"""Spatial utilities: box merging, word placement, reading order, split-line detection."""

from __future__ import annotations

import math
from typing import Any, Sequence

from geometry import Box, polygon_bounds
from models import Word, AnnotationBox, PredictedRow


SPLIT_LINE_VERTICAL_OVERLAP_THRESHOLD = 0.5
SPLIT_LINE_HORIZONTAL_OVERLAP_TOLERANCE_RATIO = 0.25
SPLIT_LINE_MAX_GAP_HEIGHT_MULTIPLIER = 2.0
LINE_END_HYPHENS = "-֊‐‑‒–—"
MULTI_BOX_SINGLE_COLUMN_LOCATION_THRESHOLD = 0.8
MULTI_BOX_SINGLE_COLUMN_OVERLAP_THRESHOLD = 0.8
MULTI_BOX_OVERLAP_TIE_TOLERANCE = 0.05
MULTI_BOX_BEGINNING_BOX_OVERLAP_TOLERANCE = 0.25
MULTI_BOX_BEGINNING_BOX_MIN_LEADING_WORDS = 2
MULTI_BOX_BEGINNING_BOX_MIN_COVERAGE = 0.35
BOX_LINE_VERTICAL_OVERLAP_THRESHOLD = 0.60
READING_ORDER_ROW_OVERLAP_THRESHOLD = 0.5
READING_ORDER_COLUMN_OVERLAP_THRESHOLD = 0.5


def merge_boxes(boxes: list[Box]) -> Box:
    return Box(
        x_min=min(box.x_min for box in boxes),
        y_min=min(box.y_min for box in boxes),
        x_max=max(box.x_max for box in boxes),
        y_max=max(box.y_max for box in boxes),
    )


def row_box(predicted_row: PredictedRow) -> Box:
    return merge_boxes([word.box for word in predicted_row.words])


def interval_overlap(
    start_a: float, end_a: float, start_b: float, end_b: float
) -> float:
    return max(0.0, min(end_a, end_b) - max(start_a, start_b))


def point_to_box_local_coordinates(
    point: tuple[float, float],
    annotation_box: AnnotationBox,
) -> tuple[float, float]:
    origin_x = annotation_box.rect.x_min
    origin_y = annotation_box.rect.y_min
    rotation_radians = math.radians(annotation_box.rotation)
    cos_theta = math.cos(rotation_radians)
    sin_theta = math.sin(rotation_radians)
    delta_x = point[0] - origin_x
    delta_y = point[1] - origin_y
    return (
        delta_x * cos_theta + delta_y * sin_theta,
        -delta_x * sin_theta + delta_y * cos_theta,
    )


def box_to_local_bounds(box: Box, annotation_box: AnnotationBox) -> Box:
    local_points = [
        point_to_box_local_coordinates(point, annotation_box)
        for point in box.to_polygon()
    ]
    return polygon_bounds(local_points)


def word_local_center(
    word: Word, annotation_box: AnnotationBox
) -> tuple[float, float]:
    return point_to_box_local_coordinates(word.center, annotation_box)


def local_box_to_list(box: Box) -> list[float]:
    return [
        round(box.x_min, 3),
        round(box.y_min, 3),
        round(box.x_max, 3),
        round(box.y_max, 3),
    ]


def rounded_box(box: Box) -> list[float]:
    return [round(value, 3) for value in box.to_list()]


def report_box(box_values: list[float]) -> Box:
    return Box(
        x_min=float(box_values[0]),
        y_min=float(box_values[1]),
        x_max=float(box_values[2]),
        y_max=float(box_values[3]),
    )


def edge_words_in_box(
    predicted_row: PredictedRow,
    annotation_box: AnnotationBox,
) -> tuple[dict[str, Any] | None, dict[str, Any] | None]:
    edge_words: list[dict[str, Any]] = []

