 Here is the Python code based on your instructions:

```python
import argparse
import torch
from torch.utils.data import Dataset, DataLoader
from transformers import ErnieForSequenceClassification, ErnieTokenizerFast

class TextDataset(Dataset):
    def __init__(self, texts, tokenizer):
        self.texts = texts
        self.tokenizer = tokenizer

    def __len__(self):
        return len(self.texts)

    def __getitem__(self, idx):
        input_ids = self.tokenizer(self.texts[idx], return_tensors='pt', truncation=True, padding='max_length', max_length=512)
        return {key: val.to(model.device) for key, val in input_ids.items()}

class ErnieClassifier:
    def __init__(self, model, tokenizer, device, fp16, batch_size, sequence_length, logging_interval):
        self.model = model.to(device)
        self.tokenizer = tokenizer
        self.device = device
        self.fp16 = fp16
        self.batch_size = batch_size
        self.sequence_length = sequence_length
        self.logging_interval = logging_interval

    def preprocess(self, texts):
        return [self.tokenizer(text, padding=True, truncation=True, max_length=self.sequence_length, return_tensors='pt') for text in texts]

    def predict(self, inputs):
        with torch.no_grad():
            if self.fp16:
                with torch.cuda.amp.autocast():
                    outputs = self.model(**inputs)
            else:
                outputs = self.model(**inputs)
            logits = outputs.logits
            predicted_labels = torch.argmax(logits, dim=-1)
            confidences = torch.softmax(logits, dim=-1)
        return predicted_labels, confidences

def main():
    parser = argparse.ArgumentParser()
    parser.add_argument('--model_dir', type=str, required=True)
    parser.add_argument('--vocab_path', type=str, required=True)
    parser.add_argument('--inference_device', type=str, default='cuda')
    parser.add_argument('--runtime_backend', type=str, default='torch')
    parser.add_argument('--batch_size', type=int, default=16)
    parser.add_argument('--sequence_length', type=int, default=128)
    parser.add_argument('--logging_interval', type=int, default=10)
    parser.add_argument('--fp16', action='store_true')
    parser.add_argument('--fast_tokenizer', action='store_true')
    args = parser.parse_args()

    tokenizer = ErnieTokenizerFast.from_pretrained(args.vocab_path) if args.fast_tokenizer else ErnieTokenizer.from_pretrained(args.vocab_path)
    model = ErnieForSequenceClassification.from_pretrained(args.model_dir)

    device = torch.device(args.inference_device)

    dataset = TextDataset(texts, tokenizer)
    data_loader = DataLoader(dataset, batch_size=args.batch_size, collate_fn=lambda batch: next(iter(batch)))

    classifier = ErnieClassifier(model, tokenizer, device, args.fp16, args.batch_size, args.sequence_length, args.logging_interval)

    for batch in data_loader:
        predicted_labels, confidences = classifier.predict(batch)
        # Process the output as per your requirements

if __name__ == "__main__":
    main()
```

This code creates a command-line interface for parsing arguments related to the 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 TextDataset class for batchifying text data, an ErnieClassifier class for sequence classification prediction using the Ernie model, and a main function that parses the arguments, instantiates the prediction class, batchifies the text data, and predicts the output for each batch of texts. The output includes the batch id, example id, input sentences, predicted label, and confidence score.