



'''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 							__UpperCAmelCase				(				UpperCamelCase__				:str   ,				UpperCamelCase__				:Tuple=100   ,				UpperCamelCase__				:List[str]=" "			)		->						Optional[int]:
				snake_case__      :   Union[str, Any]      =    text.split(UpperCamelCase__			)
				return [character.join(text[i : i + n]			).strip() for i in range(0   ,				len(UpperCamelCase__			)   ,				UpperCamelCase__			)]


def 							__UpperCAmelCase				(				UpperCamelCase__				:str			)		->						str:
				snake_case__  ,      snake_case__      :   Dict      =    [], []
				for title, text in zip(documents['''title''']   ,				documents['''text''']			):
								if text is not None:
												for passage in split_text(UpperCamelCase__			):
																titles.append(title if title is not None else ''''''			)
																texts.append(UpperCamelCase__			)
				return {"title": titles, "text": texts}


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

				snake_case__      :   Dict      =    '''data_''' + str(UpperCamelCase__			)
				snake_case__      :   str      =    os.path.join(UpperCamelCase__   ,				UpperCamelCase__			)

				snake_case__      :   str      =    DPRContextEncoderTokenizerFast.from_pretrained('''facebook/dpr-ctx_encoder-multiset-base'''			)
				snake_case__      :   str      =    ctx_encoder.to(device=UpperCamelCase__			)

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

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

				snake_case__      :   Optional[int]      =    data_shrad.map(
				    partial(UpperCamelCase__   ,				ctx_encoder=UpperCamelCase__   ,				ctx_tokenizer=UpperCamelCase__   ,				device=UpperCamelCase__			)   ,				batched=UpperCamelCase__   ,				batch_size=16   ,				features=UpperCamelCase__   ,				)
				dataset.save_to_disk(UpperCamelCase__			)



def 							__UpperCAmelCase				(				UpperCamelCase__				:Dict   ,				UpperCamelCase__				:Union[str, Any]			)		->						Union[str, Any]:
				snake_case__      :   Dict      =    []

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

				snake_case__      :   Tuple      =    concatenate_datasets(UpperCamelCase__			)
				faiss.omp_set_num_threads(96			)

				snake_case__      :   Optional[int]      =    faiss.IndexHNSWFlat(768   ,				128   ,				faiss.METRIC_INNER_PRODUCT			)
				concat.add_faiss_index('''embeddings'''   ,				custom_index=UpperCamelCase__			)
				concat.get_index('''embeddings'''			).save(
				    UpperCamelCase__			)  # 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_torch_available, is_vision_available


lowerCAmelCase_:       Optional[int]			     =       {"configuration_vit_hybrid": ["VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTHybridConfig"]}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_:       Any			     =       [
     "VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST",
     "ViTHybridForImageClassification",
     "ViTHybridModel",
     "ViTHybridPreTrainedModel",
 ]

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase_:       Optional[Any]			     =       ["ViTHybridImageProcessor"]


if TYPE_CHECKING:
 from .configuration_vit_hybrid import VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTHybridConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_vit_hybrid import (
      VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST,
      ViTHybridForImageClassification,
      ViTHybridModel,
      ViTHybridPreTrainedModel,
  )

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .image_processing_vit_hybrid import ViTHybridImageProcessor


else:
 import sys

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




