"""
Copyright 2023-2024 SGLang Team
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.
"""

"""
The entry point of inference server.
SRT = SGLang Runtime.
"""

import asyncio
import dataclasses
import json
import logging
import multiprocessing as mp
import os
import random
import threading
import time
from http import HTTPStatus
from typing import Dict, List, Optional, Union

# Fix a bug of Python threading
setattr(threading, "_register_atexit", lambda *args, **kwargs: None)

import aiohttp
import requests
import uvicorn
import uvloop
from fastapi import FastAPI, File, Form, Request, UploadFile
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import JSONResponse, Response, StreamingResponse

from sglang.lang.backend.runtime_endpoint import RuntimeEndpoint
from sglang.srt.hf_transformers_utils import get_tokenizer
from sglang.srt.managers.detokenizer_manager import run_detokenizer_process
from sglang.srt.managers.io_struct import (
    EmbeddingReqInput,
    GenerateReqInput,
    RewardReqInput,
    UpdateWeightReqInput,
)
from sglang.srt.managers.scheduler import run_scheduler_process
from sglang.srt.managers.tokenizer_manager import TokenizerManager
from sglang.srt.openai_api.adapter import (
    load_chat_template_for_openai_api,
    v1_batches,
    v1_cancel_batch,
    v1_chat_completions,
    v1_completions,
    v1_delete_file,
    v1_embeddings,
    v1_files_create,
    v1_retrieve_batch,
    v1_retrieve_file,
    v1_retrieve_file_content,
)
from sglang.srt.openai_api.protocol import ModelCard, ModelList
from sglang.srt.server_args import PortArgs, ServerArgs
from sglang.srt.utils import (
    add_api_key_middleware,
    assert_pkg_version,
    configure_logger,
    is_port_available,
    kill_child_process,
    maybe_set_triton_cache_manager,
    prepare_model_and_tokenizer,
    set_ulimit,
)
from sglang.utils import get_exception_traceback

logger = logging.getLogger(__name__)

asyncio.set_event_loop_policy(uvloop.EventLoopPolicy())


app = FastAPI()
tokenizer_manager = None

app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)


@app.get("/health")
async def health() -> Response:
    """Check the health of the http server."""
    return Response(status_code=200)


@app.get("/health_generate")
async def health_generate(request: Request) -> Response:
    """Check the health of the inference server by generating one token."""
    gri = GenerateReqInput(
        text="s", sampling_params={"max_new_tokens": 1, "temperature": 0.7}
    )
    try:
        async for _ in tokenizer_manager.generate_request(gri, request):
            break
        return Response(status_code=200)
    except Exception as e:
        logger.exception(e)
        return Response(status_code=503)


@app.get("/get_model_info")
async def get_model_info():
    """Get the model information."""
    result = {
        "model_path": tokenizer_manager.model_path,
        "is_generation": tokenizer_manager.is_generation,
    }
    return result


@app.get("/get_server_args")
async def get_server_args():
    """Get the server arguments."""
    return dataclasses.asdict(tokenizer_manager.server_args)


@app.get("/flush_cache")
async def flush_cache():
    """Flush the radix cache."""
    tokenizer_manager.flush_cache()
    return Response(
        content="Cache flushed.\nPlease check backend logs for more details. "
        "(When there are running or waiting requests, the operation will not be performed.)\n",
        status_code=200,
    )


@app.post("/update_weights")
async def update_weights(obj: UpdateWeightReqInput, request: Request):
    """Update the weights inplace without re-launching the server."""
    success, message = await tokenizer_manager.update_weights(obj, request)
    content = {"success": success, "message": message}
    if success:
        return JSONResponse(
            content,
            status_code=HTTPStatus.OK,
        )
    else:
        return JSONResponse(
            content,
            status_code=HTTPStatus.BAD_REQUEST,
        )


async def generate_request(obj: GenerateReqInput, request: Request):
    """Handle a generate request."""
    if obj.stream:

        async def stream_results():
            try:
                async for out in tokenizer_manager.generate_request(obj, request):
                    yield f"data: {json.dumps(out, ensure_ascii=False)}\n\n"
            except ValueError as e:
                out = {"error": {"message": str(e)}}
                yield f"data: {json.dumps(out, ensure_ascii=False)}\n\n"
            yield "data: [DONE]\n\n"

