import json
import time
import traceback

from flask import Response, jsonify, request

from llm_server.custom_redis import redis
from . import openai_bp
from ..helpers.http import validate_json
from ..openai_request_handler import OpenAIRequestHandler
from ..queue import decr_active_workers, decrement_ip_count, priority_queue
from ... import opts
from ...database.database import log_prompt
from ...llm.generator import generator
from ...llm.openai.oai_to_vllm import oai_to_vllm, validate_oai
from ...llm.openai.transform import generate_oai_string, transform_messages_to_prompt, trim_messages_to_fit


# TODO: add rate-limit headers?

@openai_bp.route('/chat/completions', methods=['POST'])
def openai_chat_completions():
    request_valid_json, request_json_body = validate_json(request)
    if not request_valid_json or not request_json_body.get('messages') or not request_json_body.get('model'):
        return jsonify({'code': 400, 'msg': 'invalid JSON'}), 400
    else:
        handler = OpenAIRequestHandler(incoming_request=request, incoming_json=request_json_body)
        if not request_json_body.get('stream'):
            try:
                invalid_oai_err_msg = validate_oai(request_json_body)
                if invalid_oai_err_msg:
                    return invalid_oai_err_msg
                return handler.handle_request()
            except Exception:
                traceback.print_exc()
                return 'Internal server error', 500
        else:
            if not opts.enable_streaming:
                return 'DISABLED', 401

            invalid_oai_err_msg = validate_oai(handler.request_json_body)
            if invalid_oai_err_msg:
                return invalid_oai_err_msg
            handler.request_json_body = oai_to_vllm(handler.request_json_body, hashes=False, mode=handler.cluster_backend_info['mode'])

            if opts.openai_silent_trim:
                handler.prompt = transform_messages_to_prompt(trim_messages_to_fit(handler.request.json['messages'], handler.cluster_backend_info['model_config']['max_position_embeddings'], handler.backend_url))
            else:
                handler.prompt = transform_messages_to_prompt(handler.request.json['messages'])

            response_status_code = 0
            start_time = time.time()

            request_valid, invalid_response = handler.validate_request()
            if not request_valid:
                return invalid_response
            else:
                msg_to_backend = {
                    **handler.parameters,
                    'prompt': handler.prompt,
                    'stream': True,
                }

                # Add a dummy event to the queue and wait for it to reach a worker
                event = priority_queue.put((None, handler.client_ip, handler.token, None, handler.backend_url), handler.token_priority, handler.selected_model)
                if not event:
                    log_prompt(
                        handler.client_ip,
                        handler.token,
                        handler.prompt,
                        None,
                        None,
                        handler.parameters,
                        request.headers,
                        response_status_code,
                        request.url,
                        handler.backend_url,
                    )
                    return handler.handle_ratelimited()

                # Wait for a worker to get our request and discard it.
                _, _, _ = event.wait()

                try:
                    response = generator(msg_to_backend, handler.backend_url)
                    r_headers = dict(request.headers)
                    r_url = request.url
                    model = redis.get('running_model', 'ERROR', dtype=str) if opts.openai_expose_our_model else request_json_body.get('model')
                    oai_string = generate_oai_string(30)

                    def generate():
                        try:
                            generated_text = ''
                            partial_response = b''
                            for chunk in response.iter_content(chunk_size=1):
                                partial_response += chunk
                                if partial_response.endswith(b'\x00'):
                                    json_strs = partial_response.split(b'\x00')
                                    for json_str in json_strs:
                                        if json_str:
                                            try:
                                                json_obj = json.loads(json_str.decode())
                                                new = json_obj['text'][0].split(handler.prompt + generated_text)[1]
                                                generated_text = generated_text + new
                                            except IndexError:
                                                # ????
                                                continue

                                            data = {
                                                "id": f"chatcmpl-{oai_string}",
                                                "object": "chat.completion.chunk",
                                                "created": int(time.time()),
                                                "model": model,
                                                "choices": [
                                                    {
                                                        "index": 0,
                                                        "delta": {
                                                            "content": new
                                                        },
                                                        "finish_reason": None
                                                    }
                                                ]
                                            }
                                            yield f'data: {json.dumps(data)}\n\n'
                            yield 'data: [DONE]\n\n'
                            end_time = time.time()
                            elapsed_time = end_time - start_time

                            log_prompt(
                                handler.client_ip,
                                handler.token,
                                handler.prompt,
                                generated_text,
                                elapsed_time,
                                handler.parameters,
                                r_headers,
                                response_status_code,
                                r_url,
                                handler.backend_url,
                            )
                        finally:
                            # The worker incremented it, we'll decrement it.
                            decrement_ip_count(handler.client_ip, 'processing_ips')
                            decr_active_workers(handler.selected_model, handler.backend_url)

                    return Response(generate(), mimetype='text/event-stream')
                except Exception:
                    traceback.print_exc()
                    return 'INTERNAL SERVER', 500