Upload GenDocVQA2024.py with huggingface_hub

GenDocVQA2024.py

@@ -0,0 +1,140 @@
+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""This is a data loader for the GenDocVQA-2024 Dataset."""
+
+
+import csv
+import json
+import os
+import ast
+import pandas as pd
+
+import datasets
+
+_DESCRIPTION = """\
+This dataset is dedicated to the non-extractive document visual question challenge GenDocVQA-2024.
+"""
+
+_URLS = {
+    'img_tar': 'https://huggingface.co/datasets/lenagibee/GenDocVQA2024/resolve/main/archives/gendocvqa2024_imgs.tar.gz?download=true',
+    'ocr_tar': 'https://huggingface.co/datasets/lenagibee/GenDocVQA2024/resolve/main/archives/gendocvqa2024_ocr.tar.gz?download=true',
+    'annotations_tar': 'https://huggingface.co/datasets/lenagibee/GenDocVQA2024/resolve/main/archives/gendocvqa2024_annotations.tar.gz?download=true'
+}
+
+_LICENSE = "Other"
+
+class GenDocVQA2024Small(datasets.GeneratorBasedBuilder):
+
+    VERSION = datasets.Version("1.0.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="default", version=VERSION, description="Whole dataset config"),
+    ]
+
+    DEFAULT_CONFIG_NAME = "default"
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "unique_id": datasets.Value("int64"),
+                "image_path": datasets.Value("string"),
+                "ocr": datasets.Sequence(feature={"text": datasets.Value("string"), "bbox": datasets.Sequence(datasets.Value("int64")),
+                'block_id': datasets.Value("int64"),
+                'text_id': datasets.Value("int64"),
+                'par_id': datasets.Value("int64"),
+                'line_id': datasets.Value("int64"),
+                'word_id': datasets.Value("int64")
+                }),
+                "question": datasets.Value("string"),
+                "answer": datasets.Sequence(datasets.Value("string")),
+                
+            }
+        )
+
+        return datasets.DatasetInfo(
+ 
+            features=features,
+            description=_DESCRIPTION,
+            license=_LICENSE
+        )
+
+    def _split_generators(self, dl_manager):
+        imgs_dir = dl_manager.download_and_extract(_URLS["img_tar"])
+        ocr_dir = dl_manager.download_and_extract(_URLS["ocr_tar"])
+        annotations_dir = dl_manager.download_and_extract(_URLS["annotations_tar"])
+        
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "annot_path": annotations_dir,
+                    "imgs_dir": imgs_dir,
+                    "ocr_dir": ocr_dir,
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "annot_path": annotations_dir,
+                    "imgs_dir": imgs_dir,
+                    "ocr_dir": ocr_dir,
+                    "split": "dev",
+                },
+            )
+        ]
+
+
+    def _generate_examples(self, annot_path, imgs_dir, ocr_dir, split):
+        df = pd.read_csv(os.path.join(annot_path, 'gendocvqa2024_annotations', f'{split}_v1.csv'))
+        for _, row in df.iterrows():
+            img_path = os.path.join(imgs_dir, 'gendocvqa2024_imgs', split, row['image_filename'])
+            q_id = row['unique_id']
+            ocr_path = os.path.join(ocr_dir,  'gendocvqa2024_ocr', split, row['ocr_filename'])
+            question = row['question']
+            answer = row['answer']
+            with open(ocr_path, 'r') as f:
+                ocr = json.load(f)
+            ocr_list = []
+            for item in ocr:
+                ocr_dict = {
+                    'block_id': item[0],
+                    'text_id': item[1],
+                    'par_id': item[2],
+                    'line_id': item[3],
+                    'word_id': item[4],
+                    'bbox': item[5],
+                    'text': item[6]
+                }
+                ocr_list.append(ocr_dict)
+            if split != "test":
+                answer = ast.literal_eval(answer)
+            else:
+                answer = []
+            
+            yield q_id, {
+                "unique_id": q_id,
+                "image_path": img_path,
+                "ocr": ocr_list,
+                "answer": answer,
+                "question": question,
+
+                    }
+               
+
+
+def read_image(img_path):
+    with Image.open(img_path) as f:
+        original_image = f.convert("RGB")
+    return original_image