import torch
from peft import PeftModel, PeftConfig
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
import sys
# sys.path.append("..")
# sys.path.append("../../")
from ... import utils as util
# Load peft config for pre-trained checkpoint etc.

class T5XXL_NL2TL_translator():
    def __init__(self) -> None:
        # exp_name="_mid_ascii"
        peft_model_id="model_weight/tf-ltl_eng_test_mid_ascii_gptAuged"
        self.max_target_length=128

        self.config = PeftConfig.from_pretrained(peft_model_id)

        # load base LLM model and tokenizer
        self.model = AutoModelForSeq2SeqLM.from_pretrained(self.config.base_model_name_or_path,  load_in_8bit=True,  device_map="auto")
        self.tokenizer = AutoTokenizer.from_pretrained(self.config.base_model_name_or_path, device_map="auto")

        # Load the Lora model
        self.model = PeftModel.from_pretrained(self.model, peft_model_id, device_map="auto")
        self.model.eval()

        print("Peft model loaded")

        pass
    def translate(self,input:str=""):
        input_prompt= "Generate LTL: " + input
        replace=util.Task2Preplacer()
        
        input_prompt=replace.reTask2P(input_prompt)
        # print(predicter( replace.reTask2P(input_prompt)))
        print(input_prompt)
        input_ids = self.tokenizer(input_prompt, return_tensors="pt", truncation=True).input_ids.cuda()
        outputs = self.model.generate(input_ids=input_ids, max_new_tokens=self.max_target_length, do_sample=True, top_p=0.9)
        output_txt= self.tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True)[0]
        print(output_txt)
        return replace.reP2Task(output_txt)


if __name__=="__main__":
    test_prompts=[
        "Task_1.1.1 must precede Task_1.1.2, which in turn should precede Task_1.1.3, ensuring that arranging fruits happens before preparing vegetables and prepping eggs and meats is done last.",
        "Task_1.1 must be completed before Task_1.2 starts, and Task_1.2 must be completed before Task_1.3 starts." 
    ]

    translater=T5XXL_NL2TL_translator()

    for ret in test_prompts:
        print(translater.translate(ret))    
    
    flag=True
    while flag:
        lines=[""]
        try:
            lines.append(input())
            while True:
                lines.append(input())
        except:
            pass
        ret ="".join(lines)
        print(ret)
        if ret=="":
            flag=False
        
        print(translater.translate(ret))