code: 
  
  
  
  
  
  '''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_=1_00				,							UpperCAmelCase_=" "       )      ->Union[str, Any]:
  	snake_case__  					=      text.split(UpperCAmelCase_       )
  	return [character.join(text[i : i + n]       ).strip() for i in range(0				,							len(UpperCAmelCase_       )				,							UpperCAmelCase_       )]
  
  
  
  
  def 	__lowerCamelCase (  UpperCAmelCase_       )      ->Optional[int]:
  	snake_case__					,     snake_case__  					=      [], []
  	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_       )      ->List[str]:
  	snake_case__  					=      load_dataset(
  	    'csv'				,							data_files=[csv_path]				,							split='train'				,							delimiter='\t'				,							column_names=['title', 'text']       )
  	snake_case__  					=      kb_dataset.map(
  	    UpperCAmelCase_				,							batched=UpperCAmelCase_				,							num_proc=1       )  # if you want you can load already splitted csv.
  	snake_case__  					=      [kb_dataset.shard(UpperCAmelCase_				,							UpperCAmelCase_				,							contiguous=UpperCAmelCase_       ) for i in range(UpperCAmelCase_       )]
  	snake_case__  					=      kb_list[process_num]
  
  	snake_case__  					=      'data_' + str(UpperCAmelCase_       )
  	snake_case__  					=      os.path.join(UpperCAmelCase_				,							UpperCAmelCase_       )
  
  	snake_case__  					=      DPRContextEncoderTokenizerFast.from_pretrained('facebook/dpr-ctx_encoder-multiset-base'       )
  	snake_case__  					=      ctx_encoder.to(device=UpperCAmelCase_       )
  
  	def embed(
  	    UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_       ) -> dict:
  		snake_case__  					=      ctx_tokenizer(
  		    documents['title']				,							documents['text']				,							truncation=UpperCAmelCase_				,							padding='longest'				,							return_tensors='pt'       )['input_ids']
  		snake_case__  					=      ctx_encoder(input_ids.to(device=UpperCAmelCase_       )				,							return_dict=UpperCAmelCase_       ).pooler_output
  		return {"embeddings": embeddings.detach().cpu().numpy()}
  
  	snake_case__  					=      Features(
  	    {'text': Value('string'       ), 'title': Value('string'       ), 'embeddings': Sequence(Value('float32'       )       )}       )  # optional, save as float32 instead of float64 to save space
  
  	snake_case__  					=      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_       )      ->Union[str, Any]:
  	snake_case__  					=      []
  
  	for shard_address in glob(str(UpperCAmelCase_       ) + '/*/'       ):
  		data_shard_list.append(load_from_disk(UpperCAmelCase_       )       )
  
  	snake_case__  					=      concatenate_datasets(UpperCAmelCase_       )
  	faiss.omp_set_num_threads(96       )
  
  	snake_case__  					=      faiss.IndexHNSWFlat(7_68				,							1_28				,							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
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  from collections import OrderedDict
  from typing import Mapping
  
  from packaging import version
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig
  from ...utils import logging
  
  
  a__				: Dict									=    logging.get_logger(__name__)
  
  a__				: Tuple									=    {
      '''apple/mobilevitv2-1.0''': '''https://huggingface.co/apple/mobilevitv2-1.0/resolve/main/config.json''',
  }
  
  
  
  
  
  
  class 							__snake_case    (				__magic_name__       ):
  	__lowerCAmelCase						=     '''mobilevitv2'''
  
  
  
  
  
  	def __init__(		self			,					UpperCamelCase_=3			,					UpperCamelCase_=256			,					UpperCamelCase_=2			,					UpperCamelCase_=2.0			,					UpperCamelCase_="swish"			,					UpperCamelCase_=3			,					UpperCamelCase_=32			,					UpperCamelCase_=0.1			,					UpperCamelCase_=0.0_2			,					UpperCamelCase_=1E-5			,					UpperCamelCase_=512			,					UpperCamelCase_=[6, 12, 18]			,					UpperCamelCase_=0.1			,					UpperCamelCase_=255			,					UpperCamelCase_=[2, 4, 3]			,					UpperCamelCase_=[128, 192, 256]			,					UpperCamelCase_=1.0			,					UpperCamelCase_=2			,					UpperCamelCase_=0.0			,					UpperCamelCase_=0.0			,					**UpperCamelCase_			,					)			->		Union[str, Any]:
  		super().__init__(**UpperCamelCase_		)
  
  		snake_case__  					=      num_channels
  		snake_case__  					=      image_size
  		snake_case__  					=      patch_size
  		snake_case__  					=      expand_ratio
  		snake_case__  					=      hidden_act
  		snake_case__  					=      conv_kernel_size
  		snake_case__  					=      output_stride
  		snake_case__  					=      initializer_range
  		snake_case__  					=      layer_norm_eps
  		snake_case__  					=      n_attn_blocks
  		snake_case__  					=      base_attn_unit_dims
  		snake_case__  					=      width_multiplier
  		snake_case__  					=      ffn_multiplier
  		snake_case__  					=      ffn_dropout
  		snake_case__  					=      attn_dropout
  		snake_case__  					=      classifier_dropout_prob
  
  		# decode head attributes for semantic segmentation
  		snake_case__  					=      aspp_out_channels
  		snake_case__  					=      atrous_rates
  		snake_case__  					=      aspp_dropout_prob
  		snake_case__  					=      semantic_loss_ignore_index
  
  
  
  
  
  
  class 							__snake_case    (				__magic_name__       ):
  	__lowerCAmelCase						=     version.parse('''1.11'''       )
  
  
  
  
  
  	@property
  	def      _snake_case      (		self		)			->		Mapping[str, Mapping[int, str]]:
  		return OrderedDict([('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'})]		)
  
  
  
  
  
  	@property
  	def      _snake_case      (		self		)			->		Mapping[str, Mapping[int, str]]:
  		if self.task == "image-classification":
  			return OrderedDict([('logits', {0: 'batch'})]		)
  		else:
  			return OrderedDict([('last_hidden_state', {0: 'batch'}), ('pooler_output', {0: 'batch'})]		)
  
  
  
  
  
  	@property
  	def      _snake_case      (		self		)			->		float:
  		return 1E-4
  
  
  
  