'''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 			A_   ( _lowerCamelCase			:							Tuple  ,  _lowerCamelCase			:							Optional[Any]=100  ,  _lowerCamelCase			:							Dict=" "    ):
      _lowerCAmelCase		   =   text.split(_lowerCamelCase    )
      return [character.join(text[i : i + n]    ).strip() for i in range(0  ,  len(_lowerCamelCase    )  ,  _lowerCamelCase    )]







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







def 			A_   ( _lowerCamelCase			:							Dict  ,  _lowerCamelCase			:							List[Any]  ,  _lowerCamelCase			:							List[str]  ,  _lowerCamelCase			:							Tuple  ,  _lowerCamelCase			:							int  ,  _lowerCamelCase			:							List[Any]    ):
      _lowerCAmelCase		   =   load_dataset(
          'csv'  ,  data_files=[csv_path]  ,  split='train'  ,  delimiter='\t'  ,  column_names=['title', 'text']    )
      _lowerCAmelCase		   =   kb_dataset.map(
          _lowerCamelCase  ,  batched=_lowerCamelCase  ,  num_proc=1    )  # if you want you can load already splitted csv.
      _lowerCAmelCase		   =   [kb_dataset.shard(_lowerCamelCase  ,  _lowerCamelCase  ,  contiguous=_lowerCamelCase    ) for i in range(_lowerCamelCase    )]
      _lowerCAmelCase		   =   kb_list[process_num]

      _lowerCAmelCase		   =   'data_' + str(_lowerCamelCase    )
      _lowerCAmelCase		   =   os.path.join(_lowerCamelCase  ,  _lowerCamelCase    )

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

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

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

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







def 			A_   ( _lowerCamelCase			:							str  ,  _lowerCamelCase			:							Optional[Any]    ):
      _lowerCAmelCase		   =   []

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

      _lowerCAmelCase		   =   concatenate_datasets(_lowerCamelCase    )
      faiss.omp_set_num_threads(96    )

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






'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


snake_case  =			{
    '''configuration_align''': [
        '''ALIGN_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''AlignConfig''',
        '''AlignTextConfig''',
        '''AlignVisionConfig''',
    ],
    '''processing_align''': ['''AlignProcessor'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case  =			[
     '''ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''AlignModel''',
     '''AlignPreTrainedModel''',
     '''AlignTextModel''',
     '''AlignVisionModel''',
 ]

if TYPE_CHECKING:
 from .configuration_align import (
     ALIGN_PRETRAINED_CONFIG_ARCHIVE_MAP,
     AlignConfig,
     AlignTextConfig,
     AlignVisionConfig,
 )
 from .processing_align import AlignProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_align import (
      ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST,
      AlignModel,
      AlignPreTrainedModel,
      AlignTextModel,
      AlignVisionModel,
  )

else:
 import sys

 snake_case  =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





