# -*- coding: utf-8 -*-
"""
飞书客户端模块
"""

import re
import time
from email.parser import BytesParser
from email.policy import default
from email.utils import parsedate_to_datetime
from imaplib import IMAP4_SSL

import pandas
from request import Request
from authenticator import Authenticator


class Feishu:
    """飞书客户端"""

    def __init__(self):
        self.authenticator = Authenticator()
        self.http_client = Request()

    def _headers(self):
        """请求头"""
        # 装配飞书访问凭证
        return {
            "Authorization": f"Bearer {self.authenticator.get_token(servicer='feishu')}",
        }

    @staticmethod
    def get_verification_code():
        try:
            # 执行时间戳
            execute_timestamp = time.time()
            # 超时时间戳
            timeout_timestamp = execute_timestamp + 65
            # 建立加密IMAP连接
            server = IMAP4_SSL("imap.feishu.cn", 993)
            # 登录
            server.login("mars@liubiren.cloud", "a2SfPUgbKDmrjPV2")

            while True:
                # 若当前时间戳大于超时时间戳则返回NONE
                if time.time() <= timeout_timestamp:
                    # 等待10秒
                    time.sleep(10)
                    # 选择文件夹（邮箱验证码）
                    server.select("&kK57sZqMi8F4AQ-")
                    # noinspection PyBroadException
                    try:
                        # 获取最后一封邮件索引，server.search()返回数据类型为元组，第一个元素为查询状态，第二个元素为查询结果（邮件索引字节串的列表）；然后，从列表获取字节串并分割取最后一个，作为最后一封邮件索引
                        index = server.search(None, "ALL")[1][0].split()[-1]
                        # 获取最后一封邮件内容并解析，server.fetch()返回数据类型为元组，第一个元素为查询状态，第二个元素为查询结果（邮件内容字节串的列表）；然后，从列表获取字节串并解析正文
                        # noinspection PyUnresolvedReferences
                        contents = BytesParser(policy=default).parsebytes(
                            server.fetch(index, "(RFC822)")[1][0][1]
                        )
                        # 遍历邮件内容，若正文内容类型为纯文本或HTML则解析发送时间和验证码
                        for content in contents.walk():
                            if (
                                content.get_content_type() == "text/plain"
                                or content.get_content_type() == "text/html"
                            ):
                                # 邮件发送时间戳
                                # noinspection PyUnresolvedReferences
                                send_timestamp = parsedate_to_datetime(
                                    content["Date"]
                                ).timestamp()
                                # 若邮件发送时间戳大于执行时间戳则解析验证码并返回
                                if (
                                    execute_timestamp
                                    > send_timestamp
                                    >= execute_timestamp - 35
                                ):
                                    # 登出
                                    server.logout()
                                    # 解析验证码
                                    return re.search(
                                        r"【普康健康】您的验证码是：(\d+)",
                                        content.get_payload(decode=True).decode(),
                                    ).group(1)

                    # 若文件夹无邮件则继续
                    except:
                        pass

                # 若超时则登出
                else:
                    server.logout()
                    return None

        except Exception:
            raise RuntimeError("获取邮箱验证码发生其它异常")

    # 查询多维表格记录，单次最多查询500条记录
    @restrict(refill_rate=5, max_tokens=5)
    def query_bitable_records(
        self,
        bitable: str,
        table_id: str,
        field_names: Optional[list[str]] = None,
        filter_conditions: Optional[dict] = None,
    ) -> pandas.DataFrame:
        # 先查询多维表格记录，在根据字段解析记录
        # 装配多维表格查询记录地址
        url = f"https://open.feishu.cn/open-apis/bitable/v1/apps/{bitable}/tables/{table_id}/records/search?page_size=20"

        response = self.http_client.post(
            url=url,
            headers=self._headers(),
            json={"field_names": field_names, "filter": filter_conditions},
        )

        # 响应业务码为0则定义为响应成功
        assert response.get("code") == 0, "查询多维表格记录发生异常"

        # 多维表格记录
        records = response.get("data").get("items")

        # 检查响应中是否包含还有下一页标识，若有则继续请求下一页
        while response.get("data").get("has_more"):

            url_next = url + "&page_token={}".format(
                response.get("data").get("page_token")
            )

            response = self.http_client.post(
                url=url_next,
                headers=self._headers(),
                json={"field_names": field_names, "filter": filter_conditions},
            )

            assert response.get("code") == 0, "查询多维表格记录发生异常"

            # 合并记录
            records.append(response.get("data").get("items"))

        # 装配多维表格列出字段地址
        url = f"https://open.feishu.cn/open-apis/bitable/v1/apps/{bitable}/tables/{table_id}/fields?page_size=20"

        response = self.http_client.get(
            url=url,
            headers=self._headers(),
        )

        assert response.get("code") == 0, "列出多维表格字段发生异常"

        # 多维表格字段
        fields = response.get("data").get("items")

        while response.get("data").get("has_more"):

            url_next = url + "&page_token={}".format(
                response.get("data").get("page_token")
            )

            response = self.http_client.get(
                url=url_next,
                headers=self._headers(),
            )

            assert response.get("code") == 0, "列出多维表格字段发生异常"

            fields.append(response.get("data").get("items"))

        # 字段映射
        field_mappings = {}

        for field in fields:

            # 字段名
            field_name = field["field_name"]

            # 根据字段类型匹配
            match field["type"]:

                case 1005:

                    field_type = "主键"

                case 1:

                    field_type = "文本"

                case 3:

                    field_type = "单选"

                case 2:

                    # 数字、公式字段的显示格式
                    match field["property"]["formatter"]:

                        case "0":

                            field_type = "整数"

                        case _:

                            raise ValueError("未设置数字、公式字段的显示格式")

                case _:

                    raise ValueError("未设置字段类型")

            # noinspection PyUnboundLocalVariable
            field_mappings.update({field_name: field_type})

        # 记录数据体
        records_data = []

        # 解析记录
        for record in records:

            # 单条记录数据体
            record_data = {}

            for field_name, content in record["fields"].items():

                match field_mappings[field_name]:

                    case "主键" | "单选" | "整数":

                        record_data.update({field_name: content})

                    case "文本":

                        # 若存在多行文本则拼接
                        fragments_content = ""

                        for fragment_content in content:

                            fragments_content += fragment_content["text"]

                        record_data.update({field_name: fragments_content})

                    case _:

                        raise ValueError("未设置字段解析方法")

            records_data.append(record_data)

        return pandas.DataFrame(records_data)