



'''simple docstring'''



import os
from typing import List, Union

import tensorflow as tf
from tensorflow_text import BertTokenizer as BertTokenizerLayer
from tensorflow_text import FastBertTokenizer, ShrinkLongestTrimmer, case_fold_utfa, combine_segments, pad_model_inputs

from .tokenization_bert import BertTokenizer






class 			_SCREAMING_SNAKE_CASE				(tf.keras.layers.Layer      ):



			def __init__(     self		:						str    ,			__UpperCamelCase		:						Tuple    ,			__UpperCamelCase		:						Optional[Any]    ,			__UpperCamelCase		:						int = None    ,			__UpperCamelCase		:						List[str] = None    ,			__UpperCamelCase		:						List[Any] = None    ,			__UpperCamelCase		:						Any = "longest"    ,			__UpperCamelCase		:						Union[str, Any] = True    ,			__UpperCamelCase		:						List[str] = 512    ,			__UpperCamelCase		:						str = None    ,			__UpperCamelCase		:						List[Any] = True    ,			__UpperCamelCase		:						List[str] = True    ,			__UpperCamelCase		:						List[Any] = True    ,			**__UpperCamelCase		:						Tuple    ,			)      ->      Dict:
							"""simple docstring"""





							super().__init__()
							if use_fast_bert_tokenizer:
											snake_case__      :   int      =    FastBertTokenizer(
											    _UpperCamelCase    ,			token_out_type=tf.intaa    ,			lower_case_nfd_strip_accents=_UpperCamelCase    ,			**_UpperCamelCase			)
							else:
											snake_case__      :   int      =    tf.lookup.StaticVocabularyTable(
											    tf.lookup.KeyValueTensorInitializer(
											        keys=_UpperCamelCase    ,			key_dtype=tf.string    ,			values=tf.range(tf.size(_UpperCamelCase    ,			out_type=tf.intaa			)    ,			dtype=tf.intaa			)    ,			value_dtype=tf.intaa    ,			)    ,			num_oov_buckets=1    ,			)
											snake_case__      :   int      =    BertTokenizerLayer(
											    _UpperCamelCase    ,			token_out_type=tf.intaa    ,			lower_case=_UpperCamelCase    ,			**_UpperCamelCase			)

							snake_case__      :   Union[str, Any]      =    vocab_list
							snake_case__      :   Optional[int]      =    do_lower_case
							snake_case__      :   List[str]      =    cls_token_id or vocab_list.index('''[CLS]'''			)
							snake_case__      :   str      =    sep_token_id or vocab_list.index('''[SEP]'''			)
							snake_case__      :   Optional[Any]      =    pad_token_id or vocab_list.index('''[PAD]'''			)
							snake_case__      :   Union[str, Any]      =    ShrinkLongestTrimmer(max_length - 3    ,			axis=1			)  # Allow room for special tokens
							snake_case__      :   Tuple      =    max_length
							snake_case__      :   List[Any]      =    padding
							snake_case__      :   List[str]      =    truncation
							snake_case__      :   Optional[Any]      =    pad_to_multiple_of
							snake_case__      :   Union[str, Any]      =    return_token_type_ids
							snake_case__      :   Optional[int]      =    return_attention_mask



			@classmethod
			def 				lowerCAmelCase (     cls		:						Union[str, Any]    ,			__UpperCamelCase		:						int    ,			**__UpperCamelCase		:						int			)      ->      Any:  # noqa: F821
							"""simple docstring"""





							snake_case__      :   Any      =    kwargs.pop('''do_lower_case'''    ,			_UpperCamelCase			)
							snake_case__      :   List[str]      =    tokenizer.do_lower_case if do_lower_case is None else do_lower_case
							snake_case__      :   List[str]      =    kwargs.pop('''cls_token_id'''    ,			_UpperCamelCase			)
							snake_case__      :   Optional[int]      =    tokenizer.cls_token_id if cls_token_id is None else cls_token_id
							snake_case__      :   List[Any]      =    kwargs.pop('''sep_token_id'''    ,			_UpperCamelCase			)
							snake_case__      :   Tuple      =    tokenizer.sep_token_id if sep_token_id is None else sep_token_id
							snake_case__      :   Tuple      =    kwargs.pop('''pad_token_id'''    ,			_UpperCamelCase			)
							snake_case__      :   Any      =    tokenizer.pad_token_id if pad_token_id is None else pad_token_id

