




"""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				(					lowercase_   ,       lowercase_=1_00   ,       lowercase_=" " )    ->		Optional[Any]:

    """simple docstring"""


    __UpperCamelCase											=  text.split(_SCREAMING_SNAKE_CASE )
    return [character.join(text[i : i + n] ).strip() for i in range(0   ,       len(_SCREAMING_SNAKE_CASE )   ,       _SCREAMING_SNAKE_CASE )]




def    __SCREAMING_SNAKE_CASE				(					lowercase_ )    ->		int:

    """simple docstring"""


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




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_ )    ->		List[str]:

    """simple docstring"""


    __UpperCamelCase											=  load_dataset(
        '''csv'''   ,       data_files=[csv_path]   ,       split='''train'''   ,       delimiter='''\t'''   ,       column_names=['''title''', '''text'''] )
    __UpperCamelCase											=  kb_dataset.map(
        _SCREAMING_SNAKE_CASE   ,       batched=_SCREAMING_SNAKE_CASE   ,       num_proc=1 )  # if you want you can load already splitted csv.
    __UpperCamelCase											=  [kb_dataset.shard(_SCREAMING_SNAKE_CASE   ,       _SCREAMING_SNAKE_CASE   ,       contiguous=_SCREAMING_SNAKE_CASE ) for i in range(_SCREAMING_SNAKE_CASE )]
    __UpperCamelCase											=  kb_list[process_num]

    __UpperCamelCase											=  "data_" + str(_SCREAMING_SNAKE_CASE )
    __UpperCamelCase											=  os.path.join(_SCREAMING_SNAKE_CASE   ,       _SCREAMING_SNAKE_CASE )

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

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

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

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




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_ )    ->		Tuple:

    """simple docstring"""


    __UpperCamelCase											=  []

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

    __UpperCamelCase											=  concatenate_datasets(_SCREAMING_SNAKE_CASE )
    faiss.omp_set_num_threads(96 )

    __UpperCamelCase											=  faiss.IndexHNSWFlat(7_68   ,       1_28   ,       faiss.METRIC_INNER_PRODUCT )
    concat.add_faiss_index('''embeddings'''   ,       custom_index=_SCREAMING_SNAKE_CASE )
    concat.get_index('''embeddings''' ).save(
        _SCREAMING_SNAKE_CASE )  # since we load the index in to memory,we can directly update the index in the disk












from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a_										=       {
    "configuration_marian": ["MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP", "MarianConfig", "MarianOnnxConfig"],
}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_										=       ["MarianTokenizer"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_										=       [
     "MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST",
     "MarianForCausalLM",
     "MarianModel",
     "MarianMTModel",
     "MarianPreTrainedModel",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_										=       ["TFMarianModel", "TFMarianMTModel", "TFMarianPreTrainedModel"]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_										=       ["FlaxMarianModel", "FlaxMarianMTModel", "FlaxMarianPreTrainedModel"]

if TYPE_CHECKING:
 from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_marian import MarianTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_marian import (
      MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
      MarianForCausalLM,
      MarianModel,
      MarianMTModel,
      MarianPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel

else:
 import sys

 a_										=       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






