  # Command-line interface for parsing arguments
import argparse

parser = argparse.ArgumentParser()

# Model directory
parser.add_argument("--model_dir", type=str, required=True, help="Path to the directory containing the model files")

# Tokenizer vocab path
parser.add_argument("--vocab_path", type=str, required=True, help="Path to the vocabulary file for the tokenizer")

# Inference device
parser.add_argument("--device", type=str, default="cuda", help="Device to run inference on (default: cuda)")

# Runtime backend
parser.add_argument("--backend", type=str, default="pytorch", help="Runtime backend to use (default: pytorch)")

# Batch size
parser.add_argument("--batch_size", type=int, default=32, help="Batch size for inference (default: 32)")

# Sequence length
parser.add_argument("--seq_len", type=int, default=128, help="Sequence length for inference (default: 128)")

# Logging interval
parser.add_argument("--log_interval", type=int, default=10, help="Logging interval for inference (default: 10)")

# FP16 mode
parser.add_argument("--fp16", action="store_true", help="Enable FP16 mode for inference")

# Fast tokenizer
parser.add_argument("--fast_tokenizer", action="store_true", help="Enable fast tokenizer for inference")

args = parser.parse_args()

# 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

# Sequence classification prediction using Ernie model
class ErniePredictor:
    def __init__(self, model_dir, vocab_path, device, backend, batch_size, seq_len, log_interval, fp16, fast_tokenizer):
        # Initialize tokenizer and runtime
        self.tokenizer = AutoTokenizer.from_pretrained(vocab_path, do_lower_case=True)
        self.runtime = Runtime(backend=backend, device=device)

        # Load model
        self.model = ErnieForSequenceClassification.from_pretrained(model_dir, num_labels=8)

        # Set batch size and sequence length
        self.batch_size = batch_size
        self.seq_len = seq_len

        # Set logging interval
        self.log_interval = log_interval

        # Set FP16 mode
        self.fp16 = fp16

        # Set fast tokenizer
        self.fast_tokenizer = fast_tokenizer

    def preprocess_inputs(self, texts):
        # Tokenize input texts
        inputs = self.tokenizer(texts, return_tensors="pt", padding="max_length", truncation=True, max_length=self.seq_len, return_attention_mask=True, return_token_type_ids=True)

        # Convert input tensors to FP16 if enabled
        if self.fp16:
            inputs = {k: v.half() for k, v in inputs.items()}

        return inputs

    def perform_inference(self, inputs):
        # Perform inference on input tensors
        outputs = self.runtime(self.model, inputs, return_tensors="pt")

        # Convert output tensors to FP32 if enabled
        if self.fp16:
            outputs = {k: v.float() for k, v in outputs.items()}

        return outputs

    def postprocess_inference_data(self, outputs):
        # Extract logits and attention mask from output tensors
        logits = outputs.logits
        attention_mask = outputs.attention_mask

        # Apply softmax to logits
        probs = torch.softmax(logits, dim=-1)

        # Extract predicted label and confidence score
        predicted_label = torch.argmax(probs, dim=-1)
        confidence_score = torch.max(probs, dim=-1).values

        return predicted_label, confidence_score

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

        # Preprocess input texts
        inputs = self.preprocess_inputs(batches)

        # Perform inference on input tensors
        outputs = self.perform_inference(inputs)

        # Postprocess inference data
        predicted_labels, confidence_scores = self.postprocess_inference_data(outputs)

        # Return predicted labels and confidence scores
        return predicted_labels, confidence_scores

# Main function
def main():
    # Instantiate prediction class
    predictor = ErniePredictor(args.model_dir, args.vocab_path, args.device, args.backend, args.batch_size, args.seq_len, args.log_interval, args.fp16, args.fast_tokenizer)

    # Load text data
    with open("data.txt", "r") as f:
        texts = f.readlines()

    # Batchify text data
    batches = batchify_text_data(texts, args.batch_size)

    # Predict output for each batch of texts
    for batch_id, batch in enumerate(batches):
        # Preprocess input texts
        inputs = predictor.preprocess_inputs(batch)

        # Perform inference on input tensors
        outputs = predictor.perform_inference(inputs)

        # Postprocess inference data
        predicted_labels, confidence_scores = predictor.postprocess_inference_data(outputs)

        # Print predicted labels and confidence scores
        for example_id, (predicted_label, confidence_score) in enumerate(zip(predicted_labels, confidence_scores)):
            print(f"Batch {batch_id + 1}, Example {example_id + 1}:")
            print(f"Input: {batch[example_id]}")
            print(f"Predicted Label: {predicted_label}")
            print(f"Confidence Score: {confidence_score}")
            print()

if __name__ == "__main__":
    main()