code: 
  
  """simple docstring"""
  import os
  from functools import partial
  from glob import glob
  
  import faiss
  from datasets import Features, Sequence, Value, concatenate_datasets, load_dataset, load_from_disk
  
  from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast
  
  
  
  
  def 				a__	(	lowerCAmelCase ,  lowerCAmelCase=1_00 ,  lowerCAmelCase=" "						)  ->   Union[str, Any]:
        UpperCAmelCase__ :       Tuple						=						text.split(UpperCamelCase__						)
        return [character.join(text[i : i + n]						).strip() for i in range(0 ,  len(UpperCamelCase__						) ,  UpperCamelCase__						)]
  
  
  
  
  def 				a__	(	lowerCAmelCase						)  ->   Optional[int]:
        UpperCAmelCase__ :       Optional[int]						=						[], []
        for title, text in zip(documents["""title"""] ,  documents["""text"""]						):
              if text is not None:
                    for passage in split_text(UpperCamelCase__						):
                          titles.append(title if title is not None else """"""						)
                          texts.append(UpperCamelCase__						)
        return {"title": titles, "text": texts}
  
  
  
  
  def 				a__	(	lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase						)  ->   List[Any]:
        UpperCAmelCase__ :       str						=						load_dataset(
            """csv""" ,  data_files=[csv_path] ,  split="""train""" ,  delimiter="""\t""" ,  column_names=["""title""", """text"""]						)
        UpperCAmelCase__ :       Tuple						=						kb_dataset.map(
            UpperCamelCase__ ,  batched=UpperCamelCase__ ,  num_proc=1						)  # if you want you can load already splitted csv.
        UpperCAmelCase__ :       Dict						=						[kb_dataset.shard(UpperCamelCase__ ,  UpperCamelCase__ ,  contiguous=UpperCamelCase__						) for i in range(UpperCamelCase__						)]
        UpperCAmelCase__ :       Dict						=						kb_list[process_num]
  
        UpperCAmelCase__ :       str						=						'''data_''' + str(UpperCamelCase__						)
        UpperCAmelCase__ :       int						=						os.path.join(UpperCamelCase__ ,  UpperCamelCase__						)
  
        UpperCAmelCase__ :       Tuple						=						DPRContextEncoderTokenizerFast.from_pretrained("""facebook/dpr-ctx_encoder-multiset-base"""						)
        UpperCAmelCase__ :       List[Any]						=						ctx_encoder.to(device=UpperCamelCase__						)
  
        def embed(
            lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase						) -> dict:
              UpperCAmelCase__ :       Any						=						ctx_tokenizer(
                  documents["""title"""] ,  documents["""text"""] ,  truncation=UpperCamelCase__ ,  padding="""longest""" ,  return_tensors="""pt"""						)['''input_ids''']
              UpperCAmelCase__ :       Dict						=						ctx_encoder(input_ids.to(device=UpperCamelCase__						) ,  return_dict=UpperCamelCase__						).pooler_output
              return {"embeddings": embeddings.detach().cpu().numpy()}
  
        UpperCAmelCase__ :       List[Any]						=						Features(
            {"""text""": Value("""string"""						), """title""": Value("""string"""						), """embeddings""": Sequence(Value("""float32"""						)						)}						)  # optional, save as float32 instead of float64 to save space
  
        UpperCAmelCase__ :       Tuple						=						data_shrad.map(
            partial(UpperCamelCase__ ,  ctx_encoder=UpperCamelCase__ ,  ctx_tokenizer=UpperCamelCase__ ,  device=UpperCamelCase__						) ,  batched=UpperCamelCase__ ,  batch_size=16 ,  features=UpperCamelCase__ ,  )
        dataset.save_to_disk(UpperCamelCase__						)
  
  
  
  
  
  def 				a__	(	lowerCAmelCase ,  lowerCAmelCase						)  ->   Optional[int]:
        UpperCAmelCase__ :       Dict						=						[]
  
        for shard_address in glob(str(UpperCamelCase__						) + """/*/"""						):
              data_shard_list.append(load_from_disk(UpperCamelCase__						)						)
  
        UpperCAmelCase__ :       Dict						=						concatenate_datasets(UpperCamelCase__						)
        faiss.omp_set_num_threads(96						)
  
        UpperCAmelCase__ :       Optional[int]						=						faiss.IndexHNSWFlat(7_68 ,  1_28 ,  faiss.METRIC_INNER_PRODUCT						)
        concat.add_faiss_index("""embeddings""" ,  custom_index=UpperCamelCase__						)
        concat.get_index("""embeddings"""						).save(
            UpperCamelCase__						)  # since we load the index in to memory,we can directly update the index in the disk
  
  
  
  
  
  
style_context: 
  
  """simple docstring"""
  import argparse
  import json
  
  from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  
  
  
  
  def 				a__	(	lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase						)  ->   Dict:
        # Initialise PyTorch model
        UpperCAmelCase__ :       Union[str, Any]						=						json.load(open(lowerCAmelCase ,  """r"""						)						)
        UpperCAmelCase__ :       Any						=						GPTNeoConfig(
            hidden_size=config_json["""n_embd"""] ,  num_layers=config_json["""n_layer"""] ,  num_heads=config_json["""n_head"""] ,  attention_types=config_json["""attention_types"""] ,  max_position_embeddings=config_json["""n_positions"""] ,  resid_dropout=config_json["""res_dropout"""] ,  embed_dropout=config_json["""embed_dropout"""] ,  attention_dropout=config_json["""attn_dropout"""] ,  )
        print(F"""Building PyTorch model from configuration: {config}"""						)
        UpperCAmelCase__ :       Dict						=						GPTNeoForCausalLM(lowerCAmelCase						)
  
        # Load weights from tf checkpoint
        load_tf_weights_in_gpt_neo(lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase						)
  
        # Save pytorch-model
        print(F"""Save PyTorch model to {pytorch_dump_path}"""						)
        model.save_pretrained(lowerCAmelCase						)
  
  
  if __name__ == "__main__":
      _A						=     argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
      )
      parser.add_argument(
          """--config_file""",
          default=None,
          type=str,
          required=True,
          help=(
              """The config json file corresponding to the pre-trained mesh-tf model. \n"""
              """This specifies the model architecture."""
          ),
      )
      parser.add_argument(
          """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
      )
      _A						=     parser.parse_args()
      convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
  
  