import os
import torch
import torch.nn as nn
import numpy as np
import random
from transformers import (
    BartForConditionalGeneration, 
    AutoModelForCausalLM, 
    BertModel, 
    Wav2Vec2Model,
    CLIPModel,
    AutoTokenizer
)

class MultiModalModel(nn.Module):
    def __init__(self):
        super(MultiModalModel, self).__init__()
        # 初始化子模型
        self.text_generator = BartForConditionalGeneration.from_pretrained('facebook/bart-base')
        self.code_generator = AutoModelForCausalLM.from_pretrained('gpt2')
        self.nlp_encoder = BertModel.from_pretrained('bert-base-uncased')
        self.speech_encoder = Wav2Vec2Model.from_pretrained('facebook/wav2vec2-base-960h')
        self.vision_encoder = CLIPModel.from_pretrained('openai/clip-vit-base-patch32')

        # 初始化分词器和处理器
        self.text_tokenizer = AutoTokenizer.from_pretrained('facebook/bart-base')
        self.code_tokenizer = AutoTokenizer.from_pretrained('gpt2')
        self.nlp_tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
        self.speech_processor = AutoTokenizer.from_pretrained('facebook/wav2vec2-base-960h')
        self.vision_processor = AutoTokenizer.from_pretrained('openai/clip-vit-base-patch32')
    
    def forward(self, task, inputs):
        if task == 'text_generation':
            # 确保 attention_mask 在 inputs 中
            attention_mask = inputs.get('attention_mask')
            print("输入数据:", inputs)
            outputs = self.text_generator.generate(
                inputs['input_ids'], 
                max_new_tokens=100,  # 增加生成的最大新令牌数
                pad_token_id=self.text_tokenizer.eos_token_id, 
                attention_mask=attention_mask,
                top_p=0.9,  # 调整 top_p 值
                top_k=50,  # 保持 top_k 值
                temperature=0.8,  # 调整 temperature 值
                do_sample=True
            )
            print("生成的输出:", outputs)
            return self.text_tokenizer.decode(outputs[0], skip_special_tokens=True)
        # 根据需要添加其他任务的逻辑...

# 主函数
if __name__ == "__main__":
    # 初始化模型
    model = MultiModalModel()

    # 示例任务和输入数据
    task = "text_generation"
    input_text = "This is a sample input."
    tokenizer = model.text_tokenizer
    inputs = tokenizer(input_text, return_tensors='pt')

    # 添加 attention_mask 键值对
    inputs['attention_mask'] = torch.ones_like(inputs['input_ids'])

    # 模型推理
    result = model(task, inputs)
    print("最终输出结果：", result)

    trust_remote_code=True