import time
import bitsandbytes as bnb
import torch
import transformers
from datasets import load_dataset
from typing import Dict, List, Any
from peft import (
    LoraConfig,
    PeftConfig,
    PeftModel,
    get_peft_model,
    prepare_model_for_kbit_training,
)
from transformers import (
    AutoConfig,
    LlamaTokenizer,
    LlamaForCausalLM,
    #AutoModelForCausalLM,
    #AutoTokenizer,
    BitsAndBytesConfig,
)
import json

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
)


from huggingface_hub import login
access_token_read = "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"
login(token = access_token_read)


class EndpointHandler:
    def __init__(self, path=''):
        PEFT_MODEL = path
        config = PeftConfig.from_pretrained(PEFT_MODEL)
        self.model = LlamaForCausalLM.from_pretrained(
            config.base_model_name_or_path,
            return_dict=True,
            quantization_config=bnb_config,
            device_map="auto",
            trust_remote_code=True,
        )
        self.tokenizer = LlamaTokenizer.from_pretrained(config.base_model_name_or_path)
        self.tokenizer.pad_token_id = (0)
        self.tokenizer.padding_side = "left"
        self.model = PeftModel.from_pretrained(self.model, PEFT_MODEL)
        self.generation_config = self.model.generation_config
        self.generation_config.max_new_tokens = 500
        self.generation_config.pad_token_id = self.tokenizer.eos_token_id
        self.generation_config.eos_token_id = self.tokenizer.eos_token_id




    def __call__(self, data: Dict[str, Any]):
        prompt = data.pop("inputs", data)
        DEVICE = "cuda:0"
        input_message = f"""[INST]You are Copilot, a chat assistant that helps users choose products from JioMart, JioFiber, JioCinema, Tira Beauty, netmeds and milkbasket[/INST]\nUser: {prompt}\nAssistant: """.strip()
        encoding = self.tokenizer(input_message, return_tensors="pt").to(DEVICE)
        with torch.inference_mode():
            outputs = self.model.generate(
                input_ids=encoding.input_ids,
                attention_mask=encoding.attention_mask,
                generation_config=self.generation_config
            )
            return self.tokenizer.decode(outputs[0], skip_special_tokens=True)[len(input_message):]