Upload latex-formulas.py

latex-formulas.py

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+import datasets
+import json
+
+
+RAW_METADATA_URL = r'https://huggingface.co/datasets/OleehyO/latex-formulas/resolve/main/raw_formulas.jsonl'
+
+FILTERED_METADATA_URL = r'https://huggingface.co/datasets/OleehyO/latex-formulas/resolve/main/formulas_finally.jsonl'
+FILTERED_IMG_URL = r'https://huggingface.co/datasets/OleehyO/latex-formulas/resolve/main/formulas_pic.tar.gz'
+
+
+class LatexFormulasConfig(datasets.BuilderConfig):
+    def __init__(self, img_archive_url, metadata_url, **kwargs):
+        super().__init__(**kwargs)
+        self.img_archive_url = img_archive_url
+        self.metadata_url = metadata_url
+
+
+class LatexFormulas(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [
+        LatexFormulasConfig(
+            name="raw_formulas",
+            img_archive_url=None,
+            metadata_url=RAW_METADATA_URL
+        ),
+        LatexFormulasConfig(
+            name="filtered_formulas",
+            img_archive_url=FILTERED_IMG_URL,
+            metadata_url=FILTERED_METADATA_URL
+        )
+    ]
+
+    def _info(self):
+        if self.config.name == "raw_formulas":
+            return datasets.DatasetInfo(
+                features=datasets.Features({
+                    "latex_formula": datasets.Value("string")
+                })
+            )
+        if self.config.name == "filtered_formulas":
+            return datasets.DatasetInfo(
+                features=datasets.Features({
+                    "image": datasets.Image(),
+                    "latex_formula": datasets.Value("string")
+                })
+            )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager):
+        metadata_path = dl_manager.download(self.config.metadata_url)
+
+        if self.config.name == "filtered_formulas":
+            image_archive_path = self.config.img_archive_url
+            images = dl_manager.iter_archive(image_archive_path)
+        elif self.config.name == "raw_formulas":
+            images = None
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "images": images,
+                    "metadata_path": metadata_path
+                }
+            )
+        ]
+
+    def _generate_examples(self, images, metadata_path):
+        if images is not None:
+            img_formula_pair = {}
+            with open(metadata_path, 'r', encoding="utf-8") as f:
+                for line in f:
+                    single_json = json.loads(line)
+                    img_formula_pair[single_json["id"]] = single_json["formula"]
+
+            for img_path, img_obj in images:
+                img_name = img_path.split('/')[-1]
+                if img_name in img_formula_pair:
+                    yield img_path, {
+                        "image": {"path": img_path, "bytes": img_obj.read()},
+                        "latex_formula": img_formula_pair[img_name]
+                    }
+        else:
+            with open(metadata_path, 'r', encoding="utf-8") as f:
+                for idx, line in enumerate(f):
+                    yield idx, {
+                        "latex_formula": json.loads(line)["formula"]
+                    }