import torch
from AiBase import AiBase


class QwenAi(AiBase):
    default_device = 'cuda'  # the device to load the model onto
    default_model_id = 'Qwen/Qwen1.5-1.8B-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):
        super().__init__(model_id_or_path)

    def generate(self, messages, token_count=100, **kwargs):
        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=token_count)
            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