from __future__ import annotations

from pathlib import Path

from .records import SegSampleRecord
from .utils import list_image_files, stem_without_mask_suffix


def build_paired_folder_records(
        dataset_name: str,
        image_dir: Path,
        mask_dir: Path,
        *,
        split: str | None = None,
        class_name: str | None = None,
) -> list[SegSampleRecord]:
    images = list_image_files(image_dir)
    masks = list_image_files(mask_dir)
    mask_map = {mask.name: mask for mask in masks}

    records: list[SegSampleRecord] = []
    for image in images:
        mask = mask_map.get(image.name)
        if mask is None:
            continue
        records.append(
            SegSampleRecord(
                dataset_name=dataset_name,
                image_path=image,
                mask_path=mask,
                split=split,
                sample_id=image.stem,
                class_name=class_name,
            )
        )
    return records


def build_prefixed_paired_records(
        dataset_name: str,
        image_dir: Path,
        mask_dir: Path,
        *,
        image_prefix_to_strip: str = "",
        mask_prefix_to_strip: str = "",
        split: str | None = None,
        class_name: str | None = None,
) -> list[SegSampleRecord]:
    images = list_image_files(image_dir)
    masks = list_image_files(mask_dir)

    def _normalize(path: Path, prefix: str) -> str:
        name = path.name
        if prefix and name.startswith(prefix):
            name = name[len(prefix):]
        return name

    mask_map = {_normalize(mask, mask_prefix_to_strip): mask for mask in masks}

    records: list[SegSampleRecord] = []
    for image in images:
        key = _normalize(image, image_prefix_to_strip)
        mask = mask_map.get(key)
        if mask is None:
            continue
        records.append(
            SegSampleRecord(
                dataset_name=dataset_name,
                image_path=image,
                mask_path=mask,
                split=split,
                sample_id=image.stem,
                class_name=class_name,
            )
        )
    return records


def build_stem_paired_records(
        dataset_name: str,
        image_dir: Path,
        mask_dir: Path,
        *,
        split: str | None = None,
        class_name: str | None = None,
        prefer_plain_mask: bool = True,
) -> list[SegSampleRecord]:
    images = list_image_files(image_dir)
    masks = list_image_files(mask_dir)

    grouped_masks: dict[str, list[Path]] = {}
    for mask in masks:
        key = stem_without_mask_suffix(mask.name)
        grouped_masks.setdefault(key, []).append(mask)

    records: list[SegSampleRecord] = []
    for image in images:
        key = image.stem
        candidates = sorted(grouped_masks.get(key, []))
        if not candidates:
            continue

        mask = candidates[0]
        if prefer_plain_mask:
            plain = [candidate for candidate in candidates if "_binary" not in candidate.stem.lower()]
            if plain:
                mask = plain[0]

        records.append(
            SegSampleRecord(
                dataset_name=dataset_name,
                image_path=image,
                mask_path=mask,
                split=split,
                sample_id=key,
                class_name=class_name,
                meta={"mask_candidates": str(len(candidates))},
            )
        )
    return records


def build_filename_matched_records(
        dataset_name: str,
        folder: Path,
        *,
        split: str | None = None,
        class_name: str | None = None,
) -> list[SegSampleRecord]:
    files = list_image_files(folder)
    image_map: dict[str, Path] = {}
    mask_map: dict[str, list[Path]] = {}

    for path in files:
        key = stem_without_mask_suffix(path.name)
        if "_mask" in path.stem:
            mask_map.setdefault(key, []).append(path)
        else:
            image_map[key] = path

    records: list[SegSampleRecord] = []
    for key, image in sorted(image_map.items()):
        masks = sorted(mask_map.get(key, []))
        if not masks:
            continue
        records.append(
            SegSampleRecord(
                dataset_name=dataset_name,
                image_path=image,
                mask_path=masks[0],
                split=split,
                sample_id=key,
                class_name=class_name,
                meta={"mask_count": str(len(masks))},
            )
        )
    return records


def build_pre_split_records(
        dataset_name: str,
        train_image_dir: Path,
        train_mask_dir: Path,
        test_image_dir: Path,
        test_mask_dir: Path,
) -> list[SegSampleRecord]:
    records = []
    records.extend(
        build_paired_folder_records(
            dataset_name=dataset_name,
            image_dir=train_image_dir,
            mask_dir=train_mask_dir,
            split="trainval",
        )
    )
    records.extend(
        build_paired_folder_records(
            dataset_name=dataset_name,
            image_dir=test_image_dir,
            mask_dir=test_mask_dir,
            split="test",
        )
    )
    return records


def build_xml_annotation_records(
        dataset_name: str,
        root: Path,
        *,
        split: str | None = None,
        class_name: str | None = None,
) -> list[SegSampleRecord]:
    xml_map = {path.stem: path for path in sorted(root.glob("*.xml"))}
    image_files = sorted(root.glob("*.jpg"))

    records: list[SegSampleRecord] = []
    for image in image_files:
        sample_key = image.stem.split("_")[0]
        annotation = xml_map.get(sample_key)
        if annotation is None:
            continue
        records.append(
            SegSampleRecord(
                dataset_name=dataset_name,
                image_path=image,
                mask_path=None,
                annotation_path=annotation,
                split=split,
                sample_id=image.stem,
                class_name=class_name,
                meta={"annotation_type": "xml"},
            )
        )
    return records


__all__ = [
    "build_paired_folder_records",
    "build_prefixed_paired_records",
    "build_stem_paired_records",
    "build_filename_matched_records",
    "build_pre_split_records",
    "build_xml_annotation_records",
]