from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
from langchain.llms import HuggingFacePipeline
from langchain import PromptTemplate, LLMChain
from typing import Dict, List, Any

template = """{char_name}'s Persona: {char_persona}
<START>
{chat_history}
{char_name}: {char_greeting}
<END>
{user_name}: {user_input}
{char_name}: """

class EndpointHandler():

    def __init__(self, path=""):
        pass
        # tokenizer = AutoTokenizer.from_pretrained(path)
        # model = AutoModelForCausalLM.from_pretrained(path, load_in_8bit = True, device_map = "auto")
        # local_llm = HuggingFacePipeline(
        #     pipeline = pipeline(
        #         "text-generation",
        #         model = model,
        #         tokenizer = tokenizer,
        #         max_length = 2048,
        #         temperature = 0.5,
        #         top_p = 0.9,
        #         top_k = 0,
        #         repetition_penalty = 1.1,
        #         pad_token_id = 50256,
        #         num_return_sequences = 1
        #     )
        # )
        # prompt_template = PromptTemplate(
        #     template = template,
        #     input_variables = [
        #         "user_input",
        #         "user_name",
        #         "char_name",
        #         "char_persona",
        #         "char_greeting",
        #         "chat_history"
        #     ],
        #     validate_template = True
        # )
        # self.llm_engine = LLMChain(
        #     llm = local_llm,
        #     prompt = prompt_template
        # )

    def __call__(self, data: Any) -> Any:
        return data, type(data)
        # inputs = data.pop("inputs", data)

        # return self.llm_engine.predict(
        #     user_input = inputs["user_input"],
        #     user_name = inputs["user_name"],
        #     char_name = inputs["char_name"],
        #     char_persona = inputs["char_persona"],
        #     char_greeting = inputs["char_greeting"],
        #     chat_history = inputs["chat_history"]
        # ).split("\n",1)[0]