							snake_case__      :   str      =    tokenizer.get_vocab()
							snake_case__      :   List[str]      =    sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()]    ,			key=lambda __UpperCamelCase			: x[1]			)
							snake_case__      :   Optional[Any]      =    [entry[0] for entry in vocab]
							return cls(
							    vocab_list=_UpperCamelCase    ,			do_lower_case=_UpperCamelCase    ,			cls_token_id=_UpperCamelCase    ,			sep_token_id=_UpperCamelCase    ,			pad_token_id=_UpperCamelCase    ,			**_UpperCamelCase    ,			)



			@classmethod
			def 				lowerCAmelCase (     cls		:						Dict    ,			__UpperCamelCase		:						Optional[int]    ,			*__UpperCamelCase		:						Tuple    ,			**__UpperCamelCase		:						List[str]			)      ->      Tuple:
							"""simple docstring"""





							try:
											snake_case__      :   Any      =    BertTokenizer.from_pretrained(_UpperCamelCase    ,			*_UpperCamelCase    ,			**_UpperCamelCase			)
							except:  # noqa: E722
											from .tokenization_bert_fast import BertTokenizerFast

											snake_case__      :   List[str]      =    BertTokenizerFast.from_pretrained(_UpperCamelCase    ,			*_UpperCamelCase    ,			**_UpperCamelCase			)
							return cls.from_tokenizer(_UpperCamelCase    ,			**_UpperCamelCase			)



			def 				lowerCAmelCase (     self		:						List[Any]    ,			__UpperCamelCase		:						Dict			)      ->      Tuple:
							"""simple docstring"""





							if self.do_lower_case:
											snake_case__      :   Tuple      =    case_fold_utfa(_UpperCamelCase			)
							snake_case__      :   Union[str, Any]      =    self.tf_tokenizer.tokenize(_UpperCamelCase			)
							return tokens.merge_dims(1    ,			-1			)



			def 				lowerCAmelCase (     self		:						str    ,			__UpperCamelCase		:						Optional[int]    ,			__UpperCamelCase		:						Dict=None    ,			__UpperCamelCase		:						List[str]=None    ,			__UpperCamelCase		:						Dict=None    ,			__UpperCamelCase		:						Optional[int]=None    ,			__UpperCamelCase		:						Tuple=None    ,			__UpperCamelCase		:						Union[str, Any]=None    ,			__UpperCamelCase		:						Tuple=None    ,			)      ->      Optional[int]:
							"""simple docstring"""





							if padding is None:
											snake_case__      :   int      =    self.padding
							if padding not in ("longest", "max_length"):
											raise ValueError('''Padding must be either \'longest\' or \'max_length\'!'''			)
							if max_length is not None and text_pair is not None:
											# Because we have to instantiate a Trimmer to do it properly
											raise ValueError('''max_length cannot be overridden at call time when truncating paired texts!'''			)
							if max_length is None:
											snake_case__      :   List[str]      =    self.max_length
							if truncation is None:
											snake_case__      :   Any      =    self.truncation
							if pad_to_multiple_of is None:
											snake_case__      :   Optional[Any]      =    self.pad_to_multiple_of
							if return_token_type_ids is None:
											snake_case__      :   Dict      =    self.return_token_type_ids
							if return_attention_mask is None:
											snake_case__      :   Optional[int]      =    self.return_attention_mask
							if not isinstance(_UpperCamelCase    ,			tf.Tensor			):
											snake_case__      :   Dict      =    tf.convert_to_tensor(_UpperCamelCase			)
							if text_pair is not None and not isinstance(_UpperCamelCase    ,			tf.Tensor			):
											snake_case__      :   List[Any]      =    tf.convert_to_tensor(_UpperCamelCase			)
							if text_pair is not None:
											if text.shape.rank > 1:
															raise ValueError('''text argument should not be multidimensional when a text pair is supplied!'''			)
											if text_pair.shape.rank > 1:
															raise ValueError('''text_pair should not be multidimensional!'''			)
							if text.shape.rank == 2:
											snake_case__  ,      snake_case__      :   str      =    text[:, 0], text[:, 1]
							snake_case__      :   List[Any]      =    self.unpaired_tokenize(_UpperCamelCase			)
							if text_pair is None:  # Unpaired text
											if truncation:
															snake_case__      :   Dict      =    text[:, : max_length - 2]  # Allow room for special tokens
											snake_case__  ,      snake_case__      :   Optional[int]      =    combine_segments(
											    (text,)    ,			start_of_sequence_id=self.cls_token_id    ,			end_of_segment_id=self.sep_token_id			)
							else:  # Paired text
											snake_case__      :   Any      =    self.unpaired_tokenize(_UpperCamelCase			)
											if truncation:
															snake_case__  ,      snake_case__      :   Dict      =    self.paired_trimmer.trim([text, text_pair]			)
											snake_case__  ,      snake_case__      :   Optional[Any]      =    combine_segments(
											    (text, text_pair)    ,			start_of_sequence_id=self.cls_token_id    ,			end_of_segment_id=self.sep_token_id			)
							if padding == "longest":
											snake_case__      :   Any      =    input_ids.bounding_shape(axis=1			)
											if pad_to_multiple_of is not None:
															# No ceiling division in tensorflow, so we negate floordiv instead
															snake_case__      :   Any      =    pad_to_multiple_of * (-tf.math.floordiv(-pad_length    ,			_UpperCamelCase			))
							else:
											snake_case__      :   Optional[Any]      =    max_length

