"""Strict config-driven consumer for Ultralytics end-to-end NMS ONNX — vehicle agnostic-NMS decode.

The model contract is a static batch-1 input and one fixed ``[1, 300, 6]``
output whose rows are ``x1, y1, x2, y2, confidence, model_class_id``.  NMS is
inside the ONNX graph.  Python only validates, applies the element confidence
threshold and model-class mapping from ``config.json``, unletterboxes, filters
geometrically impossible boxes, sorts, and caps the configured result count.

On top of that survivor set this miner applies ONE class-AGNOSTIC greedy NMS
pass at the element's ``nms_iou`` (vehicle kit ``constrained-C-s44122``, sweep
tag ``conf0.4-agn``, SN44-171 vehicle-sweep-on-receipt: composite 0.3827 on the
calibrated S0clean ruler, +0.002 over the served per-class config).  The pass
runs on the final integer box list — the same bytes the sweep's post-processing
consumed — greedy by descending confidence (stable sort), suppressing any
surviving box overlapping an already-kept box of ANY class by more than
``nms_iou``.  The knob is the module constant ``AGNOSTIC_NMS`` (not a
config.json field): the strict element-config schema has no key for it, and the
config that ships beside this miner stays schema-exact.

This module intentionally uses no ``dataclass`` declarations.  ScoreVision's
current ``spec_from_file_location`` loader does not register the dynamic module
in ``sys.modules`` before executing it, which makes Python dataclass decoration
fail during import.
"""

import ast
import json
import math
import os
from collections.abc import Mapping
from pathlib import Path
from typing import Any

import cv2
import numpy as np
import onnxruntime as ort
from numpy import ndarray
from pydantic import BaseModel


# The deployment rail currently recognizes this strict-inference contract
# version for every cycle-2 miner.  The tensor layout is separately enforced by
# this module's fixed [1, 300, 6] startup check.
API_VERSION = "c2-strict-v1"
# The one behavioral difference vs review-ship/miner_c2e2e.py: a final
# class-agnostic NMS pass at the element's nms_iou (SN44-171 sweep tag
# "conf0.4-agn").  Module constant, not a config.json field — the strict
# element-config schema has no key for it.
AGNOSTIC_NMS = True
STRICT_INFERENCE_ENV = "SV_STRICT_INFERENCE"
INPUT_CHANNELS = 3
OUTPUT_ROWS = 300
OUTPUT_COLUMNS = 6
IMGSZ_MIN, IMGSZ_MAX, IMGSZ_MULTIPLE = 320, 1280, 32
MAX_DET_LIMIT = 300
_ROOT_KEYS = {
    "element_id", "classes", "emit_order", "coco_ids", "missing_classes", "imgsz",
    "conf", "nms_iou", "max_det",
}


class InferenceInvariantError(RuntimeError):
    """A model tensor or decoded detection violated the serving contract."""


class BoundingBox(BaseModel):
    x1: int
    y1: int
    x2: int
    y2: int
    cls_id: int
    conf: float


class TVFrameResult(BaseModel):
    frame_id: int
    boxes: list[BoundingBox]
    keypoints: list[tuple[int, int]]


def strict_inference_from_env(environ: Mapping[str, str] | None = None) -> bool:
    raw = (os.environ if environ is None else environ).get(STRICT_INFERENCE_ENV, "")
    if raw in ("", "0"):
        return False
    if raw == "1":
        return True
    raise ValueError(
        f"{STRICT_INFERENCE_ENV} must be '1' or unset/'0', got {raw!r}"
    )


def _strict_int(value: Any, where: str, low: int, high: int) -> int:
    if isinstance(value, bool) or not isinstance(value, int) or not low <= value <= high:
        raise ValueError(f"element config {where} must be an integer in [{low}, {high}], got {value!r}")
    return value


def _strict_unit(value: Any, where: str) -> float:
    if isinstance(value, bool) or not isinstance(value, (int, float)):
        raise ValueError(f"element config {where} must be a finite number in (0, 1], got {value!r}")
    number = float(value)
    if not math.isfinite(number) or not 0.0 < number <= 1.0:
        raise ValueError(f"element config {where} must be a finite number in (0, 1], got {value!r}")
    return number


class ElementConfig:
    """Strict immutable-by-convention config without dynamic-loader dataclasses."""

