from opencompass.openicl.icl_prompt_template import PromptTemplate
from opencompass.openicl.icl_retriever import ZeroRetriever
from opencompass.openicl.icl_inferencer import ChatInferencer
from opencompass.openicl.icl_evaluator import TEvalEvaluator
from opencompass.datasets import teval_postprocess, TEvalDataset

teval_subject_mapping = {
    "instruct": ["instruct_v1"],
    "plan": ["plan_json_v1", "plan_str_v1"],
    "review": ["review_str_v1"],
    "reason_retrieve_understand": ["reason_retrieve_understand_json_v1"],
    "reason": ["reason_str_v1"],
    "retrieve": ["retrieve_str_v1"],
    "understand": ["understand_str_v1"],
}

teval_reader_cfg = dict(input_columns=["prompt"], output_column="ground_truth")

teval_infer_cfg = dict(
    prompt_template=dict(
        type=PromptTemplate,
        template=dict(
            round=[
                dict(role="HUMAN", prompt="{prompt}"),
            ],
        ),
    ),
    retriever=dict(type=ZeroRetriever),
    inferencer=dict(type=ChatInferencer),
)

teval_all_sets = list(teval_subject_mapping.keys())

teval_datasets = []
for _name in teval_all_sets:
    teval_eval_cfg = dict(
        evaluator=dict(type=TEvalEvaluator, subset=_name),
        pred_postprocessor=dict(type=teval_postprocess),
        num_gpus=1,
    )
    for subset in teval_subject_mapping[_name]:
        teval_datasets.append(
            dict(
                abbr="teval-" + subset,
                type=TEvalDataset,
                path="./data/teval/EN",
                name=subset,
                reader_cfg=teval_reader_cfg,
                infer_cfg=teval_infer_cfg,
                eval_cfg=teval_eval_cfg,
            )
        )
