import logging
from LLM.chat import Chat
from baseHandler import BaseHandler
from mlx_lm import load, stream_generate, generate
from rich.console import Console
import torch

logger = logging.getLogger(__name__)

console = Console()

WHISPER_LANGUAGE_TO_LLM_LANGUAGE = {
    "en": "english",
    "fr": "french",
    "es": "spanish",
    "zh": "chinese",
    "ja": "japanese",
    "ko": "korean",
}

class MLXLanguageModelHandler(BaseHandler):
    """
    Handles the language model part.
    """

    def setup(
        self,
        model_name="microsoft/Phi-3-mini-4k-instruct",
        device="mps",
        torch_dtype="float16",
        gen_kwargs={},
        user_role="user",
        chat_size=1,
        init_chat_role=None,
        init_chat_prompt="You are a helpful AI assistant.",
    ):
        self.model_name = model_name
        self.model, self.tokenizer = load(self.model_name)
        self.gen_kwargs = gen_kwargs

        self.chat = Chat(chat_size)
        if init_chat_role:
            if not init_chat_prompt:
                raise ValueError(
                    "An initial promt needs to be specified when setting init_chat_role."
                )
            self.chat.init_chat({"role": init_chat_role, "content": init_chat_prompt})
        self.user_role = user_role

        self.warmup()

    def warmup(self):
        logger.info(f"Warming up {self.__class__.__name__}")

        dummy_input_text = "Repeat the word 'home'."
        dummy_chat = [{"role": self.user_role, "content": dummy_input_text}]

        n_steps = 2

        for _ in range(n_steps):
            prompt = self.tokenizer.apply_chat_template(dummy_chat, tokenize=False)
            generate(
                self.model,
                self.tokenizer,
                prompt=prompt,
                max_tokens=self.gen_kwargs["max_new_tokens"],
                verbose=False,
            )

    def process(self, prompt):
        logger.debug("infering language model...")
        language_code = None

        if isinstance(prompt, tuple):
            prompt, language_code = prompt
            prompt = f"Please reply to my message in {WHISPER_LANGUAGE_TO_LLM_LANGUAGE[language_code]}. " + prompt

        self.chat.append({"role": self.user_role, "content": prompt})

        # Remove system messages if using a Gemma model
        if "gemma" in self.model_name.lower():
            chat_messages = [
                msg for msg in self.chat.to_list() if msg["role"] != "system"
            ]
        else:
            chat_messages = self.chat.to_list()

        prompt = self.tokenizer.apply_chat_template(
            chat_messages, tokenize=False, add_generation_prompt=True
        )
        output = ""
        curr_output = ""
        for t in stream_generate(
            self.model,
            self.tokenizer,
            prompt,
            max_tokens=self.gen_kwargs["max_new_tokens"],
        ):
            output += t
            curr_output += t
            if curr_output.endswith((".", "?", "!", "<|end|>")):
                yield (curr_output.replace("<|end|>", ""), language_code)
                curr_output = ""
        generated_text = output.replace("<|end|>", "")
        torch.mps.empty_cache()

        self.chat.append({"role": "assistant", "content": generated_text})