import argparse
import json
import subprocess
import sys
import time
from pathlib import Path
from typing import AsyncGenerator

import uvicorn
from fastapi import FastAPI, Request
from fastapi.responses import JSONResponse, Response, StreamingResponse
from vllm.engine.arg_utils import AsyncEngineArgs
from vllm.engine.async_llm_engine import AsyncLLMEngine
from vllm.sampling_params import SamplingParams
from vllm.transformers_utils.tokenizer import get_tokenizer
from vllm.utils import random_uuid

TIMEOUT_KEEP_ALIVE = 5  # seconds.
app = FastAPI()
engine = None
tokenizer = None

served_model = None
model_config = {}
startup_time = int(time.time())


# TODO: figure out ROPE scaling
# TODO: make sure error messages are returned in the response

def get_gpu_pstate():
    cmd = ['nvidia-smi', '--query-gpu=pstate', '--format=csv']
    output = subprocess.check_output(cmd).decode('utf-8')
    lines = output.strip().split('\n')
    if len(lines) > 1:
        return int(lines[1].strip('P'))
    else:
        return None


@app.get("/health")
async def health() -> Response:
    """Health check."""
    return Response(status_code=200)


@app.get("/info")
async def info(request: Request) -> Response:
    return JSONResponse({
        'uptime': int(time.time() - startup_time),
        'startup_time': startup_time,
        'model': served_model,
        'model_config': model_config,
        'nvidia': {
            'pstate': get_gpu_pstate()
        }
    })


@app.get("/model")
async def model(request: Request) -> Response:
    return JSONResponse({'model': served_model, 'timestamp': int(time.time())})


@app.post("/tokenize")
async def tokenize(request: Request) -> Response:
    request_dict = await request.json()
    to_tokenize = request_dict.get("input")
    if not to_tokenize:
        JSONResponse({'error': 'must have input field'}, status_code=400)
    tokens = tokenizer.tokenize(to_tokenize)
    response = {}
    if request_dict.get("return", False):
        response['tokens'] = tokens
    response['length'] = len(tokens)
    return JSONResponse(response)


@app.post("/generate")
async def generate(request: Request) -> Response:
    """Generate completion for the request.

    The request should be a JSON object with the following fields:
    - prompt: the prompt to use for the generation.
    - stream: whether to stream the results or not.
    - other fields: the sampling parameters (See `SamplingParams` for details).
    """
    request_dict = await request.json()
    prompt = request_dict.pop("prompt")
    stream = request_dict.pop("stream", False)
    sampling_params = SamplingParams(**request_dict)
    request_id = random_uuid()

    results_generator = engine.generate(prompt, sampling_params, request_id)

    # Streaming case
    async def stream_results() -> AsyncGenerator[bytes, None]:
        async for request_output in results_generator:
            prompt = request_output.prompt
            text_outputs = [
                prompt + output.text for output in request_output.outputs
            ]
            ret = {"text": text_outputs}
            yield (json.dumps(ret) + "\0").encode("utf-8")

    if stream:
        return StreamingResponse(stream_results())

    # Non-streaming case
    final_output = None
    async for request_output in results_generator:
        if await request.is_disconnected():
            # Abort the request if the client disconnects.
            await engine.abort(request_id)
            return Response(status_code=499)
        final_output = request_output

    assert final_output is not None
    prompt = final_output.prompt
    text_outputs = [prompt + output.text for output in final_output.outputs]
    ret = {"text": text_outputs}
    return JSONResponse(ret)


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--host", type=str, default=None)
    parser.add_argument("--port", type=int, default=8000)
    parser = AsyncEngineArgs.add_cli_args(parser)
    args = parser.parse_args()
    engine_args = AsyncEngineArgs.from_cli_args(args)

    # Do some init setup before loading the model.
    model_path = Path(args.model)
    served_model = model_path.name
    try:
        model_config = json.loads((model_path / 'config.json').read_text())
    except Exception as e:
        print(f"Failed to load the model's config - {e.__class__.__name__}: {e}")
        sys.exit(1)

    engine = AsyncLLMEngine.from_engine_args(engine_args)
    tokenizer = get_tokenizer(engine_args.tokenizer,
                              tokenizer_mode=args.tokenizer_mode,
                              trust_remote_code=args.trust_remote_code)

    uvicorn.run(app,
                host=args.host,
                port=args.port,
                log_level="debug",
                timeout_keep_alive=TIMEOUT_KEEP_ALIVE)