text: from torch import nn
  from transformers import T5Model, T5Tokenizer
  import json
  from typing import List
  import os
  import numpy as np
  import logging
  
  class T5(nn.Module):
      """T5 model to generate token embeddings.
  
      Each token is mapped to an output vector from BERT.
      """
      def __init__(self, model_name_or_path: str, max_seq_length: int = 128, do_lower_case: bool = True):
          super(T5, self).__init__()
          self.config_keys = ['max_seq_length', 'do_lower_case']
          self.do_lower_case = do_lower_case
  
          if max_seq_length > 512:
              logging.warning("T5 only allows a max_seq_length of 512. Value will be set to 512")
              max_seq_length = 512
          self.max_seq_length = max_seq_length
  
          self.enc_model = T5Model.from_pretrained(model_name_or_path)
          self.tokenizer = T5Tokenizer.from_pretrained(model_name_or_path, do_lower_case=do_lower_case)
          #self.cls_token_id = self.tokenizer.convert_tokens_to_ids([self.tokenizer.cls_token])[0]
          #self.sep_token_id = self.tokenizer.convert_tokens_to_ids([self.tokenizer.sep_token])[0]
  
      def forward(self, features):
          """Returns token_embeddings, cls_token"""
          output_tokens = self.enc_model(input_ids=features['input_ids'], attention_mask=features['input_mask'])[0]
          cls_tokens = output_tokens[:, 0, :]  # CLS token is first token
          features.update({'token_embeddings': output_tokens, 'cls_token_embeddings': cls_tokens, 'input_mask': features['input_mask']})
          return features
  
      def get_word_embedding_dimension(self) -> int:
          return self.enc_model.config.hidden_size
  
      def tokenize(self, text: str) -> List[int]:
          """
          Tokenizes a text and maps tokens to token-ids
          """
          return self.tokenizer.encode(text)
  
      def get_sentence_features(self, tokens: List[int], pad_seq_length: int):
          """
          Convert tokenized sentence in its embedding ids, segment ids and mask
  
          :param tokens:
              a tokenized sentence
          :param pad_seq_length:
              the maximal length of the sequence. Cannot be greater than self.sentence_transformer_config.max_seq_length
          :return: embedding ids, segment ids and mask for the sentence
          """
          pad_seq_length = min(pad_seq_length, self.max_seq_length)
  
          tokens = tokens[:pad_seq_length]
          input_ids = tokens #[self.cls_token_id] + tokens + [self.sep_token_id]
          sentence_length = len(input_ids)
  
          #pad_seq_length += 2  ##Add Space for CLS + SEP token
  
          token_type_ids = [0] * len(input_ids)
          input_mask = [1] * len(input_ids)
  
          # Zero-pad up to the sequence length. BERT: Pad to the right
          padding = [0] * (pad_seq_length - len(input_ids))
          input_ids += padding
          token_type_ids += padding
          input_mask += padding
  
          assert len(input_ids) == pad_seq_length
          assert len(input_mask) == pad_seq_length
          assert len(token_type_ids) == pad_seq_length
  
  
          return {
              'input_ids': np.asarray(input_ids, dtype=np.int64),
              'token_type_ids': np.asarray(token_type_ids, dtype=np.int64),
              'input_mask': np.asarray(input_mask, dtype=np.int64),
              'sentence_lengths': np.asarray(sentence_length, dtype=np.int64)
          }
  
      def get_config_dict(self):
          return {key: self.__dict__[key] for key in self.config_keys}
  
      def save(self, output_path: str):
          self.enc_model.save_pretrained(output_path)
          self.tokenizer.save_pretrained(output_path)
  
          with open(os.path.join(output_path, 'sentence_T5_config.json'), 'w') as fOut:
              json.dump(self.get_config_dict(), fOut, indent=2)
  
      @staticmethod
      def load(input_path: str):
          with open(os.path.join(input_path, 'sentence_T5_config.json')) as fIn:
              config = json.load(fIn)
          return T5(model_name_or_path=input_path, **config)
  
  
  
  
  
  
  