






"""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					(       __snake_case     :    int						,   __snake_case     :    Tuple=1_00						,   __snake_case     :    List[Any]=" "       ):





  '''simple docstring'''





  lowercase				       =			text.split(__lowerCAmelCase       )
  return [character.join(text[i : i + n]       ).strip() for i in range(0						,   len(__lowerCAmelCase       )						,   __lowerCAmelCase       )]
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    List[str]       ):





  '''simple docstring'''





  lowercase				       =			[], []
  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 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Dict						,   __snake_case     :    Optional[Any]						,   __snake_case     :    Any						,   __snake_case     :    Optional[Any]						,   __snake_case     :    Optional[int]						,   __snake_case     :    Union[str, Any]       ):





  '''simple docstring'''





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

  lowercase				       =			'''data_''' + str(__lowerCAmelCase       )
  lowercase				       =			os.path.join(__lowerCAmelCase						,   __lowerCAmelCase       )

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

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

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

  lowercase				       =			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 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Tuple						,   __snake_case     :    int       ):





  '''simple docstring'''





  lowercase				       =			[]

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

  lowercase				       =			concatenate_datasets(__lowerCAmelCase       )
  faiss.omp_set_num_threads(96       )

  lowercase				       =			faiss.IndexHNSWFlat(7_68						,   1_28						,   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



from typing import TYPE_CHECKING

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


__snake_case         =     {'''configuration_gptj''': ['''GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTJConfig''', '''GPTJOnnxConfig''']}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __snake_case         =     [
         '''GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''GPTJForCausalLM''',
         '''GPTJForQuestionAnswering''',
         '''GPTJForSequenceClassification''',
         '''GPTJModel''',
         '''GPTJPreTrainedModel''',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __snake_case         =     [
         '''TFGPTJForCausalLM''',
         '''TFGPTJForQuestionAnswering''',
         '''TFGPTJForSequenceClassification''',
         '''TFGPTJModel''',
         '''TFGPTJPreTrainedModel''',
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __snake_case         =     [
         '''FlaxGPTJForCausalLM''',
         '''FlaxGPTJModel''',
         '''FlaxGPTJPreTrainedModel''',
     ]


if TYPE_CHECKING:
     from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_gptj import (
              GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
              GPTJForCausalLM,
              GPTJForQuestionAnswering,
              GPTJForSequenceClassification,
              GPTJModel,
              GPTJPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_gptj import (
              TFGPTJForCausalLM,
              TFGPTJForQuestionAnswering,
              TFGPTJForSequenceClassification,
              TFGPTJModel,
              TFGPTJPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
     import sys

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