


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  (		A_  :	Union[str, Any]							,  A_  :	str=1_0_0							,  A_  :	List[str]=" "      )			->			Union[str, Any]:

   '''simple docstring'''






   UpperCamelCase__ :			List[str] 						=text.split(A_      )
   return [character.join(text[i : i + n]      ).strip() for i in range(0							,  len(A_      )							,  A_      )]
def 	_lowerCamelCase  (		A_  :	List[str]      )			->			Tuple:

   '''simple docstring'''






   UpperCamelCase__						,	UpperCamelCase__ :			Any 						=[], []
   for title, text in zip(documents["title"]							,  documents["text"]      ):
      if text is not None:
         for passage in split_text(A_      ):
            titles.append(title if title is not None else ""      )
            texts.append(A_      )
   return {"title": titles, "text": texts}
def 	_lowerCamelCase  (		A_  :	Dict							,  A_  :	List[str]							,  A_  :	Any							,  A_  :	Optional[Any]							,  A_  :	Any							,  A_  :	int      )			->			Dict:

   '''simple docstring'''






   UpperCamelCase__ :			int 						=load_dataset(
       "csv"							,  data_files=[csv_path]							,  split="train"							,  delimiter="\t"							,  column_names=["title", "text"]      )
   UpperCamelCase__ :			Optional[int] 						=kb_dataset.map(
       A_							,  batched=A_							,  num_proc=1      )  # if you want you can load already splitted csv.
   UpperCamelCase__ :			Any 						=[kb_dataset.shard(A_							,  A_							,  contiguous=A_      ) for i in range(A_      )]
   UpperCamelCase__ :			Union[str, Any] 						=kb_list[process_num]

   UpperCamelCase__ :			int 						="data_" + str(A_      )
   UpperCamelCase__ :			Optional[int] 						=os.path.join(A_							,  A_      )

   UpperCamelCase__ :			Any 						=DPRContextEncoderTokenizerFast.from_pretrained("facebook/dpr-ctx_encoder-multiset-base"      )
   UpperCamelCase__ :			List[str] 						=ctx_encoder.to(device=A_      )

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

   UpperCamelCase__ :			Union[str, Any] 						=Features(
       {"text": Value("string"      ), "title": Value("string"      ), "embeddings": Sequence(Value("float32"      )      )}      )  # optional, save as float32 instead of float64 to save space

   UpperCamelCase__ :			Optional[Any] 						=data_shrad.map(
       partial(A_							,  ctx_encoder=A_							,  ctx_tokenizer=A_							,  device=A_      )							,  batched=A_							,  batch_size=1_6							,  features=A_							,  )
   dataset.save_to_disk(A_      )
def 	_lowerCamelCase  (		A_  :	int							,  A_  :	Tuple      )			->			str:

   '''simple docstring'''






   UpperCamelCase__ :			Union[str, Any] 						=[]

   for shard_address in glob(str(A_      ) + "/*/"      ):
      data_shard_list.append(load_from_disk(A_      )      )

   UpperCamelCase__ :			List[str] 						=concatenate_datasets(A_      )
   faiss.omp_set_num_threads(9_6      )

   UpperCamelCase__ :			int 						=faiss.IndexHNSWFlat(7_6_8							,  1_2_8							,  faiss.METRIC_INNER_PRODUCT      )
   concat.add_faiss_index("embeddings"							,  custom_index=A_      )
   concat.get_index("embeddings"      ).save(
       A_      )  # 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,
)


__UpperCAmelCase      			=  {
    """configuration_switch_transformers""": [
        """SWITCH_TRANSFORMERS_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """SwitchTransformersConfig""",
        """SwitchTransformersOnnxConfig""",
    ]
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __UpperCAmelCase      			=  [
         """SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """SwitchTransformersEncoderModel""",
         """SwitchTransformersForConditionalGeneration""",
         """SwitchTransformersModel""",
         """SwitchTransformersPreTrainedModel""",
         """SwitchTransformersTop1Router""",
         """SwitchTransformersSparseMLP""",
     ]


if TYPE_CHECKING:
     from .configuration_switch_transformers import (
         SWITCH_TRANSFORMERS_PRETRAINED_CONFIG_ARCHIVE_MAP,
         SwitchTransformersConfig,
         SwitchTransformersOnnxConfig,
     )

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_switch_transformers import (
              SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST,
              SwitchTransformersEncoderModel,
              SwitchTransformersForConditionalGeneration,
              SwitchTransformersModel,
              SwitchTransformersPreTrainedModel,
              SwitchTransformersSparseMLP,
              SwitchTransformersTopaRouter,
          )


else:
     import sys

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

