





"""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 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase=100			, _UpperCAmelCase=" " ):






  """simple docstring"""





  A_  :	Union[str, Any]    						=  text.split(A__ )
  return [character.join(text[i : i + n] ).strip() for i in range(0			, len(A__ )			, A__ )]





def 						UpperCAmelCase__		(					_UpperCAmelCase ):






  """simple docstring"""





  A_  :	List[str]    						=  [], []
  for title, text in zip(documents['title']			, documents['text'] ):
    if text is not None:
      for passage in split_text(A__ ):
        titles.append(title if title is not None else '' )
        texts.append(A__ )
  return {"title": titles, "text": texts}





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  A_  :	Optional[Any]    						=  load_dataset(
      'csv'			, data_files=[csv_path]			, split='train'			, delimiter='\t'			, column_names=['title', 'text'] )
  A_  :	str    						=  kb_dataset.map(
      A__			, batched=A__			, num_proc=1 )  # if you want you can load already splitted csv.
  A_  :	Dict    						=  [kb_dataset.shard(A__			, A__			, contiguous=A__ ) for i in range(A__ )]
  A_  :	List[str]    						=  kb_list[process_num]

  A_  :	List[str]    						=  'data_' + str(A__ )
  A_  :	Optional[Any]    						=  os.path.join(A__			, A__ )

  A_  :	int    						=  DPRContextEncoderTokenizerFast.from_pretrained('facebook/dpr-ctx_encoder-multiset-base' )
  A_  :	Any    						=  ctx_encoder.to(device=A__ )

  def embed(
      _UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase ) -> dict:
    A_  :	Optional[int]    						=  ctx_tokenizer(
        documents['title']			, documents['text']			, truncation=A__			, padding='longest'			, return_tensors='pt' )['input_ids']
    A_  :	str    						=  ctx_encoder(input_ids.to(device=A__ )			, return_dict=A__ ).pooler_output
    return {"embeddings": embeddings.detach().cpu().numpy()}

  A_  :	Union[str, Any]    						=  Features(
      {'text': Value('string' ), 'title': Value('string' ), 'embeddings': Sequence(Value('float32' ) )} )  # optional, save as float32 instead of float64 to save space

  A_  :	Optional[Any]    						=  data_shrad.map(
      partial(A__			, ctx_encoder=A__			, ctx_tokenizer=A__			, device=A__ )			, batched=A__			, batch_size=16			, features=A__			, )
  dataset.save_to_disk(A__ )





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  A_  :	int    						=  []

  for shard_address in glob(str(A__ ) + '/*/' ):
    data_shard_list.append(load_from_disk(A__ ) )

  A_  :	Union[str, Any]    						=  concatenate_datasets(A__ )
  faiss.omp_set_num_threads(96 )

  A_  :	str    						=  faiss.IndexHNSWFlat(768			, 128			, faiss.METRIC_INNER_PRODUCT )
  concat.add_faiss_index('embeddings'			, custom_index=A__ )
  concat.get_index('embeddings' ).save(
      A__ )  # since we load the index in to memory,we can directly update the index in the disk











"""simple docstring"""




from functools import partial
from typing import Any, Callable, Dict, List, Type, TypeVar, Union, overload

import torch
import torch.nn as nn
import torch.types





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  if not inplace:
    A_  :	Optional[int]    						=  ma + ma
  else:
    ma += ma

  return ma





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  A_  :	Tuple    						=  -1 * len(_UpperCAmelCase )
  A_  :	List[str]    						=  list(range(len(tensor.shape[:zero_index] ) ) )
  return tensor.permute(first_inds + [zero_index + i for i in inds] )





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  return t.reshape(t.shape[:-no_dims] + (-1,) )





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase = 1E-4 ):






  """simple docstring"""





  A_  :	Optional[Any]    						=  mask.expand(*value.shape )
  return torch.sum(mask * value			, dim=_UpperCAmelCase ) / (eps + torch.sum(_UpperCAmelCase			, dim=_UpperCAmelCase ))





