#!/usr/bin/env python
import os
import random
import torch
from transformers import T5Tokenizer, T5ForConditionalGeneration
from superprompter.download_models import download_models

global tokenizer, model
modelDir = os.path.expanduser("~") + "/.superprompter/model_files"

def load_models():
 
    if not all(os.path.exists(modelDir) for file in modelDir):
        print("Model files not found. Downloading...\n")
        download_models()
    else:
        print("Model files found. Skipping download.\n")

    print("Loading SuperPrompt-v1 model...\n")


    global tokenizer, model
    tokenizer = T5Tokenizer.from_pretrained(modelDir)
    model = T5ForConditionalGeneration.from_pretrained(modelDir, torch_dtype=torch.float16)

    print("SuperPrompt-v1 model loaded successfully.\n")

def unload_models():
    global tokenizer, model
    del tokenizer
    del model

    for file in os.listdir(modelDir):
        os.remove(os.path.join(modelDir, file))
    os.rmdir(modelDir)



def answer(input_text="", max_new_tokens=512, repetition_penalty=1.2, temperature=0.5, top_p=1, top_k = 1 , seed=-1):
       
    # if the seed is "0", generate a random seed and log it to the output
    if seed == -1:
        seed = random.randint(1, 1000000)

    torch.manual_seed(seed)

    if torch.cuda.is_available():
        device = 'cuda'
    else:
        device = 'cpu'

    input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to(device)
    if torch.cuda.is_available():
        model.to('cuda')

    outputs = model.generate(input_ids, max_new_tokens=max_new_tokens, repetition_penalty=repetition_penalty,
                            do_sample=True, temperature=temperature, top_p=top_p, top_k=top_k)

    dirty_text = tokenizer.decode(outputs[0])
    text = dirty_text.replace("<pad>", "").replace("</s>", "").strip()
    # print("Temperature: {temperature}\nTop P: {top_p}\nTop K: {top_k}\nSeed: {seed}\nOutput:\n\n")
    
    # print(text)
    return text