#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
@Time    : 2024/1/4 16:32
@Author  : alexanderwu
@File    : context.py
"""
import os
from pathlib import Path
from typing import Any, Optional

from pydantic import BaseModel, ConfigDict

from metagpt.config2 import Config
from metagpt.configs.llm_config import LLMConfig
from metagpt.provider.base_llm import BaseLLM
from metagpt.provider.llm_provider_registry import create_llm_instance
from metagpt.utils.cost_manager import CostManager
from metagpt.utils.git_repository import GitRepository
from metagpt.utils.project_repo import ProjectRepo


class AttrDict(BaseModel):
    """A dict-like object that allows access to keys as attributes, compatible with Pydantic."""

    model_config = ConfigDict(extra="allow")

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

    def __getattr__(self, key):
        return self.__dict__.get(key, None)

    def __setattr__(self, key, value):
        self.__dict__[key] = value

    def __delattr__(self, key):
        if key in self.__dict__:
            del self.__dict__[key]
        else:
            raise AttributeError(f"No such attribute: {key}")

    def set(self, key, val: Any):
        self.__dict__[key] = val

    def get(self, key, default: Any = None):
        return self.__dict__.get(key, default)

    def remove(self, key):
        if key in self.__dict__:
            self.__delattr__(key)


class Context(BaseModel):
    """Env context for MetaGPT"""

    model_config = ConfigDict(arbitrary_types_allowed=True)

    kwargs: AttrDict = AttrDict()
    config: Config = Config.default()

    repo: Optional[ProjectRepo] = None
    git_repo: Optional[GitRepository] = None
    src_workspace: Optional[Path] = None
    cost_manager: CostManager = CostManager()

    _llm: Optional[BaseLLM] = None

    def new_environ(self):
        """Return a new os.environ object"""
        env = os.environ.copy()
        # i = self.options
        # env.update({k: v for k, v in i.items() if isinstance(v, str)})
        return env

    # def use_llm(self, name: Optional[str] = None, provider: LLMType = LLMType.OPENAI) -> BaseLLM:
    #     """Use a LLM instance"""
    #     self._llm_config = self.config.get_llm_config(name, provider)
    #     self._llm = None
    #     return self._llm

    def llm(self) -> BaseLLM:
        """Return a LLM instance, fixme: support cache"""
        # if self._llm is None:
        self._llm = create_llm_instance(self.config.llm)
        if self._llm.cost_manager is None:
            self._llm.cost_manager = self.cost_manager
        return self._llm

    def llm_with_cost_manager_from_llm_config(self, llm_config: LLMConfig) -> BaseLLM:
        """Return a LLM instance, fixme: support cache"""
        # if self._llm is None:
        llm = create_llm_instance(llm_config)
        if llm.cost_manager is None:
            llm.cost_manager = self.cost_manager
        return llm