
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 						A_			(				snake_case				: Tuple			,   snake_case				: str=100			,   snake_case				: Optional[int]=" "			)   ->     int:



						'''simple docstring'''

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




def 						A_			(				snake_case				: str			)   ->     Optional[int]:



						'''simple docstring'''

						__UpperCamelCase  	=      [], []
						for title, text in zip(documents['''title''']			,   documents['''text''']			):
												if text is not None:
																		for passage in split_text(__SCREAMING_SNAKE_CASE			):
																								titles.append(title if title is not None else ''''''			)
																								texts.append(__SCREAMING_SNAKE_CASE			)
						return {"title": titles, "text": texts}




def 						A_			(				snake_case				: Optional[int]			,   snake_case				: Tuple			,   snake_case				: List[Any]			,   snake_case				: str			,   snake_case				: Optional[Any]			,   snake_case				: List[Any]			)   ->     Tuple:



						'''simple docstring'''

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

						__UpperCamelCase  	=      '''data_''' + str(__SCREAMING_SNAKE_CASE			)
						__UpperCamelCase  	=      os.path.join(__SCREAMING_SNAKE_CASE			,   __SCREAMING_SNAKE_CASE			)

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

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

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

						__UpperCamelCase  	=      data_shrad.map(
						    partial(__SCREAMING_SNAKE_CASE			,   ctx_encoder=__SCREAMING_SNAKE_CASE			,   ctx_tokenizer=__SCREAMING_SNAKE_CASE			,   device=__SCREAMING_SNAKE_CASE			)			,   batched=__SCREAMING_SNAKE_CASE			,   batch_size=16			,   features=__SCREAMING_SNAKE_CASE			,   )
						dataset.save_to_disk(__SCREAMING_SNAKE_CASE			)




def 						A_			(				snake_case				: str			,   snake_case				: Optional[Any]			)   ->     Any:



						'''simple docstring'''

						__UpperCamelCase  	=      []

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

						__UpperCamelCase  	=      concatenate_datasets(__SCREAMING_SNAKE_CASE			)
						faiss.omp_set_num_threads(96			)

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






'''simple docstring'''







from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class 					lowerCAmelCase__  (    lowerCamelCase_	):
					lowerCAmelCase_	  =	['''image_processor''', '''tokenizer''']
					lowerCAmelCase_	  =	'''EfficientNetImageProcessor'''
					lowerCAmelCase_	  =	('''BertTokenizer''', '''BertTokenizerFast''')






					def __init__(  self							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						):


											"""simple docstring"""





											super().__init__(__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						)






					def __call__(  self							,				__SCREAMING_SNAKE_CASE=None							,				__SCREAMING_SNAKE_CASE=None							,				__SCREAMING_SNAKE_CASE="max_length"							,				__SCREAMING_SNAKE_CASE=64							,				__SCREAMING_SNAKE_CASE=None							,				**__SCREAMING_SNAKE_CASE						):


											"""simple docstring"""





											if text is None and images is None:
																	raise ValueError('''You have to specify either text or images. Both cannot be none.'''						)

											if text is not None:
																	lowercase_      :	str						=			self.tokenizer(
																	    __SCREAMING_SNAKE_CASE							,				padding=__SCREAMING_SNAKE_CASE							,				max_length=__SCREAMING_SNAKE_CASE							,				return_tensors=__SCREAMING_SNAKE_CASE							,				**__SCREAMING_SNAKE_CASE						)

											if images is not None:
																	lowercase_      :	int						=			self.image_processor(__SCREAMING_SNAKE_CASE							,				return_tensors=__SCREAMING_SNAKE_CASE							,				**__SCREAMING_SNAKE_CASE						)

											if text is not None and images is not None:
																	lowercase_      :	Any						=			image_features.pixel_values
																	return encoding
											elif text is not None:
																	return encoding
											else:
																	return BatchEncoding(data=dict(**__SCREAMING_SNAKE_CASE						)							,				tensor_type=__SCREAMING_SNAKE_CASE						)






					def       _snake_case       (  self							,				*__SCREAMING_SNAKE_CASE							,				**__SCREAMING_SNAKE_CASE						):


											"""simple docstring"""





											return self.tokenizer.batch_decode(*__SCREAMING_SNAKE_CASE							,				**__SCREAMING_SNAKE_CASE						)






					def       _snake_case       (  self							,				*__SCREAMING_SNAKE_CASE							,				**__SCREAMING_SNAKE_CASE						):


											"""simple docstring"""





											return self.tokenizer.decode(*__SCREAMING_SNAKE_CASE							,				**__SCREAMING_SNAKE_CASE						)






					@property
					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	Dict						=			self.tokenizer.model_input_names
											lowercase_      :	Optional[int]						=			self.image_processor.model_input_names
											return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names						)						)



