from typing import Dict, Any, List
import logging

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftConfig, PeftModel
import torch.cuda

dtype = torch.bfloat16 if torch.cuda.get_device_capability()[0]==8 else torch.float16

# LOGGER = logging.getLogger(__name__)
# logging.basicConfig(level=logging.INFO)
# device = "cuda" if torch.cuda.is_available() else "cpu"


class EndpointHandler():
    def __init__(self, path=""):
        tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True)
        model = AutoModelForCausalLM.from_pretrained(
            path,
            return_dict=True,
            device_map="auto",
            load_in_8bit=True,
            torch_dtype=dtype,
            trust_remote_code=True,
        )

        generation_config = model.generation_config
        generation_config.max_new_tokens=512
        generation_config.temperation = 0
        generation_config.num_return_sequences=1
        generation_config.pad_token_id = tokenizer.eos_token_id
        generation_config.eos_token_id = tokenizer.eos_token_id
        self.generation_config = generation_config

        self.pipeline = transformers.pipeline(
            "text-generation",model=model,tokenizer=tokenizer
        )


        # config = PeftConfig.from_pretrained(path)
        # model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, load_in_8bit=True, device_map='auto')
        # self.tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
        # # Load the Lora model
        # self.model = PeftModel.from_pretrained(model, path)

    def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]:
        # """
        # Args:
        #     data (Dict): The payload with the text prompt and generation parameters.
        # """
        # LOGGER.info(f"Received data: {data}")
        # Get inputs
        prompt = data.pop("inputs", None)
        # parameters = data.pop("parameters", None)
        # if prompt is None:
        #     raise ValueError("Missing prompt.")
        # # Preprocess
        # input_ids = self.tokenizer(prompt, return_tensors="pt").input_ids.to(device)
        # # Forward
        # LOGGER.info(f"Start generation.")
        # if parameters is not None:
        #     output = self.model.generate(input_ids=input_ids, **parameters)
        # else:
        #     output = self.model.generate(input_ids=input_ids)
        # # Postprocess
        # prediction = self.tokenizer.decode(output[0])
        # LOGGER.info(f"Generated text: {prediction}")
        # return {"generated_text": prediction}
        result = self.pipeline(prompt,generation_config=self.generation_config)
        return result