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# Necessary imports
import os
import sys
from dotenv import load_dotenv
from typing import Any
import torch
from transformers import AutoModel, AutoTokenizer, AutoProcessor
# Local imports
from src.logger import logging
from src.exception import CustomExceptionHandling
# Load the Environment Variables from .env file
load_dotenv()
# Access token for using the model
access_token = os.environ.get("ACCESS_TOKEN")
def load_model_tokenizer_and_processor(model_name: str, device: str) -> Any:
"""
Load the model, tokenizer and processor.
Args:
- model_name (str): The name of the model to load.
- device (str): The device to load the model onto.
Returns:
- model: The loaded model.
- tokenizer: The loaded tokenizer.
- processor: The loaded processor.
"""
try:
# Load the model, tokenizer and processor
model = AutoModel.from_pretrained(
model_name,
trust_remote_code=True,
attn_implementation="sdpa",
torch_dtype=torch.bfloat16,
init_vision=True,
init_audio=False,
init_tts=False,
token=access_token
)
model = model.eval().to(device=device)
tokenizer = AutoTokenizer.from_pretrained(
model_name, trust_remote_code=True, token=access_token
)
processor = AutoProcessor.from_pretrained(
model_name, trust_remote_code=True, token=access_token
)
# Log the successful loading of the model, tokenizer and processor
logging.info("Model, tokenizer and processor loaded successfully.")
# Return the model, tokenizer and processor
return model, tokenizer, processor
# Handle exceptions that may occur during model, tokenizer and processor loading
except Exception as e:
# Custom exception handling
raise CustomExceptionHandling(e, sys) from e
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