




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 						lowercase		(     _lowerCAmelCase   ,     _lowerCAmelCase=100   ,     _lowerCAmelCase=" "      ):
  UpperCAmelCase__    						=   text.split(UpperCamelCase__      )
  return [character.join(text[i : i + n]      ).strip() for i in range(0   ,     len(UpperCamelCase__      )   ,     UpperCamelCase__      )]


def 						lowercase		(     _lowerCAmelCase      ):
  UpperCAmelCase__    						=   [], []
  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 						lowercase		(     _lowerCAmelCase   ,     _lowerCAmelCase   ,     _lowerCAmelCase   ,     _lowerCAmelCase   ,     _lowerCAmelCase   ,     _lowerCAmelCase      ):
  UpperCAmelCase__    						=   load_dataset(
      """csv"""   ,     data_files=[csv_path]   ,     split="""train"""   ,     delimiter="""\t"""   ,     column_names=["""title""", """text"""]      )
  UpperCAmelCase__    						=   kb_dataset.map(
      UpperCamelCase__   ,     batched=UpperCamelCase__   ,     num_proc=1      )  # if you want you can load already splitted csv.
  UpperCAmelCase__    						=   [kb_dataset.shard(UpperCamelCase__   ,     UpperCamelCase__   ,     contiguous=UpperCamelCase__      ) for i in range(UpperCamelCase__      )]
  UpperCAmelCase__    						=   kb_list[process_num]

  UpperCAmelCase__    						=   '''data_''' + str(UpperCamelCase__      )
  UpperCAmelCase__    						=   os.path.join(UpperCamelCase__   ,     UpperCamelCase__      )

  UpperCAmelCase__    						=   DPRContextEncoderTokenizerFast.from_pretrained("""facebook/dpr-ctx_encoder-multiset-base"""      )
  UpperCAmelCase__    						=   ctx_encoder.to(device=UpperCamelCase__      )

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

  UpperCAmelCase__    						=   Features(
      {"""text""": Value("""string"""      ), """title""": Value("""string"""      ), """embeddings""": Sequence(Value("""float32"""      )      )}      )  # optional, save as float32 instead of float64 to save space

  UpperCAmelCase__    						=   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 						lowercase		(     _lowerCAmelCase   ,     _lowerCAmelCase      ):
  UpperCAmelCase__    						=   []

  for shard_address in glob(str(UpperCamelCase__      ) + """/*/"""      ):
    data_shard_list.append(load_from_disk(UpperCamelCase__      )      )

  UpperCAmelCase__    						=   concatenate_datasets(UpperCamelCase__      )
  faiss.omp_set_num_threads(96      )

  UpperCAmelCase__    						=   faiss.IndexHNSWFlat(768   ,     128   ,     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










import unittest

import numpy as np

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
   import tensorflow as tf

   from transformers.activations_tf import get_tf_activation





@require_tf
class     snake_case  (	unittest.TestCase							):


   '''simple docstring'''







   def 	UpperCAmelCase       (					self     :	Tuple			)					->Union[str, Any]:
     '''simple docstring'''







     UpperCAmelCase__    						=   tf.constant([-100, -1.0, -0.1, 0, 0.1, 1.0, 100.0]			)
     UpperCAmelCase__    						=   get_tf_activation("""gelu"""			)
     UpperCAmelCase__    						=   get_tf_activation("""gelu_10"""			)

     UpperCAmelCase__    						=   gelu(lowerCamelCase_			)
     UpperCAmelCase__    						=   geluaa(lowerCamelCase_			)

     UpperCAmelCase__    						=   tf.where(y_gelu_aa < 10.0						,    1.0						,    0.0			)

     self.assertEqual(tf.math.reduce_max(lowerCamelCase_			).numpy().item()						,    10.0			)
     self.assertTrue(np.allclose(y_gelu * clipped_mask						,    y_gelu_aa * clipped_mask			)			)







   def 	UpperCAmelCase       (					self     :	List[Any]			)					->Any:
     '''simple docstring'''







     get_tf_activation("""gelu"""			)
     get_tf_activation("""gelu_10"""			)
     get_tf_activation("""gelu_fast"""			)
     get_tf_activation("""gelu_new"""			)
     get_tf_activation("""glu"""			)
     get_tf_activation("""mish"""			)
     get_tf_activation("""quick_gelu"""			)
     get_tf_activation("""relu"""			)
     get_tf_activation("""sigmoid"""			)
     get_tf_activation("""silu"""			)
     get_tf_activation("""swish"""			)
     get_tf_activation("""tanh"""			)
     with self.assertRaises(lowerCamelCase_			):
       get_tf_activation("""bogus"""			)
     with self.assertRaises(lowerCamelCase_			):
       get_tf_activation(lowerCamelCase_			)