							snake_case__  ,      snake_case__      :   Tuple      =    pad_model_inputs(_UpperCamelCase    ,			max_seq_length=_UpperCamelCase    ,			pad_value=self.pad_token_id			)
							snake_case__      :   Optional[Any]      =    {'''input_ids''': input_ids}
							if return_attention_mask:
											snake_case__      :   Union[str, Any]      =    attention_mask
							if return_token_type_ids:
											snake_case__  ,      snake_case__      :   Optional[Any]      =    pad_model_inputs(
											    _UpperCamelCase    ,			max_seq_length=_UpperCamelCase    ,			pad_value=self.pad_token_id			)
											snake_case__      :   Tuple      =    token_type_ids
							return output







			def 				lowerCAmelCase (     self		:						List[Any]			)      ->      Union[str, Any]:
							"""simple docstring"""





							return {
							    "vocab_list": self.vocab_list,
							    "do_lower_case": self.do_lower_case,
							    "cls_token_id": self.cls_token_id,
							    "sep_token_id": self.sep_token_id,
							    "pad_token_id": self.pad_token_id,
							}












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_							:       Dict     ,							UpperCamelCase_							:       Union[str, Any]=100     ,							UpperCamelCase_							:       Optional[int]=" "		)	-> int:

						"""simple docstring"""



						lowerCAmelCase__         =    text.split(UpperCamelCase_		)
						return [character.join(text[i : i + n]		).strip() for i in range(0     ,							len(UpperCamelCase_		)     ,							UpperCamelCase_		)]







def 						_UpperCamelCase (     UpperCamelCase_							:       List[str]		)	-> Any:

						"""simple docstring"""



						lowerCAmelCase__    ,							lowerCAmelCase__         =    [], []
						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 						_UpperCamelCase (     UpperCamelCase_							:       Union[str, Any]     ,							UpperCamelCase_							:       int     ,							UpperCamelCase_							:       Optional[int]     ,							UpperCamelCase_							:       Tuple     ,							UpperCamelCase_							:       str     ,							UpperCamelCase_							:       Tuple		)	-> Optional[Any]:

						"""simple docstring"""



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

						lowerCAmelCase__         =    'data_' + str(UpperCamelCase_		)
						lowerCAmelCase__         =    os.path.join(UpperCamelCase_     ,							UpperCamelCase_		)

						lowerCAmelCase__         =    DPRContextEncoderTokenizerFast.from_pretrained('facebook/dpr-ctx_encoder-multiset-base'		)
						lowerCAmelCase__         =    ctx_encoder.to(device=UpperCamelCase_		)

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

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

						lowerCAmelCase__         =    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 						_UpperCamelCase (     UpperCamelCase_							:       List[Any]     ,							UpperCamelCase_							:       Dict		)	-> Optional[Any]:

						"""simple docstring"""



						lowerCAmelCase__         =    []

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

						lowerCAmelCase__         =    concatenate_datasets(UpperCamelCase_		)
						faiss.omp_set_num_threads(96		)

						lowerCAmelCase__         =    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