        return StreamingResponse(
            stream_results(),
            media_type="text/event-stream",
            background=tokenizer_manager.create_abort_task(obj),
        )
    else:
        try:
            ret = await tokenizer_manager.generate_request(obj, request).__anext__()
            return ret
        except ValueError as e:
            return JSONResponse(
                {"error": {"message": str(e)}}, status_code=HTTPStatus.BAD_REQUEST
            )


app.post("/generate")(generate_request)
app.put("/generate")(generate_request)


async def encode_request(obj: EmbeddingReqInput, request: Request):
    """Handle an embedding request."""
    try:
        ret = await tokenizer_manager.generate_request(obj, request).__anext__()
        return ret
    except ValueError as e:
        return JSONResponse(
            {"error": {"message": str(e)}}, status_code=HTTPStatus.BAD_REQUEST
        )


app.post("/encode")(encode_request)
app.put("/encode")(encode_request)


async def judge_request(obj: RewardReqInput, request: Request):
    """Handle a reward model request."""
    try:
        ret = await tokenizer_manager.generate_request(obj, request).__anext__()
        return ret
    except ValueError as e:
        return JSONResponse(
            {"error": {"message": str(e)}}, status_code=HTTPStatus.BAD_REQUEST
        )


app.post("/judge")(judge_request)
app.put("/judge")(judge_request)


@app.post("/v1/completions")
async def openai_v1_completions(raw_request: Request):
    return await v1_completions(tokenizer_manager, raw_request)


@app.post("/v1/chat/completions")
async def openai_v1_chat_completions(raw_request: Request):
    return await v1_chat_completions(tokenizer_manager, raw_request)


@app.post("/v1/embeddings")
async def openai_v1_embeddings(raw_request: Request):
    response = await v1_embeddings(tokenizer_manager, raw_request)
    return response


@app.get("/v1/models")
def available_models():
    """Show available models."""
    served_model_names = [tokenizer_manager.served_model_name]
    model_cards = []
    for served_model_name in served_model_names:
        model_cards.append(ModelCard(id=served_model_name, root=served_model_name))
    return ModelList(data=model_cards)


@app.post("/v1/files")
async def openai_v1_files(file: UploadFile = File(...), purpose: str = Form("batch")):
    return await v1_files_create(
        file, purpose, tokenizer_manager.server_args.file_storage_pth
    )


@app.delete("/v1/files/{file_id}")
async def delete_file(file_id: str):
    # https://platform.openai.com/docs/api-reference/files/delete
    return await v1_delete_file(file_id)


@app.post("/v1/batches")
async def openai_v1_batches(raw_request: Request):
    return await v1_batches(tokenizer_manager, raw_request)


@app.post("/v1/batches/{batch_id}/cancel")
async def cancel_batches(batch_id: str):
    # https://platform.openai.com/docs/api-reference/batch/cancel
    return await v1_cancel_batch(tokenizer_manager, batch_id)


@app.get("/v1/batches/{batch_id}")
async def retrieve_batch(batch_id: str):
    return await v1_retrieve_batch(batch_id)


@app.get("/v1/files/{file_id}")
async def retrieve_file(file_id: str):
    # https://platform.openai.com/docs/api-reference/files/retrieve
    return await v1_retrieve_file(file_id)


@app.get("/v1/files/{file_id}/content")
async def retrieve_file_content(file_id: str):
    # https://platform.openai.com/docs/api-reference/files/retrieve-contents
    return await v1_retrieve_file_content(file_id)


def launch_server(
    server_args: ServerArgs,
    pipe_finish_writer: Optional[mp.connection.Connection] = None,
):
    """Launch an HTTP server."""
    global tokenizer_manager

    # Configure global environment
    configure_logger(server_args)
    server_args.check_server_args()
    _set_envs_and_config(server_args)

    # Allocate ports for inter-process communications
    port_args = PortArgs.init_new(server_args)
    logger.info(f"{server_args=}")

    # If using model from www.modelscope.cn, first download the model.
    server_args.model_path, server_args.tokenizer_path = prepare_model_and_tokenizer(
        server_args.model_path, server_args.tokenizer_path
    )

