import torch
import numpy as np
import random

def get_batch_text_representation(texts, model, tokenizer, batch_size=1):
    """
    Get mean-pooled representations of given texts in batches.
    """
    mean_pooled_batch = []
    for i in range(0, len(texts), batch_size):
        batch_texts = texts[i:i+batch_size]
        inputs = tokenizer(batch_texts, return_tensors="pt", padding=True, truncation=True)
        with torch.no_grad():
            outputs = model(**inputs, output_hidden_states=False)
        last_hidden_states = outputs.last_hidden_state
        input_mask_expanded = inputs['attention_mask'].unsqueeze(-1).expand(last_hidden_states.size()).float()
        sum_embeddings = torch.sum(last_hidden_states * input_mask_expanded, 1)
        sum_mask = input_mask_expanded.sum(1)
        mean_pooled = sum_embeddings / sum_mask
        mean_pooled_batch.extend(mean_pooled.cpu().detach().numpy())
    return np.array(mean_pooled_batch)