#
# Copyright 2016 The BigDL Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# Modified from vllm_worker
# https://github.com/lm-sys/FastChat/blob/v0.2.28/fastchat/serve/vllm_worker.py

"""
A model worker that executes the model based on vLLM.

See documentations at docs/vllm_integration.md
"""

import argparse
import asyncio
import json
from typing import List

from fastapi import FastAPI, Request, BackgroundTasks
from fastapi.responses import StreamingResponse, JSONResponse
import uvicorn

from vllm.engine.arg_utils import AsyncEngineArgs
from vllm.sampling_params import SamplingParams
from vllm.utils import random_uuid

from fastchat.serve.base_model_worker import BaseModelWorker
from fastchat.serve.model_worker import (
    logger,
    worker_id,
)
from fastchat.utils import get_context_length, is_partial_stop
from typing import TYPE_CHECKING
if TYPE_CHECKING:
    from ipex_llm.vllm.cpu.engine import IPEXLLMAsyncLLMEngine as AsyncLLMEngine


app = FastAPI()


class VLLMWorker(BaseModelWorker):
    def __init__(
        self,
        controller_addr: str,
        worker_addr: str,
        worker_id: str,
        model_path: str,
        model_names: List[str],
        limit_worker_concurrency: int,
        no_register: bool,
        llm_engine: 'AsyncLLMEngine',
        conv_template: str,
    ):
        super().__init__(
            controller_addr,
            worker_addr,
            worker_id,
            model_path,
            model_names,
            limit_worker_concurrency,
            conv_template,
        )

        logger.info(
            f"Loading the model {self.model_names} on worker {worker_id}, "
            f"worker type: vLLM worker..."
        )
        self.tokenizer = llm_engine.engine.tokenizer
        # This is to support vllm >= 0.2.7 where TokenizerGroup was introduced
        # and llm_engine.engine.tokenizer was no longer a raw tokenizer
        if hasattr(self.tokenizer, "tokenizer"):
            self.tokenizer = llm_engine.engine.tokenizer.tokenizer
        self.context_len = get_context_length(llm_engine.engine.model_config.hf_config)

        if not no_register:
            self.init_heart_beat()

    async def generate_stream(self, params):
        self.call_ct += 1

        context = params.pop("prompt")
        request_id = params.pop("request_id")
        temperature = float(params.get("temperature", 1.0))
        top_p = float(params.get("top_p", 1.0))
        top_k = params.get("top_k", -1.0)
        presence_penalty = float(params.get("presence_penalty", 0.0))
        frequency_penalty = float(params.get("frequency_penalty", 0.0))
        max_new_tokens = params.get("max_new_tokens", 256)
        stop_str = params.get("stop", None)
        stop_token_ids = params.get("stop_token_ids", None) or []
        if self.tokenizer.eos_token_id is not None:
            stop_token_ids.append(self.tokenizer.eos_token_id)
        echo = params.get("echo", True)
        use_beam_search = params.get("use_beam_search", False)
        best_of = params.get("best_of", None)

        request = params.get("request", None)

        # Handle stop_str
        stop = set()
        if isinstance(stop_str, str) and stop_str != "":
            stop.add(stop_str)
        elif isinstance(stop_str, list) and stop_str != []:
            stop.update(stop_str)

        for tid in stop_token_ids:
            if tid is not None:
                s = self.tokenizer.decode(tid)
                if s != "":
                    stop.add(s)

        # make sampling params in vllm
        top_p = max(top_p, 1e-5)
        if temperature <= 1e-5:
            top_p = 1.0

        sampling_params = SamplingParams(
            n=1,
            temperature=temperature,
            top_p=top_p,
            use_beam_search=use_beam_search,
            stop=list(stop),
            stop_token_ids=stop_token_ids,
            max_tokens=max_new_tokens,
            top_k=top_k,
            presence_penalty=presence_penalty,
            frequency_penalty=frequency_penalty,
            best_of=best_of,
        )
        results_generator = engine.generate(context, sampling_params, request_id)

        async for request_output in results_generator:
            prompt = request_output.prompt
            if echo:
                text_outputs = [
                    prompt + output.text for output in request_output.outputs
                ]
            else:
                text_outputs = [output.text for output in request_output.outputs]
            text_outputs = " ".join(text_outputs)

            partial_stop = any(is_partial_stop(text_outputs, i) for i in stop)
            # prevent yielding partial stop sequence
            if partial_stop:
                continue

            aborted = False
            if request and await request.is_disconnected():
                await engine.abort(request_id)
                request_output.finished = True
                aborted = True
                for output in request_output.outputs:
                    output.finish_reason = "abort"

