run.py

import os
import json
import torch
import argparse
from transformers import AutoTokenizer
from modeling_custom import BertForTokenClassificationWithSixO
from labeler import Labeler


class ModelPipeline:
    """
    Pipeline for text processing using the BertForTokenClassificationWithFourO model.
    Handles preprocessing, inference, and postprocessing.
    """

    def __init__(self, model_path=None):
        """
        Initialize the pipeline with model and tokenizer.

        Args:
            model_path: Path to the model directory. Defaults to current directory.
        """
        # Use current directory if no path specified
        if model_path is None:
            model_path = os.path.dirname(os.path.abspath(__file__))

        # Load tokenizer and model
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        print(f"Using device: {self.device}")

        self.tokenizer = AutoTokenizer.from_pretrained(model_path)
        self.model = BertForTokenClassificationWithSixO.from_pretrained(model_path)
        self.model.to(self.device)
        self.model.eval()

        # Load config for any custom settings
        config_path = os.path.join(model_path, "config.json")
        with open(config_path, 'r') as f:
            self.config = json.load(f)

        # Initialize labeler for postprocessing
        self.labeler = Labeler(tags=(1, 2),
                               regexes=(r'[^\S\r\n\v\f]', r'\u200c'),
                               chars=(" ", "‌"),
                               class_count=2)

    def _tokenize(self, chars, chunk_size=512):
        input_ids = []
        attention_masks = []

        ids_ = [101] + [self.tokenizer.encode(char)[1] for char in chars] + [102]
        for i in range(0, len(ids_), chunk_size):
            chunked_ids = ids_[i:i + chunk_size]
            attention_mask = [1] * len(chunked_ids)

            if len(chunked_ids) != chunk_size:
                attention_mask += [0] * (chunk_size - len(chunked_ids))  # padding the attention mask accordingly
                chunked_ids += [0] * (chunk_size - len(chunked_ids))  # padding the last chunk to chunk size

            input_ids.append(chunked_ids)
            attention_masks.append(attention_mask)

        return input_ids, attention_masks

    def preprocess(self, text):
        chars, labels = self.labeler.label_text(text, corpus_type=Labeler.WHOLE_RAW)
        input_ids, attention_mask = self._tokenize(chars[0], chunk_size=512)
        input_ids = torch.tensor(input_ids)
        attention_mask = torch.tensor(attention_mask)
        labels = [0] + labels[0] + [0]
        return {"input_ids": input_ids, "attention_mask": attention_mask}, chars, labels

    def predict(self, encoded_inputs):
        """
        Run model inference on preprocessed inputs.

        Args:
            encoded_inputs: Tokenized inputs from preprocess method

        Returns:
            Model predictions
        """
        # Move input tensors to the same device as the model
        input_ids = encoded_inputs["input_ids"].to(self.device)
        attention_mask = encoded_inputs["attention_mask"].to(self.device)

        with torch.no_grad():
            outputs = self.model(
                input_ids=input_ids,
                attention_mask=attention_mask
            )

        # Get predicted class for each token
        predictions = torch.argmax(outputs.logits, dim=-1)
        return predictions, outputs.logits

    def postprocess_spacing(self, predictions, chars):
        # Move predictions back to CPU if needed for postprocessing
        predictions_cpu = predictions.cpu()
        predictions_flat = [label for sample in predictions_cpu.tolist() for label in sample]
        return self.labeler.text_generator([' '] + chars[0] + [' '],
                                           predictions_flat[:len(chars[0]) + 2],
                                           corpus_type=Labeler.WHOLE_RAW).strip()

    def postprocess_correcting(self, logits, chars, labels, alpha):
        # Process the labels to match the sequence length

        sequence_length = logits.size(1)
        if len(labels) < sequence_length:
            labels.extend([0] * (sequence_length - len(labels)))  # Padding with 0

        else:
            labels = labels[:sequence_length]  # Truncate if longer

        # Convert labels to one-hot encoding
        num_classes = logits.size(-1)
        user_labels_tensor = torch.tensor([labels], device=self.device)
        user_labels_one_hot = torch.nn.functional.one_hot(user_labels_tensor, num_classes=num_classes).float()

        # Expand dimensions to match logits shape if needed
        if logits.dim() > user_labels_one_hot.dim():
            user_labels_one_hot = user_labels_one_hot.unsqueeze(0)

        # Combine logits and user labels
        combined_logits = logits * (1 - alpha) + user_labels_one_hot * alpha

        # Apply softmax to get probabilities
        combined_probs = torch.nn.functional.softmax(combined_logits, dim=-1)

        # Get the final predictions
        final_predictions = torch.argmax(combined_probs, dim=-1)

        # Move to CPU for postprocessing
        final_predictions_cpu = final_predictions.cpu()

        # Flatten predictions
        predictions_flat = [label for sample in final_predictions_cpu.tolist() for label in sample]

        # Generate text with combined predictions
        return self.labeler.text_generator([' '] + chars[0] + [' '],
                                           predictions_flat[:len(chars[0]) + 2],
                                           corpus_type=Labeler.WHOLE_RAW).strip()

    def process_text(self, text, mode):
        """
        Process text through the entire pipeline.

        Args:
            text: Input text as string or list of strings
            mode: Processing mode ('space' or 'correct')

        Returns:
            Processed text with labels applied
        """
        # Run the full pipeline
        encoded_inputs, chars, labels = self.preprocess(text)

        predictions, logits = self.predict(encoded_inputs)

        if mode == 'space':
            result = self.postprocess_spacing(predictions, chars)
        elif mode == 'correct':
            result = self.postprocess_correcting(logits, chars, labels, 0.5)
        else:
            raise ValueError(f"Unrecognized mode: {mode}")
        return result


def main():
    """Command line interface for the model pipeline."""
    parser = argparse.ArgumentParser(description="Process text using token classification model")
    parser.add_argument("--text", type=str, help="Text to process")
    parser.add_argument("--file", type=str, help="Path to file containing text to process")
    parser.add_argument("--output", type=str, help="Path to output file")
    parser.add_argument("--mode", type=str, choices=['space', 'correct'], default='space',
                        help="Processing mode: 'space' uses model outputs only, 'correct' combines model results with "
                             "original text spacing")
    parser.add_argument("--model_path", type=str, default=None, help="Path to model directory")
    args = parser.parse_args()

    # Initialize pipeline
    pipeline = ModelPipeline(args.model_path)

    # Get input text
    if args.text:
        input_text = args.text
    elif args.file:
        with open(args.file, 'r', encoding='utf-8') as f:
            input_text = f.read()
    else:
        print("Please provide either --text or --file")
        return

    # Process the text
    result = pipeline.process_text(input_text, args.mode)

    # Output results
    if args.output:
        with open(args.output, 'w', encoding='utf-8') as f:
            if isinstance(result, list):
                f.write('\n'.join(result))
            else:
                f.write(result)
    else:
        print("\nProcessed Text:")
        if isinstance(result, list):
            for item in result:
                print(item)
        else:
            print(result)


if __name__ == "__main__":
    main()