0.14.2" description = "Lightning toolbox for across the our ecosystem." optional = false python-versions = ">=3.9" +groups = ["main"] files = [ - {file = "lightning_utilities-0.14.1-py3-none-any.whl", hash = "sha256:badc40a70d8e933706aa0b4f63b12392dbf8208728743a40b55edb5e81797311"}, - {file = "lightning_utilities-0.14.1.tar.gz", hash = "sha256:9fb56c76dc07a46c075e1f78594fcb161091eae5944b34c2b43258d23c202791"}, + {file = "lightning_utilities-0.14.2-py3-none-any.whl", hash = "sha256:da791fcaa731f651ec76a1a3b12994ed05af4d6841f2e78760233552709ef05d"}, + {file = "lightning_utilities-0.14.2.tar.gz", hash = "sha256:0466a4f1bb9dff1c7190d4c7a32d1a8a1109f94fb816931efe8fb8b12bb0ab8d"}, ] [package.dependencies] @@ -2175,6 +3108,7 @@ version = "5.3.1" description = "Powerful and Pythonic XML processing library combining libxml2/libxslt with the ElementTree API." optional = false python-versions = ">=3.6" +groups = ["main"] files = [ {file = "lxml-5.3.1-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:a4058f16cee694577f7e4dd410263cd0ef75644b43802a689c2b3c2a7e69453b"}, {file = "lxml-5.3.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:364de8f57d6eda0c16dcfb999af902da31396949efa0e583e12675d09709881b"}, @@ -2329,6 +3263,7 @@ version = "3.0.0" description = "Python port of markdown-it. Markdown parsing, done right!" optional = false python-versions = ">=3.8" +groups = ["main"] files = [ {file = "markdown-it-py-3.0.0.tar.gz", hash = "sha256:e3f60a94fa066dc52ec76661e37c851cb232d92f9886b15cb560aaada2df8feb"}, {file = "markdown_it_py-3.0.0-py3-none-any.whl", hash = "sha256:355216845c60bd96232cd8d8c40e8f9765cc86f46880e43a8fd22dc1a1a8cab1"}, @@ -2353,6 +3288,7 @@ version = "2.1.2" description = "A markdown parser with high extensibility." optional = false python-versions = ">=3.8" +groups = ["main"] files = [ {file = "marko-2.1.2-py3-none-any.whl", hash = "sha256:c14aa7a77468aaaf53cf056dcd3d32398b9df4c3fb81f5e120dd37cbb9f8c859"}, {file = "marko-2.1.2.tar.gz", hash = "sha256:a9170006b879376e6845c91b1ae3dce2992772954b99b70175ff888537186011"}, @@ -2369,6 +3305,7 @@ version = "3.0.2" description = "Safely add untrusted strings to HTML/XML markup." optional = false python-versions = ">=3.9" +groups = ["main"] files = [ {file = "MarkupSafe-3.0.2-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:7e94c425039cde14257288fd61dcfb01963e658efbc0ff54f5306b06054700f8"}, {file = "MarkupSafe-3.0.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:9e2d922824181480953426608b81967de705c3cef4d1af983af849d7bd619158"}, @@ -2439,6 +3376,7 @@ version = "3.10.1" description = "Python plotting package" optional = false python-versions = ">=3.10" +groups = ["main"] files = [ {file = "matplotlib-3.10.1-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:ff2ae14910be903f4a24afdbb6d7d3a6c44da210fc7d42790b87aeac92238a16"}, {file = "matplotlib-3.10.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:0721a3fd3d5756ed593220a8b86808a36c5031fce489adb5b31ee6dbb47dd5b2"}, @@ -2496,6 +3434,7 @@ version = "0.1.7" description = "Inline Matplotlib backend for Jupyter" optional = false python-versions = ">=3.8" +groups = ["dev"] files = [ {file = "matplotlib_inline-0.1.7-py3-none-any.whl", hash = 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"sha256:661e1abd9198507b1409a20c02106d9670b2576e916d58f520316666abca6729"}, @@ -6072,6 +7448,7 @@ version = "4.0.13" description = "Jupyter interactive widgets for Jupyter Notebook" optional = false python-versions = ">=3.7" +groups = ["dev"] files = [ {file = "widgetsnbextension-4.0.13-py3-none-any.whl", hash = "sha256:74b2692e8500525cc38c2b877236ba51d34541e6385eeed5aec15a70f88a6c71"}, {file = "widgetsnbextension-4.0.13.tar.gz", hash = "sha256:ffcb67bc9febd10234a362795f643927f4e0c05d9342c727b65d2384f8feacb6"}, @@ -6083,6 +7460,7 @@ version = "1.17.2" description = "Module for decorators, wrappers and monkey patching." optional = false python-versions = ">=3.8" +groups = ["dev"] files = [ {file = "wrapt-1.17.2-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:3d57c572081fed831ad2d26fd430d565b76aa277ed1d30ff4d40670b1c0dd984"}, {file = "wrapt-1.17.