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__=1_00			,					lowerCamelCase__=" "       )					->int:

  '''simple docstring'''




  a__  		=  text.split(_UpperCamelCase       )
  return [character.join(text[i : i + n]       ).strip() for i in range(0			,					len(_UpperCamelCase       )			,					_UpperCamelCase       )]
def 					__lowercase						(    lowerCamelCase__       )					->Optional[Any]:

  '''simple docstring'''




  a__		,a__  		=  [], []
  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__       )					->Optional[Any]:

  '''simple docstring'''




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

  a__  		=  "data_" + str(_UpperCamelCase       )
  a__  		=  os.path.join(_UpperCamelCase			,					_UpperCamelCase       )

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

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

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

  a__  		=  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__       )					->Optional[Any]:

  '''simple docstring'''




  a__  		=  []

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

  a__  		=  concatenate_datasets(_UpperCamelCase       )
  faiss.omp_set_num_threads(96       )

  a__  		=  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





"""simple docstring"""







import inspect
import json
import os
import tempfile
import unittest

from transformers.models.speech_to_text_a import SpeechaTextaTokenizer
from transformers.models.speech_to_text_a.tokenization_speech_to_text_a import VOCAB_FILES_NAMES

from ...test_tokenization_common import TokenizerTesterMixin
class 				lowerCamelCase							(      __SCREAMING_SNAKE_CASE					,       unittest.TestCase			):


							"""simple docstring"""
							SCREAMING_SNAKE_CASE              =   SpeechaTextaTokenizer
							SCREAMING_SNAKE_CASE              =   False






							def        lowerCAmelCase  (    self			:  Dict					):
													super().setUp()

													UpperCAmelCase__												=     '''<s> <pad> </s> <unk> here@@ a couple of@@ words for the he@@ re@@ vocab'''.split(''' '''					)
													UpperCAmelCase__												=     ['''he re</w> 123''', '''here a 1456''']
													UpperCAmelCase__												=     dict(zip(lowerCamelCase_		,			range(len(lowerCamelCase_					)					)					)					)

													UpperCAmelCase__												=     {'''pad_token''': '''<pad>''', '''unk_token''': '''<unk>''', '''bos_token''': '''<s>''', '''eos_token''': '''</s>'''}

													UpperCAmelCase__												=     tempfile.mkdtemp()
													UpperCAmelCase__												=     os.path.join(self.tmpdirname		,			VOCAB_FILES_NAMES['''vocab_file''']					)
													UpperCAmelCase__												=     os.path.join(self.tmpdirname		,			VOCAB_FILES_NAMES['''merges_file''']					)

													with open(self.vocab_file		,			'''w'''		,			encoding='''utf-8'''					) as fp:
																			fp.write(json.dumps(lowerCamelCase_					) + '''\n'''					)

													with open(self.merges_file		,			'''w'''					) as fp:
																			fp.write('''\n'''.join(lowerCamelCase_					)					)






							def        lowerCAmelCase  (    self			:  Union[str, Any]					):
													UpperCAmelCase__												=     list(self.get_tokenizer().get_vocab().keys()					)

													self.assertEqual(vocab_keys[0]		,			'''<s>'''					)
													self.assertEqual(vocab_keys[1]		,			'''<pad>'''					)
													self.assertEqual(vocab_keys[-1]		,			'''vocab'''					)
													self.assertEqual(len(lowerCamelCase_					)		,			14					)






							def        lowerCAmelCase  (    self			:  Optional[Any]					):
													self.assertEqual(self.get_tokenizer().vocab_size		,			14					)






							def        lowerCAmelCase  (    self			:  Any					):
													UpperCAmelCase__												=     SpeechaTextaTokenizer.from_pretrained(self.tmpdirname					)

													# make sure @@ is correctly concatenated
													UpperCAmelCase__												=     [4, 6, 8, 7, 10]  # ["here@@", "couple", "words", "of@@", "the"]
													UpperCAmelCase__												=     tokenizer.decode(lowerCamelCase_					)

													self.assertTrue(output_string == '''herecouple words ofthe'''					)






							def        lowerCAmelCase  (    self			:  int					):
													UpperCAmelCase__												=     SpeechaTextaTokenizer.from_pretrained(self.tmpdirname					)

													with tempfile.TemporaryDirectory() as tmp_dirname:
																			tokenizer.save_pretrained(lowerCamelCase_					)
																			os.remove(os.path.join(lowerCamelCase_		,			'''merges.txt'''					)					)

																			# load tokenizer without merges file should not throw an error
																			UpperCAmelCase__												=     SpeechaTextaTokenizer.from_pretrained(lowerCamelCase_					)

													with tempfile.TemporaryDirectory() as tmp_dirname:
																			# save tokenizer and load again
																			tokenizer.save_pretrained(lowerCamelCase_					)
																			UpperCAmelCase__												=     SpeechaTextaTokenizer.from_pretrained(lowerCamelCase_					)

													self.assertIsNotNone(lowerCamelCase_					)






							def        lowerCAmelCase  (    self			:  int					):
													if not self.test_slow_tokenizer:
																			return

													UpperCAmelCase__												=     inspect.signature(self.tokenizer_class.__init__					)
													UpperCAmelCase__												=     self.get_tokenizer()

													for parameter_name, parameter in signature.parameters.items():
																			if parameter.default != inspect.Parameter.empty and parameter_name != "merges_file":
																									self.assertIn(lowerCamelCase_		,			tokenizer.init_kwargs					)