import argparse

def batchify_text_data(text_data, batch_size):
    # Function to batchify text data
    pass

class ErnieSequenceClassificationPrediction:
    def __init__(self, model_dir, tokenizer_vocab_path, inference_device, runtime_backend, batch_size, sequence_length, logging_interval, fp16_mode, fast_tokenizer):
        # Initialize tokenizer and runtime
        pass
    
    def preprocess_input_texts(self, input_texts):
        # Preprocess input texts
        pass
    
    def perform_inference(self, preprocessed_texts):
        # Perform inference
        pass
    
    def postprocess_inference_data(self, inference_results):
        # Postprocess inference data
        pass
    
    def predict_output(self, input_texts):
        # Predict output for given texts
        pass

def main():
    parser = argparse.ArgumentParser(description='Sequence Classification Prediction using Ernie Model')
    parser.add_argument('--model_dir', type=str, help='Model directory path')
    parser.add_argument('--tokenizer_vocab_path', type=str, help='Tokenizer vocab path')
    parser.add_argument('--inference_device', type=str, help='Inference device')
    parser.add_argument('--runtime_backend', type=str, help='Runtime backend')
    parser.add_argument('--batch_size', type=int, help='Batch size')
    parser.add_argument('--sequence_length', type=int, help='Sequence length')
    parser.add_argument('--logging_interval', type=int, help='Logging interval')
    parser.add_argument('--fp16_mode', action='store_true', help='Enable FP16 mode')
    parser.add_argument('--fast_tokenizer', action='store_true', help='Use fast tokenizer')
    
    args = parser.parse_args()
    
    prediction_model = ErnieSequenceClassificationPrediction(args.model_dir, args.tokenizer_vocab_path, args.inference_device, args.runtime_backend, args.batch_size, args.sequence_length, args.logging_interval, args.fp16_mode, args.fast_tokenizer)
    
    text_data = ["example text 1", "example text 2", "example text 3"]  # Example text data
    batched_text_data = batchify_text_data(text_data, args.batch_size)
    
    for batch_id, batch_texts in enumerate(batched_text_data):
        input_texts = prediction_model.preprocess_input_texts(batch_texts)
        inference_results = prediction_model.perform_inference(input_texts)
        postprocessed_results = prediction_model.postprocess_inference_data(inference_results)
        
        for example_id, (input_text, predicted_label, confidence_score) in enumerate(postprocessed_results):
            print(f"Batch ID: {batch_id}, Example ID: {example_id}, Input Sentence: {input_text}, Predicted Label: {predicted_label}, Confidence Score: {confidence_score}")

if __name__ == "__main__":
    main()