import atexit
import json
import multiprocessing
import warnings
from typing import Dict, List, Optional, Union

import aiohttp
import requests

from sglang.global_config import global_config
from sglang.lang.backend.base_backend import BaseBackend
from sglang.lang.chat_template import get_chat_template, get_chat_template_by_model_path
from sglang.lang.choices import ChoicesDecision, ChoicesSamplingMethod
from sglang.lang.interpreter import StreamExecutor
from sglang.lang.ir import (
    REGEX_BOOL,
    REGEX_FLOAT,
    REGEX_INT,
    REGEX_STR,
    SglSamplingParams,
)
from sglang.utils import http_request


class RuntimeEndpoint(BaseBackend):
    def __init__(
        self,
        base_url: str,
        api_key: Optional[str] = None,
        verify: Optional[str] = None,
        chat_template_name: Optional[str] = None,
    ):
        super().__init__()
        self.support_concate_and_append = True

        self.base_url = base_url
        self.api_key = api_key
        self.verify = verify

        res = http_request(
            self.base_url + "/get_model_info",
            api_key=self.api_key,
            verify=self.verify,
        )
        self._assert_success(res)
        self.model_info = res.json()

        if chat_template_name:
            self.chat_template = get_chat_template(chat_template_name)
        else:
            self.chat_template = get_chat_template_by_model_path(
                self.model_info["model_path"]
            )

    def get_model_name(self):
        return self.model_info["model_path"]

    def flush_cache(self):
        res = http_request(
            self.base_url + "/flush_cache",
            api_key=self.api_key,
            verify=self.verify,
            method="POST",
        )
        self._assert_success(res)

    def get_server_info(self):
        res = http_request(
            self.base_url + "/get_server_info",
            api_key=self.api_key,
            verify=self.verify,
        )
        self._assert_success(res)
        return res.json()

    def get_chat_template(self):
        return self.chat_template

    def cache_prefix(self, prefix_str: str):
        res = http_request(
            self.base_url + "/generate",
            json={"text": prefix_str, "sampling_params": {"max_new_tokens": 0}},
            api_key=self.api_key,
            verify=self.verify,
        )
        self._assert_success(res)

    def start_profile(self):
        res = http_request(
            self.base_url + "/start_profile",
            api_key=self.api_key,
            verify=self.verify,
        )
        self._assert_success(res)

    def stop_profile(self):
        res = http_request(
            self.base_url + "/stop_profile",
            api_key=self.api_key,
            verify=self.verify,
        )
        self._assert_success(res)

    def commit_lazy_operations(self, s: StreamExecutor):
        data = {"text": s.text_, "sampling_params": {"max_new_tokens": 0}}
        self._add_images(s, data)
        self._add_videos(s, data)
        res = http_request(
            self.base_url + "/generate",
            json=data,
            api_key=self.api_key,
            verify=self.verify,
        )
        self._assert_success(res)

    def fill_image(self, s: StreamExecutor):
        data = {"text": s.text_, "sampling_params": {"max_new_tokens": 0}}
        self._add_images(s, data)

        res = http_request(
            self.base_url + "/generate",
            json=data,
            api_key=self.api_key,
            verify=self.verify,
        )
        self._assert_success(res)

    def _handle_dtype_to_regex(self, sampling_params: SglSamplingParams):
        if sampling_params.dtype is None:
            return

        if sampling_params.stop == ():
            sampling_params.stop = []

        dtype_regex = None
        if sampling_params.dtype in ["int", int]:

            dtype_regex = REGEX_INT
            sampling_params.stop.extend([" ", "\n"])
        elif sampling_params.dtype in ["float", float]:

            dtype_regex = REGEX_FLOAT
            sampling_params.stop.extend([" ", "\n"])
        elif sampling_params.dtype in ["str", str]:

            dtype_regex = REGEX_STR
        elif sampling_params.dtype in ["bool", bool]:

            dtype_regex = REGEX_BOOL
        else:
            raise RuntimeError(f"Invalid dtype: {sampling_params.dtype}")

        if dtype_regex is not None and sampling_params.regex is not None:
            warnings.warn(
                f"Both dtype and regex are set. Only dtype will be used. dtype: {sampling_params.dtype}, regex: {sampling_params.regex}"
            )

