code: 
  
  
  
  
  
  
  import os
  from typing import Optional, Union
  
  import numpy as np
  from huggingface_hub import hf_hub_download
  
  from ... import AutoTokenizer
  from ...utils import logging
  
  
  lowerCamelCase				  =  '''block_records.npy'''
  
  
  lowerCamelCase				  =  logging.get_logger(__name__)
  def        UpperCAmelCase      (   _lowercase,      _lowercase      ):
  
  
      '''simple docstring'''
  
  
  
      import tensorflow.compat.va as tf
  
      _lowercase     =	tf.data.TFRecordDataset(_lowercase,      buffer_size=512 * 1024 * 1024      )
      _lowercase     =	blocks_dataset.batch(_lowercase,      drop_remainder=_lowercase      )
      _lowercase     =	next(blocks_dataset.take(1      ).as_numpy_iterator()      )
  
      return np_record
  
  class 					snake_case_							:
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
     def __init__(    self:	Dict   ,       snake_case__:	Optional[Any]   ,       snake_case__:	List[Any]   ,       snake_case__:	List[Any]=2   ,       snake_case__:	Optional[int]=1_000   ,       snake_case__:	List[str]=100   ,       snake_case__:	Union[str, Any]=100_000   ,       )  ->		Union[str, Any]:
  
  
         """simple docstring"""
  
  
  
         from scann.scann_ops.py.scann_ops_pybind import builder as Builder
  
         _lowercase     =	Builder(db=snake_case__   ,       num_neighbors=snake_case__   ,       distance_measure='dot_product'	)
         _lowercase     =	builder.tree(
             num_leaves=snake_case__   ,       num_leaves_to_search=snake_case__   ,       training_sample_size=snake_case__	)
         _lowercase     =	builder.score_ah(dimensions_per_block=snake_case__	)
  
         _lowercase     =	builder.build()
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Union[str, Any]   ,       snake_case__:	int	)  ->		Tuple:
  
  
         """simple docstring"""
  
  
  
         _lowercase ,    _lowercase     =	self.searcher.search_batched(question_projection.detach().cpu()	)
         return retrieved_block_ids.astype('int64'	)
  
  
  
  
  
  
  class 					snake_case_							:
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
     def __init__(    self:	Optional[Any]   ,       snake_case__:	Tuple   ,       snake_case__:	Dict	)  ->		Optional[int]:
  
  
         """simple docstring"""
  
  
  
         super().__init__()
         _lowercase     =	block_records
         _lowercase     =	tokenizer
  
     def __call__(    self:	Optional[int]   ,       snake_case__:	Tuple   ,       snake_case__:	Union[str, Any]   ,       snake_case__:	str   ,       snake_case__:	Union[str, Any]=None   ,       snake_case__:	int="pt"	)  ->		List[str]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	np.take(self.block_records   ,       indices=snake_case__   ,       axis=0	)
  
         _lowercase     =	self.tokenizer.decode(question_input_ids[0]   ,       skip_special_tokens=snake_case__	)
  
         _lowercase     =	[]
         _lowercase     =	[]
         for retrieved_block in retrieved_blocks:
             text.append(snake_case__	)
             text_pair.append(retrieved_block.decode()	)
  
         _lowercase     =	self.tokenizer(
             snake_case__   ,       snake_case__   ,       padding=snake_case__   ,       truncation=snake_case__   ,       return_special_tokens_mask=snake_case__   ,       max_length=snake_case__	)
         _lowercase     =	concat_inputs.convert_to_tensors(snake_case__	)
  
         if answer_ids is not None:
             return self.block_has_answer(snake_case__   ,       snake_case__	) + (concat_inputs_tensors,)
         else:
             return (None, None, None, concat_inputs_tensors)
  
     @classmethod
     def 			SCREAMING_SNAKE_CASE__				(    cls:	List[Any]   ,       snake_case__:	Optional[Union[str, os.PathLike]]   ,       *snake_case__:	Optional[Any]   ,       **snake_case__:	int	)  ->		Optional[int]:
  
  
         """simple docstring"""
  
  
  
         if os.path.isdir(snake_case__	):
             _lowercase     =	os.path.join(snake_case__   ,       _REALM_BLOCK_RECORDS_FILENAME	)
         else:
             _lowercase     =	hf_hub_download(
                 repo_id=snake_case__   ,       filename=_REALM_BLOCK_RECORDS_FILENAME   ,       **snake_case__	)
         _lowercase     =	np.load(snake_case__   ,       allow_pickle=snake_case__	)
  
