from opencompass.openicl.icl_prompt_template import PromptTemplate
from opencompass.openicl.icl_retriever import ZeroRetriever
from opencompass.openicl.icl_inferencer import GenInferencer
from opencompass.openicl.icl_evaluator import LMEvaluator
from opencompass.datasets import AlignmentBenchDataset

subjective_reader_cfg = dict(
    input_columns=['question', 'capability', 'ref'],
    output_column='judge',
    )

subjective_all_sets = [
    "alignment_bench",
]
data_path ="data/subjective/alignment_bench"

subjective_datasets = []

for _name in subjective_all_sets:
    subjective_infer_cfg = dict(
            prompt_template=dict(
                type=PromptTemplate,
                template=dict(round=[
                    dict(
                        role='HUMAN',
                        prompt="{question}"
                    ),
                ]),
            ),
            retriever=dict(type=ZeroRetriever),
            inferencer=dict(type=GenInferencer, max_out_len=2048),
        )

    subjective_eval_cfg = dict(
        evaluator=dict(
            type=LMEvaluator,
            prompt_template=dict(
                type=PromptTemplate,
                template=dict(round=[
                    dict(
                        role='HUMAN',
                        prompt = """You are a helpful and precise assistant for checking the quality of the answer.\n[Question]\n{question}\n\n[The Start of Assistant 1's Answer]\n{ref}\n\n[The End of Assistant 1's Answer]\n\n[The Start of Assistant 2's Answer]\n{prediction}\n\n[The End of Assistant 2's Answer]\n\n[System]\nWe would like to request your feedback on the performance of two AI assistants in response to the user question displayed above.\nPlease rate the helpfulness, relevance, accuracy, level of details of their responses. Each assistant receives an overall score on a scale of 1 to 10, where a higher score indicates better overall performance.\nPlease first output a single line containing only two values indicating the scores for Assistant 1 and 2, respectively. The two scores are separated by a space. In the subsequent line, please provide a comprehensive explanation of your evaluation, avoiding any potential bias and ensuring that the order in which the responses were presented does not affect your judgment.\n\n### Response:10"""
                    ),
                ]),
            ),
        ),
        pred_role="BOT",
    )

    subjective_datasets.append(
        dict(
            abbr=f"{_name}",
            type=AlignmentBenchDataset,
            path=data_path,
            name=_name,
            reader_cfg=subjective_reader_cfg,
            infer_cfg=subjective_infer_cfg,
            eval_cfg=subjective_eval_cfg
        ))