from openmind import AutoTokenizer, AutoModel, is_torch_npu_available
from openmind_hub import snapshot_download
import torch
import argparse
import torch.nn.functional as F


# 均值池化 - 考虑注意力掩码以进行正确的平均
def mean_pooling(model_output, attention_mask):
    token_embeddings = model_output[0] # model_output的第一个元素包含所有token嵌入
    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)

def parse_args():
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--model_name_or_path",
        type=str,
        help="Path to model",
        default="Rose/cross-en-es-pt-roberta-sentence-transformer",
    )
    args = parser.parse_args()
    return args

def main():
    args = parse_args()
    model_path = args.model_name_or_path

    if is_torch_npu_available():
        device = "npu:0"
    else:
        device = "cpu"

    # 我们想要获取句子嵌入的句子
    sentences = ['This is an example sentence', 'Each sentence is converted']

    # 从openmind_hub加载模型
    tokenizer = AutoTokenizer.from_pretrained(model_path)
    model = AutoModel.from_pretrained(model_path)

    # 对句子进行分词
    encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')

    # 计算token嵌入
    with torch.no_grad():
        model_output = model(**encoded_input)

    # 执行池化
    sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])

    # 归一化嵌入
    sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1)

    print("Sentence embeddings:")
    print(sentence_embeddings)


if __name__ == "__main__":
    main()