    for index, word in enumerate(predicted_row.words):
        local_box = box_to_local_bounds(word.box, annotation_box)
        edge_words.append(
            {
                "index": index,
                "text": word.text,
                "global_box": [round(value, 3) for value in word.box.to_list()],
                "local_box": local_box,
            }
        )

    if not edge_words:
        return (None, None)

    first_word = min(
        edge_words, key=lambda item: (item["local_box"].x_min, item["index"])
    )
    last_word = max(
        edge_words, key=lambda item: (item["local_box"].x_max, -item["index"])
    )
    return (first_word, last_word)


def word_coverage_in_box(word: Word, annotation_box: AnnotationBox) -> float:
    center_x, center_y = word.center
    return 1.0 if annotation_box.contains_point(center_x, center_y) else 0.0


def word_overlap_coverage_in_box(word: Word, annotation_box: AnnotationBox) -> float:
    if word.box.area <= 0:
        return 0.0
    overlap_area = annotation_box.overlap_area_with_box(word.box)
    return overlap_area / word.box.area


def word_non_overlapping_area(word: Word, annotation_box: AnnotationBox) -> float:
    overlap_area = annotation_box.overlap_area_with_box(word.box)
    return max(0.0, word.box.area - overlap_area)


def non_empty_words(predicted_row: PredictedRow) -> list[Word]:
    return [word for word in predicted_row.words if word.text.strip()]


def uncovered_words_against_box(
    predicted_row: PredictedRow,
    annotation_box: AnnotationBox,
) -> list[dict[str, Any]]:
    uncovered_words: list[dict[str, Any]] = []

    for word in non_empty_words(predicted_row):
        if word_coverage_in_box(word, annotation_box) >= 1.0:
            continue

        non_overlapping_area = word_non_overlapping_area(word, annotation_box)
        uncovered_words.append(
            {
                "text": word.text,
                "word_box": [round(value, 3) for value in word.box.to_list()],
                "non_overlapping_area": round(non_overlapping_area, 6),
                "non_overlapping_fraction": (
                    round(non_overlapping_area / word.box.area, 6)
                    if word.box.area > 0
                    else 0.0
                ),
                "overlap_coverage": round(
                    word_overlap_coverage_in_box(word, annotation_box), 6
                ),
            }
        )

    return uncovered_words


def single_uncovered_word_against_box(
    predicted_row: PredictedRow,
    annotation_box: AnnotationBox,
) -> dict[str, Any] | None:
    uncovered_words = uncovered_words_against_box(predicted_row, annotation_box)
    if len(uncovered_words) != 1:
        return None
    return uncovered_words[0]


def row_location_coverage(
    predicted_row: PredictedRow, annotation_box: AnnotationBox
) -> float:
    words = non_empty_words(predicted_row)
    total_words = len(words)
    if total_words == 0:
        return 0.0
    matched_words = sum(word_coverage_in_box(word, annotation_box) for word in words)
    return matched_words / total_words


def row_overlap_coverage(
    predicted_row: PredictedRow, annotation_box: AnnotationBox
) -> float:
    words = non_empty_words(predicted_row)
    total_area = sum(word.box.area for word in words)
    if total_area <= 0:
        return 0.0

    covered_area = 0.0
    for word in words:
        covered_area += annotation_box.overlap_area_with_box(word.box)
    return covered_area / total_area


def classify_row(touched_box_ids: list[str], all_word_box_ids: list[str]) -> str:
    if not touched_box_ids:
        return "no_box"
    if len(all_word_box_ids) == 1 and len(touched_box_ids) == 1:
        return "exactly_one_box"
    if len(touched_box_ids) > 1 or len(all_word_box_ids) > 1:
        return "multiple_boxes"
    return "no_box"


def best_box_detail_for_multiple_row(
    per_box_details: list[dict[str, Any]],
) -> dict[str, Any] | None:
    if not per_box_details:
        return None