    __slots__ = (
        "element_id", "classes", "emit_order", "output_cls_ids", "coco_ids", "emitted_cls_ids",
        "missing_classes", "imgsz", "conf", "nms_iou", "max_det",
    )

    def __init__(
        self,
        element_id: str,
        classes: tuple[str, ...],
        coco_ids: tuple[int, ...],
        emitted_cls_ids: tuple[int, ...],
        missing_classes: tuple[str, ...],
        imgsz: int,
        conf: float,
        nms_iou: float,
        max_det: int,
        *,
        emit_order: tuple[str, ...] | None = None,
        output_cls_ids: tuple[int, ...] = (),
    ) -> None:
        self.element_id = element_id
        self.classes = classes
        self.emit_order = emit_order
        self.output_cls_ids = output_cls_ids
        self.coco_ids = coco_ids
        self.emitted_cls_ids = emitted_cls_ids
        self.missing_classes = missing_classes
        self.imgsz = imgsz
        self.conf = conf
        self.nms_iou = nms_iou
        self.max_det = max_det

    @classmethod
    def from_mapping(cls, data: dict[str, Any]) -> "ElementConfig":
        required = {"element_id", "classes", "coco_ids", "imgsz", "conf", "nms_iou", "max_det"}
        missing = sorted(required - set(data))
        if missing:
            raise ValueError(f"element config missing required field(s): {', '.join(missing)}")
        unknown = sorted(set(data) - _ROOT_KEYS)
        if unknown:
            raise ValueError(f"element config root has unknown key(s): {', '.join(unknown)}")
        element_id = data["element_id"]
        classes = data["classes"]
        coco_ids = data["coco_ids"]
        missing_classes = data.get("missing_classes", [])
        if not isinstance(element_id, str) or not element_id:
            raise ValueError("element config element_id must be a non-empty string")
        if not isinstance(classes, list) or not classes or not all(isinstance(name, str) and name for name in classes):
            raise ValueError("element config classes must be a non-empty list of strings")
        if len(set(classes)) != len(classes):
            raise ValueError("element config classes must not contain duplicates")
        emit_order_raw = data.get("emit_order")
        if "emit_order" not in data:
            emit_order = None
        else:
            if not isinstance(emit_order_raw, list) or not emit_order_raw \
                    or not all(isinstance(name, str) and name for name in emit_order_raw):
                raise ValueError("element config emit_order must be a non-empty list of class names")
            if len(set(emit_order_raw)) != len(emit_order_raw):
                raise ValueError("element config emit_order must not contain duplicates")
            unknown = sorted(set(emit_order_raw) - set(classes))
            omitted = sorted(set(classes) - set(emit_order_raw))
            if unknown or omitted or len(emit_order_raw) != len(classes):
                raise ValueError(
                    "element config emit_order must be an exact permutation of classes; "
                    f"unknown={unknown}, missing={omitted}"
                )
            emit_order = tuple(emit_order_raw)
        output_order = emit_order if emit_order is not None else tuple(classes)
        output_cls_ids = tuple(output_order.index(name) for name in classes)
        if not isinstance(missing_classes, list) or not all(isinstance(name, str) for name in missing_classes):
            raise ValueError("element config missing_classes must be a list of strings")
        if len(set(missing_classes)) != len(missing_classes) or any(name not in classes for name in missing_classes):
            raise ValueError("element config missing_classes must be unique members of classes")
        if not isinstance(coco_ids, list) or not all(
            isinstance(class_id, int) and not isinstance(class_id, bool) and class_id >= 0
            for class_id in coco_ids
        ):
            raise ValueError("element config coco_ids must be non-negative integer model class ids")
        if len(set(coco_ids)) != len(coco_ids):
            raise ValueError("element config coco_ids must not contain duplicates")
        emitted_cls_ids = tuple(index for index, name in enumerate(classes) if name not in missing_classes)
        if len(coco_ids) != len(emitted_cls_ids):
            raise ValueError(
                "element config classes/coco_ids length mismatch: "
                f"{len(classes)} classes minus {len(missing_classes)} missing classes requires "
                f"{len(emitted_cls_ids)} ids, got {len(coco_ids)}"
            )
        imgsz = _strict_int(data["imgsz"], "imgsz", IMGSZ_MIN, IMGSZ_MAX)
        if imgsz % IMGSZ_MULTIPLE:
            raise ValueError(f"element config imgsz must be a multiple of {IMGSZ_MULTIPLE}, got {imgsz}")
        return cls(
            element_id,
            tuple(classes),
            tuple(coco_ids),
            emitted_cls_ids,
            tuple(missing_classes),
            imgsz,
            _strict_unit(data["conf"], "conf"),
            _strict_unit(data["nms_iou"], "nms_iou"),
            _strict_int(data["max_det"], "max_det", 1, MAX_DET_LIMIT),
            emit_order=emit_order,
            output_cls_ids=output_cls_ids,
        )