            prompt_tokens = len(request_output.prompt_token_ids)
            completion_tokens = sum(
                len(output.token_ids) for output in request_output.outputs
            )
            ret = {
                "text": text_outputs,
                "error_code": 0,
                "usage": {
                    "prompt_tokens": prompt_tokens,
                    "completion_tokens": completion_tokens,
                    "total_tokens": prompt_tokens + completion_tokens,
                },
                "cumulative_logprob": [
                    output.cumulative_logprob for output in request_output.outputs
                ],
                "finish_reason": request_output.outputs[0].finish_reason
                if len(request_output.outputs) == 1
                else [output.finish_reason for output in request_output.outputs],
            }
            # Emit twice here to ensure a 'finish_reason' with empty
            # content in the OpenAI API response.
            # This aligns with the behavior of model_worker.
            if request_output.finished:
                yield (json.dumps({**ret, **{"finish_reason": None}}) + "\0").encode()
            yield (json.dumps(ret) + "\0").encode()

            if aborted:
                break

    async def generate(self, params):
        async for x in self.generate_stream(params):
            pass
        return json.loads(x[:-1].decode())


def release_worker_semaphore():
    worker.semaphore.release()


def acquire_worker_semaphore():
    if worker.semaphore is None:
        worker.semaphore = asyncio.Semaphore(worker.limit_worker_concurrency)
    return worker.semaphore.acquire()


def create_background_tasks(request_id):
    async def abort_request() -> None:
        await engine.abort(request_id)

    background_tasks = BackgroundTasks()
    background_tasks.add_task(release_worker_semaphore)
    background_tasks.add_task(abort_request)
    return background_tasks


@app.post("/worker_generate_stream")
async def api_generate_stream(request: Request):
    params = await request.json()
    await acquire_worker_semaphore()
    request_id = random_uuid()
    params["request_id"] = request_id
    params["request"] = request
    generator = worker.generate_stream(params)
    background_tasks = create_background_tasks(request_id)
    return StreamingResponse(generator, background=background_tasks)


@app.post("/worker_generate")
async def api_generate(request: Request):
    params = await request.json()
    await acquire_worker_semaphore()
    request_id = random_uuid()
    params["request_id"] = request_id
    params["request"] = request
    output = await worker.generate(params)
    release_worker_semaphore()
    await engine.abort(request_id)
    return JSONResponse(output)


@app.post("/worker_get_status")
async def api_get_status(request: Request):
    return worker.get_status()


@app.post("/count_token")
async def api_count_token(request: Request):
    params = await request.json()
    return worker.count_token(params)


@app.post("/worker_get_conv_template")
async def api_get_conv(request: Request):
    return worker.get_conv_template()


@app.post("/model_details")
async def api_model_details(request: Request):
    return {"context_length": worker.context_len}


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--host", type=str, default="localhost")
    parser.add_argument("--port", type=int, default=21002)
    parser.add_argument("--worker-address", type=str, default="http://localhost:21002")
    parser.add_argument(
        "--controller-address", type=str, default="http://localhost:21001"
    )
    parser.add_argument("--model-path", type=str, default="lmsys/vicuna-7b-v1.5")
    parser.add_argument(
        "--model-names",
        type=lambda s: s.split(","),
        help="Optional display comma separated names",
    )
    parser.add_argument("--limit-worker-concurrency", type=int, default=1024)
    parser.add_argument("--no-register", action="store_true")
    parser.add_argument("--num-gpus", type=int, default=1)
    parser.add_argument(
        "--conv-template", type=str, default=None, help="Conversation prompt template."
    )
    parser.add_argument(
        "--load-in-low-bit", type=str, default="sym_int4", help="Low-bit quantization format"
    )
    parser.add_argument(
        "--trust_remote_code",
        action="store_false",
        default=True,
        help="Trust remote code (e.g., from HuggingFace) when"
        "downloading the model and tokenizer.",
    )

    parser = AsyncEngineArgs.add_cli_args(parser)
    args = parser.parse_args()
    if args.model_path:
        args.model = args.model_path
    if args.num_gpus > 1:
        args.tensor_parallel_size = args.num_gpus

    engine_args = AsyncEngineArgs.from_cli_args(args)
    if args.device == 'cpu':
        from ipex_llm.vllm.cpu.engine import IPEXLLMAsyncLLMEngine as AsyncLLMEngine
    elif args.device == 'xpu':
        from ipex_llm.vllm.xpu.engine import IPEXLLMAsyncLLMEngine as AsyncLLMEngine
    engine = AsyncLLMEngine.from_engine_args(engine_args, load_in_low_bit=args.load_in_low_bit)
    worker = VLLMWorker(
        args.controller_address,
        args.worker_address,
        worker_id,
        args.model_path,
        args.model_names,
        args.limit_worker_concurrency,
        args.no_register,
        engine,
        args.conv_template,
    )
    uvicorn.run(app, host=args.host, port=args.port, log_level="info")
