from typing import Dict, List, Any
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline, StoppingCriteria, StoppingCriteriaList



class EndpointHandler():
    def __init__(self, path=""):
        # Preload all the elements you are going to need at inference.
        tokenizer = AutoTokenizer.from_pretrained(path)
        model = AutoModelForCausalLM.from_pretrained(path)
        tokenizer.pad_token = tokenizer.eos_token
        self.pipeline = pipeline('text-generation', model=model, tokenizer=tokenizer)
        self.stopping_criteria = StoppingCriteriaList([StopAtPeriodCriteria(tokenizer)])

    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
        """
       data args:
            inputs (:obj: `str` | `PIL.Image` | `np.array`)
            kwargs
      Return:
            A :obj:`list` | `dict`: will be serialized and returned
        """
        inputs = data.pop("inputs", data)

        prediction = self.pipeline(inputs, stopping_criteria=self.stopping_criteria, max_new_tokens=100)
        return prediction


class StopAtPeriodCriteria(StoppingCriteria):
    def __init__(self, tokenizer):
        self.tokenizer = tokenizer

    def __call__(self, input_ids, scores, **kwargs):
        # Decode the last generated token to text
        last_token_text = self.tokenizer.decode(input_ids[:, -1], skip_special_tokens=True)
        # Check if the decoded text ends with a period
        return '.' in last_token_text