"""Evaluation runner helper (Quick Start)."""

from typing import Any, Dict, Optional

import torch
import torch.nn as nn
from torch.utils.data import DataLoader

from llm_lab.config import EvalConfig
from .full_evaluator import FullEvaluator
from .checklist import InsightChecklist


def run_evaluation(
    model: nn.Module,
    tokenizer: Any,
    val_dataloader: DataLoader,
    device: torch.device = None,
    dtype: torch.dtype = torch.bfloat16,
    metrics_history: Optional[Dict[str, list]] = None,
    config: Optional[EvalConfig] = None,
) -> Dict[str, Any]:
    """Runs all evaluations in one call.

    Usage (Colab):
    ```python
    from llm_lab.evaluation import run_evaluation

    # After training is complete
    report = run_evaluation(
        model=trainer.model,
        tokenizer=tokenizer,
        val_dataloader=val_dl,
        metrics_history=trainer.metrics.history,
    )
    ```
    """
    if device is None:
        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

    evaluator = FullEvaluator(
        model=model,
        tokenizer=tokenizer,
        val_dataloader=val_dataloader,
        device=device,
        config=config,
        dtype=dtype,
        metrics_history=metrics_history,
    )

    report = evaluator.run_full_evaluation()

    # Insight checklist
    InsightChecklist.run_checklist(report, metrics_history)

    return report