import os
from datetime import datetime

import chardet
import pandas as pd
from sqlalchemy import create_engine, text, inspect

from utils.log.trans_log import logger


def get_engine(host='192.168.50.235', port=30306, user='root', pwd='admin123456', db='datang'):
    engine = create_engine(f'mysql+pymysql://{user}:{pwd}@{host}:{port}/{db}')
    return engine


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def print_log(message: str = '', bool_print_log=False, pre_time=None):
    log_now = datetime.now()
    if bool_print_log:
        if pre_time is None:
            logger.info(message)
        else:
            logger.info('%s,耗时:%s', message, log_now - pre_time)
    return log_now


# 获取文件编码
def detect_file_encoding(filename):
    with open(filename, 'rb') as f:
        rawdata = f.read(1000)
    result = chardet.detect(rawdata)
    encoding = result['encoding']

    if encoding is None:
        encoding = 'gb18030'

    if encoding.lower() in ['utf-8', 'ascii', 'utf8', 'utf-8-sig']:
        return 'utf-8'

    return 'gb18030'


def df_save_file(df: pd.DataFrame = None, file_name: str = '', bool_add_time: bool = True, bool_print_log: bool = True):
    if df is None:
        raise Exception('df不能为None')

    res_file_name = file_name
    if bool_add_time:
        now = datetime.now()
        now_str = now.strftime('%Y%m%d%H%M%S')
        res_file_name = os.path.join(os.path.dirname(file_name), now_str + '-' + os.path.basename(file_name))
    log_now = print_log(f'开始保存df:{df.shape}到{res_file_name}', bool_print_log)
    os.makedirs(os.path.dirname(file_name), exist_ok=True)
    if file_name.endswith('csv'):
        df.to_csv(res_file_name, index=False, encoding='utf8')
    elif file_name.endswith('xls') or file_name.endswith('xlsx'):
        df.to_excel(res_file_name, index=False)
    elif file_name.endswith('parquet'):
        df.to_parquet(res_file_name, index=False)
    else:
        raise Exception('需要添加映射')
    print_log(f'完成保存df:{df.shape}到{res_file_name}', bool_print_log, pre_time=log_now)
    return res_file_name


def read_file(file_path, read_cols: list = None, header: int = 0,
              nrows: int = None, bool_print_log: bool = True) -> pd.DataFrame:
    # 参数验证
    if not file_path or not isinstance(file_path, str):
        raise ValueError("文件路径不能为空且必须是字符串")

    if not os.path.exists(file_path):
        raise FileNotFoundError(f"文件不存在: {file_path}")

    file_ext = file_path.lower()
    # 构建读取参数的通用字典
    read_params = {'header': header}
    if nrows is not None:
        read_params['nrows'] = nrows

    try:
        log_now = print_log(f'开始读取文件: {file_path}', bool_print_log)

        # 根据文件扩展名选择读取方式
        if file_ext.endswith('.csv'):
            encoding = detect_file_encoding(file_path)
            read_params['encoding'] = encoding

            if read_cols:
                df = pd.read_csv(file_path, usecols=read_cols, **read_params)
            else:
                df = pd.read_csv(file_path, **read_params)

        elif file_ext.endswith(('.xls', '.xlsx')):
            if read_cols:
                df = pd.read_excel(file_path, usecols=read_cols, **read_params)
            else:
                df = pd.read_excel(file_path, **read_params)

        elif file_ext.endswith('.parquet'):
            # parquet文件使用不同的参数名
            parquet_params = {'columns': read_cols} if read_cols else {}
            df = pd.read_parquet(file_path, **parquet_params)

        elif file_ext.endswith('.json'):
            if read_cols:
                df = pd.read_json(file_path, **read_params)[read_cols]
            else:
                df = pd.read_json(file_path, **read_params)

        else:
            supported_formats = ['.csv', '.xls', '.xlsx', '.parquet', '.json']
            raise ValueError(
                f"不支持的文件格式: {os.path.splitext(file_path)[1]}\n"
                f"支持的文件格式: {', '.join(supported_formats)}"
            )

        print_log(f'文件读取成功: {file_path}, 数据量: {df.shape}', bool_print_log, pre_time=log_now)
        return df

    except pd.errors.EmptyDataError:
        print_log(f'文件为空: {file_path}', bool_print_log)
        return pd.DataFrame()
    except Exception as e:
        print_log(f'读取文件失败: {file_path}, 错误: {str(e)}', bool_print_log)
        raise


def df_save_table(engine, df: pd.DataFrame, table_name, pre_save: str = None):
    """
    engine: 数据库引擎
    df: DataFrame
    table_name: 表名
    pre_save: 删除表(DROP),清空表(TRUNCATE)或者不处理(None)
    """
    log_time = print_log(f'开始保存到表{table_name},数据量:{df.shape},前置处理表:{pre_save}', bool_print_log=True)
    if not pre_save is None:
        with engine.connect() as conn:
            # 检查表是否存在
            inspector = inspect(engine)
            if table_name in inspector.get_table_names():
                # 删除表
                conn.execute(text(f"{pre_save}  TABLE {table_name}"))
                conn.commit()

    df.to_sql(table_name, con=engine, if_exists='append', index=False)
    print_log(f'开始保存到表{table_name},数据量:{df.shape},前置处理表:{pre_save}', bool_print_log=True,
              pre_time=log_time)


from pathlib import Path


def get_all_files(read_path: str, pre_filter: tuple = None):
    result = list()
    if os.path.isfile(read_path):
        if pre_filter is None:
            result.append(str(Path(read_path)))
        else:
            if read_path.split('.')[-1].endswith(pre_filter):
                result.append(str(Path(read_path)))
    else:
        for root, dir, files in os.walk(read_path):
            for file in files:
                whole_path = os.path.join(root, file)
                if pre_filter is None:
                    result.append(str(Path(whole_path)))
                else:
                    if whole_path.split('.')[-1].endswith(pre_filter):
                        result.append(str(Path(whole_path)))
    return result