    # Launch tensor parallel scheduler processes
    scheduler_procs = []
    scheduler_pipe_readers = []
    tp_size_per_node = server_args.tp_size // server_args.nnodes
    tp_rank_range = range(
        tp_size_per_node * server_args.node_rank,
        tp_size_per_node * (server_args.node_rank + 1),
    )
    for tp_rank in tp_rank_range:
        reader, writer = mp.Pipe(duplex=False)
        gpu_id = tp_rank % tp_size_per_node
        proc = mp.Process(
            target=run_scheduler_process,
            args=(server_args, port_args, gpu_id, tp_rank, writer),
        )
        proc.start()
        scheduler_procs.append(proc)
        scheduler_pipe_readers.append(reader)

    if server_args.node_rank >= 1:
        # For other nodes, they do not need to run tokenizer or detokenizer,
        # so they can just wait here.
        while True:
            pass

    # Launch detokenizer process
    detoken_proc = mp.Process(
        target=run_detokenizer_process,
        args=(
            server_args,
            port_args,
        ),
    )
    detoken_proc.start()

    # Launch tokenizer process
    tokenizer_manager = TokenizerManager(server_args, port_args)
    if server_args.chat_template:
        load_chat_template_for_openai_api(tokenizer_manager, server_args.chat_template)

    # Wait for model to finish loading
    for i in range(len(scheduler_pipe_readers)):
        scheduler_pipe_readers[i].recv()

    # Add api key authorization
    if server_args.api_key:
        add_api_key_middleware(app, server_args.api_key)

    # Send a warmup request
    t = threading.Thread(
        target=_wait_and_warmup, args=(server_args, pipe_finish_writer, os.getpid())
    )
    t.start()

    try:
        # Listen for HTTP requests
        uvicorn.run(
            app,
            host=server_args.host,
            port=server_args.port,
            log_level=server_args.log_level_http or server_args.log_level,
            timeout_keep_alive=5,
            loop="uvloop",
        )
    finally:
        t.join()


def _set_envs_and_config(server_args: ServerArgs):
    # Set global environments
    os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
    os.environ["NCCL_CUMEM_ENABLE"] = "0"
    os.environ["NCCL_NVLS_ENABLE"] = "0"
    os.environ["TORCH_NCCL_AVOID_RECORD_STREAMS"] = "1"
    os.environ["CUDA_DEVICE_MAX_CONNECTIONS"] = "1"

    # Set ulimit
    set_ulimit()

    # Fix triton bugs
    if server_args.tp_size * server_args.dp_size > 1:
        # FIXME: remove this after https://github.com/triton-lang/triton/pull/4295 is used as a dependency.
        maybe_set_triton_cache_manager()

    # Check flashinfer version
    if server_args.attention_backend == "flashinfer":
        assert_pkg_version(
            "flashinfer",
            "0.1.6",
            "Please uninstall the old version and "
            "reinstall the latest version by following the instructions "
            "at https://docs.flashinfer.ai/installation.html.",
        )

    mp.set_start_method("spawn", force=True)


def _wait_and_warmup(server_args, pipe_finish_writer, pid):
    headers = {}
    url = server_args.url()
    if server_args.api_key:
        headers["Authorization"] = f"Bearer {server_args.api_key}"

    # Wait until the server is launched
    success = False
    for _ in range(120):
        time.sleep(1)
        try:
            res = requests.get(url + "/get_model_info", timeout=5, headers=headers)
            assert res.status_code == 200, f"{res=}, {res.text=}"
            success = True
            break
        except (AssertionError, requests.exceptions.RequestException):
            last_traceback = get_exception_traceback()
            pass

    if not success:
        if pipe_finish_writer is not None:
            pipe_finish_writer.send(last_traceback)
        logger.error(f"Initialization failed. warmup error: {last_traceback}")
        kill_child_process(pid, including_parent=False)
        return

    model_info = res.json()

    # Send a warmup request
    request_name = "/generate" if model_info["is_generation"] else "/encode"
    max_new_tokens = 8 if model_info["is_generation"] else 1
    json_data = {
        "sampling_params": {
            "temperature": 0,
            "max_new_tokens": max_new_tokens,
        },
    }
    if server_args.skip_tokenizer_init:
        json_data["input_ids"] = [10, 11, 12]
    else:
        json_data["text"] = "The capital city of France is"

    try:
        for _ in range(server_args.dp_size):
            res = requests.post(
                url + request_name,
                json=json_data,
                headers=headers,
                timeout=600,
            )
            assert res.status_code == 200, f"{res}"
    except Exception:
        last_traceback = get_exception_traceback()
        if pipe_finish_writer is not None:
            pipe_finish_writer.send(last_traceback)
        logger.error(f"Initialization failed. warmup error: {last_traceback}")
        kill_child_process(pid, including_parent=False)
        return

    logger.info("The server is fired up and ready to roll!")
    if pipe_finish_writer is not None:
        pipe_finish_writer.send("ready")


class Runtime:
    """
    A wrapper for the server.
    This is used for launching the server in a python program without
    using the commond line interface.
    """

    def __init__(
        self,
        log_level: str = "error",
        *args,
        **kwargs,
    ):
        """See the arguments in server_args.py::ServerArgs"""
        self.server_args = ServerArgs(*args, log_level=log_level, **kwargs)

