#!/usr/bin/env python3

"""Evaluate CSV `group_row` predictions against annotated text boxes.

Input 1: Label Studio-style JSON annotations.
We use one text-bearing result per annotation id and convert each box from
`x, y, width, height` to `x1, y1, x2, y2`.

Input 2: CSV with predicted word boxes and `group_row`.
Rows are grouped by `group_row`, then each row is checked against the
annotation boxes.

Row classification:
- `exactly_one_box`: exactly one annotation box contains every word in the row,
  and no other annotation box significantly contains any word from that row.
  We also treat a row as `exactly_one_box` when it touches multiple boxes but
  one box covers almost all of that row, which usually means one stray word was
  pulled across columns by OCR.
- `multiple_boxes`: words from the row significantly fall into multiple
  annotation boxes.
- `no_box`: the row does not fit cleanly into any annotation box.

Coverage in this script is location-first:
- a word belongs to an annotation box when the word center lies inside that box
- row coverage is the fraction of words in the row whose centers lie inside
  a given annotation box

Label Studio box rotation is taken into account using the rotated rectangle
geometry stored in the annotation results.
"""

from __future__ import annotations

import json
import sys
from collections.abc import Callable
from pathlib import Path
from typing import Any

_BOX_GROUPING = str(Path(__file__).resolve().parent.parent / "box_grouping")
if _BOX_GROUPING not in sys.path:
    sys.path.insert(0, _BOX_GROUPING)

# Geometry primitives (re-exported for callers and tests)
from geometry import Box, polygon_bounds, rotated_rectangle_points

# Domain models and constants
from models import (
    AnnotationBox,
    HEADER_TITLE_LIKE_LABELS,
    IMAGE_HEADER_FILTER_LABELS,
    IMAGE_RELATED_LABELS,
    PredictedRow,
    Word,
    annotation_box_metadata,
    annotation_box_type,
    gt_box_report,
)

# Loading (re-exported for callers and tests)
from loading import (
    NON_ARMENIAN_BOX_LETTER_RATIO_THRESHOLD,
    is_watermark_row,
    load_annotation_boxes,
    load_predicted_rows,
    non_armenian_letter_ratio,
    parse_args,
)

# Spatial utilities (re-exported for callers and tests)
from spatial import row_box

# Box-grouping entry point (spatial assignment)
from group import group_words_into_regions

# Text metrics
from text_metrics import (
    CER_BUCKET_KEYS,
    HIGH_IMPACT_REGION_EXAMPLE_COUNT,
    build_cer_bucket_summary,
    cer_bucket_key,
    compute_text_metrics,
    edit_distance,
    normalize_punctuation_chars,
    safe_error_rate,
    safe_mean,
    safe_rate,
    summarize_region_example,
)

# Prediction builders
from prediction import (
    build_region_predicted_text,
    count_empty_words_in_non_empty_boxes,
)

# Report builders
from reports import (
    build_ocr_region_reports,
    build_region_summary,
    filtered_box_report,
)


def report_excluded_labels() -> dict[str, list[str]]:
    return {
        "image_related_boxes": sorted(IMAGE_RELATED_LABELS),
        "header_title_like_boxes": sorted(HEADER_TITLE_LIKE_LABELS),
        "image_header_boxes": sorted(IMAGE_HEADER_FILTER_LABELS),
    }


FILTER_NON_ARMENIAN = "non-armenian"
FILTER_LABEL_GROUPS: dict[str, frozenset[str]] = {
    "graphics": frozenset({"Graphics"}),
    "photo": frozenset({"Photo"}),
    "image": IMAGE_RELATED_LABELS,
    "header": HEADER_TITLE_LIKE_LABELS,
    "image-header": IMAGE_HEADER_FILTER_LABELS,
}
FILTER_ALIASES = {
    "nonarmenian": FILTER_NON_ARMENIAN,
    "non-armenian": FILTER_NON_ARMENIAN,
    "non_armenian": FILTER_NON_ARMENIAN,
    "latin": FILTER_NON_ARMENIAN,
    "latin-cyrillic": FILTER_NON_ARMENIAN,
    "latin_or_cyrillic": FILTER_NON_ARMENIAN,
    "image-header": "image-header",
    "image_header": "image-header",
    "imageheader": "image-header",
    "image-related": "image",
    "image_related": "image",
    "images": "image",
    "headers": "header",
}
AVAILABLE_FILTERS = (FILTER_NON_ARMENIAN, *FILTER_LABEL_GROUPS.keys())


