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__	(       __A ,			__A=100 ,			__A=" "						):







    '''simple docstring'''
    _lowerCamelCase  :		str			    =    text.split(__A						)
    return [character.join(text[i : i + n]						).strip() for i in range(0 ,			len(__A						) ,			__A						)]







def     A__	(       __A						):







    '''simple docstring'''
    _lowerCamelCase    ,				_lowerCamelCase  :		int			    =    [], []
    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     A__	(       __A ,			__A ,			__A ,			__A ,			__A ,			__A						):







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

    _lowerCamelCase  :		List[str]			    =    """data_""" + str(__A						)
    _lowerCamelCase  :		List[str]			    =    os.path.join(__A ,			__A						)

    _lowerCamelCase  :		Tuple			    =    DPRContextEncoderTokenizerFast.from_pretrained("""facebook/dpr-ctx_encoder-multiset-base"""						)
    _lowerCamelCase  :		List[str]			    =    ctx_encoder.to(device=__A						)

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

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

    _lowerCamelCase  :		int			    =    data_shrad.map(
        partial(__A ,			ctx_encoder=__A ,			ctx_tokenizer=__A ,			device=__A						) ,			batched=__A ,			batch_size=16 ,			features=__A ,			)
    dataset.save_to_disk(__A						)







def     A__	(       __A ,			__A						):







    '''simple docstring'''
    _lowerCamelCase  :		List[Any]			    =    []

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

    _lowerCamelCase  :		Optional[Any]			    =    concatenate_datasets(__A						)
    faiss.omp_set_num_threads(96						)

    _lowerCamelCase  :		Optional[Any]			    =    faiss.IndexHNSWFlat(768 ,			128 ,			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





import copy
from typing import Dict

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
from ..bit import BitConfig


lowerCAmelCase      :				List[Any]     			=logging.get_logger(__name__)

lowerCAmelCase      :				Optional[int]     			={
    "google/vit-hybrid-base-bit-384": "https://huggingface.co/vit-hybrid-base-bit-384/resolve/main/config.json",
    # See all ViT hybrid models at https://huggingface.co/models?filter=vit
}




class 						__snake_case						(			__lowerCAmelCase		):





      '''simple docstring'''






      _snake_case     		=							'vit-hybrid'



      def __init__(       self  :					str     ,	_UpperCamelCase  :					int=None     ,	_UpperCamelCase  :					Union[str, Any]=768     ,	_UpperCamelCase  :					List[Any]=12     ,	_UpperCamelCase  :					List[Any]=12     ,	_UpperCamelCase  :					Optional[int]=3072     ,	_UpperCamelCase  :					Optional[int]="gelu"     ,	_UpperCamelCase  :					int=0.0     ,	_UpperCamelCase  :					Tuple=0.0     ,	_UpperCamelCase  :					Dict=0.0_2     ,	_UpperCamelCase  :					Dict=1E-1_2     ,	_UpperCamelCase  :					Union[str, Any]=224     ,	_UpperCamelCase  :					str=1     ,	_UpperCamelCase  :					Optional[int]=3     ,	_UpperCamelCase  :					Dict=[1, 1024, 24, 24]     ,	_UpperCamelCase  :					Optional[Any]=True     ,	**_UpperCamelCase  :					List[Any]     ,	)					->str:


          """simple docstring"""


          super().__init__(**_UpperCamelCase)

          if backbone_config is None:
              logger.info("""`backbone_config` is `None`. Initializing the config with a `BiT` backbone.""")
              _lowerCamelCase  :		Tuple			    =    {
                  """global_padding""": """same""",
                  """layer_type""": """bottleneck""",
                  """depths""": [3, 4, 9],
                  """out_features""": ["""stage3"""],
                  """embedding_dynamic_padding""": True,
              }

          if isinstance(_UpperCamelCase     ,	_UpperCamelCase):
              if "model_type" in backbone_config:
                  _lowerCamelCase  :		int			    =    CONFIG_MAPPING[backbone_config["""model_type"""]]
              else:
                  logger.info(
                      """`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class.""")
                  _lowerCamelCase  :		Optional[int]			    =    BitConfig
              _lowerCamelCase  :		List[Any]			    =    backbone_config_class(**_UpperCamelCase)

          _lowerCamelCase  :		Any			    =    backbone_featmap_shape
          _lowerCamelCase  :		Dict			    =    backbone_config
          _lowerCamelCase  :		Tuple			    =    hidden_size
          _lowerCamelCase  :		Union[str, Any]			    =    num_hidden_layers
          _lowerCamelCase  :		Optional[Any]			    =    num_attention_heads
          _lowerCamelCase  :		Optional[Any]			    =    intermediate_size
          _lowerCamelCase  :		List[Any]			    =    hidden_act
          _lowerCamelCase  :		Dict			    =    hidden_dropout_prob
          _lowerCamelCase  :		Optional[Any]			    =    attention_probs_dropout_prob
          _lowerCamelCase  :		Union[str, Any]			    =    initializer_range
          _lowerCamelCase  :		Any			    =    layer_norm_eps
          _lowerCamelCase  :		Optional[int]			    =    image_size
          _lowerCamelCase  :		Any			    =    patch_size
          _lowerCamelCase  :		Optional[Any]			    =    num_channels
          _lowerCamelCase  :		List[str]			    =    qkv_bias




      def 						_SCREAMING_SNAKE_CASE     (       self  :					Optional[Any])					->Dict[str, any]:


          """simple docstring"""


          _lowerCamelCase  :		Dict			    =    copy.deepcopy(self.__dict__)
          _lowerCamelCase  :		List[Any]			    =    self.backbone_config.to_dict()
          _lowerCamelCase  :		List[str]			    =    self.__class__.model_type
          return output




