





'''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_							(   __snake_case							:    List[str]     , __snake_case							:    Union[str, Any]=100     , __snake_case							:    int=" "     )							->		List[Any]:



       """simple docstring"""
       lowerCamelCase_							=text.split(__snake_case     )
       return [character.join(text[i : i + n]     ).strip() for i in range(0     , len(__snake_case     )     , __snake_case     )]





def   a_							(   __snake_case							:    Dict     )							->		int:



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





def   a_							(   __snake_case							:    Union[str, Any]     , __snake_case							:    Union[str, Any]     , __snake_case							:    List[str]     , __snake_case							:    Tuple     , __snake_case							:    Dict     , __snake_case							:    Any     )							->		List[Any]:



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

       lowerCamelCase_							='''data_''' + str(__snake_case     )
       lowerCamelCase_							=os.path.join(__snake_case     , __snake_case     )

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

       def embed(
           __snake_case							:    dict     , __snake_case							:    DPRContextEncoder     , __snake_case							:    DPRContextEncoderTokenizerFast     , __snake_case							:    Any     ) -> dict:
              lowerCamelCase_							=ctx_tokenizer(
                  documents['''title''']     , documents['''text''']     , truncation=__snake_case     , padding='''longest'''     , return_tensors='''pt'''     )['''input_ids''']
              lowerCamelCase_							=ctx_encoder(input_ids.to(device=__snake_case     )     , return_dict=__snake_case     ).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(__snake_case     , ctx_encoder=__snake_case     , ctx_tokenizer=__snake_case     , device=__snake_case     )     , batched=__snake_case     , batch_size=16     , features=__snake_case     , )
       dataset.save_to_disk(__snake_case     )





def   a_							(   __snake_case							:    Optional[Any]     , __snake_case							:    Union[str, Any]     )							->		Union[str, Any]:



       """simple docstring"""
       lowerCamelCase_							=[]

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

       lowerCamelCase_							=concatenate_datasets(__snake_case     )
       faiss.omp_set_num_threads(96     )

       lowerCamelCase_							=faiss.IndexHNSWFlat(768     , 128     , faiss.METRIC_INNER_PRODUCT     )
       concat.add_faiss_index('''embeddings'''     , custom_index=__snake_case     )
       concat.get_index('''embeddings'''     ).save(
           __snake_case     )  # 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_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a_   :	Union[str, Any]							   = {
    """configuration_openai""": ["""OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """OpenAIGPTConfig"""],
    """tokenization_openai""": ["""OpenAIGPTTokenizer"""],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     a_   :	Optional[int]							   = ["""OpenAIGPTTokenizerFast"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     a_   :	int							   = [
         """OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """OpenAIGPTDoubleHeadsModel""",
         """OpenAIGPTForSequenceClassification""",
         """OpenAIGPTLMHeadModel""",
         """OpenAIGPTModel""",
         """OpenAIGPTPreTrainedModel""",
         """load_tf_weights_in_openai_gpt""",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     a_   :	Dict							   = [
         """TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TFOpenAIGPTDoubleHeadsModel""",
         """TFOpenAIGPTForSequenceClassification""",
         """TFOpenAIGPTLMHeadModel""",
         """TFOpenAIGPTMainLayer""",
         """TFOpenAIGPTModel""",
         """TFOpenAIGPTPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
     from .tokenization_openai import OpenAIGPTTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_openai_fast import OpenAIGPTTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_openai import (
              OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
              OpenAIGPTDoubleHeadsModel,
              OpenAIGPTForSequenceClassification,
              OpenAIGPTLMHeadModel,
              OpenAIGPTModel,
              OpenAIGPTPreTrainedModel,
              load_tf_weights_in_openai_gpt,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_openai import (
              TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFOpenAIGPTDoubleHeadsModel,
              TFOpenAIGPTForSequenceClassification,
              TFOpenAIGPTLMHeadModel,
              TFOpenAIGPTMainLayer,
              TFOpenAIGPTModel,
              TFOpenAIGPTPreTrainedModel,
          )

else:
     import sys

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