        sampling_params.regex = dtype_regex

    def generate(
        self,
        s: StreamExecutor,
        sampling_params: SglSamplingParams,
    ):
        self._handle_dtype_to_regex(sampling_params)
        data = {
            "text": s.text_,
            "sampling_params": {
                "skip_special_tokens": global_config.skip_special_tokens_in_output,
                "spaces_between_special_tokens": global_config.spaces_between_special_tokens_in_out,
                **sampling_params.to_srt_kwargs(),
            },
        }

        for item in [
            "return_logprob",
            "logprob_start_len",
            "top_logprobs_num",
            "return_text_in_logprobs",
        ]:
            value = getattr(sampling_params, item, None)
            if value is not None:
                data[item] = value

        self._add_images(s, data)
        self._add_videos(s, data)

        res = http_request(
            self.base_url + "/generate",
            json=data,
            api_key=self.api_key,
            verify=self.verify,
        )
        self._assert_success(res)

        obj = res.json()
        comp = obj["text"]
        return comp, obj["meta_info"]

    def generate_stream(
        self,
        s: StreamExecutor,
        sampling_params: SglSamplingParams,
    ):
        self._handle_dtype_to_regex(sampling_params)

        data = {
            "text": s.text_,
            "sampling_params": {
                "skip_special_tokens": global_config.skip_special_tokens_in_output,
                "spaces_between_special_tokens": global_config.spaces_between_special_tokens_in_out,
                **sampling_params.to_srt_kwargs(),
            },
        }

        for item in [
            "return_logprob",
            "logprob_start_len",
            "top_logprobs_num",
            "return_text_in_logprobs",
        ]:
            value = getattr(sampling_params, item, None)
            if value is not None:
                data[item] = value

        data["stream"] = True
        self._add_images(s, data)
        self._add_videos(s, data)

        res = http_request(
            self.base_url + "/generate",
            json=data,
            stream=True,
            api_key=self.api_key,
            verify=self.verify,
        )
        self._assert_success(res)
        pos = 0

        for chunk in res.iter_lines(decode_unicode=False):
            chunk = chunk.decode("utf-8")
            if chunk and chunk.startswith("data:"):
                if chunk == "data: [DONE]":
                    break
                data = json.loads(chunk[5:].strip("\n"))
                chunk_text = data["text"][pos:]
                meta_info = data["meta_info"]
                pos += len(chunk_text)
                yield chunk_text, meta_info

    def select(
        self,
        s: StreamExecutor,
        choices: List[str],
        temperature: float,
        choices_method: ChoicesSamplingMethod,
    ) -> ChoicesDecision:
        assert temperature <= 1e-5

        # Cache common prefix
        data = {"text": s.text_, "sampling_params": {"max_new_tokens": 0}}
        obj = self._generate_http_request(s, data)
        prompt_len = obj["meta_info"]["prompt_tokens"]
        logprob_start_len = max(prompt_len - 2, 0)  # For token healing

        # Compute logprob
        data = {
            "text": [s.text_ + c for c in choices],
            "sampling_params": {
                "max_new_tokens": 0,
                "temperature": 0,
            },
            "return_logprob": True,
            "return_text_in_logprobs": True,
            "logprob_start_len": logprob_start_len,
        }
        obj = self._generate_http_request(s, data)

        input_token_logprobs = [r["meta_info"]["input_token_logprobs"] for r in obj]
        output_token_logprobs = [r["meta_info"]["output_token_logprobs"] for r in obj]
        normalized_prompt_logprobs = [
            compute_normalized_prompt_logprobs(r["meta_info"]["input_token_logprobs"])
            for r in obj
        ]

        # Remove extra token if no token healing occurred
        for i in range(len(input_token_logprobs)):
            healed_token_str = input_token_logprobs[i][0][-1]
            if s.text_.endswith(healed_token_str):
                healed_token_logprob = input_token_logprobs[i][0][0]
                normalized_prompt_logprobs[i] = (
                    normalized_prompt_logprobs[i] * len(input_token_logprobs[i])
                    - healed_token_logprob
                ) / (len(input_token_logprobs[i]) - 1)
                input_token_logprobs[i] = input_token_logprobs[i][1:]