def parse_filter_names(
    raw_filters: str | list[str] | tuple[str, ...] | None,
) -> tuple[str, ...]:
    if raw_filters is None:
        return ()

    tokens: list[str] = []
    if isinstance(raw_filters, str):
        tokens = raw_filters.split(",")
    else:
        for raw_filter in raw_filters:
            tokens.extend(str(raw_filter).split(","))

    selected_filters: list[str] = []
    seen_filters: set[str] = set()
    for token in tokens:
        normalized = token.strip().lower().replace(" ", "-")
        if not normalized:
            continue
        canonical = FILTER_ALIASES.get(normalized, normalized)
        if canonical not in AVAILABLE_FILTERS:
            available = ", ".join(AVAILABLE_FILTERS)
            raise ValueError(
                f"Unknown filter '{token}'. Available filters: {available}"
            )
        if canonical not in seen_filters:
            selected_filters.append(canonical)
            seen_filters.add(canonical)
    return tuple(selected_filters)


def labels_for_filters(filter_names: tuple[str, ...]) -> frozenset[str]:
    labels: set[str] = set()
    for filter_name in filter_names:
        labels.update(FILTER_LABEL_GROUPS.get(filter_name, ()))
    return frozenset(labels)


def filter_matches_for_box(
    annotation_box: AnnotationBox,
    filter_names: tuple[str, ...],
) -> list[str]:
    matches: list[str] = []
    box_labels = set(annotation_box.labels)
    for filter_name in filter_names:
        if (
            filter_name == FILTER_NON_ARMENIAN
            and annotation_box.excluded_as_non_armenian_text
        ):
            matches.append(filter_name)
            continue
        label_group = FILTER_LABEL_GROUPS.get(filter_name)
        if label_group and box_labels & label_group:
            matches.append(filter_name)
    return matches


def should_exclude_box_for_filters(
    filter_names: tuple[str, ...],
) -> Callable[[AnnotationBox], bool] | None:
    if not filter_names:
        return None

    def should_exclude_box(annotation_box: AnnotationBox) -> bool:
        return bool(filter_matches_for_box(annotation_box, filter_names))

    return should_exclude_box


def filtered_region_box_report(
    annotation_box: AnnotationBox,
    filter_names: tuple[str, ...],
) -> dict[str, Any]:
    report = filtered_box_report(annotation_box)
    report["matched_filters"] = filter_matches_for_box(annotation_box, filter_names)
    report["letter_count"] = annotation_box.letter_count
    report["latin_or_cyrillic_letter_count"] = (
        annotation_box.latin_or_cyrillic_letter_count
    )
    report["non_armenian_letter_ratio"] = round(
        annotation_box.non_armenian_letter_ratio,
        6,
    )
    report["non_armenian_letter_percentage"] = round(
        annotation_box.non_armenian_letter_ratio * 100,
        6,
    )
    return report


def build_region_filter_report(
    annotation_boxes: list[AnnotationBox],
    filter_names: tuple[str, ...],
) -> dict[str, Any]:
    text_boxes = [box for box in annotation_boxes if box.has_transcription]
    excluded_boxes = [
        box for box in text_boxes if filter_matches_for_box(box, filter_names)
    ]
    report = {
        "filters": list(filter_names),
        "text_box_count": len(text_boxes),
        "excluded_box_count": len(excluded_boxes),
        "excluded_box_rate": safe_rate(len(excluded_boxes), len(text_boxes)),
        "included_box_count": len(text_boxes) - len(excluded_boxes),
        "excluded_boxes": [
            filtered_region_box_report(box, filter_names) for box in excluded_boxes
        ],
    }
    if FILTER_NON_ARMENIAN in filter_names:
        report["threshold"] = NON_ARMENIAN_BOX_LETTER_RATIO_THRESHOLD
    label_filters = labels_for_filters(filter_names)
    if label_filters:
        report["labels"] = sorted(label_filters)
    return report


def summarize_region_filter_report(filter_report: dict[str, Any]) -> dict[str, Any]:
    return {
        key: value
        for key, value in filter_report.items()
        if key != "excluded_boxes"
    }


def nonzero_cer_records(records: list[dict[str, Any]]) -> list[dict[str, Any]]:
    return [
        record
        for record in records
        if (record.get("text_metrics") or {}).get("cer", 0.0) != 0.0
    ]