def load_config(path: Path) -> ElementConfig:
    try:
        data = json.loads(path.read_text(encoding="utf-8"))
    except Exception as exc:
        raise ValueError(f"cannot parse element config {path}: {exc}") from exc
    if not isinstance(data, dict):
        raise ValueError("element config root must be a JSON object")
    return ElementConfig.from_mapping(data)


class Miner:
    api_version = API_VERSION
    strict_inference = False
    inference_failures = 0
    invariant_failures = 0
    input_dims_source = "unbound"
    input_shape_declared: list[Any] = []
    min_side = 3.0
    max_aspect_ratio = 10.0

    def __init__(self, path_hf_repo: Path) -> None:
        repo = Path(path_hf_repo)
        self.config = load_config(repo / "config.json")
        self.class_names = list(self.config.emit_order or self.config.classes)
        self.conf_threshold = self.config.conf
        self.nms_iou = self.config.nms_iou  # retained for API parity; NMS is in the graph
        self.max_det = self.config.max_det
        self.strict_inference = strict_inference_from_env()
        self.inference_failures = 0
        self.invariant_failures = 0

        # The referee grants one physical core (two SMT threads).  Let ORT own
        # both threads for the convolution graph, but keep OpenCV's short
        # resize/blob stages single-threaded: a second OpenCV worker is slower
        # and adds tail jitter on two sibling logical CPUs.
        cv2.setNumThreads(1)

        options = ort.SessionOptions()
        options.intra_op_num_threads = 2
        options.inter_op_num_threads = 1
        options.execution_mode = ort.ExecutionMode.ORT_SEQUENTIAL
        options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
        model_path = repo / "weights.onnx"
        try:
            self.session = ort.InferenceSession(
                str(model_path), sess_options=options,
                providers=["CUDAExecutionProvider", "CPUExecutionProvider"],
            )
        except Exception as exc:
            print(f"CUDA session failed, falling back to CPU: {exc}")
            self.session = ort.InferenceSession(
                str(model_path), sess_options=options, providers=["CPUExecutionProvider"]
            )
        self.input_name = self.session.get_inputs()[0].name
        self.output_names = [output.name for output in self.session.get_outputs()]
        self._bind_input_dims(self.session.get_inputs()[0].shape)
        self._check_output_shape(self.session.get_outputs()[0].shape)
        self.model_names = self._read_model_names()
        self._bind_class_map()
        self._warmup(3)
        print(
            f"api_version={self.api_version} element={self.config.element_id} "
            f"imgsz={self.config.imgsz} conf={self.conf_threshold} "
            f"providers={self.session.get_providers()} warmups=3"
        )

    @staticmethod
    def _static_dim(value: Any) -> bool:
        return isinstance(value, int) and not isinstance(value, bool) and value > 0

    def _bind_input_dims(self, shape: Any) -> None:
        if not isinstance(shape, (list, tuple)) or len(shape) != 4:
            raise ValueError(f"model input must be rank-4 NCHW, got {shape!r}")
        batch, channels, height, width = shape
        self.input_shape_declared = [value if self._static_dim(value) else str(value) for value in shape]
        if self._static_dim(batch) and batch != 1:
            raise ValueError(f"model input batch must be 1, got {batch}")
        if self._static_dim(channels) and channels != INPUT_CHANNELS:
            raise ValueError(f"model input channels must be 3, got {channels}")
        if not (self._static_dim(height) and self._static_dim(width)):
            raise ValueError(f"e2e shipping export must have static spatial dims, got {height}x{width}")
        if (height, width) != (self.config.imgsz, self.config.imgsz):
            raise ValueError(
                f"model input spatial dims {height}x{width} != config imgsz "
                f"{self.config.imgsz}x{self.config.imgsz}"
            )
        self.input_height = self.input_width = self.config.imgsz
        self.input_dims_source = "static"

