"""Dataset loaders for the reproduction pipeline.

Uses the dataset classes from ``src/dataset/sequence_classification.py``
(released alongside the fine-tuning stage) so that train / validation /
test splits match exactly what was used to fine-tune the encoders.

Supported sources (from config ``data.source``):
- ``hf_glue_cola``     -> ``CoLa()``
- ``hf_sst5``          -> ``SST5()``
- ``hf_toxigen``       -> ``ToxigenDataset()``
- ``hf_newsgroups``    -> ``NewsGroups()``
- ``hf_goemotions``    -> ``GoEmotions()``
- ``hf_yelp``          -> ``Yelp()``
- ``tsv``              -> legacy paper TSV (2-split only)

For HF-based sources, ``load_splits`` returns a dict with keys
``train``, ``validation`` and ``test``, each a ``pd.DataFrame`` with
columns ``sentence``, ``label``, ``idx``.

For backward compatibility, when a caller asks for the legacy ``dev``
split it is silently mapped to ``test`` (the held-out evaluation set in
this pipeline's protocol).
"""
from __future__ import annotations

import importlib.util
import sys
from pathlib import Path
from typing import Dict

import pandas as pd


# Import the dataset classes via an explicit file load, since both ``src``
# directories (this one, and the top-level one) collide on the package name.
_REPO_ROOT = Path(__file__).resolve().parents[2]
_DATASET_CLASSES_PATH = _REPO_ROOT / "training" / "src" / "dataset" / "sequence_classification.py"


def _import_dataset_module():
    spec = importlib.util.spec_from_file_location(
        "dataset_module", str(_DATASET_CLASSES_PATH))
    mod = importlib.util.module_from_spec(spec)
    spec.loader.exec_module(mod)
    return mod


COLA_COLUMNS = ["source", "label", "label_notes", "sentence"]


# ---------------------------------------------------------------------------
# Legacy TSV loader (En-CoLA paper data)
# ---------------------------------------------------------------------------
def load_cola_tsv(path: str | Path, text_col: str = "sentence", label_col: str = "label") -> pd.DataFrame:
    df = pd.read_csv(path, sep="\t", header=None, names=COLA_COLUMNS, na_filter=False)
    df = df[[text_col, label_col]].copy()
    df[label_col] = df[label_col].astype(int)
    df["idx"] = range(len(df))
    return df


# ---------------------------------------------------------------------------
# Dataset-class-backed loaders
# ---------------------------------------------------------------------------
def _to_df(ds, text_col_candidates=("text", "sentence")) -> pd.DataFrame:
    """Convert a HF Dataset row-set to the (sentence, label, idx) frame the
    pipeline expects."""
    cols = set(ds.column_names)
    text_col = next((c for c in text_col_candidates if c in cols), None)
    if text_col is None:
        raise ValueError(f"Dataset has no text column among {text_col_candidates}: {cols}")
    df = pd.DataFrame({"sentence": list(ds[text_col]), "label": list(ds["label"])})
    df["idx"] = range(len(df))
    return df


def _load_class_splits(class_name: str) -> Dict[str, pd.DataFrame]:
    m = _import_dataset_module()
    cls = getattr(m, class_name)
    ds = cls().load()
    return {
        "train":      _to_df(ds["train"]),
        "validation": _to_df(ds["validation"]),
        "test":       _to_df(ds["test"]),
    }


def load_cola_splits() -> Dict[str, pd.DataFrame]:
    """80% train + 20% validation of GLUE-CoLA train, plus GLUE-CoLA validation as test."""
    return _load_class_splits("CoLa")


def load_sst5_splits() -> Dict[str, pd.DataFrame]:
    """SetFit/sst5 train + validation + test (no 80/20 subsplit)."""
    return _load_class_splits("SST5")


def load_toxigen_splits() -> Dict[str, pd.DataFrame]:
    """skg/toxigen-data with an 80/20 split of train, plus its original test."""
    return _load_class_splits("ToxigenDataset")


