'''simple docstring'''





from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase       :     int			=    logging.get_logger(__name__)

UpperCamelCase       :     Tuple			=    {
    """uw-madison/nystromformer-512""": """https://huggingface.co/uw-madison/nystromformer-512/resolve/main/config.json""",
    # See all Nystromformer models at https://huggingface.co/models?filter=nystromformer
}



class   UpperCamelCase					(       a_       ):







    """simple docstring"""





    A     :    Optional[Any]				=			"nystromformer"





    def __init__(					self     :						List[str]							,							UpperCAmelCase_     :						Optional[int]=3_0_0_0_0							,							UpperCAmelCase_     :						Optional[Any]=7_6_8							,							UpperCAmelCase_     :						Optional[int]=1_2							,							UpperCAmelCase_     :						Union[str, Any]=1_2							,							UpperCAmelCase_     :						List[Any]=3_0_7_2							,							UpperCAmelCase_     :						Optional[Any]="gelu_new"							,							UpperCAmelCase_     :						Optional[Any]=0.1							,							UpperCAmelCase_     :						Tuple=0.1							,							UpperCAmelCase_     :						Union[str, Any]=5_1_0							,							UpperCAmelCase_     :						Dict=2							,							UpperCAmelCase_     :						Dict=6_4							,							UpperCAmelCase_     :						Optional[int]=6_4							,							UpperCAmelCase_     :						Union[str, Any]=6_5							,							UpperCAmelCase_     :						Tuple=False							,							UpperCAmelCase_     :						Any=0.02							,							UpperCAmelCase_     :						Optional[Any]=1e-5							,							UpperCAmelCase_     :						str=1							,							UpperCAmelCase_     :						str=0							,							UpperCAmelCase_     :						str=2							,							**UpperCAmelCase_     :						Union[str, Any]							,							):


         """simple docstring"""







         a					:							List[Any]							= vocab_size
         a					:							List[Any]							= max_position_embeddings
         a					:							List[str]							= hidden_size
         a					:							List[str]							= num_hidden_layers
         a					:							List[Any]							= num_attention_heads
         a					:							Optional[int]							= intermediate_size
         a					:							Tuple							= hidden_act
         a					:							List[str]							= hidden_dropout_prob
         a					:							Tuple							= attention_probs_dropout_prob
         a					:							str							= initializer_range
         a					:							List[str]							= type_vocab_size
         a					:							Any							= segment_means_seq_len
         a					:							List[Any]							= num_landmarks
         a					:							Dict							= conv_kernel_size
         a					:							List[Any]							= inv_coeff_init_option
         a					:							Dict							= layer_norm_eps
         super().__init__(pad_token_id=UpperCAmelCase_							,							bos_token_id=UpperCAmelCase_							,							eos_token_id=UpperCAmelCase_							,							**UpperCAmelCase_)


'''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 SCREAMING_SNAKE_CASE__  (				snake_case       :				List[Any]				,	snake_case       :				List[str]=100				,	snake_case       :				Any=" "  )   ->				Any:







     """simple docstring"""
     a					:							Union[str, Any]							= text.split(snake_case  )
     return [character.join(text[i : i + n]  ).strip() for i in range(0				,	len(snake_case  )				,	snake_case  )]






def SCREAMING_SNAKE_CASE__  (				snake_case       :				Optional[int]  )   ->				Union[str, Any]:







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






def SCREAMING_SNAKE_CASE__  (				snake_case       :				int				,	snake_case       :				str				,	snake_case       :				List[Any]				,	snake_case       :				Dict				,	snake_case       :				Union[str, Any]				,	snake_case       :				int  )   ->				Dict:







     """simple docstring"""
     a					:							Tuple							= load_dataset(
         'csv'				,	data_files=[csv_path]				,	split='train'				,	delimiter='\t'				,	column_names=['title', 'text']  )
     a					:							Union[str, Any]							= kb_dataset.map(
         snake_case				,	batched=snake_case				,	num_proc=1  )  # if you want you can load already splitted csv.
     a					:							Optional[Any]							= [kb_dataset.shard(snake_case				,	snake_case				,	contiguous=snake_case  ) for i in range(snake_case  )]
     a					:							List[Any]							= kb_list[process_num]

     a					:							Dict							= 'data_' + str(snake_case  )
     a					:							int							= os.path.join(snake_case				,	snake_case  )

     a					:							Any							= DPRContextEncoderTokenizerFast.from_pretrained('facebook/dpr-ctx_encoder-multiset-base'  )
     a					:							List[str]							= ctx_encoder.to(device=snake_case  )

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

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

     a					:							List[str]							= data_shrad.map(
         partial(snake_case				,	ctx_encoder=snake_case				,	ctx_tokenizer=snake_case				,	device=snake_case  )				,	batched=snake_case				,	batch_size=16				,	features=snake_case				,	)
     dataset.save_to_disk(snake_case  )






def SCREAMING_SNAKE_CASE__  (				snake_case       :				Any				,	snake_case       :				Any  )   ->				List[Any]:







     """simple docstring"""
     a					:							int							= []

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

     a					:							Tuple							= concatenate_datasets(snake_case  )
     faiss.omp_set_num_threads(96  )

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