def select_no_box_failure_examples(
    rows: list[dict[str, Any]],
    example_count: int,
) -> list[dict[str, Any]]:
    non_empty_rows = [row for row in rows if row["row_text"].strip()]
    if non_empty_rows:
        return non_empty_rows[:example_count]
    return rows[:1]


def build_failure_examples(
    details: list[dict[str, Any]],
    ocr_regions: list[dict[str, Any]],
    example_count: int,
    split_line_groups: list[dict[str, Any]] | None = None,
) -> dict[str, list[dict[str, Any]]]:
    def simplify(row: dict[str, Any]) -> dict[str, Any]:
        simplified = {
            "row_id": row["row_id"],
            "row_text": row["row_text"],
            "dominant_box_id": row["dominant_box_id"],
            "dominant_coverage": row["dominant_coverage"],
            "touched_box_ids": row["touched_box_ids"],
            "candidate_boxes": row["per_box_coverages"][:3],
        }
        if row["status"] == "no_box":
            simplified["single_uncovered_word_against_dominant_box"] = row[
                "single_uncovered_word_against_dominant_box"
            ]
            simplified["uncovered_words_against_dominant_box"] = row[
                "uncovered_words_against_dominant_box"
            ]
        return simplified

    multiple_rows = [row for row in details if row["status"] == "multiple_boxes"]
    no_box_rows = [row for row in details if row["status"] == "no_box"]
    detected_empty_rows = [row for row in details if row.get("is_detected_empty")]
    multiple_examples = sorted(
        multiple_rows,
        key=lambda row: (
            -len(row["touched_box_ids"]),
            row["dominant_coverage"],
            row["row_id"],
        ),
    )[:example_count]
    no_box_examples = select_no_box_failure_examples(no_box_rows, example_count)
    high_impact_examples = sorted(
        [
            region
            for region in ocr_regions
            if region["text_metrics"]["char_edit_distance"] > 0
        ],
        key=lambda region: (
            -region["text_metrics"]["char_edit_distance"],
            region["region_id"],
        ),
    )[:HIGH_IMPACT_REGION_EXAMPLE_COUNT]
    normal_single_box_error_examples = sorted(
        [
            region
            for region in ocr_regions
            if (
                region.get("normal_single_box_region")
                and region["text_metrics"]["char_edit_distance"] > 0
            )
        ],
        key=lambda region: (
            -region["text_metrics"]["cer"],
            -region["text_metrics"]["char_edit_distance"],
            region["region_id"],
        ),
    )[:example_count]

    return {
        "multiple_boxes": [simplify(row) for row in multiple_examples],
        "no_box": [simplify(row) for row in no_box_examples],
        "detected_empty": [simplify(row) for row in detected_empty_rows[:example_count]],
        "split_line": (
            [] if split_line_groups is None else split_line_groups[:example_count]
        ),
        "high_impact_regions": [
            summarize_region_example(region, include_error_stats=True)
            for region in high_impact_examples
        ],
        "normal_single_box_region_errors": [
            summarize_region_example(region, include_error_stats=True)
            for region in normal_single_box_error_examples
        ],
    }


def evaluate_rows(
    predicted_rows: list[PredictedRow],
    annotation_boxes: list[AnnotationBox],
    coverage_threshold: float,
    failure_example_count: int,
    hide_zero_cer_details: bool = True,
    filters: str | list[str] | tuple[str, ...] | None = None,
    unit_level: str = "word",
) -> dict[str, Any]:
    filter_names = parse_filter_names(filters)
    should_exclude_box = should_exclude_box_for_filters(filter_names)

    grouping = group_words_into_regions(
        predicted_rows=predicted_rows,
        annotation_boxes=annotation_boxes,
        coverage_threshold=coverage_threshold,
        unit_level=unit_level,
    )
    details = grouping["assignments"]
    ignored_rows = grouping["watermark_rows"]
    split_line_groups = grouping["split_line_groups"]
    best_coverages = grouping["best_coverages"]
    counts = grouping["counts"]

    total_rows = len(details)
    predicted_rows_by_id = {row.row_id: row for row in predicted_rows}
    total_detected_word_boxes = sum(len(row.words) for row in predicted_rows)
    missing_text_boxes = count_empty_words_in_non_empty_boxes(
        predicted_rows,
        annotation_boxes,
    )
    gt_text_boxes = [box for box in annotation_boxes if box.has_transcription]
    gt_box_count = len(gt_text_boxes)
    gt_char_count = sum(len(box.text) for box in gt_text_boxes)