    @staticmethod
    def _check_output_shape(shape: Any) -> None:
        if list(shape) != [1, OUTPUT_ROWS, OUTPUT_COLUMNS]:
            raise ValueError(
                f"e2e model output must be exactly [1, {OUTPUT_ROWS}, {OUTPUT_COLUMNS}], got {shape!r}"
            )

    def _read_model_names(self) -> tuple[str, ...]:
        try:
            raw = self.session.get_modelmeta().custom_metadata_map["names"]
            parsed = ast.literal_eval(raw)
            if not isinstance(parsed, dict):
                raise TypeError("names metadata is not a dict")
            keys = sorted(parsed)
            if keys != list(range(len(keys))):
                raise ValueError(f"names metadata keys are not consecutive from zero: {keys[:8]}")
            names = tuple(str(parsed[index]) for index in keys)
        except Exception as exc:
            raise ValueError(f"e2e ONNX requires valid consecutive names metadata: {exc}") from exc
        if not names:
            raise ValueError("e2e ONNX names metadata is empty")
        return names

    def _bind_class_map(self) -> None:
        self.model_to_element: dict[int, int] = {}
        for model_id, element_id in zip(self.config.coco_ids, self.config.emitted_cls_ids):
            if model_id >= len(self.model_names):
                raise ValueError(
                    f"config model class id {model_id} outside ONNX names range 0..{len(self.model_names)-1}"
                )
            expected = self.config.classes[element_id]
            actual = self.model_names[model_id]
            if actual != expected:
                raise ValueError(
                    f"config class mapping mismatch: model class {model_id} is {actual!r}, "
                    f"element class {element_id} is {expected!r}"
                )
            self.model_to_element[model_id] = element_id

    @staticmethod
    def _letterbox(image: ndarray, new_shape: tuple[int, int], color=(114, 114, 114)):
        height, width = image.shape[:2]
        new_width, new_height = new_shape
        ratio = min(new_width / width, new_height / height)
        resized_width, resized_height = int(round(width * ratio)), int(round(height * ratio))
        if (resized_width, resized_height) != (width, height):
            image = cv2.resize(
                image, (resized_width, resized_height),
                interpolation=cv2.INTER_CUBIC if ratio > 1.0 else cv2.INTER_LINEAR,
            )
        dw, dh = (new_width - resized_width) / 2.0, (new_height - resized_height) / 2.0
        padded = cv2.copyMakeBorder(
            image,
            int(round(dh - 0.1)), int(round(dh + 0.1)),
            int(round(dw - 0.1)), int(round(dw + 0.1)),
            cv2.BORDER_CONSTANT, value=color,
        )
        return padded, ratio, (dw, dh)

    def _preprocess(self, image: ndarray):
        original_height, original_width = image.shape[:2]
        padded, ratio, pad = self._letterbox(image, (self.input_width, self.input_height))
        blob = cv2.dnn.blobFromImage(padded, scalefactor=1 / 255.0, swapRB=True)
        return blob, ratio, pad, (original_width, original_height)

    @staticmethod
    def _check_image(image: np.ndarray) -> np.ndarray:
        if not isinstance(image, np.ndarray) or image.ndim != 3 or image.shape[2] != 3:
            raise ValueError(f"expected HWC 3-channel image, got {getattr(image, 'shape', type(image))}")
        return image if image.dtype == np.uint8 else image.astype(np.uint8)

    @staticmethod
    def _check_finite(tensor: np.ndarray, where: str) -> None:
        if not np.isfinite(tensor).all():
            raise InferenceInvariantError(
                f"{where} must be finite everywhere: NaN={int(np.isnan(tensor).sum())} "
                f"Inf={int(np.isinf(tensor).sum())}"
            )

    def _check_outputs(self, outputs: Any) -> np.ndarray:
        if not isinstance(outputs, (list, tuple)) or len(outputs) != len(self.output_names):
            raise ValueError(f"session returned invalid output list, expected {len(self.output_names)} tensors")
        for name, tensor in zip(self.output_names, outputs):
            if not isinstance(tensor, np.ndarray):
                raise ValueError(f"model output {name!r} is not an ndarray")
            if np.issubdtype(tensor.dtype, np.floating):
                self._check_finite(tensor, f"model output {name!r}")
        output = outputs[0]
        if not np.issubdtype(output.dtype, np.floating) or list(output.shape) != [1, OUTPUT_ROWS, OUTPUT_COLUMNS]:
            raise ValueError(
                f"e2e output tensor must be float [1, {OUTPUT_ROWS}, {OUTPUT_COLUMNS}], "
                f"got {output.dtype} {list(output.shape)}"
            )
        return output

