import multiprocessing
import os

from trans.ExecParam import ExecParam
from utils.file.trans_methods import read_excel_files, split_array, find_header, read_file_to_df
from utils.log.import_data_log import log_print
from utils.systeminfo.sysinfo import use_files_get_max_cpu_count


class Axis0DataImpl(object):

    def __init__(self, id, process_count, now_count, exec_param: ExecParam, save_db=True):
        self.id = id
        self.process_count = process_count
        self.now_count = now_count
        self.exec_param = exec_param
        self.save_db = save_db
        # self.lock_map = dict()
        # for i in range(1000):
        #     self.lock_map[i] = multiprocessing.Manager().Lock()
        self.lock = multiprocessing.Manager().Lock()
        self.field_dict = multiprocessing.Manager().dict()

    def get_lock(self, split_col_value, col_name):
        boolean_first_time = False
        if split_col_value:
            filed_name = f'{split_col_value}_{col_name}'
        else:
            filed_name = col_name

        exists_count = len(self.field_dict.keys())
        # if exists_count >= 4:
        #     return self.lock

        if filed_name not in self.field_dict:
            boolean_first_time = True
            self.field_dict[filed_name] = len(self.field_dict.keys()) + 1

        # return boolean_first_time, self.lock_map[self.field_dict[filed_name]]
        return boolean_first_time, self.lock

    def read_and_save_file(self, file_path):
        if self.exec_param.has_header:
            header = find_header(file_path, self.exec_param.use_cols)
            if header is None:
                raise Exception(f"文件{os.path.basename(file_path)}没有找到列名")
        else:
            header = None

        df = read_file_to_df(file_path, header=header, use_cols=self.exec_param.use_cols)
        col_map = {'file_name': 'file_name'}
        if 'sheet_name' in df.columns:
            col_map['sheet_name'] = 'sheet_name'
            pass

        df.rename(mapper=self.exec_param.mapping_cols, errors='ignore', inplace=True)

        if self.exec_param.split_cols:
            df['split_col'] = df[self.exec_param.split_cols].apply(
                lambda x: '_'.join([str(i).replace(' ', '_').replace(':', '_') for i in x.values]),
                axis=1)
        else:
            df['split_col'] = 'All'

        split_col = df['split_col'].unique()

        if len(split_col) >= 1000:
            log_print(f"{file_path}切割文件太多,大于等于1000个")
            raise Exception(f"{file_path}切割文件太多,大于等于1000个")

        general_fields = list(df.columns)
        general_fields.remove('split_col')
        general_fields.remove('file_name')
        if 'sheet_name' in general_fields:
            general_fields.remove('sheet_name')

        for col in self.exec_param.index_cols:
            general_fields.remove(col)
        for split_col_value in split_col:
            for col in general_fields:
                now_cols = self.exec_param.index_cols
                now_cols.append(col)
                now_df = df[df['split_col'] == split_col_value][now_cols]

                boolean_first_time, lock = self.get_lock(split_col_value, col)
                with lock:
                    path = self.exec_param.path_param.get_process_tmp_path()
                    os.makedirs(path, exist_ok=True)
                    if boolean_first_time:
                        now_df.to_csv(os.path.join(path, f'{split_col_value}.csv'), index=False, encoding='utf-8')
                    else:
                        now_df.to_csv(os.path.join(path, f'{split_col_value}.csv'), index=False, mode='a', header=False,
                                      encoding='utf-8')


def run(self):
    all_files = read_excel_files(self.exec_param.path_param.get_unzip_tmp_path())
    split_count = use_files_get_max_cpu_count(all_files)
    all_arrays = split_array(all_files, split_count)

    log_print("开始读取竖向合并文件,文件总数:", len(all_files), ",文件分片数:", split_count)

    with multiprocessing.Pool(split_count) as pool:
        pool.map(self.read_and_save_file, all_arrays)