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import os
import io
import datasets
from PIL import ImageOps, Image
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {Processed KHATT paragrpah dataset},
author={Ahmed Alnaggar},
year={2024}
}
"""
_DESCRIPTION = """\
A curated version of KHATT paragraph dataset containing 3996 images and their crossponding Arabic paragraphs
"""
_HOMEPAGE = "https://huggingface.co/datasets/a-alnaggar/khatt-paragraphs"
_LICENSE = ""
_REPO = "https://huggingface.co/datasets/a-alnaggar/khatt-paragraphs"
class KhattPara(datasets.GeneratorBasedBuilder):
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
'text': datasets.Value("string"),
'image': datasets.Image(),
}
),
supervised_keys=None,
homepage=_HOMEPAGE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
images_archive = dl_manager.download(f"{_REPO}/resolve/main/khatt-paragraphs-images.tar.gz")
image_iters = dl_manager.iter_archive(images_archive)
text_archive = dl_manager.download_and_extract(f"{_REPO}/resolve/main/khatt-paragraphs-text.tar.gz")
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"images": image_iters,
"text_archive_path": text_archive
}
),
]
def _generate_examples(self, images, text_archive_path):
"""Returns inverted image and Arabic text."""
for idx, (filepath, image) in enumerate(images):
text_path = os.path.join("proc_text", str(os.path.basename(filepath)[:-4] + ".txt"))
text = self.read_arabic_text_file(os.path.join(text_archive_path,text_path))
yield idx, {
"image": {"path": filepath, "bytes": image.read()},
"text": text,
}
@staticmethod
def read_arabic_text_file(file_path):
with open(file_path, 'r', encoding='windows-1256') as file:
lines = file.readlines()
return ''.join(lines)
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