Delete loading script

KHATT.py

@@ -1,103 +0,0 @@
-# coding=utf-8
-# Copyright 2022 the HuggingFace Datasets Authors.
-#
-# 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.
-
-import datasets
-
-
-_CITATION = """\
-@article{Pattern Recognition,
-    Author = {bri A. Mahmoud, Irfan Ahmad, Wasfi G. Al-Khatib, Mohammad Alshayeb, Mohammad Tanvir Parvez, Volker Märgner, Gernot A. Fink},
-    Title = { {KHATT: An Open Arabic Offline Handwritten Text Database} },
-    Year = {2013},
-    doi = {10.1016/j.patcog.2013.08.009},
-}
-"""
-
-_HOMEPAGE = "https://khatt.ideas2serve.net/KHATTAgreement.php"
-
-_DESCRIPTION = """\
-KHATT (KFUPM Handwritten Arabic TexT) database is a database of unconstrained handwritten Arabic Text written by 1000 different writers. This research database’s development was undertaken by a research group from KFUPM, Dhahran, S audi Arabia headed by Professor Sabri Mahmoud in collaboration with Professor Fink from TU-Dortmund, Germany and Dr. Märgner from TU-Braunschweig, Germany.
-"""
-
-_DATA_URL = {
-    "train": [
-        "https://huggingface.co/datasets/benhachem/KHATT/resolve/main/data/train.zip"
-    ],
-    "validation": [
-        "https://huggingface.co/datasets/benhachem/KHATT/resolve/main/data/validation.zip"
-    ],
-
-}
-
-
-class KHATT(datasets.GeneratorBasedBuilder):
-    VERSION = datasets.Version("1.0.0")
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "image": datasets.Image(),
-                    "text": datasets.Value("string"),
-                }
-            ),
-            homepage=_HOMEPAGE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        archives = dl_manager.download(_DATA_URL)
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "archives": [dl_manager.iter_archive(archive) for archive in archives["train"]],
-                    "split": "train",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "archives": [dl_manager.iter_archive(archive) for archive in archives["validation"]],
-                    "split": "validation",
-                },
-            ),
-        ]
-    def _generate_examples(self, archives, split):
-        """Yields examples."""
-        idx = 0
-
-        for archive in archives:
-            for path, file in archive:
-                # If we have an image
-                if path.endswith(".tif"):
-                    if split != "test":
-                        img_file = file                          
-                    else:
-                        text = ""
-
-                elif path.endswith(".txt"):
-
-                    text = file.read()
-                    text = text.decode('utf-8')
-
-                    ex = {"image": {"path": path, "bytes": img_file.read()}, "text": text}
-
-                    yield idx, ex
-
-                    idx += 1