def load_newsgroups_splits() -> Dict[str, pd.DataFrame]:
    """sklearn 20-newsgroups with an 80/20 split of train, plus its original test."""
    return _load_class_splits("NewsGroups")


def load_goemotions_splits() -> Dict[str, pd.DataFrame]:
    """GoEmotions with an 80/10/10 split (max 5k samples per split)."""
    return _load_class_splits("GoEmotions")


def load_yelp_splits() -> Dict[str, pd.DataFrame]:
    """Yelp 3-star sentiment (HF: $YELP_REPO)."""
    return _load_class_splits("Yelp")


def load_amazon_splits() -> Dict[str, pd.DataFrame]:
    return _load_class_splits("Amazon")


def load_sst2_splits() -> Dict[str, pd.DataFrame]:
    return _load_class_splits("SST2")


def load_imdb_splits() -> Dict[str, pd.DataFrame]:
    return _load_class_splits("IMDB")


# ---------------------------------------------------------------------------
# Legacy single-split loaders (kept for direct callers — they call the
# 3-split loaders under the hood and slice the requested view).
# ---------------------------------------------------------------------------
def _legacy(splits, split_name):
    """Map legacy ``train|dev|validation|test`` to the pipeline's 3-split layout.

    Old call sites used "dev" for the held-out evaluation set. We map it to
    "test" (which is what this protocol calls the held-out test split).
    """
    if split_name == "dev":
        split_name = "test"
    return splits[split_name]


def load_hf_glue_cola(split: str) -> pd.DataFrame:
    return _legacy(load_cola_splits(), split)


def load_hf_sst5(split: str) -> pd.DataFrame:
    return _legacy(load_sst5_splits(), split)


def load_hf_toxigen(split: str) -> pd.DataFrame:
    return _legacy(load_toxigen_splits(), split)


def load_hf_newsgroups(split: str) -> pd.DataFrame:
    return _legacy(load_newsgroups_splits(), split)


def load_hf_goemotions(split: str) -> pd.DataFrame:
    return _legacy(load_goemotions_splits(), split)


def load_hf_yelp(split: str) -> pd.DataFrame:
    return _legacy(load_yelp_splits(), split)


# ---------------------------------------------------------------------------
# Top-level loader used by extract scripts
# ---------------------------------------------------------------------------
def load_splits(cfg: Dict) -> Dict[str, pd.DataFrame]:
    """Return ``{"train": ..., "validation": ..., "test": ...}`` for HF sources.

    For the legacy TSV (En-CoLA) source we still return ``{"train", "dev"}``
    plus optional ``ood`` because no 3-split layout is defined there.
    """
    data_cfg = cfg["data"]
    source = data_cfg.get("source", "tsv")
    if source == "tsv":
        splits = {
            "train": load_cola_tsv(data_cfg["train_tsv"], data_cfg["text_col"], data_cfg["label_col"]),
            "dev":   load_cola_tsv(data_cfg["dev_tsv"], data_cfg["text_col"], data_cfg["label_col"]),
        }
        if data_cfg.get("ood_tsv"):
            splits["ood"] = load_cola_tsv(data_cfg["ood_tsv"], data_cfg["text_col"], data_cfg["label_col"])
        return splits
    if source == "hf_glue_cola":
        return load_cola_splits()
    if source == "hf_sst5":
        return load_sst5_splits()
    if source == "hf_toxigen":
        return load_toxigen_splits()
    if source == "hf_newsgroups":
        return load_newsgroups_splits()
    if source == "hf_goemotions":
        return load_goemotions_splits()
    if source == "hf_yelp":
        return load_yelp_splits()
    if source == "hf_amazon":
        return load_amazon_splits()
    if source == "hf_sst2":
        return load_sst2_splits()
    if source == "hf_imdb":
        return load_imdb_splits()
    raise ValueError(f"unknown data source: {source}")