from typing import List, Tuple, TypedDict
from re import sub

from transformers import DPRQuestionEncoder, DPRQuestionEncoderTokenizer, logging
from transformers import AutoModelForQuestionAnswering, DPRReaderTokenizer, DPRReader
from transformers import QuestionAnsweringPipeline
from transformers import AutoTokenizer, PegasusXForConditionalGeneration, PegasusTokenizerFast
import torch

max_answer_len = 8
logging.set_verbosity_error()


def summarize_text(tokenizer: PegasusTokenizerFast, model: PegasusXForConditionalGeneration,
                   input_texts: List[str]):
  inputs = tokenizer(input_texts, padding=True,
                     return_tensors='pt', truncation=True).to(1)
  with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=False, enable_mem_efficient=False):
    summary_ids = model.generate(inputs["input_ids"])
  summaries = tokenizer.batch_decode(summary_ids, skip_special_tokens=True,
                                     clean_up_tokenization_spaces=False, batch_size=len(input_texts))
  return summaries


def get_summarizer(model_id="seonglae/resrer") -> Tuple[PegasusTokenizerFast, PegasusXForConditionalGeneration]:
  tokenizer = PegasusTokenizerFast.from_pretrained(model_id)
  model = PegasusXForConditionalGeneration.from_pretrained(model_id).to(1)
  model = torch.compile(model)
  return tokenizer, model


# OpenAI reader


class AnswerInfo(TypedDict):
  score: float
  start: int
  end: int
  answer: str


@torch.inference_mode()
def ask_reader(tokenizer: AutoTokenizer, model: AutoModelForQuestionAnswering,
               questions: List[str], ctxs: List[str]) -> List[AnswerInfo]:
  with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=False, enable_mem_efficient=False):
    pipeline = QuestionAnsweringPipeline(
        model=model, tokenizer=tokenizer, device='cuda', max_answer_len=max_answer_len)
    answer_infos: List[AnswerInfo] = pipeline(
        question=questions, context=ctxs)
  for answer_info in answer_infos:
    answer_info['answer'] = sub(r'[.\(\)"\',]', '', answer_info['answer'])
  return answer_infos


def get_reader(model_id="mrm8488/longformer-base-4096-finetuned-squadv2"):
  tokenizer = DPRReaderTokenizer.from_pretrained(model_id)
  model = DPRReader.from_pretrained(model_id).to(0)
  return tokenizer, model


def encode_dpr_question(tokenizer: DPRQuestionEncoderTokenizer, model: DPRQuestionEncoder, questions: List[str]) -> torch.FloatTensor:
  """Encode a question using DPR question encoder.
  https://huggingface.co./docs/transformers/model_doc/dpr#transformers.DPRQuestionEncoder

  Args:
      question (str): question string to encode
      model_id (str, optional): Default for NQ or "facebook/dpr-question_encoder-multiset-base
  """
  batch_dict = tokenizer(questions, return_tensors="pt",
                         padding=True, truncation=True,).to(0)
  with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=False, enable_mem_efficient=False):
    embeddings: torch.FloatTensor = model(**batch_dict).pooler_output
  return embeddings


def get_dpr_encoder(model_id="facebook/dpr-question_encoder-single-nq-base") -> Tuple[DPRQuestionEncoder, DPRQuestionEncoderTokenizer]:
  """Encode a question using DPR question encoder.
  https://huggingface.co./docs/transformers/model_doc/dpr#transformers.DPRQuestionEncoder

  Args:
      question (str): question string to encode
      model_id (str, optional): Default for NQ or "facebook/dpr-question_encoder-multiset-base
  """
  tokenizer = DPRQuestionEncoderTokenizer.from_pretrained(model_id)
  model = DPRQuestionEncoder.from_pretrained(model_id).to(0)
  return tokenizer, model