from typing import Callable, List, Literal, Optional

import certifi
import openai
from pydantic import Field

from ...core.main import ChatMessage
from .base import LLM

CHAT_MODELS = {
    "gpt-3.5-turbo",
    "gpt-3.5-turbo-16k",
    "gpt-4",
    "gpt-3.5-turbo-0613",
    "gpt-4-32k",
}
MAX_TOKENS_FOR_MODEL = {
    "gpt-3.5-turbo": 4096,
    "gpt-3.5-turbo-0613": 4096,
    "gpt-3.5-turbo-16k": 16_384,
    "gpt-4": 8192,
    "gpt-35-turbo-16k": 16_384,
    "gpt-35-turbo-0613": 4096,
    "gpt-35-turbo": 4096,
    "gpt-4-32k": 32_768,
}


class OpenAI(LLM):
    """
    The OpenAI class can be used to access OpenAI models like gpt-4 and gpt-3.5-turbo.

    If you are locally serving a model that uses an OpenAI-compatible server, you can simply change the `api_base` in the `OpenAI` class like this:

    ```python title="~/.continue/config.py"
    from continuedev.libs.llm.openai import OpenAI

    config = ContinueConfig(
        ...
        models=Models(
            default=OpenAI(
                api_key="EMPTY",
                model="<MODEL_NAME>",
                api_base="http://localhost:8000", # change to your server
            )
        )
    )
    ```

    Options for serving models locally with an OpenAI-compatible server include:

    - [text-gen-webui](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/openai#setup--installation)
    - [FastChat](https://github.com/lm-sys/FastChat/blob/main/docs/openai_api.md)
    - [LocalAI](https://localai.io/basics/getting_started/)
    - [llama-cpp-python](https://github.com/abetlen/llama-cpp-python#web-server)
    """

    api_key: str = Field(
        ...,
        description="OpenAI API key",
    )

    proxy: Optional[str] = Field(None, description="Proxy URL to use for requests.")

    api_base: Optional[str] = Field(None, description="OpenAI API base URL.")

    api_type: Optional[Literal["azure", "openai"]] = Field(
        None, description="OpenAI API type."
    )

    api_version: Optional[str] = Field(
        None, description="OpenAI API version. For use with Azure OpenAI Service."
    )

    engine: Optional[str] = Field(
        None, description="OpenAI engine. For use with Azure OpenAI Service."
    )

    async def start(
        self, unique_id: Optional[str] = None, write_log: Callable[[str], None] = None
    ):
        await super().start(write_log=write_log, unique_id=unique_id)

        if self.context_length is None:
            self.context_length = MAX_TOKENS_FOR_MODEL.get(self.model, 4096)

        openai.api_key = self.api_key
        if self.api_type is not None:
            openai.api_type = self.api_type
        if self.api_base is not None:
            openai.api_base = self.api_base
        if self.api_version is not None:
            openai.api_version = self.api_version

        if self.verify_ssl is not None and self.verify_ssl is False:
            openai.verify_ssl_certs = False

        if self.proxy is not None:
            openai.proxy = self.proxy

        openai.ca_bundle_path = self.ca_bundle_path or certifi.where()

    def collect_args(self, options):
        args = super().collect_args(options)
        if self.engine is not None:
            args["engine"] = self.engine

        if not args["model"].endswith("0613") and "functions" in args:
            del args["functions"]

        return args

    async def _stream_complete(self, prompt, options):
        args = self.collect_args(options)
        args["stream"] = True

        if args["model"] in CHAT_MODELS:
            async for chunk in await openai.ChatCompletion.acreate(
                messages=[{"role": "user", "content": prompt}],
                **args,
                headers=self.headers,
            ):
                if len(chunk.choices) > 0 and "content" in chunk.choices[0].delta:
                    yield chunk.choices[0].delta.content
        else:
            async for chunk in await openai.Completion.acreate(prompt=prompt, **args, headers=self.headers):
                if len(chunk.choices) > 0:
                    yield chunk.choices[0].text

    async def _stream_chat(self, messages: List[ChatMessage], options):
        args = self.collect_args(options)

        async for chunk in await openai.ChatCompletion.acreate(
            messages=messages,
            stream=True,
            **args,
            headers=self.headers,
        ):
            if not hasattr(chunk, "choices") or len(chunk.choices) == 0:
                continue
            yield chunk.choices[0].delta

    async def _complete(self, prompt: str, options):
        args = self.collect_args(options)

        if args["model"] in CHAT_MODELS:
            resp = await openai.ChatCompletion.acreate(
                messages=[{"role": "user", "content": prompt}],
                **args,
                headers=self.headers,
            )
            return resp.choices[0].message.content
        else:
            return (
                (await openai.Completion.acreate(prompt=prompt, **args, headers=self.headers)).choices[0].text
            )