"""
{{PAPER_TITLE}} — Evaluation Metrics

Paper: https://arxiv.org/abs/{{ARXIV_ID}}
Implements: Metric computation for {{METRICS_LISTED}}

Section references:
  {{§SECTION}} — {{evaluation methodology description}}

NOTE: This file provides metric computation functions only.
      It does NOT handle data loading or model inference.
      Pass predictions and targets to compute metrics.
"""

from typing import Dict, List, Optional

import torch
import numpy as np


def compute_{{METRIC_1}}(
    predictions: torch.Tensor,
    targets: torch.Tensor,
) -> float:
    """§{{SECTION}} — Compute {{metric name}}.
    
    "{{Quote from paper about evaluation metric}}"
    
    Args:
        predictions: model output — shape: (batch, {{dims}})
        targets: ground truth — shape: (batch, {{dims}})
        
    Returns:
        {{metric_name}} score as a float
        
    NOTE: {{any caveats about metric implementation, e.g.,
           "Different BLEU implementations give different numbers.
            We use sacrebleu for reproducibility."}}
    """
    # §{{SECTION}} — metric computation
    pass  # REPLACE with actual metric


def compute_all_metrics(
    predictions: torch.Tensor,
    targets: torch.Tensor,
) -> Dict[str, float]:
    """Compute all evaluation metrics reported in the paper.
    
    §{{SECTION}} — "We report {{metric_1}}, {{metric_2}}, and {{metric_3}}"
    
    Args:
        predictions: model output — shape: (batch, {{dims}})
        targets: ground truth — shape: (batch, {{dims}})
    
    Returns:
        Dict mapping metric name to value
    """
    return {
        "{{metric_1}}": compute_{{METRIC_1}}(predictions, targets),
        # Add more metrics as needed
    }