import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from utils.conversation import save_conversation_json


class ChatQwen:
    default_device = 'cuda'  # the device to load the model onto
    # default_model_id = 'Qwen/Qwen1.5-0.5B-Chat'
    default_model_id = 'Qwen/Qwen1.5-1.8B-Chat'
    # default_model_id = 'Qwen/Qwen1.5-4B-Chat'

    default_instruction = {'role': 'system',
                           'name': 'system',
                           'content': 'Your name is "Laura". You are an AI created by Alice.'}

    def __init__(self, model_id_or_path=default_model_id):
        # model_id = model_id_or_path if not load_from_disk else os.path.abspath(sys.argv[1])

        print('Loading ' + model_id_or_path)
        self.model_id_or_path = model_id_or_path
        self.model = AutoModelForCausalLM.from_pretrained(model_id_or_path, torch_dtype='auto', device_map='auto')
        self.tokenizer = AutoTokenizer.from_pretrained(model_id_or_path)
        # print(self.tokenizer.default_chat_template)
        # print(type(self.model))
        # print(type(self.tokenizer))
        print('Loaded')

    def generate(self, messages):
        try:
            # prepare
            messages = [m for m in messages if m['role'] != 'system']
            input_messages = [self.default_instruction] + messages

            # generate
            text = self.tokenizer.apply_chat_template(input_messages, tokenize=False, add_generation_prompt=True)
            model_inputs = self.tokenizer([text], return_tensors='pt').to(self.default_device)
            generated_ids = self.model.generate(model_inputs.input_ids, max_new_tokens=300)
            generated_ids = [
                output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
            ]
            response = self.tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]

            # add response and save conversation
            response_entry = {'role': 'assistant', 'name': 'assistant', 'content': response}
            messages.append(response_entry)
            self.record_conversation(input_messages, response_entry)

            return messages
        finally:
            torch.cuda.empty_cache()  # clear cache or the gpu mem will be used a lot

    def record_conversation(self, messages, response):
        messages = messages + [response]
        save_conversation_json(self.model_id_or_path, messages)