    def _decode_e2e(
        self,
        output: np.ndarray,
        ratio: float,
        pad: tuple[float, float],
        original_size: tuple[int, int],
    ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
        rows = output[0]
        boxes = rows[:, :4].astype(np.float32, copy=True)
        scores = rows[:, 4].astype(np.float32, copy=False)
        raw_classes = rows[:, 5]
        if (scores < 0).any() or (scores > 1).any():
            raise InferenceInvariantError("e2e confidence outside [0, 1]")
        rounded = np.rint(raw_classes)
        if not np.array_equal(raw_classes, rounded):
            raise InferenceInvariantError("e2e model class ids must be exact integers")
        model_classes = rounded.astype(np.int64)
        if (model_classes < 0).any() or (model_classes >= len(self.model_names)).any():
            bad = model_classes[(model_classes < 0) | (model_classes >= len(self.model_names))]
            raise InferenceInvariantError(f"e2e model class id outside range: {bad[:8].tolist()}")

        active = scores > 0.0
        reversed_rows = active & ((boxes[:, 2] < boxes[:, 0]) | (boxes[:, 3] < boxes[:, 1]))
        if reversed_rows.any():
            raise InferenceInvariantError(f"e2e output contains reversed boxes: {boxes[reversed_rows][:4].tolist()}")
        mapped = np.fromiter((int(class_id) in self.model_to_element for class_id in model_classes), bool, len(rows))
        keep = mapped & (scores >= np.float32(self.conf_threshold))
        if not keep.any():
            return (
                np.empty((0, 4), np.float32),
                np.empty((0,), np.float32),
                np.empty((0,), np.int64),
            )
        boxes = boxes[keep]
        scores = scores[keep]
        model_classes = model_classes[keep]
        element_classes = np.fromiter(
            (self.model_to_element[int(class_id)] for class_id in model_classes),
            dtype=np.int64,
            count=len(model_classes),
        )
        boxes[:, [0, 2]] = (boxes[:, [0, 2]] - pad[0]) / ratio
        boxes[:, [1, 3]] = (boxes[:, [1, 3]] - pad[1]) / ratio
        width, height = original_size
        boxes[:, [0, 2]] = np.clip(boxes[:, [0, 2]], 0, width - 1)
        boxes[:, [1, 3]] = np.clip(boxes[:, [1, 3]], 0, height - 1)
        self._check_decoded(boxes, scores, element_classes, original_size)

        box_widths = boxes[:, 2] - boxes[:, 0]
        box_heights = boxes[:, 3] - boxes[:, 1]
        areas = box_widths * box_heights
        image_area = float(width * height)
        sane = (
            (box_widths >= self.min_side)
            & (box_heights >= self.min_side)
            & (areas <= 0.95 * image_area)
            & (
                np.maximum(
                    box_widths / np.maximum(box_heights, 1e-6),
                    box_heights / np.maximum(box_widths, 1e-6),
                )
                <= self.max_aspect_ratio
            )
        )
        boxes, scores, element_classes = boxes[sane], scores[sane], element_classes[sane]
        if len(scores):
            order = np.argsort(-scores, kind="stable")[: self.max_det]
            boxes, scores, element_classes = boxes[order], scores[order], element_classes[order]
        # Thresholds, model-name checks and all head semantics above stay in
        # ``classes`` order.  Only the validator-facing ids are remapped here.
        if len(element_classes):
            element_classes = np.asarray(self.config.output_cls_ids, dtype=np.int64)[element_classes]
        return boxes, scores, element_classes

    def _check_decoded(
        self,
        boxes: np.ndarray,
        scores: np.ndarray,
        class_ids: np.ndarray,
        original_size: tuple[int, int],
    ) -> None:
        if not len(boxes):
            return
        width, height = original_size
        if not np.isfinite(boxes).all():
            raise InferenceInvariantError("decoded boxes contain non-finite coordinates")
        if (boxes[:, 2] < boxes[:, 0]).any() or (boxes[:, 3] < boxes[:, 1]).any():
            raise InferenceInvariantError("decoded boxes are reversed")
        xs, ys = boxes[:, [0, 2]], boxes[:, [1, 3]]
        if (xs < 0).any() or (xs > width - 1).any() or (ys < 0).any() or (ys > height - 1).any():
            raise InferenceInvariantError(f"decoded boxes outside {width}x{height} image")
        if not np.isfinite(scores).all() or (scores < 0).any() or (scores > 1).any():
            raise InferenceInvariantError("decoded confidence outside [0, 1]")
        if (class_ids < 0).any() or (class_ids >= len(self.class_names)).any():
            raise InferenceInvariantError("decoded cls_id outside element class range")

