


'''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 				lowerCAmelCase__	(       UpperCAmelCase    ,       UpperCAmelCase=100    ,       UpperCAmelCase=" "     ):






       """simple docstring"""





       snake_case__					:	Any          =					text.split(lowercase_     )
       return [character.join(text[i : i + n]     ).strip() for i in range(0    ,       len(lowercase_     )    ,       lowercase_     )]



def 				lowerCAmelCase__	(       UpperCAmelCase     ):






       """simple docstring"""





       snake_case__       ,   snake_case__					:	Tuple          =					[], []
       for title, text in zip(documents["""title"""]    ,       documents["""text"""]     ):
              if text is not None:
                     for passage in split_text(lowercase_     ):
                            titles.append(title if title is not None else """"""     )
                            texts.append(lowercase_     )
       return {"title": titles, "text": texts}



def 				lowerCAmelCase__	(       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase     ):






       """simple docstring"""





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

       snake_case__					:	Union[str, Any]          =					"""data_""" + str(lowercase_     )
       snake_case__					:	int          =					os.path.join(lowercase_    ,       lowercase_     )

       snake_case__					:	Union[str, Any]          =					DPRContextEncoderTokenizerFast.from_pretrained("""facebook/dpr-ctx_encoder-multiset-base"""     )
       snake_case__					:	List[str]          =					ctx_encoder.to(device=lowercase_     )

       def embed(
           UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase     ) -> dict:
              snake_case__					:	Tuple          =					ctx_tokenizer(
                  documents["""title"""]    ,       documents["""text"""]    ,       truncation=lowercase_    ,       padding="""longest"""    ,       return_tensors="""pt"""     )["""input_ids"""]
              snake_case__					:	int          =					ctx_encoder(input_ids.to(device=lowercase_     )    ,       return_dict=lowercase_     ).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[Any]          =					data_shrad.map(
           partial(lowercase_    ,       ctx_encoder=lowercase_    ,       ctx_tokenizer=lowercase_    ,       device=lowercase_     )    ,       batched=lowercase_    ,       batch_size=16    ,       features=lowercase_    ,       )
       dataset.save_to_disk(lowercase_     )






def 				lowerCAmelCase__	(       UpperCAmelCase    ,       UpperCAmelCase     ):






       """simple docstring"""





       snake_case__					:	Tuple          =					[]

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

       snake_case__					:	List[Any]          =					concatenate_datasets(lowercase_     )
       faiss.omp_set_num_threads(96     )

       snake_case__					:	Dict          =					faiss.IndexHNSWFlat(768    ,       128    ,       faiss.METRIC_INNER_PRODUCT     )
       concat.add_faiss_index("""embeddings"""    ,       custom_index=lowercase_     )
       concat.get_index("""embeddings"""     ).save(
           lowercase_     )  # since we load the index in to memory,we can directly update the index in the disk










'''simple docstring'''







from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowerCamelCase				:		List[str]				      =    logging.get_logger(__name__)

_lowerCamelCase				:		Optional[Any]				      =    {
    'MBZUAI/swiftformer-xs': 'https://huggingface.co/MBZUAI/swiftformer-xs/resolve/main/config.json',
}





class 	lowerCamelCase_				(			UpperCAmelCase_				):




					'''simple docstring'''






					lowercase__       =					"""swiftformer"""






					def __init__(       self,    __UpperCamelCase=3,    __UpperCamelCase=[3, 3, 6, 4],    __UpperCamelCase=[48, 56, 112, 220],    __UpperCamelCase=4,    __UpperCamelCase=[True, True, True, True],    __UpperCamelCase="gelu",    __UpperCamelCase=3,    __UpperCamelCase=2,    __UpperCamelCase=1,    __UpperCamelCase=0.0,    __UpperCamelCase=True,    __UpperCamelCase=1E-5,    __UpperCamelCase=1E-5,    **__UpperCamelCase,    ):
							super().__init__(**__UpperCamelCase					)
							_snake_case      			=			num_channels
							_snake_case      			=			depths
							_snake_case      			=			embed_dims
							_snake_case      			=			mlp_ratio
							_snake_case      			=			downsamples
							_snake_case      			=			hidden_act
							_snake_case      			=			down_patch_size
							_snake_case      			=			down_stride
							_snake_case      			=			down_pad
							_snake_case      			=			drop_path_rate
							_snake_case      			=			use_layer_scale
							_snake_case      			=			layer_scale_init_value
							_snake_case      			=			batch_norm_eps





class 	lowerCamelCase_				(			UpperCAmelCase_				):




					'''simple docstring'''






					lowercase__       =					version.parse("""1.11"""				)






					@property
					def 							__SCREAMING_SNAKE_CASE      (       self					):
							return OrderedDict(
							    [
							        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
							    ]					)






					@property
					def 							__SCREAMING_SNAKE_CASE      (       self					):
							return 1E-4