        # Pre-allocate ports
        for port in range(10000, 40000):
            if is_port_available(port):
                break
            port += 1
        self.server_args.port = port

        self.url = self.server_args.url()
        self.generate_url = self.url + "/generate"

        # NOTE: We store pid instead of proc to fix some issues during __delete__
        self.pid = None
        pipe_reader, pipe_writer = mp.Pipe(duplex=False)

        proc = mp.Process(
            target=launch_server,
            args=(self.server_args, pipe_writer),
        )
        proc.start()
        pipe_writer.close()
        self.pid = proc.pid

        try:
            init_state = pipe_reader.recv()
        except EOFError:
            init_state = ""

        if init_state != "ready":
            self.shutdown()
            raise RuntimeError(
                "Initialization failed. Please see the error messages above."
            )

        self.endpoint = RuntimeEndpoint(self.url)

    def shutdown(self):
        if self.pid is not None:
            kill_child_process(self.pid)
            self.pid = None

    def cache_prefix(self, prefix: str):
        self.endpoint.cache_prefix(prefix)

    def get_tokenizer(self):
        return get_tokenizer(
            self.server_args.tokenizer_path,
            tokenizer_mode=self.server_args.tokenizer_mode,
            trust_remote_code=self.server_args.trust_remote_code,
        )

    async def async_generate(
        self,
        prompt: str,
        sampling_params: Optional[Dict] = None,
    ):
        if self.server_args.skip_tokenizer_init:
            json_data = {
                "input_ids": prompt,
                "sampling_params": sampling_params,
                "stream": True,
            }
        else:
            json_data = {
                "text": prompt,
                "sampling_params": sampling_params,
                "stream": True,
            }
        pos = 0

        timeout = aiohttp.ClientTimeout(total=3 * 3600)
        async with aiohttp.ClientSession(timeout=timeout, trust_env=True) as session:
            async with session.post(self.generate_url, json=json_data) as response:
                async for chunk, _ in response.content.iter_chunks():
                    chunk = chunk.decode("utf-8")
                    if chunk and chunk.startswith("data:"):
                        if chunk == "data: [DONE]\n\n":
                            break
                        data = json.loads(chunk[5:].strip("\n"))
                        if "text" in data:
                            cur = data["text"][pos:]
                            if cur:
                                yield cur
                            pos += len(cur)
                        else:
                            yield data

    add_request = async_generate

    def generate(
        self,
        prompt: Union[str, List[str]],
        sampling_params: Optional[Dict] = None,
        return_logprob: Optional[Union[List[bool], bool]] = False,
        logprob_start_len: Optional[Union[List[int], int]] = None,
        top_logprobs_num: Optional[Union[List[int], int]] = None,
        lora_path: Optional[List[Optional[str]]] = None,
    ):
        json_data = {
            "text": prompt,
            "sampling_params": sampling_params,
            "return_logprob": return_logprob,
            "logprob_start_len": logprob_start_len,
            "top_logprobs_num": top_logprobs_num,
            "lora_path": lora_path,
        }
        assert not isinstance(lora_path, list) or len(lora_path) == len(prompt)
        response = requests.post(
            self.url + "/generate",
            json=json_data,
        )
        return json.dumps(response.json())

    def encode(
        self,
        prompt: Union[str, List[str], List[Dict], List[List[Dict]]],
    ):
        if isinstance(prompt, str) or isinstance(prompt[0], str):
            # embedding
            json_data = {
                "text": prompt,
            }
            response = requests.post(
                self.url + "/encode",
                json=json_data,
            )
        else:
            # reward
            json_data = {
                "conv": prompt,
            }
            response = requests.post(
                self.url + "/judge",
                json=json_data,
            )
        return json.dumps(response.json())

    def __del__(self):
        self.shutdown()