    strongest_match = max(
        per_box_details,
        key=lambda item: (
            item["overlap_coverage"],
            item["coverage"],
            item["matched_words"],
            -item["first_word_index"],
            item["leading_word_count"],
            item["box_id"],
        ),
    )
    beginning_match = min(
        per_box_details,
        key=lambda item: (
            item["first_word_index"],
            -item["leading_word_count"],
            -item["coverage"],
            -item["overlap_coverage"],
            item["box_id"],
        ),
    )
    if (
        beginning_match["first_word_index"] == 0
        and (
            beginning_match["leading_word_count"]
            >= MULTI_BOX_BEGINNING_BOX_MIN_LEADING_WORDS
            or beginning_match["coverage"] >= MULTI_BOX_BEGINNING_BOX_MIN_COVERAGE
        )
        and strongest_match["overlap_coverage"]
        - beginning_match["overlap_coverage"]
        <= MULTI_BOX_BEGINNING_BOX_OVERLAP_TOLERANCE
    ):
        return beginning_match

    tied_matches = [
        item
        for item in per_box_details
        if strongest_match["overlap_coverage"] - item["overlap_coverage"]
        <= MULTI_BOX_OVERLAP_TIE_TOLERANCE
    ]
    return max(
        tied_matches,
        key=lambda item: (
            item["leading_word_count"],
            -item["first_word_index"],
            item["matched_words"],
            item["coverage"],
            item["overlap_coverage"],
            item["box_id"],
        ),
    )


def box_detail_meets_single_box_threshold(
    box_detail: dict[str, Any] | None,
) -> bool:
    if box_detail is None:
        return False
    return (
        box_detail["coverage"] >= MULTI_BOX_SINGLE_COLUMN_LOCATION_THRESHOLD
        or box_detail["overlap_coverage"] >= MULTI_BOX_SINGLE_COLUMN_OVERLAP_THRESHOLD
    )


def treat_multiple_row_as_single_box(per_box_details: list[dict[str, Any]]) -> bool:
    best_match = best_box_detail_for_multiple_row(per_box_details)
    return box_detail_meets_single_box_threshold(best_match)


def _fix_armenian_yev_at_join(prev: str, next_line: str) -> tuple[str, str]:
    """Replace ե-|վ split across a hyphenation boundary with the ligature և."""
    if prev.endswith("ե") and next_line.startswith("վ"):
        prev = prev[:-1] + "և"
        next_line = next_line[1:]
    return prev, next_line


def join_box_lines_with_hyphenation(
    line_texts: list[str],
) -> tuple[str, frozenset[int]]:
    """Join lines, stripping line-end hyphens.

    Returns (text, hyphen_join_word_indices) where hyphen_join_word_indices is
    the set of word indices (in the space-normalised result) whose text was
    formed by merging across a hyphen boundary.  Callers that only need the
    text can unpack with ``text, _ = join_box_lines_with_hyphenation(...)``.
    """
    merged_lines: list[str] = []
    words_before_current: int = 0  # word count of all finalized lines
    hyphen_join_word_indices: set[int] = set()

    for line_text in line_texts:
        normalized_line = normalize_whitespace(line_text)
        if not normalized_line:
            continue

        if merged_lines and merged_lines[-1].rstrip().endswith(
            tuple(LINE_END_HYPHENS)
        ):
            prev = merged_lines[-1].rstrip()[:-1]
            next_line = normalized_line.lstrip()

            prev, next_line = _fix_armenian_yev_at_join(prev, next_line)

            words_in_prev = len(prev.split())
            if words_in_prev > 0:
                hyphen_join_word_indices.add(words_before_current + words_in_prev - 1)

            merged_lines[-1] = prev + next_line
        else:
            if merged_lines:
                words_before_current += len(merged_lines[-1].split())
            merged_lines.append(normalized_line)

    return "\n".join(merged_lines), frozenset(hyphen_join_word_indices)


