import datasets
import csv
import random

class ppb_affinity(datasets.GeneratorBasedBuilder):
    VERSION = datasets.Version("1.0.1")
    
    BUILDER_CONFIGS = [
        datasets.BuilderConfig(name="raw", description="Raw parsed PDBs dataset with critical filtrations only."),
        datasets.BuilderConfig(name="filtered", description="Raw dataset with additional cleaning and train/val/test splits."),
        datasets.BuilderConfig(name="filtered_random", description="Filtered dataset with random 80-10-10 splits."),
    ]
    
    def _info(self):
        return datasets.DatasetInfo()
    
    def _split_generators(self, dl_manager):
        if self.config.name == "raw":
            filepath = dl_manager.download_and_extract("raw.csv")
            return [datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={"filepath": filepath}
            )]
        elif self.config.name == "filtered":
            filepath = dl_manager.download_and_extract("filtered.csv")
            return [
                datasets.SplitGenerator(
                    name=datasets.Split.TRAIN,
                    gen_kwargs={"filepath": filepath, "split": "train"},
                ),
                datasets.SplitGenerator(
                    name=datasets.Split.VALIDATION,
                    gen_kwargs={"filepath": filepath, "split": "val"},
                ),
                datasets.SplitGenerator(
                    name=datasets.Split.TEST,
                    gen_kwargs={"filepath": filepath, "split": "test"},
                ),
            ]
        elif self.config.name == "filtered_random":
            filepath = dl_manager.download_and_extract("filtered.csv")
            # Read all rows to determine splits
            with open(filepath, encoding="utf-8") as f:
                reader = csv.DictReader(f)
                rows = list(reader)
            n_total = len(rows)
            # Generate shuffled indices with fixed seed
            indices = list(range(n_total))
            rng = random.Random(42)  # Fixed seed for reproducibility
            rng.shuffle(indices)
            # Calculate split sizes
            n_train = int(0.8 * n_total)
            n_val = int(0.1 * n_total)
            n_test = n_total - n_train - n_val  # Handle remainder
            # Split indices into ranges
            return [
                datasets.SplitGenerator(
                    name=datasets.NamedSplit("train_rand"),
                    gen_kwargs={
                        "filepath": filepath,
                        "shuffled_indices": indices,
                        "split_start": 0,
                        "split_end": n_train,
                    },
                ),
                datasets.SplitGenerator(
                    name=datasets.NamedSplit("validation_rand"),
                    gen_kwargs={
                        "filepath": filepath,
                        "shuffled_indices": indices,
                        "split_start": n_train,
                        "split_end": n_train + n_val,
                    },
                ),
                datasets.SplitGenerator(
                    name=datasets.NamedSplit("test_rand"),
                    gen_kwargs={
                        "filepath": filepath,
                        "shuffled_indices": indices,
                        "split_start": n_train + n_val,
                        "split_end": n_total,
                    },
                ),
            ]
    
    def _generate_examples(self, filepath, split=None, shuffled_indices=None, split_start=None, split_end=None):
        with open(filepath, encoding="utf-8") as f:
            reader = csv.DictReader(f)
            rows = list(reader)
            if self.config.name == "raw":
                for idx, row in enumerate(rows):
                    yield idx, row
            elif self.config.name == "filtered":
                for idx, row in enumerate(rows):
                    if row["split"] == split:
                        del row["split"]
                        yield idx, row
            elif self.config.name == "filtered_random":
                # Iterate over the range [split_start, split_end) in shuffled_indices
                for global_idx in range(split_start, split_end):
                    original_idx = shuffled_indices[global_idx]
                    row = rows[original_idx]
                    del row["split"]  # Remove original split column
                    yield global_idx, row  # Key is global shuffled index