"""Batching helpers for future GPU workers."""

from __future__ import annotations

from collections import defaultdict
from typing import Iterable


def bucket_pages(page_records: Iterable[dict]) -> dict[str, list[dict]]:
    buckets: dict[str, list[dict]] = defaultdict(list)
    for page in page_records:
        task = str(page.get("task", "parse"))
        resolution = str(page.get("resolution_bucket", "default"))
        buckets[f"{task}:{resolution}"].append(page)
    return dict(buckets)


def batch_gpu_tasks(tasks: Iterable[dict], max_batch_size: int = 4) -> list[dict]:
    buckets: dict[tuple[str, str, str, str, str, str], list[dict]] = defaultdict(list)
    for task in tasks:
        key = (
            str(task.get("provider", "huggingface_spaces")),
            str(task.get("space_name", "zeroshotGPU")),
            str(task.get("backend", "transformers")),
            str(task.get("task_type", "unknown")),
            str(task.get("model_role", "vlm")),
            str(task.get("model_id", "")),
        )
        buckets[key].append(task)

    batches: list[dict] = []
    safe_batch_size = max(int(max_batch_size), 1)
    for (provider, space_name, backend, task_type, model_role, model_id), bucket in sorted(buckets.items()):
        ordered = sorted(bucket, key=lambda item: (-int(item.get("priority", 0)), str(item.get("task_id", ""))))
        for offset in range(0, len(ordered), safe_batch_size):
            batch_tasks = ordered[offset : offset + safe_batch_size]
            batches.append(
                {
                    "batch_id": f"gb{len(batches) + 1}",
                    "provider": provider,
                    "space_name": space_name,
                    "backend": backend,
                    "task_type": task_type,
                    "model_role": model_role,
                    "model_id": model_id or None,
                    "task_count": len(batch_tasks),
                    "tasks": batch_tasks,
                }
            )
    return batches