    @staticmethod
    def _to_bounding_boxes(
        boxes: np.ndarray,
        scores: np.ndarray,
        class_ids: np.ndarray,
    ) -> list[BoundingBox]:
        return [
            BoundingBox(
                x1=int(math.floor(box[0])), y1=int(math.floor(box[1])),
                x2=int(math.ceil(box[2])), y2=int(math.ceil(box[3])),
                cls_id=int(class_id), conf=float(score),
            )
            for box, score, class_id in zip(boxes, scores, class_ids)
            if box[2] > box[0] and box[3] > box[1]
        ]

    def _predict_single(self, image: np.ndarray) -> list[BoundingBox]:
        image = self._check_image(image)
        blob, ratio, pad, original_size = self._preprocess(image)
        outputs = self.session.run(self.output_names, {self.input_name: blob})
        output = self._check_outputs(outputs)
        arrays = self._decode_e2e(output, ratio, pad, original_size)
        boxes = self._to_bounding_boxes(*arrays)
        if AGNOSTIC_NMS:
            boxes = self._agnostic_nms(boxes, self.nms_iou)
        return boxes

    @staticmethod
    def _agnostic_nms(boxes: list[BoundingBox], iou_threshold: float) -> list[BoundingBox]:
        """One class-agnostic greedy NMS pass over the decoded boxes.

        Input and output are the final integer-box list (descending confidence,
        capped).  A survivor is kept unless it overlaps an already-kept box of
        ANY class by more than ``iou_threshold`` — the exact pass the
        vehicle-sweep lane measured as tag ``conf<bar>-agn`` (SN44-171).
        """
        keep: list[BoundingBox] = []
        for cand in sorted(boxes, key=lambda b: -b.conf):
            suppressed = False
            for kept in keep:
                xx1, yy1 = max(cand.x1, kept.x1), max(cand.y1, kept.y1)
                xx2, yy2 = min(cand.x2, kept.x2), min(cand.y2, kept.y2)
                inter = max(0, xx2 - xx1) * max(0, yy2 - yy1)
                union = ((cand.x2 - cand.x1) * (cand.y2 - cand.y1)
                         + (kept.x2 - kept.x1) * (kept.y2 - kept.y1) - inter)
                iou = inter / union if union > 0 else 0.0
                if iou > iou_threshold:
                    suppressed = True
                    break
            if not suppressed:
                keep.append(cand)
        return keep

    def _warmup(self, iterations: int) -> None:
        dummy = np.zeros((720, 1280, 3), dtype=np.uint8)
        original_strict = self.strict_inference
        self.strict_inference = True
        try:
            for _ in range(iterations):
                self.predict_batch(batch_images=[dummy], offset=0, n_keypoints=0)
        except Exception as exc:
            raise RuntimeError(f"warmup failed, refusing to serve: {type(exc).__name__}: {exc}") from exc
        finally:
            self.strict_inference = original_strict

    def _record_failure(self, frame_id: int, exc: Exception) -> None:
        self.inference_failures += 1
        invariant = isinstance(exc, InferenceInvariantError)
        if invariant:
            self.invariant_failures += 1
        print(json.dumps({
            "event": "inference_failure",
            "frame_id": frame_id,
            "error_type": type(exc).__name__,
            "error": str(exc),
            "invariant": invariant,
            "inference_failures": self.inference_failures,
            "invariant_failures": self.invariant_failures,
        }, sort_keys=True))

    def predict_batch(
        self,
        batch_images: list[np.ndarray],
        offset: int,
        n_keypoints: int,
    ) -> list[TVFrameResult]:
        _ = n_keypoints
        results: list[TVFrameResult] = []
        for index, image in enumerate(batch_images):
            frame_id = offset + index
            try:
                boxes = self._predict_single(image)
            except Exception as exc:
                if self.strict_inference:
                    raise
                self._record_failure(frame_id, exc)
                boxes = []
            results.append(TVFrameResult(frame_id=frame_id, boxes=boxes, keypoints=[]))
        return results

    def __repr__(self) -> str:
        return (
            f"E2EONNXRuntime(session={type(self.session).__name__}, "
            f"providers={self.session.get_providers()})"
        )