# import subprocess
import coremltools as ct
from transformers import AutoTokenizer
import numpy as np

model = ct.models.CompiledMLModel('./msmarco_distilbert_base_tas_b_512_single_quantized.mlmodelc')
tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/msmarco-distilbert-base-tas-b")

def tokenize(text):
    return tokenizer(
        text,
        add_special_tokens=True, # Adds [CLS] and [SEP]
        max_length=512,
        padding='max_length',
        truncation=True,
        return_attention_mask=True,
        return_tensors='np'
    )

def embed(text):
    result = tokenize(text)
    token_ids = result['input_ids'].astype(np.float32)#.flatten().reshape(1, 512)
    mask = result['attention_mask'].astype(np.float32)#.flatten().reshape(1, 512)
    print(f"Tokens: {token_ids}")
    print(f"Mask: {mask}")
    predictions = model.predict({"input_ids": token_ids, "attention_mask": mask})
    return predictions['embeddings'][0]


string = "test: hello, world! calling swift executable from python, what will we think of next?"
print(f"🔮 Embedding string: {string}")
embeddings = embed(string)
print(f"🔮 Embeddings (0-10): {embeddings[:10]}")