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:b5e251054542ae57ac7f3fba5d10bfff615b6c2fb09abeb37d2f1463f841ae22"}, @@ -6171,6 +7549,7 @@ version = "3.2.2" description = "A Python module for creating Excel XLSX files." optional = false python-versions = ">=3.6" +groups = ["main"] files = [ {file = "XlsxWriter-3.2.2-py3-none-any.whl", hash = "sha256:272ce861e7fa5e82a4a6ebc24511f2cb952fde3461f6c6e1a1e81d3272db1471"}, {file = "xlsxwriter-3.2.2.tar.gz", hash = "sha256:befc7f92578a85fed261639fb6cde1fd51b79c5e854040847dde59d4317077dc"}, @@ -6182,6 +7561,7 @@ version = "0.14.2" description = "Makes working with XML feel like you are working with JSON" optional = false python-versions = ">=3.6" +groups = ["dev"] files = [ {file = "xmltodict-0.14.2-py2.py3-none-any.whl", hash = "sha256:20cc7d723ed729276e808f26fb6b3599f786cbc37e06c65e192ba77c40f20aac"}, {file = "xmltodict-0.14.2.tar.gz", hash = "sha256:201e7c28bb210e374999d1dde6382923ab0ed1a8a5faeece48ab525b7810a553"}, @@ -6193,6 +7573,7 @@ version = "3.5.0" description = "Python binding for xxHash" optional = false python-versions = ">=3.7" +groups = ["main"] files = [ {file = "xxhash-3.5.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:ece616532c499ee9afbb83078b1b952beffef121d989841f7f4b3dc5ac0fd212"}, {file = "xxhash-3.5.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:3171f693dbc2cef6477054a665dc255d996646b4023fe56cb4db80e26f4cc520"}, @@ -6325,6 +7706,7 @@ version = "1.18.3" description = "Yet another URL library" optional = false python-versions = ">=3.9" +groups = ["main"] files = [ {file = "yarl-1.18.3-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:7df647e8edd71f000a5208fe6ff8c382a1de8edfbccdbbfe649d263de07d8c34"}, {file = "yarl-1.18.3-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:c69697d3adff5aa4f874b19c0e4ed65180ceed6318ec856ebc423aa5850d84f7"}, @@ -6421,20 +7803,21 @@ version = "3.21.0" description = "Backport of pathlib-compatible object wrapper for zip files" optional = false python-versions = ">=3.9" +groups = ["dev"] files = [ {file = "zipp-3.21.0-py3-none-any.whl", hash = "sha256:ac1bbe05fd2991f160ebce24ffbac5f6d11d83dc90891255885223d42b3cd931"}, {file = "zipp-3.21.0.tar.gz", hash = "sha256:2c9958f6430a2040341a52eb608ed6dd93ef4392e02ffe219417c1b28b5dd1f4"}, ] [package.extras] -check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)"] +check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1) ; sys_platform != \"cygwin\""] cover = ["pytest-cov"] doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] enabler = ["pytest-enabler (>=2.2)"] -test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"] +test = ["big-O", "importlib-resources ; python_version < \"3.9\"", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"] type = ["pytest-mypy"] [metadata] -lock-version = "2.0" +lock-version = "2.1" python-versions = "^3.10" -content-hash = "da4b4c6218ec1304a9bbb51f9735660ebb0314126334a2eaa2da789bdfbef25e" +content-hash = "6bf222772f1020c2a70dbddfb282755736510269742b97061c77b726efce4477" diff --git a/pyproject.toml b/pyproject.toml index 092c3da0..50610157 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -31,9 +31,9 @@ lxml = "^5.3.0" datasets = "^3.2.0" apted = "^1.0.3" Distance = "^0.1.3" -docling = {extras = ["vlm"], version = "~2.26.0"} +docling = {extras = ["vlm"], version = "2.26.0"} matplotlib = "^3.10.0" -torch = "^2.5.1" +torch = "2.2.2" torchmetrics = "^1.6.0" pycocotools = "^2.0.8" tabulate = "^0.9.0" @@ -42,6 +42,13 @@ pillow = "^10.3.0" evaluate = "^0.4.3" nltk = "^3.9.1" beautifulsoup4 = "^4.12.3" +boto3 = "^1.37.8" +google-cloud-documentai = "^3.2.0" +azure-ai-documentintelligence = "^1.0.0" +azure-ai-formrecognizer = "^3.3.0" +azure-common = "^1.1.28" +azure-core = "^1.32.0" +jiwer = "^3.1.0" [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "^24.4.2"} @@ -62,6 +69,8 @@ ipywidgets = "^8.1.5" nbqa = "^1.9.0" types-openpyxl = "^3.1.5.20241114" xmltodict = "^0.14.2" +types-protobuf = "^5.29.1.20250315" +boto3-stubs = "^1.37.19" [tool.poetry.scripts] evaluate = "docling_eval.cli.main:app" @@ -120,4 +129,4 @@ branch = "main" # (note that they must be a subset of the configured allowed types): parser_angular_allowed_types = "build,chore,ci,docs,feat,fix,perf,style,refactor,test" parser_angular_minor_types = "feat" -parser_angular_patch_types = "fix,perf" +parser_angular_patch_types = "fix,perf" \ No newline at end of file diff --git a/tests/test_dataset_builder.py b/tests/test_dataset_builder.py index 62964f94..209b751e 100644 --- a/tests/test_dataset_builder.py +++ b/tests/test_dataset_builder.py @@ -27,9 +27,6 @@ OmniDocBenchDatasetBuilder, ) from docling_eval_next.dataset_builders.xfund_builder import XFUNDDatasetBuilder -from docling_eval_next.dataset_builders.omnidocbench_builder import ( - OmniDocBenchDatasetBuilder, -) from docling_eval_next.prediction_providers.prediction_provider import ( DoclingPredictionProvider, NullPredictionProvider,