def local_boxes_share_line(left: Box, right: Box) -> bool:
    min_height = min(left.height, right.height)
    if min_height <= 0:
        return False
    vertical_overlap = interval_overlap(
        left.y_min, left.y_max, right.y_min, right.y_max
    )
    vertical_overlap_ratio = vertical_overlap / min_height
    return vertical_overlap_ratio >= BOX_LINE_VERTICAL_OVERLAP_THRESHOLD


def horizontal_overlap_ratio(left: Box, right: Box) -> float:
    min_width = min(left.width, right.width)
    if min_width <= 0:
        return 0.0
    return (
        interval_overlap(left.x_min, left.x_max, right.x_min, right.x_max)
        / min_width
    )


def row_sequence_prefix(row_id: str) -> str:
    parts = row_id.split("_", 1)
    return parts[0]


def boxes_share_reading_row(left: Box, right: Box) -> bool:
    min_height = min(left.height, right.height)
    if min_height <= 0:
        return False
    vertical_overlap = interval_overlap(
        left.y_min, left.y_max, right.y_min, right.y_max
    )
    return (vertical_overlap / min_height) >= READING_ORDER_ROW_OVERLAP_THRESHOLD


def boxes_share_reading_column(left: Box, right: Box) -> bool:
    min_width = min(left.width, right.width)
    if min_width <= 0:
        return False
    horizontal_overlap = interval_overlap(
        left.x_min, left.x_max, right.x_min, right.x_max
    )
    return (
        horizontal_overlap / min_width
    ) >= READING_ORDER_COLUMN_OVERLAP_THRESHOLD


def _group_items_spatially(
    items: Sequence[Any],
    box_getter: Any,
    primary_axis: str,
) -> list[list[Any]]:
    ltr = primary_axis == "x"
    shares_band = boxes_share_reading_column if ltr else boxes_share_reading_row
    primary_sort = (
        (lambda b: (b.x_min, b.y_min, b.x_max, b.y_max))
        if ltr
        else (lambda b: (b.y_min, b.x_min, b.y_max, b.x_max))
    )
    secondary_sort = (
        (lambda b: (b.y_min, b.x_min, b.y_max, b.x_max))
        if ltr
        else (lambda b: (b.x_min, b.x_max, b.y_min, b.y_max))
    )
    center = (
        (lambda b: (b.x_min + b.x_max) / 2.0)
        if ltr
        else (lambda b: (b.y_min + b.y_max) / 2.0)
    )

    ordered_items = sorted(items, key=lambda item: primary_sort(box_getter(item)))
    groups: list[dict[str, Any]] = []
    for item in ordered_items:
        item_box = box_getter(item)
        best_group: dict[str, Any] | None = None
        best_distance = float("inf")
        for group in groups:
            if not shares_band(group["box"], item_box):
                continue
            distance = abs(center(group["box"]) - center(item_box))
            if distance < best_distance:
                best_distance = distance
                best_group = group
        if best_group is None:
            groups.append({"items": [item], "box": item_box})
        else:
            best_group["items"].append(item)
            best_group["box"] = merge_boxes([best_group["box"], item_box])

    return [
        sorted(g["items"], key=lambda item: secondary_sort(box_getter(item)))
        for g in sorted(groups, key=lambda g: primary_sort(g["box"]))
    ]


def group_items_left_to_right_top_to_bottom(
    items: Sequence[Any], box_getter: Any
) -> list[list[Any]]:
    return _group_items_spatially(items, box_getter, "x")


def flatten_text_units(text_units: list[str]) -> str:
    flattened_units: list[str] = []
    for text_unit in text_units:
        unit_lines = split_text_lines(text_unit)
        if not unit_lines:
            continue
        text, _ = join_box_lines_with_hyphenation(unit_lines)
        flattened_units.append(text)
    return "\n".join(flattened_units)


