from opencompass.openicl.icl_prompt_template import PromptTemplate
from opencompass.openicl.icl_retriever import FixKRetriever
from opencompass.openicl.icl_inferencer import PPLInferencer
from opencompass.openicl.icl_evaluator import AccEvaluator
from opencompass.datasets import HFDataset


_hint = "The following are semantic matching questions. \n" \
    "Please determine whether the following two sentences are semantically equivalent: " \
    "0 means not equivalent, 1 means equivalent.\n"
MRPC_infer_cfg = dict(
    ice_template=dict(
        type=PromptTemplate,
        template="Sentence one: {sentence1}\nSentence two: {sentence2}\nResult: {label}",
    ),
    prompt_template=dict(
        type=PromptTemplate,
        template={
            answer:
            f"{_hint}</E>Sentence one: {{sentence1}}\nSentence two: {{sentence2}}\nResult: {answer}"
            for answer in [0, 1]
        },
        ice_token='</E>',
    ),
    retriever=dict(type=FixKRetriever),
    inferencer=dict(type=PPLInferencer, fix_id_list=[0, 1, 2, 3, 4]))

MRPC_eval_cfg = dict(evaluator=dict(type=AccEvaluator), )


MRPC_datasets = []
for _split in ["validation", "test"]:

    MRPC_reader_cfg = dict(
        input_columns=['sentence1', 'sentence2'],
        output_column='label',
        train_split="train",
        test_split=_split
    )

    MRPC_datasets.append(
        dict(
            abbr=f'MRPC-{_split}',
            type=HFDataset,
            path='glue',
            name='mrpc',
            reader_cfg=MRPC_reader_cfg,
            infer_cfg=MRPC_infer_cfg,
            eval_cfg=MRPC_eval_cfg
        )
    )
