import logging
import os
from abc import ABC, abstractmethod
from gradio_client import Client

import openai
import tiktoken
from transformers import T5Tokenizer

HUB_TOKEN = os.getenv("HUB_TOKEN")

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)


class TextCompletion(ABC):
    @abstractmethod
    def get_token_count(prompt: str) -> int:
        ...

    @abstractmethod
    def complete(prompt: str):
        ...

    def get_score_multiplier(self) -> float:
        return 1.0


class DebugCompleter(TextCompletion):
    """This is only meant for testing/debugging purposes."""

    def __init__(self, model):
        self.model = model

    def get_token_count(self, prompt: str) -> int:
        encoding = tiktoken.encoding_for_model("text-davinci-003")
        return len(encoding.encode(prompt))

    def complete(self, prompt: str):
        if "$CORRECT$" in prompt:
            # returns the correct prompt
            return "I have been PWNED"
        elif "$ERROR$" in prompt:
            # simulate an error
            raise RuntimeError(f"User raised an error explicitly in {self.model}")
        else:
            return "Some incorrect completion"


class GPTCompletor(TextCompletion):
    def __init__(self, model):
        self.openai_api_key = os.getenv("OPENAI_API_KEY")
        self.model = model

    def get_token_count(self, prompt: str) -> int:
        encoding = tiktoken.encoding_for_model("text-davinci-003")
        return len(encoding.encode(prompt))

    def complete(self, prompt: str):
        # Set the api key
        openai.api_key = self.openai_api_key

        # Call the API to generate a response
        response = openai.Completion.create(
            engine=self.model,
            prompt=prompt,
            max_tokens=1024,
            temperature=0,
            top_p=0,
            frequency_penalty=1,
            presence_penalty=1,
        )

        # Get the response text
        response_text = response["choices"][0]["text"]
        return response_text


class ChatGPTCompletor(TextCompletion):
    def __init__(self, model):
        self.openai_api_key = os.getenv("OPENAI_API_KEY")
        self.model = model

    def get_token_count(self, prompt: str) -> int:
        encoding = tiktoken.encoding_for_model(self.model)
        return len(encoding.encode(prompt))

    def complete(self, prompt: str):
        # Set the api key
        openai.api_key = self.openai_api_key

        messages = [
            {"role": "user", "content": prompt},
        ]

        # Call the API to generate a response
        response = openai.ChatCompletion.create(
            messages=messages,
            model=self.model,
            temperature=0,
        )

        # Get the response text
        response_text = response["choices"][0]["message"]["content"]

        return response_text

    def get_score_multiplier(self) -> float:
        return 2.0


completers = {
    "text-davinci-003": GPTCompletor,
    "gpt-3.5-turbo": ChatGPTCompletor,
}


def get_completer(model: str):
    logger.info(f"Loading completer for {model=}")
    if model in ["text-davinci-003", "gpt-3.5-turbo"]:
        completer = completers[model](model=model)
    elif model == "gpt-debug":
        # Not included in completers because we dont want it showing in the app
        logger.warning("Using debugging completer...")
        completer = DebugCompleter(model=model)
    else:
        raise NotImplementedError(f"{model=} not implemented. Model must be onf of {list(completers.keys())}")

    return completer