





'''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










'''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