         _lowercase     =	AutoTokenizer.from_pretrained(snake_case__   ,       *snake_case__   ,       **snake_case__	)
  
         return cls(snake_case__   ,       snake_case__	)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Any   ,       snake_case__:	Tuple	)  ->		List[Any]:
  
  
         """simple docstring"""
  
  
  
         np.save(os.path.join(snake_case__   ,       _REALM_BLOCK_RECORDS_FILENAME	)   ,       self.block_records	)
         # save tokenizer
         self.tokenizer.save_pretrained(snake_case__	)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	int   ,       snake_case__:	Optional[Any]   ,       snake_case__:	int	)  ->		List[str]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	[]
         _lowercase     =	[]
         _lowercase     =	[]
         _lowercase     =	0
  
         for input_id in concat_inputs.input_ids:
             _lowercase     =	input_id.tolist()
             # Check answers between two [SEP] tokens
             _lowercase     =	input_id_list.index(self.tokenizer.sep_token_id	)
             _lowercase     =	first_sep_idx + 1 + input_id_list[first_sep_idx + 1 :].index(self.tokenizer.sep_token_id	)
  
             start_pos.append([]	)
             end_pos.append([]	)
             for answer in answer_ids:
                 for idx in range(first_sep_idx + 1   ,       snake_case__	):
                     if answer[0] == input_id_list[idx]:
                         if input_id_list[idx : idx + len(snake_case__	)] == answer:
                             start_pos[-1].append(snake_case__	)
                             end_pos[-1].append(idx + len(snake_case__	) - 1	)
  
             if len(start_pos[-1]	) == 0:
                 has_answers.append(snake_case__	)
             else:
                 has_answers.append(snake_case__	)
                 if len(start_pos[-1]	) > max_answers:
                     _lowercase     =	len(start_pos[-1]	)
  
          # Pad -1 to max_answers
         for start_pos_, end_pos_ in zip(snake_case__   ,       snake_case__	):
             if len(start_pos_	) < max_answers:
                 _lowercase     =	[-1] * (max_answers - len(start_pos_	))
                 start_pos_ += padded
                 end_pos_ += padded
         return has_answers, start_pos, end_pos
  
style_context: 
  
  
  
  
  
  
  import argparse
  
  import torch
  
  from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging
  
  
  logging.set_verbosity_info()
  lowerCamelCase										=  logging.get_logger(__name__)
  
  lowerCamelCase										=  ['''UtteranceLevel''']
  @torch.no_grad()
  def        UpperCAmelCase      (   _lowercase,      _lowercase,      _lowercase,      _lowercase      ):
  
  
      '''simple docstring'''
  
  
  
      _lowercase     =	torch.load(_lowercase,      map_location='cpu'      )
      if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
          raise NotImplementedError(f'''The supported s3prl models are {SUPPORTED_MODELS}'''      )
  
      _lowercase     =	checkpoint['Downstream']
  
      _lowercase     =	HubertConfig.from_pretrained(_lowercase      )
      _lowercase     =	HubertForSequenceClassification.from_pretrained(_lowercase,      config=_lowercase      )
      _lowercase     =	WavaVecaFeatureExtractor.from_pretrained(
          _lowercase,      return_attention_mask=_lowercase,      do_normalize=_lowercase      )
  
      if hf_congfig.use_weighted_layer_sum:
          _lowercase     =	checkpoint['Featurizer']['weights']
  
      _lowercase     =	downstream_dict['projector.weight']
      _lowercase     =	downstream_dict['projector.bias']
      _lowercase     =	downstream_dict['model.post_net.linear.weight']
      _lowercase     =	downstream_dict['model.post_net.linear.bias']
  
      hf_feature_extractor.save_pretrained(_lowercase      )
      hf_model.save_pretrained(_lowercase      )
  
  
  if __name__ == "__main__":
     lowerCamelCase										=  argparse.ArgumentParser()
     parser.add_argument(
         '''--base_model_name''', default=None, type=str, help='''Name of the huggingface pretrained base model.'''
     )
     parser.add_argument('''--config_path''', default=None, type=str, help='''Path to the huggingface classifier config.''')
     parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to the s3prl checkpoint.''')
     parser.add_argument('''--model_dump_path''', default=None, type=str, help='''Path to the final converted model.''')
     lowerCamelCase										=  parser.parse_args()
     convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
  