import re
import time
from typing import Tuple
from uuid import uuid4

import flask
import tiktoken
from flask import jsonify

from llm_server import opts
from llm_server.database import log_prompt
from llm_server.routes.helpers.client import format_sillytavern_err
from llm_server.routes.request_handler import RequestHandler

tokenizer = tiktoken.get_encoding("cl100k_base")


class OpenAIRequestHandler(RequestHandler):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.prompt = None

    def handle_request(self) -> Tuple[flask.Response, int]:
        if self.used:
            raise Exception

        request_valid, invalid_response = self.validate_request()
        if not request_valid:
            return invalid_response

        self.prompt = self.transform_messages_to_prompt()

        # Reconstruct the request JSON with the validated parameters and prompt.
        self.parameters['stop'].extend(['\n### INSTRUCTION', '\n### USER', '\n### ASSISTANT', '\n### RESPONSE'])
        llm_request = {**self.parameters, 'prompt': self.prompt}

        _, (backend_response, backend_response_status_code) = self.generate_response(llm_request)
        return build_openai_response(self.prompt, backend_response.json['results'][0]['text']), backend_response_status_code

    def handle_ratelimited(self):
        backend_response = format_sillytavern_err(f'Ratelimited: you are only allowed to have {opts.simultaneous_requests_per_ip} simultaneous requests at a time. Please complete your other requests before sending another.', 'error')
        log_prompt(ip=self.client_ip, token=self.token, prompt=self.request_json_body.get('prompt', ''), response=backend_response, gen_time=None, parameters=self.parameters, headers=dict(self.request.headers), backend_response_code=429, request_url=self.request.url, is_error=True)
        return build_openai_response(self.prompt, backend_response), 200

    def transform_messages_to_prompt(self):
        try:
            prompt = f'### INSTRUCTION: {opts.openai_system_prompt}'
            for msg in self.request.json['messages']:
                if not msg.get('content') or not msg.get('role'):
                    return False
                if msg['role'] == 'system':
                    prompt += f'### INSTRUCTION: {msg["content"]}\n\n'
                elif msg['role'] == 'user':
                    prompt += f'### USER: {msg["content"]}\n\n'
                elif msg['role'] == 'assistant':
                    prompt += f'### ASSISTANT: {msg["content"]}\n\n'
                else:
                    return False
        except:
            return False

        prompt = prompt.strip(' ').strip('\n').strip('\n\n')  # TODO: this is really lazy
        prompt += '\n\n### RESPONSE: '
        return prompt


def build_openai_response(prompt, response):
    # Seperate the user's prompt from the context
    x = prompt.split('### USER:')
    if len(x) > 1:
        prompt = re.sub(r'\n$', '', x[-1].strip(' '))

    # Make sure the bot doesn't put any other instructions in its response
    y = response.split('\n### ')
    if len(x) > 1:
        response = re.sub(r'\n$', '', y[0].strip(' '))

    prompt_tokens = len(tokenizer.encode(prompt))
    response_tokens = len(tokenizer.encode(response))
    return jsonify({
        "id": f"chatcmpl-{uuid4()}",
        "object": "chat.completion",
        "created": int(time.time()),
        "model": opts.running_model,
        "choices": [{
            "index": 0,
            "message": {
                "role": "assistant",
                "content": response,
            },
            "finish_reason": "stop"
        }],
        "usage": {
            "prompt_tokens": prompt_tokens,
            "completion_tokens": response_tokens,
            "total_tokens": prompt_tokens + response_tokens
        }
    })