  Here is the code you requested:
```
import argparse
import os
import sys
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, Dataset
from transformers import ErnieTokenizer, ErnieForSequenceClassification

# Define the command-line interface
parser = argparse.ArgumentParser()
parser.add_argument("--model_dir", type=str, help="Path to the model directory")
parser.add_argument("--tokenizer_vocab_path", type=str, help="Path to the tokenizer vocabulary")
parser.add_argument("--inference_device", type=str, help="Device to perform inference on")
parser.add_argument("--runtime_backend", type=str, help="Runtime backend to use")
parser.add_argument("--batch_size", type=int, help="Batch size for inference")
parser.add_argument("--sequence_length", type=int, help="Sequence length for inference")
parser.add_argument("--logging_interval", type=int, help="Logging interval for inference")
parser.add_argument("--fp16", action="store_true", help="Use FP16 mode for inference")
parser.add_argument("--fast_tokenizer", action="store_true", help="Use fast tokenizer for inference")

# Define the function to batchify text data
def batchify_text_data(texts, batch_size):
    batches = []
    for i in range(0, len(texts), batch_size):
        batches.append(texts[i:i+batch_size])
    return batches

# Define the class for sequence classification prediction using the Ernie model
class ErnieSequenceClassifier:
    def __init__(self, model_dir, tokenizer_vocab_path, inference_device, runtime_backend, batch_size, sequence_length, logging_interval, fp16, fast_tokenizer):
        self.model_dir = model_dir
        self.tokenizer_vocab_path = tokenizer_vocab_path
        self.inference_device = inference_device
        self.runtime_backend = runtime_backend
        self.batch_size = batch_size
        self.sequence_length = sequence_length
        self.logging_interval = logging_interval
        self.fp16 = fp16
        self.fast_tokenizer = fast_tokenizer

        # Load the model and tokenizer
        self.model = ErnieForSequenceClassification.from_pretrained(model_dir)
        self.tokenizer = ErnieTokenizer.from_pretrained(tokenizer_vocab_path)

    def preprocess_input_texts(self, texts):
        # Tokenize the input texts
        tokenized_texts = self.tokenizer.batch_encode_plus(
            texts,
            add_special_tokens=True,
            max_length=self.sequence_length,
            padding="max_length",
            truncation=True,
            return_attention_mask=True,
            return_tensors="pt"
        )

        # Convert the tokenized texts to tensors
        input_ids = torch.tensor(tokenized_texts["input_ids"])
        attention_mask = torch.tensor(tokenized_texts["attention_mask"])

        # Return the preprocessed input texts
        return input_ids, attention_mask

    def perform_inference(self, input_ids, attention_mask):
        # Move the input tensors to the inference device
        input_ids = input_ids.to(self.inference_device)
        attention_mask = attention_mask.to(self.inference_device)

        # Perform inference on the input tensors
        with torch.no_grad():
            outputs = self.model(input_ids, attention_mask=attention_mask)

        # Return the output of the model
        return outputs

    def postprocess_inference_data(self, outputs):
        # Get the predicted label and confidence score
        predicted_label = torch.argmax(outputs.logits)
        confidence_score = torch.softmax(outputs.logits, dim=1)[predicted_label]

        # Return the postprocessed inference data
        return predicted_label, confidence_score

    def predict(self, texts):
        # Batchify the input texts
        batches = batchify_text_data(texts, self.batch_size)

        # Perform inference on each batch of texts
        for batch_id, batch in enumerate(batches):
            # Preprocess the input texts
            input_ids, attention_mask = self.preprocess_input_texts(batch)

            # Perform inference on the input tensors
            outputs = self.perform_inference(input_ids, attention_mask)

            # Postprocess the inference data
            predicted_label, confidence_score = self.postprocess_inference_data(outputs)

            # Print the output for the current batch
            print(f"Batch {batch_id}:")
            for example_id, (text, predicted_label, confidence_score) in enumerate(zip(batch, predicted_label, confidence_score)):
                print(f"Example {example_id}:")
                print(f"Input text: {text}")
                print(f"Predicted label: {predicted_label}")
                print(f"Confidence score: {confidence_score}")

# Define the main function
def main():
    # Parse the command-line arguments
    args = parser.parse_args()

    # Instantiate the prediction class
    ernie_sequence_classifier = ErnieSequenceClassifier(
        args.model_dir,
        args.tokenizer_vocab_path,
        args.inference_device,
        args.runtime_backend,
        args.batch_size,
        args.sequence_length,
        args.logging_interval,
        args.fp16,
        args.fast_tokenizer
    )

    # Batchify the input texts
    batches = batchify_text_data(args.texts, args.batch_size)

    # Perform inference on each batch of texts
    for batch_id, batch in enumerate(batches):
        # Preprocess the input texts
        input_ids, attention_mask = ernie_sequence_classifier.preprocess_input_texts(batch)

        # Perform inference on the input tensors
        outputs = ernie_sequence_classifier.perform_inference(input_ids, attention_mask)

        # Postprocess the inference data
        predicted_label, confidence_score = ernie_sequence_classifier.postprocess_inference_data(outputs)

        # Print the output for the current batch
        print(f"Batch {batch_id}:")
        for example_id, (text, predicted_label, confidence_score) in enumerate(zip(batch, predicted_label, confidence_score)):
            print(f"Example {example_id}:")
            print(f"Input text: {text}")
            print(f"Predicted label: {predicted_label}")
            print(f"Confidence score: {confidence_score}")

if __name__ == "__main__":
    main()
```
This code defines a command-line interface for parsing arguments related to model directory, tokenizer vocab path, inference device, runtime backend, batch size, sequence length, logging interval, and usage of FP16 mode and fast tokenizer. It also defines a function to batchify text data and a class for sequence classification prediction using the Ernie model. The main function parses the command-line arguments, instantiates the prediction class, batchifies the input texts, and predicts the output for each batch of texts. The output includes the batch id, example id, input sentences, predicted label, and confidence score.