import pandas as pd
from sentence_transformers.util import cos_sim

from utils.models import SBert


def p0_originality(df: pd.DataFrame, model_name: str) -> pd.DataFrame:
    assert 'prompt' in df.columns
    assert 'response' in df.columns
    model = SBert(model_name)

    def get_cos_sim(model, prompt: str, response: str) -> float:
        prompt_vec = model(prompt)
        response_vec = model(response)
        score = cos_sim(prompt_vec, response_vec).item()
        return score

    df['originality'] = df.apply(lambda x: 1 - get_cos_sim(model, x['prompt'], x['response']), axis=1)
    return df


def p1_flexibility(df: pd.DataFrame, model_name: str) -> pd.DataFrame:
    df = p0_originality(df, model_name)
    assert 'id' in df.columns
    df_out = df.groupby(by=['id', 'prompt']) \
        .agg({'id': 'first', 'prompt': 'first', 'originality': 'mean'}) \
        .rename(columns={'originality': 'flexibility'}) \
        .reset_index(drop=True)
    return df_out


if __name__ == '__main__':
    _df_input = pd.read_csv('data/example_3.csv')
    _df_0 = p0_originality(_df_input, 'paraphrase-multilingual-MiniLM-L12-v2')
    _df_1 = p1_flexibility(_df_input, 'paraphrase-multilingual-MiniLM-L12-v2')