
"""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     _lowerCamelCase(__UpperCamelCase    , __UpperCamelCase=100    , __UpperCamelCase=" "       )				-> Optional[int]:
	_lowerCAmelCase								=text.split(__UpperCamelCase       )
	return [character.join(text[i : i + n]       ).strip() for i in range(0    , len(__UpperCamelCase       )    , __UpperCamelCase       )]



def     _lowerCamelCase(__UpperCamelCase       )				-> Optional[Any]:
	_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     _lowerCamelCase(__UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase       )				-> int:
	_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    , __UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase       ) -> 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     _lowerCamelCase(__UpperCamelCase    , __UpperCamelCase       )				-> Tuple:
	_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





"""simple docstring"""


import enum
import inspect
import warnings
from functools import wraps
from typing import Callable, Optional

from .logging import get_logger


__A       						=       set()
__A       						=       get_logger(__name__)



def     _lowerCamelCase(__UpperCamelCase = None       )				-> Optional[Any]:

	def decorator(__UpperCamelCase       ):
		global _emitted_deprecation_warnings

		if inspect.isclass(__UpperCamelCase       ):
			_lowerCAmelCase								=deprecated_class_or_function.__init__
			_lowerCAmelCase								=deprecated_class_or_function.__name__
		else:
			_lowerCAmelCase								=deprecated_class_or_function
			_lowerCAmelCase								=deprecated_function.__name__
			# Support deprecating __init__ class method: class name instead
			_lowerCAmelCase								=name if name != """__init__""" else deprecated_function.__qualname__.split("""."""       )[-2]

		_lowerCAmelCase								=(
		    F'''{name} is deprecated and will be removed in the next major version of datasets.''' + F''' {help_message}'''
		    if help_message
		    else """"""
		)

		@wraps(__UpperCamelCase       )
		def wrapper(*__UpperCamelCase    , **__UpperCamelCase       ):
			_lowerCAmelCase								=hash(__UpperCamelCase       )
			if func_hash not in _emitted_deprecation_warnings:
				warnings.warn(__UpperCamelCase    , category=__UpperCamelCase    , stacklevel=2       )
				_emitted_deprecation_warnings.add(__UpperCamelCase       )
			return deprecated_function(*__UpperCamelCase    , **__UpperCamelCase       )

		_lowerCAmelCase								="""deprecated"""

		if inspect.isclass(__UpperCamelCase       ):
			_lowerCAmelCase								=wrapper
			return deprecated_class_or_function
		else:
			return wrapper

	return decorator







class 		lowerCamelCase__					(					enum.EnumMeta				):





			'''simple docstring'''



			def __getattribute__(							cls     ,     __UpperCAmelCase  ) ->     Optional[int]:
				_lowerCAmelCase								=super().__getattribute__(__UpperCAmelCase  )
				if isinstance(__UpperCAmelCase     ,     enum.Enum  ) and obj._on_access:
					obj._on_access()
				return obj



			def __getitem__(							cls     ,     __UpperCAmelCase  ) ->     List[str]:
				_lowerCAmelCase								=super().__getitem__(__UpperCAmelCase  )
				if member._on_access:
					member._on_access()
				return member



			def __call__(							cls     ,     __UpperCAmelCase     ,     __UpperCAmelCase=None     ,     *, __UpperCAmelCase=None     ,     __UpperCAmelCase=None     ,     __UpperCAmelCase=None     ,     __UpperCAmelCase=1  ) ->     Optional[Any]:
				_lowerCAmelCase								=super().__call__(__UpperCAmelCase     ,     __UpperCAmelCase     ,     module=__UpperCAmelCase     ,     qualname=__UpperCAmelCase     ,     type=__UpperCAmelCase     ,     start=__UpperCAmelCase  )
				if isinstance(__UpperCAmelCase     ,     enum.Enum  ) and obj._on_access:
					obj._on_access()
				return obj







class 		lowerCamelCase__					(					enum.Enum ,	metaclass=__magic_name__				):





			'''simple docstring'''



			def __new__(							cls     ,     __UpperCAmelCase  ) ->     Optional[int]:
				_lowerCAmelCase								=object.__new__(cls  )
				_lowerCAmelCase								=value
				_lowerCAmelCase								=member.deprecate
				return member



			@property
			def 					_lowerCAmelCase		(							self  ) ->     List[Any]:
				return ""



			def 					_lowerCAmelCase		(							self  ) ->     Tuple:
				_lowerCAmelCase								=f''' {self.help_message}''' if self.help_message else """"""
				warnings.warn(
				    f'''\'{self.__objclass__.__name__}\' is deprecated and will be removed in the next major version of datasets.'''
				    + help_message     ,     __UpperCAmelCase     ,     stacklevel=3     ,     )



