

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					(			_lowerCAmelCase     :						str,  _lowerCAmelCase     :						Union[str, Any]=100,  _lowerCAmelCase     :						Union[str, Any]=" " )				->			int:
	_UpperCAmelCase    :							Optional[Any]									=					text.split(_lowerCAmelCase )
	return [character.join(text[i : i + n] ).strip() for i in range(0,  len(_lowerCAmelCase ),  _lowerCAmelCase )]
def       UpperCamelCase					(			_lowerCAmelCase     :						Optional[Any] )				->			str:
	_UpperCAmelCase						,   _UpperCAmelCase    :							Optional[int]									=					[], []
	for title, text in zip(documents["""title"""],  documents["""text"""] ):
		if text is not None:
			for passage in split_text(_lowerCAmelCase ):
				titles.append(title if title is not None else """""" )
				texts.append(_lowerCAmelCase )
	return {"title": titles, "text": texts}
def       UpperCamelCase					(			_lowerCAmelCase     :						Tuple,  _lowerCAmelCase     :						List[Any],  _lowerCAmelCase     :						List[str],  _lowerCAmelCase     :						Union[str, Any],  _lowerCAmelCase     :						Dict,  _lowerCAmelCase     :						Any )				->			Optional[int]:
	_UpperCAmelCase    :							int									=					load_dataset(
	    """csv""",  data_files=[csv_path],  split="""train""",  delimiter="""\t""",  column_names=["""title""", """text"""] )
	_UpperCAmelCase    :							int									=					kb_dataset.map(
	    _lowerCAmelCase,  batched=_lowerCAmelCase,  num_proc=1 )  # if you want you can load already splitted csv.
	_UpperCAmelCase    :							Any									=					[kb_dataset.shard(_lowerCAmelCase,  _lowerCAmelCase,  contiguous=_lowerCAmelCase ) for i in range(_lowerCAmelCase )]
	_UpperCAmelCase    :							Optional[int]									=					kb_list[process_num]

	_UpperCAmelCase    :							List[Any]									=					"""data_""" + str(_lowerCAmelCase )
	_UpperCAmelCase    :							Optional[Any]									=					os.path.join(_lowerCAmelCase,  _lowerCAmelCase )

	_UpperCAmelCase    :							Optional[int]									=					DPRContextEncoderTokenizerFast.from_pretrained("""facebook/dpr-ctx_encoder-multiset-base""" )
	_UpperCAmelCase    :							Any									=					ctx_encoder.to(device=_lowerCAmelCase )

	def embed(
	    _lowerCAmelCase     :						Dict,  _lowerCAmelCase     :						List[Any],  _lowerCAmelCase     :						int,  _lowerCAmelCase     :						List[str] ) -> dict:
		_UpperCAmelCase    :							str									=					ctx_tokenizer(
		    documents["""title"""],  documents["""text"""],  truncation=_lowerCAmelCase,  padding="""longest""",  return_tensors="""pt""" )["""input_ids"""]
		_UpperCAmelCase    :							Optional[Any]									=					ctx_encoder(input_ids.to(device=_lowerCAmelCase ),  return_dict=_lowerCAmelCase ).pooler_output
		return {"embeddings": embeddings.detach().cpu().numpy()}

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

	_UpperCAmelCase    :							Dict									=					data_shrad.map(
	    partial(_lowerCAmelCase,  ctx_encoder=_lowerCAmelCase,  ctx_tokenizer=_lowerCAmelCase,  device=_lowerCAmelCase ),  batched=_lowerCAmelCase,  batch_size=16,  features=_lowerCAmelCase,  )
	dataset.save_to_disk(_lowerCAmelCase )





def       UpperCamelCase					(			_lowerCAmelCase     :						Optional[Any],  _lowerCAmelCase     :						List[Any] )				->			Optional[int]:
	_UpperCAmelCase    :							Optional[Any]									=					[]

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

	_UpperCAmelCase    :							List[Any]									=					concatenate_datasets(_lowerCAmelCase )
	faiss.omp_set_num_threads(96 )

	_UpperCAmelCase    :							int									=					faiss.IndexHNSWFlat(768,  128,  faiss.METRIC_INNER_PRODUCT )
	concat.add_faiss_index("""embeddings""",  custom_index=_lowerCAmelCase )
	concat.get_index("""embeddings""" ).save(
	    _lowerCAmelCase )  # since we load the index in to memory,we can directly update the index in the disk








"""simple docstring"""






def       UpperCamelCase					(			_lowerCAmelCase     :						float,  _lowerCAmelCase     :						float )				->			float:
	_UpperCAmelCase    :							List[Any]									=					float()
	try:
		_UpperCAmelCase    :							Optional[Any]									=					mass * acceleration
	except Exception:
		return -0.0
	return force


if __name__ == "__main__":
						import doctest

						# run doctest
						doctest.testmod()

						# demo
						lowerCamelCase__				:  Any													=  12.5
						lowerCamelCase__				:  Union[str, Any]													=  10
						lowerCamelCase__				:  Optional[Any]													=  newtons_second_law_of_motion(mass, acceleration)
						print('''The force is ''', force, '''N''')