def detect_split_line_pair(
    left_row: dict[str, Any],
    right_row: dict[str, Any],
    left_predicted_row: PredictedRow,
    right_predicted_row: PredictedRow,
    annotation_box: AnnotationBox,
) -> dict[str, Any] | None:
    left_first_word, left_last_word = edge_words_in_box(
        left_predicted_row, annotation_box
    )
    right_first_word, right_last_word = edge_words_in_box(
        right_predicted_row, annotation_box
    )
    if (
        left_first_word is None
        or left_last_word is None
        or right_first_word is None
        or right_last_word is None
    ):
        return None

    left_last_local_box = left_last_word["local_box"]
    right_first_local_box = right_first_word["local_box"]

    vertical_overlap = interval_overlap(
        left_last_local_box.y_min,
        left_last_local_box.y_max,
        right_first_local_box.y_min,
        right_first_local_box.y_max,
    )
    min_height = min(left_last_local_box.height, right_first_local_box.height)
    if min_height <= 0:
        return None

    vertical_overlap_ratio = vertical_overlap / min_height
    if vertical_overlap_ratio < SPLIT_LINE_VERTICAL_OVERLAP_THRESHOLD:
        return None

    horizontal_gap = right_first_local_box.x_min - left_last_local_box.x_max
    if horizontal_gap < (
        -SPLIT_LINE_HORIZONTAL_OVERLAP_TOLERANCE_RATIO * min_height
    ):
        return None
    if horizontal_gap > SPLIT_LINE_MAX_GAP_HEIGHT_MULTIPLIER * max(
        left_last_local_box.height,
        right_first_local_box.height,
    ):
        return None

    return {
        "box_id": annotation_box.box_id,
        "left_row_id": left_row["row_id"],
        "right_row_id": right_row["row_id"],
        "horizontal_gap": round(horizontal_gap, 6),
        "vertical_overlap_ratio": round(vertical_overlap_ratio, 6),
        "left_row_text": left_row["row_text"],
        "right_row_text": right_row["row_text"],
        "left_last_word_text": left_last_word["text"],
        "right_first_word_text": right_first_word["text"],
        "left_last_word_box": left_last_word["global_box"],
        "right_first_word_box": right_first_word["global_box"],
        "left_last_word_local_box": local_box_to_list(left_last_local_box),
        "right_first_word_local_box": local_box_to_list(right_first_local_box),
    }


def split_line_row_order_key(
    predicted_row: PredictedRow,
    annotation_box: AnnotationBox,
) -> tuple[float, float, float, float, str]:
    if not predicted_row.words:
        return (
            float("inf"),
            float("inf"),
            float("inf"),
            float("inf"),
            predicted_row.row_id,
        )
    local_bounds = box_to_local_bounds(row_box(predicted_row), annotation_box)
    return (
        local_bounds.x_min,
        local_bounds.x_max,
        local_bounds.y_min,
        local_bounds.y_max,
        predicted_row.row_id,
    )


def order_words_in_box_context(
    words: list[Word], annotation_box: AnnotationBox
) -> list[Word]:
    return sorted(
        list(words),
        key=lambda word: (
            point_to_box_local_coordinates(word.center, annotation_box)[0],
            point_to_box_local_coordinates(word.center, annotation_box)[1],
            word.box.x_min,
            word.box.y_min,
        ),
    )


def words_in_box(predicted_row: PredictedRow, annotation_box: AnnotationBox) -> list[Word]:
    words = [
        word
        for word in predicted_row.words
        if annotation_box.contains_point(word.center[0], word.center[1])
    ]
    return order_words_in_box_context(words, annotation_box)


def sorted_row_words(predicted_row: PredictedRow) -> list[Word]:
    return sorted(
        predicted_row.words,
        key=lambda word: (
            word.box.y_min,
            word.box.x_min,
            word.box.x_max,
            word.text,
        ),
    )


def normalize_whitespace(text: str) -> str:
    return " ".join(text.split())


def split_text_lines(text: str) -> list[str]:
    return [
        normalize_whitespace(line)
        for line in text.splitlines()
        if normalize_whitespace(line)
    ]