import time
import pandas as pd
import polars as pl
import torch
from datasets import Dataset
from sentence_transformers import SentenceTransformer
from sentence_transformers.util import paraphrase_mining


def mining(modelname, path, score):
    st = time.time()
    data = Dataset.from_pandas(pd.read_csv(path, on_bad_lines='skip', header=0, sep="\t"))
    original_df = pd.read_csv(path, on_bad_lines='skip', header=0, sep="\t")

    device = "cuda" if torch.cuda.is_available() else "cpu"
    model = SentenceTransformer(
        modelname,
        device=device,
        trust_remote_code=True,
    )

    paraphrases = paraphrase_mining(
        model,
        data["text"],
        corpus_chunk_size=len(data),
        show_progress_bar=True,
        batch_size=1024,
        max_pairs=len(data) ** 2,
    )

    df_pd = pd.DataFrame(paraphrases)
    df = pl.from_pandas(df_pd)
    df = df.rename({"0": "score", "1": "sentence_1", "2": "sentence_2"})

    union_df = pl.DataFrame(data.to_pandas())

    original_columns = original_df.columns.tolist()

    additional_cols = []
    for col in original_columns:
        if col != "text":
            additional_cols.extend([
                union_df.select(pl.col(col)).to_series()[df["sentence_1"].cast(pl.Int32)].alias(f"{col}_1"),
                union_df.select(pl.col(col)).to_series()[df["sentence_2"].cast(pl.Int32)].alias(f"{col}_2")
            ])

    df = df.with_columns([
        pl.col("score").round(3).cast(pl.Float32),
        union_df.select(pl.col("text")).to_series()[df["sentence_1"].cast(pl.Int32)].alias("sentence_1"),
        union_df.select(pl.col("text")).to_series()[df["sentence_2"].cast(pl.Int32)].alias("sentence_2"),
        *additional_cols
    ]).filter(pl.col("score") > score).sort(["score"], descending=True)

    elapsed_time = time.time() - st
    print('Execution time:', time.strftime("%H:%M:%S", time.gmtime(elapsed_time)))

    return df