Create characters.py

characters.py

@@ -0,0 +1,71 @@
+# coding=utf-8
+# 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.Wikipedia
+
+# Lint as: python3
+"""Dream!n character datasets"""
+
+import datasets
+
+_CITATION = """
+"""
+
+_DESCRIPTION = "Dream!n character datasets"
+
+
+_DATASET_URL = "https://huggingface.co/datasets/JAWCF/character-demo/resolve/main/images.tar.gz"
+
+
+class Characters(datasets.GeneratorBasedBuilder):
+
+    def _info(self):
+        features = datasets.Features({
+            'image': datasets.Image(),
+            'text': datasets.Value('string')
+        })
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # This defines the different columns of the dataset and their types
+            features=features,  # Here we define them above because they are different between the two configurations
+            supervised_keys=None,
+            # Homepage of the dataset for documentation
+            homepage="",
+            # License for the dataset if available
+            license="",
+            # Citation for the dataset
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        path = dl_manager.download(_DATASET_URL)
+        image_iters = dl_manager.iter_archive(path)
+        splits = [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "images": image_iters
+                }
+            ),
+        ]
+        return splits
+
+    def _generate_examples(self, images):
+        """Yields examples."""
+        for filepath, image in images:
+            print(filepath)
+            yield {
+                "image": {"path": filepath, "bytes": image.read()},
+                "text" : "alal",
+            }