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase = 2.3_125			, _UpperCAmelCase = 21.6_875			, _UpperCAmelCase = 64 ):






  """simple docstring"""





  A_  :	List[str]    						=  torch.linspace(_UpperCAmelCase			, _UpperCAmelCase			, no_bins - 1			, device=pts.device )
  A_  :	Optional[Any]    						=  torch.sqrt(torch.sum((pts.unsqueeze(-2 ) - pts.unsqueeze(-3 )) ** 2			, dim=-1 ) )
  return torch.bucketize(_UpperCAmelCase			, _UpperCAmelCase )





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  A_  :	Any    						=  dicts[0]
  A_  :	int    						=  {}
  for k, v in first.items():
    A_  :	Dict    						=  [d[k] for d in dicts]
    if isinstance(_UpperCAmelCase			, _UpperCAmelCase ):
      A_  :	List[Any]    						=  dict_multimap(_UpperCAmelCase			, _UpperCAmelCase )
    else:
      A_  :	Union[str, Any]    						=  fn(_UpperCAmelCase )

  return new_dict





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  A_  :	Tuple    						=  v_bins.view(((1,) * len(x.shape )) + (len(_UpperCAmelCase ),) )
  A_  :	Any    						=  x[..., None] - reshaped_bins
  A_  :	List[str]    						=  torch.argmin(torch.abs(_UpperCAmelCase )			, dim=-1 )
  return nn.functional.one_hot(_UpperCAmelCase			, num_classes=len(_UpperCAmelCase ) ).float()





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase = 0			, _UpperCAmelCase = 0 ):






  """simple docstring"""





  A_  :	List[Union[slice, torch.Tensor]]    						=  []
  for i, s in enumerate(data.shape[:no_batch_dims] ):
    A_  :	List[Any]    						=  torch.arange(_UpperCAmelCase )
    A_  :	Dict    						=  r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape ) - i - 1))) )
    ranges.append(_UpperCAmelCase )

  A_  :	List[Union[slice, torch.Tensor]]    						=  [slice(_UpperCAmelCase ) for _ in range(len(data.shape ) - no_batch_dims )]
  A_  :	Optional[Any]    						=  inds
  ranges.extend(_UpperCAmelCase )
  # Matt note: Editing this to get around the behaviour of using a list as an array index changing
  # in recent Numpy versions
  return data[tuple(_UpperCAmelCase )]


lowerCamelCase_					:						Union[str, Any]      = TypeVar('T')





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  A_  :	Dict[Any, Union[dict, list, tuple, Any]]    						=  {}
  for k, v in dic.items():
    if isinstance(_UpperCAmelCase			, _UpperCAmelCase ):
      A_  :	int    						=  dict_map(_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase )
    else:
      A_  :	Any    						=  tree_map(_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase )

  return new_dict





@overload
def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  ...





@overload
def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  ...





@overload
def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  ...





@overload
def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  ...





def 						UpperCAmelCase__		(					_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase ):






  """simple docstring"""





  if isinstance(_UpperCAmelCase			, _UpperCAmelCase ):
    return dict_map(_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase )
  elif isinstance(_UpperCAmelCase			, _UpperCAmelCase ):
    return [tree_map(_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase ) for x in tree]
  elif isinstance(_UpperCAmelCase			, _UpperCAmelCase ):
    return tuple(tree_map(_UpperCAmelCase			, _UpperCAmelCase			, _UpperCAmelCase ) for x in tree )
  elif isinstance(_UpperCAmelCase			, _UpperCAmelCase ):
    return fn(_UpperCAmelCase )
  else:
    print(type(_UpperCAmelCase ) )
    raise ValueError('Not supported' )





lowerCamelCase_					:						Optional[int]      = partial(tree_map, leaf_type=torch.Tensor)