




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




	"""simple docstring"""






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

def 		lowercase_   (		_UpperCAmelCase						):




	"""simple docstring"""






	A_ ,			A_	:					List[str]						 =		[], []
	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_   (		_UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase						):




	"""simple docstring"""






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

	A_	:					Tuple						 =		'''data_''' + str(_UpperCAmelCase						)
	A_	:					str						 =		os.path.join(_UpperCAmelCase	,   _UpperCAmelCase						)

	A_	:					List[str]						 =		DPRContextEncoderTokenizerFast.from_pretrained('''facebook/dpr-ctx_encoder-multiset-base'''						)
	A_	:					str						 =		ctx_encoder.to(device=_UpperCAmelCase						)

	def embed(
	    _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase						) -> dict:
		A_	:					List[Any]						 =		ctx_tokenizer(
		    documents['''title''']	,   documents['''text''']	,   truncation=_UpperCAmelCase	,   padding='''longest'''	,   return_tensors='''pt'''						)['''input_ids''']
		A_	:					str						 =		ctx_encoder(input_ids.to(device=_UpperCAmelCase						)	,   return_dict=_UpperCAmelCase						).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_	:					int						 =		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_   (		_UpperCAmelCase	,   _UpperCAmelCase						):




	"""simple docstring"""






	A_	:					Optional[int]						 =		[]

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

	A_	:					List[str]						 =		concatenate_datasets(_UpperCAmelCase						)
	faiss.omp_set_num_threads(96						)

	A_	:					int						 =		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











"""simple docstring"""

import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_xglm import XGLMTokenizer
else:
		_lowerCamelCase	:      Union[str, Any]   =							None


_lowerCamelCase	:      Optional[Any]   =							logging.get_logger(__name__)

_lowerCamelCase	:      Tuple   =							{'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

_lowerCamelCase	:      Union[str, Any]   =							{
    'vocab_file': {
        'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model',
    },
    'tokenizer_file': {
        'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json',
    },
}

_lowerCamelCase	:      Dict   =							{
    'facebook/xglm-564M': 2048,
}

class 							lowercase   (     __UpperCAmelCase):
				__lowerCAmelCase     :     Optional[int]								= VOCAB_FILES_NAMES
				__lowerCAmelCase     :     Tuple								= PRETRAINED_VOCAB_FILES_MAP
				__lowerCAmelCase     :     List[Any]								= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__lowerCAmelCase     :     Union[str, Any]								= ["""input_ids""", """attention_mask"""]
				__lowerCAmelCase     :     List[str]								= XGLMTokenizer







				def __init__(				self :       str   ,  _lowerCamelCase :       int=None   ,  _lowerCamelCase :       Any=None   ,  _lowerCamelCase :       List[str]="<s>"   ,  _lowerCamelCase :       int="</s>"   ,  _lowerCamelCase :       Any="</s>"   ,  _lowerCamelCase :       Optional[int]="<s>"   ,  _lowerCamelCase :       List[Any]="<unk>"   ,  _lowerCamelCase :       Optional[int]="<pad>"   ,  **_lowerCamelCase :       List[Any]   ,  ):



					"""simple docstring"""

					A_	:					Tuple						 =		7
					A_	:					Dict						 =		[F"""<madeupword{i}>""" for i in range(self.num_madeup_words							)]

					A_	:					str						 =		kwargs.get('''additional_special_tokens'''   ,  []							)
					kwargs["additional_special_tokens"] += [
					    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
					]

					super().__init__(
					    _lowerCamelCase   ,  tokenizer_file=_lowerCamelCase   ,  bos_token=_lowerCamelCase   ,  eos_token=_lowerCamelCase   ,  sep_token=_lowerCamelCase   ,  cls_token=_lowerCamelCase   ,  unk_token=_lowerCamelCase   ,  pad_token=_lowerCamelCase   ,  **_lowerCamelCase   ,  )

					A_	:					Tuple						 =		vocab_file
					A_	:					str						 =		False if not self.vocab_file else True







				def        a_	(				self :       List[Any]   ,  _lowerCamelCase :       List[int]   ,  _lowerCamelCase :       Optional[List[int]] = None							):



					"""simple docstring"""

					if token_ids_a is None:
						return [self.sep_token_id] + token_ids_a
					A_	:					List[Any]						 =		[self.sep_token_id]
					return sep + token_ids_a + sep + sep + token_ids_a







				def        a_	(				self :       List[str]   ,  _lowerCamelCase :       List[int]   ,  _lowerCamelCase :       Optional[List[int]] = None							):



					"""simple docstring"""

					A_	:					Dict						 =		[self.sep_token_id]

					if token_ids_a is None:
						return len(sep + token_ids_a							) * [0]
					return len(sep + token_ids_a + sep + sep + token_ids_a							) * [0]







				def        a_	(				self :       List[str]   ,  _lowerCamelCase :       str   ,  _lowerCamelCase :       Optional[str] = None							):



					"""simple docstring"""

					if not self.can_save_slow_tokenizer:
						raise ValueError(
						    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
						    '''tokenizer.'''							)

					if not os.path.isdir(_lowerCamelCase							):
						logger.error(F"""Vocabulary path ({save_directory}) should be a directory."""							)
						return
					A_	:					Tuple						 =		os.path.join(
					    _lowerCamelCase   ,  (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']							)

					if os.path.abspath(self.vocab_file							) != os.path.abspath(_lowerCamelCase							):
						copyfile(self.vocab_file   ,  _lowerCamelCase							)

					return (out_vocab_file,)





