Delete markushgrapher-datasets.py

markushgrapher-datasets.py

@@ -1,100 +0,0 @@
-import os
-from datasets import (
-    DatasetInfo,
-    GeneratorBasedBuilder,
-    SplitGenerator,
-    Split,
-    BuilderConfig,
-    Features,
-    Value,
-    Sequence,
-    Image,
-    Dataset
-)
-import datasets
-
-
-class MarkushGrapherConfig(BuilderConfig):
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
-
-
-class MarkushGrapherDataset(GeneratorBasedBuilder):
-    BUILDER_CONFIGS = [
-        MarkushGrapherConfig(name="m2s", description="Molecule-to-SMILES synthetic subset"),
-        MarkushGrapherConfig(name="uspto-markush", description="Real Markush structures from USPTO patents"),
-        MarkushGrapherConfig(name="markushgrapher-synthetic", description="Synthetic Markush subset"),
-        MarkushGrapherConfig(name="markushgrapher-synthetic-training", description="Training set with synthetic Markush structures"),
-    ]
-
-    def _info(self):
-        if self.config.name in ["m2s", "uspto-markush"]:
-            return DatasetInfo(
-                features=Features({
-                    "id": Value("int64"),
-                    "image_name": Value("string"),
-                    "page_image": Image(decode=True),
-                    "description": Value("string"),
-                    "annotation": Value("string"),
-                    "mol": Value("string"),
-                    "cxsmiles_dataset": Value("string"),
-                    "cxsmiles": Value("string"),
-                    "cxsmiles_opt": Value("string"),
-                    "keypoints": Value("null"),
-                    "cells": Sequence({
-                        "bbox": Sequence(Value("float64")),
-                        "text": Value("string"),
-                    }),
-                }),
-            )
-
-        elif self.config.name in ["markushgrapher-synthetic", "markushgrapher-synthetic-training"]:
-            return DatasetInfo(
-                features=Features({
-                    "id": Value("int64"),
-                    "page_image_path": Value("string"),
-                    "description": Value("string"),
-                    "annotation": Value("string"),
-                    "mol": Value("string"),
-                    "cxsmiles_dataset": Value("string"),
-                    "cxsmiles": Value("string"),
-                    "cxsmiles_opt": Value("string"),
-                    "keypoints": Sequence(Sequence(Value("float64"))),
-                    "cells": Sequence({
-                        "bbox": Sequence(Value("float64")),
-                        "text": Value("string"),
-                    }),
-                    "page_image": Image(decode=True),
-                }),
-            )
-
-        else:
-            raise ValueError(f"Unsupported config: {self.config.name}")
-
-    def _split_generators(self, dl_manager):
-        # Point directly to the config subfolder
-        config_path = dl_manager.download_and_extract(self.config.name)
-
-        splits = []
-        for split in ["train", "test"]:
-            split_path = os.path.join(config_path, split)
-            if os.path.exists(split_path):
-                splits.append(SplitGenerator(
-                    name=Split.TRAIN if split == "train" else Split.TEST,
-                    gen_kwargs={"data_dir": split_path},
-                ))
-
-        return splits
-
-    def _generate_examples(self, data_dir):
-        print(f"[DEBUG] LOADING CONFIG: {self.config.name}")
-        print(f"[DEBUG] Looking for arrow files under: {data_dir}")
-
-        for root, _, files in os.walk(data_dir):
-            for fname in files:
-                if fname.endswith(".arrow"):
-                    fpath = os.path.join(root, fname)
-                    print(f"[DEBUG] Reading file: {fpath}")  # <--- KEY
-                    dataset = Dataset.from_file(fpath)
-                    for i, row in enumerate(dataset):
-                        yield f"{fpath}_{i}", row