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

				snake_case__ :     List[str] 							=				'data_' + str(A__   )
				snake_case__ :     Optional[Any] 							=				os.path.join(A__      ,			A__   )

				snake_case__ :     int 							=				DPRContextEncoderTokenizerFast.from_pretrained('facebook/dpr-ctx_encoder-multiset-base'   )
				snake_case__ :     Any 							=				ctx_encoder.to(device=A__   )

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

				snake_case__ :     Union[str, Any] 							=				Features(
				    {'text': Value('string'   ), 'title': Value('string'   ), 'embeddings': Sequence(Value('float32'   )   )}   )  # optional, save as float32 instead of float64 to save space

				snake_case__ :     Optional[Any] 							=				data_shrad.map(
				    partial(A__      ,			ctx_encoder=A__      ,			ctx_tokenizer=A__      ,			device=A__   )      ,			batched=A__      ,			batch_size=16      ,			features=A__      ,			)
				dataset.save_to_disk(A__   )

def 			UpperCamelCase__						(    A__      ,			A__   )       -> Optional[int]:
				snake_case__ :     int 							=				[]

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

				snake_case__ :     Union[str, Any] 							=				concatenate_datasets(A__   )
				faiss.omp_set_num_threads(96   )

				snake_case__ :     str 							=				faiss.IndexHNSWFlat(768      ,			128      ,			faiss.METRIC_INNER_PRODUCT   )
				concat.add_faiss_index('embeddings'      ,			custom_index=A__   )
				concat.get_index('embeddings'   ).save(
				    A__   )  # since we load the index in to memory,we can directly update the index in the disk



def 			UpperCamelCase__						(    A__   )       -> int:
				if number != int(A__   ):
								raise ValueError('factorial() only accepts integral values'   )
				if number < 0:
								raise ValueError('factorial() not defined for negative values'   )
				snake_case__ :     Optional[Any] 							=				1
				for i in range(1      ,			number + 1   ):
								value *= i
				return value
def 			UpperCamelCase__						(    A__   )       -> int:
				if not isinstance(A__      ,			A__   ):
								raise ValueError('factorial() only accepts integral values'   )
				if n < 0:
								raise ValueError('factorial() not defined for negative values'   )
				return 1 if n == 0 or n == 1 else n * factorial(n - 1   )


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				lowerCAmelCase__					:       Any		=       int(input('''Enter a positive integer: ''').strip() or 0)
				print(F'''factorial{n} is {factorial(n)}''')


