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


# class EndpointHandler():
#     def __init__(self, path=""):
#         tokenizer = AutoTokenizer.from_pretrained(path)
#         tokenizer.pad_token = tokenizer.eos_token
#         self.model = AutoModelForCausalLM.from_pretrained(path).to('cuda')
#         self.tokenizer = tokenizer
#         self.stopping_criteria = StoppingCriteriaList([StopAtPeriodCriteria(tokenizer)])

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


#         # 3070, 10456, [313, 334] corresponds to "(*", and we do not want to output a comment
#         # 13 is a newline character
#         # [1976, 441, 29889], [4920, 441, 29889] is "Abort." [4920, 18054, 29889] is "Aborted."
#         # [2087, 29885, 4430, 29889] is "Admitted."
#         bad_words_ids = [[3070], [313, 334], [10456], [13], [1976, 441, 29889], [2087, 29885, 4430, 29889], [4920, 441], [4920, 441, 29889], [4920, 18054, 29889]]
#         bad_words_ids.extend(additional_bad_words_ids)

#         input_ids = self.tokenizer.encode(inputs, return_tensors="pt").to('cuda')
#         max_generation_length = 75  # Desired number of tokens to generate
#         # max_input_length = 4092 - max_generation_length  # Maximum input length to allow space for generation

#         # # Truncate input_ids to the most recent tokens that fit within the max_input_length
#         # if input_ids.shape[1] > max_input_length:
#         #     input_ids = input_ids[:, -max_input_length:]

#         max_length = input_ids.shape[1] + max_generation_length
        
#         generated_ids = self.model.generate(
#             input_ids,
#             max_length=max_length,  # 50 new tokens
#             bad_words_ids=bad_words_ids,
#             temperature=1,
#             top_k=40,
#             do_sample=True,
#             stopping_criteria=self.stopping_criteria,
#         )

#         generated_text = self.tokenizer.decode(generated_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
#         prediction = [{"generated_text": generated_text, "generated_ids": generated_ids[0][input_ids.shape[1]:].tolist()}]
#         return prediction

class EndpointHandler():
    def __init__(self, path=""):
        self.model_path = path
        tokenizer = AutoTokenizer.from_pretrained(path)
        tokenizer.pad_token = tokenizer.eos_token
        self.tokenizer = tokenizer
        # Initialize the pipeline for text generation
        self.text_generation_pipeline = pipeline("text-generation", model=path, tokenizer=self.tokenizer, device=0)  # device=0 for CUDA

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

        # Define bad words to avoid in the output
        bad_words_ids = [[3070], [313, 334], [10456], [13], [1976, 441, 29889], [2087, 29885, 4430, 29889], [4920, 441], [4920, 441, 29889], [4920, 18054, 29889]]
        bad_words_ids.extend(additional_bad_words_ids)

        # Generate text using the pipeline
        generation_kwargs = {
            "max_new_tokens": 75,
            "temperature": 0.7,
            "top_k": 40,
            "bad_words_ids": bad_words_ids,
            "pad_token_id": self.tokenizer.eos_token_id,
            "return_full_text": False,  # Only return the new generated tokens
        }
        generated_outputs = self.text_generation_pipeline(inputs, **generation_kwargs)

        # Format the output
        predictions = [{"generated_text": output["generated_text"]} for output in generated_outputs]
        return predictions


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