from typing import Dict, Optional, Set

from helm.benchmark.annotation.model_as_judge import AnnotatorModelInfo, LLMAsJuryAnnotator
from helm.clients.auto_client import AutoClient


PROMPT_TEMPLATE = """You are a medical expert tasked with evaluating responses to consumer medication questions.

Your goal is to assess how well the response captures the information asked,
and how it compares to the gold response in terms of accuracy, completeness, and clarity.

The question provided in these tags:
<medication_question>
{QUESTION}
</medication_question>

The response will be provided in these tags:
<response>
{RESPONSE}
</response>

The reference response will be provided in these tags:
<gold_response>
{GOLD_RESPONSE}
</gold_response>

Carefully review the <response> and compare it to the <gold_response> when needed.
Please evaluate the response on a scale of 1-5 (1 = poor, 5 = excellent) for each of these three key dimensions:

Evaluation Criteria:
Accuracy (1-5)
- Is the information about the medication factually correct?

Completeness (1-5)
- Does the response fully answer the question, covering all important points?

Clarity (1-5)
- Is the response easy for a consumer to understand?

Output Format:
Output your evaluation as a single valid JSON object matching the following structure:
{
    "accuracy": {
        "score": 0,
        "explanation": "Brief explanation of why this score was given."
    },
    "completeness": {
        "score": 0,
        "explanation": "Brief explanation of why this score was given."
    },
    "clarity": {
        "score": 0,
        "explanation": "Brief explanation of why this score was given."
    }
}

Ensure the output is valid JSON:
- Use **double quotes** (") for all keys and string values.
- When quoting text or sections inside the explanations, use escaped double quotes (\") to
  maintain valid JSON formatting.
- Do not include any additional information in the output.
"""

ANNOTATION_CRITERIA: Dict[str, Set[str]] = {
    "accuracy": {"score", "explanation"},
    "completeness": {"score", "explanation"},
    "clarity": {"score", "explanation"},
}


class MedicationQAAnnotator(LLMAsJuryAnnotator):
    """The MedicationQA autograder."""

    def __init__(
        self,
        auto_client: AutoClient,
        annotator_models: Dict[str, AnnotatorModelInfo],
        template_name: Optional[str] = None,
    ):
        super().__init__(
            name="medication_qa",
            auto_client=auto_client,
            prompt_template=PROMPT_TEMPLATE,
            annotation_criteria=ANNOTATION_CRITERIA,
            annotator_models=annotator_models,
        )
