'''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    (      lowerCamelCase_					:							List[str]	,     lowerCamelCase_					:							Optional[Any]=100	,     lowerCamelCase_					:							Optional[int]=" "   ):



     """simple docstring"""





     UpperCAmelCase_							:   List[str]   =      text.split(lowerCamelCase_   )
     return [character.join(text[i : i + n]   ).strip() for i in range(0	,     len(lowerCamelCase_   )	,     lowerCamelCase_   )]


def 					_lowerCamelCase    (      lowerCamelCase_					:							Optional[int]   ):



     """simple docstring"""





     UpperCAmelCase_   ,			UpperCAmelCase_							:   Optional[int]   =      [], []
     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 					_lowerCamelCase    (      lowerCamelCase_					:							Dict	,     lowerCamelCase_					:							List[str]	,     lowerCamelCase_					:							List[str]	,     lowerCamelCase_					:							Dict	,     lowerCamelCase_					:							str	,     lowerCamelCase_					:							Any   ):



     """simple docstring"""





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

     UpperCAmelCase_							:   str   =      'data_' + str(lowerCamelCase_   )
     UpperCAmelCase_							:   Optional[int]   =      os.path.join(lowerCamelCase_	,     lowerCamelCase_   )

     UpperCAmelCase_							:   Optional[int]   =      DPRContextEncoderTokenizerFast.from_pretrained('facebook/dpr-ctx_encoder-multiset-base'   )
     UpperCAmelCase_							:   int   =      ctx_encoder.to(device=lowerCamelCase_   )

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

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

     UpperCAmelCase_							:   List[Any]   =      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 					_lowerCamelCase    (      lowerCamelCase_					:							Optional[int]	,     lowerCamelCase_					:							Tuple   ):



     """simple docstring"""





     UpperCAmelCase_							:   Tuple   =      []

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

     UpperCAmelCase_							:   Optional[Any]   =      concatenate_datasets(lowerCamelCase_   )
     faiss.omp_set_num_threads(96   )

     UpperCAmelCase_							:   Optional[int]   =      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_tokenizers_available, is_torch_available


snake_case__     :			int      			=  {
    '''configuration_markuplm''': ['''MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MarkupLMConfig'''],
    '''feature_extraction_markuplm''': ['''MarkupLMFeatureExtractor'''],
    '''processing_markuplm''': ['''MarkupLMProcessor'''],
    '''tokenization_markuplm''': ['''MarkupLMTokenizer'''],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case__     :			List[str]      			=  ['''MarkupLMTokenizerFast''']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case__     :			Dict      			=  [
     '''MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''MarkupLMForQuestionAnswering''',
     '''MarkupLMForSequenceClassification''',
     '''MarkupLMForTokenClassification''',
     '''MarkupLMModel''',
     '''MarkupLMPreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_markuplm import MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP, MarkupLMConfig
 from .feature_extraction_markuplm import MarkupLMFeatureExtractor
 from .processing_markuplm import MarkupLMProcessor
 from .tokenization_markuplm import MarkupLMTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_markuplm_fast import MarkupLMTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_markuplm import (
      MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST,
      MarkupLMForQuestionAnswering,
      MarkupLMForSequenceClassification,
      MarkupLMForTokenClassification,
      MarkupLMModel,
      MarkupLMPreTrainedModel,
  )


else:
 import sys

 snake_case__     :			Optional[Any]      			=  _LazyModule(__name__, globals()['''__file__'''], _import_structure)






