"""Auto-generated stub for module: result_formatter."""
from typing import Any, Optional

# Constants
MetricPayload: Any

# Functions
def format_classification_results(split: str, predictions: Any, output: Any, target: Any, index_to_labels: Any[str, str], metric_functions: Any[str, Any]) -> Any:
    """
    Build standardized single-label classification metric payload.
    
        Args:
            split: Split name such as ``train`` or ``val``.
            predictions: Predicted class ids from inference runner.
            output: Model output tensor or array used for metrics.
            target: Ground-truth target tensor or array.
            index_to_labels: Mapping of class indices to label names.
            metric_functions: Mapping containing required metric callables.
    
        Returns:
            Normalized classification metric payload records.
    """
    ...
def format_detection_results(split: str, precision: Any[Any], recall: Any[Any], f1_score: Any[Any], index_to_labels: Any[str, str], mAP50: Any | None = None, mAP50_95: Any | None = None) -> Any:
    """
    Build standardized detection payload from per-class vectors.
    
        Args:
            split: Split name such as ``train`` or ``val``.
            precision: Iterable of per-class precision values.
            recall: Iterable of per-class recall values.
            f1_score: Iterable of per-class F1 values.
            index_to_labels: Mapping of class indices to label names.
            mAP50: Optional mean average precision at IoU 0.50.
            mAP50_95: Optional mean average precision over IoU range.
    
        Returns:
            Normalized detection metric payload records.
    """
    ...
def format_keypoint_results(split: str, keypoint_names: Any[str] | None = None, mAP: Any | None = None, mAP50: Any | None = None, oks_mAP: Any | None = None, pck: Any | None = None, per_keypoint_pck: Any[Any] | None = None) -> Any:
    """
    Build normalized payload for keypoint or pose estimation metrics.
    
        Args:
            split: Split name such as ``train`` or ``val``.
            keypoint_names: Optional names for each keypoint index.
            mAP: Optional mean average precision value.
            mAP50: Optional mean average precision at IoU 0.50.
            oks_mAP: Optional OKS-based mean average precision.
            pck: Optional overall PCK value.
            per_keypoint_pck: Optional iterable of per-keypoint PCK values.
    
        Returns:
            Normalized keypoint metric payload records.
    """
    ...
def format_multilabel_results(split: str, per_class_precision: Any[Any], per_class_recall: Any[Any], per_class_f1: Any[Any], macro_f1: Any | None, micro_f1: Any | None, mAP: Any | None, index_to_labels: Any[str, str]) -> Any:
    """
    Build normalized payload for multi-label classification metrics.
    
        Args:
            split: Split name such as ``train`` or ``val``.
            per_class_precision: Iterable of per-class precision values.
            per_class_recall: Iterable of per-class recall values.
            per_class_f1: Iterable of per-class F1 values.
            macro_f1: Optional macro-averaged F1 value.
            micro_f1: Optional micro-averaged F1 value.
            mAP: Optional mean average precision value.
            index_to_labels: Mapping of class indices to label names.
    
        Returns:
            Normalized multi-label metric payload records.
    """
    ...
def format_ocr_results(split: str, cer: Any | None = None, wer: Any | None = None, precision: Any | None = None, recall: Any | None = None, f1_score: Any | None = None) -> Any:
    """
    Build normalized payload for OCR or text pipeline metrics.
    
        Args:
            split: Split name such as ``train`` or ``val``.
            cer: Optional character error rate.
            wer: Optional word error rate.
            precision: Optional precision value.
            recall: Optional recall value.
            f1_score: Optional F1 score value.
    
        Returns:
            Normalized OCR metric payload records.
    """
    ...
def format_tracking_results(split: str, mota: Any | None = None, motp: Any | None = None, idf1: Any | None = None, hota: Any | None = None, fp: Any | None = None, fn: Any | None = None, ids: Any | None = None) -> Any:
    """
    Build normalized payload for MOT-style tracking metrics.
    
        Args:
            split: Split name such as ``train`` or ``val``.
            mota: Optional MOTA value.
            motp: Optional MOTP value.
            idf1: Optional IDF1 value.
            hota: Optional HOTA value.
            fp: Optional false-positive count.
            fn: Optional false-negative count.
            ids: Optional identity-switch count.
    
        Returns:
            Normalized tracking metric payload records.
    """
    ...
def format_yolo_results(yolo_result: Any, split: str) -> Any:
    """
    Build detection payload from Ultralytics YOLO validation result object.
    
        Args:
            yolo_result: Ultralytics validation result object with metrics and per-class stats.
            split: Split name such as ``train`` or ``val``.
    
        Returns:
            Normalized detection payload records derived from YOLO result.
    """
    ...