    ocr_regions, ocr_region_summary = build_ocr_region_reports(
        details=details,
        annotation_boxes=annotation_boxes,
        predicted_rows_by_id=predicted_rows_by_id,
        should_exclude_box=should_exclude_box,
    )
    filter_report = (
        build_region_filter_report(annotation_boxes, filter_names)
        if filter_names
        else None
    )
    summary = {
        "unit_level": unit_level,
        "total_rows": total_rows,
        "ignored_watermark_rows": len(ignored_rows),
        "exactly_one_box": counts["exactly_one_box"],
        "exactly_one_box_rate": safe_rate(counts["exactly_one_box"], total_rows),
        "multiple_boxes": counts["multiple_boxes"],
        "multiple_boxes_rate": safe_rate(counts["multiple_boxes"], total_rows),
        "no_box": counts["no_box"],
        "no_box_rate": safe_rate(counts["no_box"], total_rows),
        "detected_empty": counts["detected_empty"],
        "split_line": len(split_line_groups),
        "split_line_rate": safe_rate(len(split_line_groups), total_rows),
        "split_line_rows": counts["split_line"],
        "split_line_rows_rate": safe_rate(counts["split_line"], total_rows),
        "mean_best_coverage": safe_mean(best_coverages),
        "gt_box_count": gt_box_count,
        "gt_char_count": gt_char_count,
        "ocr_region_count": ocr_region_summary["ocr_region_count"],
        "multibox_region_count": ocr_region_summary["multibox_region_count"],
        "ocr_region_mean_cer": ocr_region_summary["mean_cer"],
        "ocr_region_gt_char_count": ocr_region_summary["gt_char_count"],
        "ocr_region_char_edit_distance": ocr_region_summary["char_edit_distance"],
        "ocr_region_char_edit_distance_lowercase": ocr_region_summary["char_edit_distance_lowercase"],
        "ocr_region_cer": ocr_region_summary["cer"],
        "ocr_region_cer_lowercase": ocr_region_summary["cer_lowercase"],
        "ocr_region_cer_buckets": ocr_region_summary["cer_buckets"],
        "normal_single_box_region": ocr_region_summary["normal_single_box_region"],
        "missing_text_boxes": missing_text_boxes,
        "total_detected_word_boxes": total_detected_word_boxes,
        "missing_text_box_rate": safe_rate(
            missing_text_boxes,
            total_detected_word_boxes,
        ),
    }
    if filter_report is not None:
        summary["filter"] = summarize_region_filter_report(filter_report)

    failure_examples = build_failure_examples(
        details,
        ocr_regions,
        failure_example_count,
        split_line_groups,
    )
    report = {
        "summary": summary,
        "ocr_regions": (
            nonzero_cer_records(ocr_regions) if hide_zero_cer_details else ocr_regions
        ),
        "failure_examples": failure_examples,
        "split_line_groups": split_line_groups,
        "ignored_rows": ignored_rows,
        "rows": details,
        "excluded_labels": report_excluded_labels(),
    }
    if filter_report is not None:
        report["filter"] = filter_report
        report["filtered_text_boxes"] = filter_report["excluded_boxes"]
    return report


def main() -> None:
    args = parse_args()
    annotation_boxes = load_annotation_boxes(args.annotations_json)
    predicted_rows = load_predicted_rows(args.predictions_csv, unit_level=args.unit_level)
    try:
        filters = parse_filter_names(args.filters)
    except ValueError as error:
        raise SystemExit(str(error)) from error
    report = evaluate_rows(
        predicted_rows=predicted_rows,
        annotation_boxes=annotation_boxes,
        coverage_threshold=args.coverage_threshold,
        failure_example_count=args.failure_example_count,
        hide_zero_cer_details=True,
        filters=filters,
        unit_level=args.unit_level,
    )

    print(
        json.dumps(
            {
                "summary": report["summary"],
                "ocr_regions": report["ocr_regions"],
            },
            ensure_ascii=False,
            indent=2,
        )
    )

    if args.output:
        args.output.write_text(
            json.dumps(report, ensure_ascii=False, indent=2), encoding="utf-8"
        )


if __name__ == "__main__":
    main()