        # Compute unconditional logprobs if required
        if choices_method.requires_unconditional_logprobs:
            input_ids = [[el[1] for el in subl] for subl in input_token_logprobs]
            data = {
                "input_ids": input_ids,
                "sampling_params": {"max_new_tokens": 0},
                "return_logprob": True,
            }
            obj = self._generate_http_request(s, data)
            unconditional_token_logprobs = [
                r["meta_info"]["input_token_logprobs"] for r in obj
            ]
        else:
            unconditional_token_logprobs = None

        return choices_method(
            choices=choices,
            normalized_prompt_logprobs=normalized_prompt_logprobs,
            input_token_logprobs=input_token_logprobs,
            output_token_logprobs=output_token_logprobs,
            unconditional_token_logprobs=unconditional_token_logprobs,
        )

    def concatenate_and_append(self, src_rids: List[str], dst_rid: str):
        res = http_request(
            self.base_url + "/concate_and_append_request",
            json={"src_rids": src_rids, "dst_rid": dst_rid},
            api_key=self.api_key,
            verify=self.verify,
        )
        self._assert_success(res)

    def _generate_http_request(self, s: StreamExecutor, data):
        self._add_images(s, data)
        self._add_videos(s, data)

        res = http_request(
            self.base_url + "/generate",
            json=data,
            api_key=self.api_key,
            verify=self.verify,
        )
        self._assert_success(res)
        return res.json()

    def _add_images(self, s: StreamExecutor, data):
        if s.images_:
            assert len(s.images_) == 1, "Only support one image."
            data["image_data"] = s.images_[0][1]

    def _add_videos(self, s: StreamExecutor, data):
        if s.videos_:
            assert len(s.videos_) == 1, "Only support one video."
            data["video_data"] = s.videos_

    def _assert_success(self, res):
        if res.status_code != 200:
            try:
                content = res.json()
            except json.JSONDecodeError:
                content = res.text
            raise RuntimeError(content)


def compute_normalized_prompt_logprobs(input_logprobs):
    values = [x[0] for x in input_logprobs if x[0]]
    return sum(values) / len(values)


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

    It is mainly used for the frontend language.
    You should use the Engine class if you want to do normal offline processing without the frontend language.
    """

    def __init__(
        self,
        log_level: str = "error",
        *args,
        **kwargs,
    ):
        """See the arguments in server_args.py::ServerArgs"""
        # We delay the import of any `sglang.srt` components in `sglang.lang`, so users can run
        # client code without installing SRT server and its dependency if they want.
        from sglang.srt.entrypoints.http_server import launch_server
        from sglang.srt.server_args import ServerArgs
        from sglang.srt.utils import is_port_available

        self.server_args = ServerArgs(*args, log_level=log_level, **kwargs)

        # Pre-allocate ports
        for port in range(self.server_args.port, 40000):
            if is_port_available(port):
                break
        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 = multiprocessing.Pipe(duplex=False)

        ctx = multiprocessing.get_context("spawn")
        proc = ctx.Process(
            target=launch_server,
            args=(self.server_args, pipe_writer),
        )
        proc.start()
        pipe_writer.close()
        self.pid = proc.pid

        # Before python program terminates, call shutdown implicitly. Therefore, users don't have to explicitly call .shutdown()
        atexit.register(self.shutdown)

        # TODO: remove this pipe_writer mechanism and use `/health_generate` instead.
        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):
        from sglang.srt.utils import kill_process_tree

        if self.pid is not None:
            kill_process_tree(self.pid)
            self.pid = None

    def start_profile(self):
        self.endpoint.start_profile()

    def stop_profile(self):
        self.endpoint.stop_profile()

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

    def get_tokenizer(self):
        from sglang.srt.utils.hf_transformers_utils import get_tokenizer

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

    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]]],
    ):
        json_data = {"text": prompt}
        response = requests.post(self.url + "/encode", json=json_data)
        return json.dumps(response.json())

    async def get_server_info(self):
        async with aiohttp.ClientSession() as session:
            async with session.get(f"{self.url}/get_server_info") as response:
                if response.status == 200:
                    return await response.json()
                else:
                    error_data = await response.json()
                    raise RuntimeError(
                        f"Failed to get server info. {error_data['error']['message']}"
                    )

    def __del__(self):
        self.shutdown()
