from optimum.onnxruntime import ORTModelForSequenceClassification
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline


# 转换 onnx 模型
def convert(path, onnx_path, onnx_path):
    onnx_model = ORTModelForSequenceClassification.from_pretrained(path, from_transformers=True)
    tokenizer = AutoTokenizer.from_pretrained(path)

    onnx_model.save_pretrained(onnx_path)
    tokenizer.save_pretrained(onnx_path)


# 加载模型，用pipeline包装
def load_model(model_name):
    model = AutoModelForSequenceClassification.from_pretrained(model_name)
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    text_classification_pipeline = pipeline("text-classification", model=model, tokenizer=tokenizer)
    print(text_classification_pipeline('这是一个简单的demo，用来防止忘记'))
    return text_classification_pipeline


# 加载 onnx 模型，用pipeline包装
def load_onnx_model(onnx_path):
    lang_tokenizer = AutoTokenizer.from_pretrained(onnx_path)
    lang_model = ORTModelForSequenceClassification.from_pretrained(onnx_path)
    lang_detecter = pipeline("text-classification", model=lang_model, tokenizer=lang_tokenizer, truncation=True)
    print(lang_detecter('这是一个简单的demo，用来防止忘记'))
    return lang_detecter