'''
MIT License

Copyright (c) 2024 Mo Zhou <lumin@debian.org>

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
'''
from typing import List, Dict, Union
from rich.status import Status
from rich.panel import Panel
from rich.markup import escape
import argparse
import os
import json
import rich
import uuid
import sys
from . import defaults
console = rich.get_console()


def _check(messages: List[Dict]):
    '''
    communitation protocol.
    both huggingface transformers and openapi api use this
    '''
    assert isinstance(messages, list)
    assert all(isinstance(x, dict) for x in messages)
    assert all('role' in x.keys() for x in messages)
    assert all('content' in x.keys() for x in messages)
    assert all(isinstance(x['role'], str) for x in messages)
    assert all(isinstance(x['content'], str) for x in messages)
    assert all(x['role'] in ('system', 'user', 'assistant') for x in messages)


class AbstractFrontend():
    '''
    The frontend instance holds the whole chat session. The context is the whole
    session for the next LLM query. Historical chats is also a part of the
    context for following up questions. You may feel LLMs smart when they
    get information from the historical chat in the same session.
    '''

    NAME = 'AbstractFrontend'

    def __init__(self, args):
        self.uuid = uuid.uuid4()
        self.session = []
        self.debgpt_home = args.debgpt_home
        console.log(f'{self.NAME}> Starting conversation {self.uuid}')

    def reset(self):
        '''
        clear the context. No need to change UUID I think.
        '''
        self.session = []

    def query(self, messages):
        '''
        the messages format can be found in _check(...) function above.
        '''
        raise NotImplementedError


    def update_session(self, messages: Union[List, Dict, str]) -> None:
        if isinstance(messages, list):
            # reset the chat with provided message list
            self.session = messages
        elif isinstance(messages, dict):
            # just append a new dict
            self.session.append(messages)
        elif isinstance(messages, str):
            # just append a new user dict
            self.session.append({'role': 'user', 'content': messages})
        else:
            raise TypeError(type(messages))
        _check(self.session)

    def __call__(self, *args, **kwargs):
        return self.query(*args, **kwargs)

    def dump(self):
        fpath = os.path.join(self.debgpt_home, str(self.uuid) + '.json')
        with open(fpath, 'wt') as f:
            json.dump(self.session, f, indent=2)
        console.log(f'{self.NAME}> Conversation saved at {fpath}')


class OpenAIFrontend(AbstractFrontend):
    '''
    https://platform.openai.com/docs/quickstart?context=python
    '''
    NAME: str = 'OpenAIFrontend'
    debug: bool = False
    stream: bool = True
    system_message: str = defaults.OPENAI_SYSTEM_MESSAGE

    def __init__(self, args):
        super().__init__(args)
        from openai import OpenAI
        self.client = OpenAI(api_key=args.openai_api_key,
                             base_url=args.openai_base_url)
        self.session.append({"role": "system", "content": self.system_message})
        self.model = args.openai_model
        self.kwargs = {'temperature': args.temperature, 'top_p': args.top_p}
        if args.verbose:
            console.log(f'{self.NAME}> model={repr(self.model)}, '
                    + f'temperature={args.temperature}, top_p={args.top_p}.')

    def query(self, messages: Union[List, Dict, str]) -> list:
        # add the message into the session
        self.update_session(messages)
        if self.debug:
            console.log('send:', self.session[-1])
        completion = self.client.chat.completions.create(
            model=self.model, messages=self.session, stream=self.stream,
            **self.kwargs)
        if self.stream:
            chunks = []
            for chunk in completion:
                if chunk.choices[0].delta.content is not None:
                    piece = chunk.choices[0].delta.content
                    chunks.append(piece)
                    print(piece, end="")
                    sys.stdout.flush()
            generated_text = ''.join(chunks)
            if not generated_text.endswith('\n'):
                print()
                sys.stdout.flush()
        else:
            generated_text = completion.choices[0].message.content
        new_message = {'role': 'assistant', 'content': generated_text}
        self.update_session(new_message)
        if self.debug:
            console.log('recv:', self.session[-1])
        return self.session[-1]['content']


class ZMQFrontend(AbstractFrontend):
    '''
    ZMQ frontend communicates with a self-hosted ZMQ backend.
    '''
    NAME = 'ZMQFrontend'
    debug: bool = False
    stream: bool = False

    def __init__(self, args):
        import zmq
        super().__init__(args)
        self.zmq_backend = args.zmq_backend
        self.socket = zmq.Context().socket(zmq.REQ)
        self.socket.connect(self.zmq_backend)
        console.log(
            f'{self.NAME}> Connected to ZMQ backend {self.zmq_backend}.')
        #
        if hasattr(args, 'temperature'):
            console.log(
                'warning! --temperature not yet supported for this frontend')
        if hasattr(args, 'top_p'):
            console.log('warning! --top_p not yet supported for this frontend')

    def query(self, content: Union[List, Dict, str]) -> list:
        if isinstance(content, list):
            self.session = content
        elif isinstance(content, dict):
            self.session.append(content)
        elif isinstance(content, str):
            self.session.append({'role': 'user', 'content': content})
        _check(self.session)
        msg_json = json.dumps(self.session)
        if self.debug:
            console.log('send:', msg_json)
        self.socket.send_string(msg_json)
        msg = self.socket.recv()
        self.session = json.loads(msg)
        _check(self.session)
        if self.debug:
            console.log('recv:', self.session[-1])
        return self.session[-1]['content']


def create_frontend(args):
    if args.frontend == 'zmq':
        frontend = ZMQFrontend(args)
    elif args.frontend == 'openai':
        frontend = OpenAIFrontend(args)
    elif args.frontend == 'dryrun':
        frontend = None
    else:
        raise NotImplementedError
    return frontend


def query_once(f: AbstractFrontend, text: str) -> None:
    '''
    we have prepared text -- let frontend send it to LLM, and this function
    will print the LLM reply
    '''
    if f.stream:
        lprompt = f'[bold green]LLM[{1+len(f.session)}]>[/bold green] '
        console.print(lprompt, end='')
        reply = f(text)
    else:
        with Status('LLM', spinner='line'):
            reply = f(text)
        console.print(Panel(escape(reply), title='LLM Reply'))
    # console.print('LLM>', reply)


if __name__ == '__main__':
    ag = argparse.ArgumentParser()
    ag.add_argument('--zmq_backend', '-B', default='tcp://localhost:11177')
    ag.add_argument('--frontend', '-F', default='zmq',
                    choices=('zmq', 'openai'))
    ag.add_argument('--debgpt_home', default=os.path.expanduser('~/.debgpt'))
    ag = ag.parse_args()
    console.print(ag)

    frontend = create_frontend(ag)
    f = frontend
    import IPython
    IPython.embed(colors='neutral')
