from __future__ import annotations

import random
from collections import defaultdict
from pathlib import Path

from .builder import build_dataset_index
from .records import SegSampleRecord


PROJECT_SPLIT_ROOT = Path("splits") / "project"
PROJECT_SPLIT_DATASETS = {"BUS-UCLM", "BUSI", "BUS-BRA", "BUS_UC", "CCAUI", "DDTI"}


def _project_split_dir(root: str | Path) -> Path:
    return Path(root) / PROJECT_SPLIT_ROOT


def get_project_split_file(
        root: str | Path,
        split: str,
) -> Path:
    return _project_split_dir(root) / f"{split}.txt"


def load_project_split_ids(
        root: str | Path,
        split: str,
) -> list[str]:
    path = get_project_split_file(root, split)
    if not path.exists():
        raise FileNotFoundError(f"Project split file not found: {path}")
    return [
        line.strip()
        for line in path.read_text(encoding="utf-8", errors="ignore").splitlines()
        if line.strip()
    ]


def _write_split_ids(path: Path, sample_ids: list[str]) -> None:
    path.parent.mkdir(parents=True, exist_ok=True)
    if sample_ids:
        path.write_text("\n".join(sample_ids) + "\n", encoding="utf-8")
    else:
        path.write_text("", encoding="utf-8")


def _deduplicate_records(
        dataset_name: str,
        records: list[SegSampleRecord],
) -> list[SegSampleRecord]:
    if dataset_name != "BUS_UC":
        return records

    # BUS_UC 的 All 与 Benign/Malignant 是重复样本，默认只保留 All 作为正式划分基底。
    all_records = [record for record in records if record.class_name == "all"]
    return all_records if all_records else records


def select_project_split_base_records(
        dataset_name: str,
        records: list[SegSampleRecord],
) -> list[SegSampleRecord]:
    return _deduplicate_records(dataset_name, records)


def _group_records_for_split(
        records: list[SegSampleRecord],
) -> dict[str, list[SegSampleRecord]]:
    groups: dict[str, list[SegSampleRecord]] = defaultdict(list)
    for record in records:
        key = record.class_name or "__default__"
        groups[key].append(record)
    return groups


def _split_group(
        group_records: list[SegSampleRecord],
        *,
        val_ratio: float,
        rng: random.Random,
) -> tuple[list[SegSampleRecord], list[SegSampleRecord]]:
    shuffled = list(group_records)
    rng.shuffle(shuffled)

    val_count = int(round(len(shuffled) * val_ratio))
    if len(shuffled) >= 2:
        val_count = max(1, min(len(shuffled) - 1, val_count))
    elif len(shuffled) == 1:
        val_count = 0

    val_records = shuffled[:val_count]
    train_records = shuffled[val_count:]
    return train_records, val_records


def generate_project_splits(
        dataset_name: str,
        root: str | Path,
        *,
        val_ratio: float = 0.2,
        seed: int = 42,
        stratify_by_class: bool = True,
        reuse_existing: bool = True,
) -> dict[str, list[str]]:
    if dataset_name not in PROJECT_SPLIT_DATASETS:
        raise ValueError(
            f"Dataset '{dataset_name}' is not enabled for project split generation."
        )
    if not 0.0 < val_ratio < 1.0:
        raise ValueError(f"val_ratio must be between 0 and 1, got {val_ratio}.")

    train_path = get_project_split_file(root, "train")
    val_path = get_project_split_file(root, "val")
    if reuse_existing and train_path.exists() and val_path.exists():
        return {
            "train": load_project_split_ids(root, "train"),
            "val": load_project_split_ids(root, "val"),
        }

    records = build_dataset_index(dataset_name, root)
    records = _deduplicate_records(dataset_name, records)
    rng = random.Random(seed)

    train_records: list[SegSampleRecord] = []
    val_records: list[SegSampleRecord] = []

    if stratify_by_class:
        groups = _group_records_for_split(records)
        for group_records in groups.values():
            group_train, group_val = _split_group(group_records, val_ratio=val_ratio, rng=rng)
            train_records.extend(group_train)
            val_records.extend(group_val)
    else:
        train_records, val_records = _split_group(records, val_ratio=val_ratio, rng=rng)

    train_ids = sorted(record.sample_id for record in train_records if record.sample_id is not None)
    val_ids = sorted(record.sample_id for record in val_records if record.sample_id is not None)

    split_dir = _project_split_dir(root)
    split_dir.mkdir(parents=True, exist_ok=True)

    _write_split_ids(train_path, train_ids)
    _write_split_ids(val_path, val_ids)

    return {
        "train": train_ids,
        "val": val_ids,
    }


__all__ = [
    "PROJECT_SPLIT_DATASETS",
    "PROJECT_SPLIT_ROOT",
    "generate_project_splits",
    "get_project_split_file",
    "load_project_split_ids",
    "select_project_split_base_records",
]