import re
import string
from pathlib import Path
import logging

import pandas as pd

import datasets
from datasets import DatasetInfo, SplitDict, SplitInfo, load_dataset

ALPHABET = string.ascii_lowercase

def temp_list(num_list):
    return map(lambda x: "temp_" + x, ALPHABET[: len(num_list)])


def extract_placeholders(text):
    pattern = r"<<(.*?)>>"
    matches = re.findall(pattern, text)
    return matches


def multiple_replace(text, replacement_dict):
    for k, v in replacement_dict.items():
        text = text.replace(k, v)
    return text
    # if replacement_dict:
    #     pattern = "|".join(map(re.escape, replacement_dict.keys()))
    #     return re.sub(pattern, lambda m: replacement_dict[m.group()], text)
    # else:
    #     return text


def solution_human(solution, num_list):
    eqs = extract_placeholders(solution)
    num_list = {key: str(value) for key, value in zip(temp_list(num_list), num_list)}

    modified = []
    cached = {}
    for eq in eqs:
        eq = multiple_replace(eq, num_list)
        eq = multiple_replace(eq, cached)
        try:
            res = eval(eq)
            be_eval = True
        except Exception:
            res = eq
            be_eval = False
        cached[eq] = str(res)
        num_ops = sum([1 for char in eq if char in "+-*/"])
        if num_ops and be_eval:
            modified.append(f"{eq}={cached[eq]}")
        else:
            modified.append(f"{eq}")

    text = solution
    for t, rt in zip(eqs, modified):
        text = text.replace(t, rt, 1)

    return text


def get_expre(example):
    seq = example["target_template"]
    new_seq = []
    for comp in seq[2:]:
        if comp.startswith("temp"):
            new_seq.append("{" + comp + "}")
        elif comp == "PI":
            new_seq.append("3.14")
        elif comp == "^":
            new_seq.append("**")
        else:
            new_seq.append(comp)
    # num_list = list(set(sorted(num_list)))
    # alphabet = string.ascii_lowercase
    # num_list = list(map(lambda x: "temp_" + x, alphabet[: len(example["num_list"])]))
    eqs = "".join(new_seq)
    return {"expression": eqs}


# 获取字母表
def regular(example):
    if example["id"] in ["17520"]:
        return False
    num_list = list(temp_list(example["num_list"]))
    eqs = example["expression"].format(**dict(zip(num_list, example["num_list"])))
    return eval(eqs) == example["answer"]


_DATA_FILES = ["data/math23k.csv"]


class DatasetBuilder(datasets.DatasetBuilder):
    def _info(self):
        return DatasetInfo()

    def __init__(self, **kwargs):
        super().__init__(**kwargs)

    # def download_and_prepare(self, *args, **kwargs):

    #     return self

    def _download_and_prepare(
        self, dl_manager, verification_mode, **prepare_split_kwargs
    ):
        downloaded_files = dl_manager.download(_DATA_FILES)
        split_dict = SplitDict(dataset_name=self.name)
        split_info = SplitInfo(name="train", shard_lengths=downloaded_files[0])
        split_dict.add(split_info)
        self.info.splits = split_dict
        self.info.download_size = dl_manager.downloaded_size

    def as_dataset(self, split, **kwargs):
        df_file=self.info.splits[split].shard_lengths
        logging.info("Loading dataset %s split %s from %s", self.name, split, df_file)
        df = pd.read_csv(df_file)
        ds = load_dataset("Gxg/Math23K", self.config.name, split=split)
        ds = ds.map(get_expre).filter(regular)
        ds = ds.add_column("solution", df["answers"])
        ds = ds.map(
            lambda exa: {
                "solution_human": solution_human(exa["solution"], exa["num_list"])
            }
        )
        ds = ds.select_columns(["original_text", "solution_human"])

        ds = ds.rename_columns(
            {"original_text": "question", "solution_human": "answer"}
        )
        return ds