import json
import pprint 


def read_json(file_path): 
    with open(file_path, 'r', encoding='utf-8') as file:
        data = json.load(file)
    return data

def write_json(file_path, data):
    with open(file_path, 'w', encoding='utf-8') as file:
        json.dump(data, file, ensure_ascii=False, indent=4)
        
data = read_json("DataSet/train_samples_all_tuning.json")       
        

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "Model/QwQ-32B-Preview"
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)



def chat_QwQ(prompt):
    
    messages = [
        {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
        {"role": "user", "content": prompt}
    ]
    
    text = tokenizer.apply_chat_template(
        messages,
        tokenize=False,
        add_generation_prompt=True
    )
    
    model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

    generated_ids = model.generate(
        **model_inputs,
        max_new_tokens=512
    )
    
    generated_ids = [
        output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
    ]

    response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
    # print(response)
    return response




# from transformers import MarianMTModel, MarianTokenizer

# model_name = "Model/opus-mt-en-zh"
# tokenizer = MarianTokenizer.from_pretrained(model_name)
# model = MarianMTModel.from_pretrained(model_name)



    

for i in data:
    sent1 = i['conversations'][0]['value']
    sent2 = i['conversations'][1]['value']
    sentence = sent1 + sent2 
    prompt = "This is a question-answering datapoint based on image information. Determine whether the answer can be judged without relying on the image. If it can, this is considered bad data; if it requires the image, it is considered good data. Rate this datapoint on a scale from 1 (bad) to 5 (good). ###### " + sentence
    answer = chat_QwQ(prompt)
    
 
    # english_text = answer
    # inputs = tokenizer.encode(english_text, return_tensors="pt", truncation=True)
    # translated = model.generate(inputs, max_length=40, num_beams=4, early_stopping=True)
    # chinese_translation = tokenizer.decode(translated[0], skip_special_tokens=True)
    pprint.pprint(prompt)
    pprint.pprint(answer)
    break