
'''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       __SCREAMING_SNAKE_CASE   (						_SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE=100							,      _SCREAMING_SNAKE_CASE=" "   ):
							_snake_case     =						text.split(_A   )
							return [character.join(text[i : i + n]   ).strip() for i in range(0							,      len(_A   )							,      _A   )]






def       __SCREAMING_SNAKE_CASE   (						_SCREAMING_SNAKE_CASE   ):
							_snake_case,							_snake_case     =						[], []
							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       __SCREAMING_SNAKE_CASE   (						_SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE   ):
							_snake_case     =						load_dataset(
							    """csv"""							,      data_files=[csv_path]							,      split="""train"""							,      delimiter="""\t"""							,      column_names=["""title""", """text"""]   )
							_snake_case     =						kb_dataset.map(
							    _A							,      batched=_A							,      num_proc=1   )  # if you want you can load already splitted csv.
							_snake_case     =						[kb_dataset.shard(_A							,      _A							,      contiguous=_A   ) for i in range(_A   )]
							_snake_case     =						kb_list[process_num]

							_snake_case     =						"""data_""" + str(_A   )
							_snake_case     =						os.path.join(_A							,      _A   )

							_snake_case     =						DPRContextEncoderTokenizerFast.from_pretrained("""facebook/dpr-ctx_encoder-multiset-base"""   )
							_snake_case     =						ctx_encoder.to(device=_A   )

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

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

							_snake_case     =						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       __SCREAMING_SNAKE_CASE   (						_SCREAMING_SNAKE_CASE							,      _SCREAMING_SNAKE_CASE   ):
							_snake_case     =						[]

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

							_snake_case     =						concatenate_datasets(_A   )
							faiss.omp_set_num_threads(96   )

							_snake_case     =						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

'''simple docstring'''
from typing import List

import datasets
from datasets.tasks import ImageClassification

from ..folder_based_builder import folder_based_builder


__lowerCAmelCase  							= datasets.utils.logging.get_logger(__name__)


class     _lowerCAmelCase					(					folder_based_builder.FolderBasedBuilderConfig   ):







							'''simple docstring'''

							lowerCAmelCase_      		=		None
							lowerCAmelCase_      		=		None


class     _lowerCAmelCase					(					folder_based_builder.FolderBasedBuilder   ):







							'''simple docstring'''

							lowerCAmelCase_      		=		datasets.Image()
							lowerCAmelCase_      		=		"image"
							lowerCAmelCase_      		=		ImageFolderConfig
							lowerCAmelCase_      		=		42  # definition at the bottom of the script
							lowerCAmelCase_      		=		ImageClassification(image_column="image"  ,							label_column="label"   )
__lowerCAmelCase  							= [
    '.blp',
    '.bmp',
    '.dib',
    '.bufr',
    '.cur',
    '.pcx',
    '.dcx',
    '.dds',
    '.ps',
    '.eps',
    '.fit',
    '.fits',
    '.fli',
    '.flc',
    '.ftc',
    '.ftu',
    '.gbr',
    '.gif',
    '.grib',
    '.h5',
    '.hdf',
    '.png',
    '.apng',
    '.jp2',
    '.j2k',
    '.jpc',
    '.jpf',
    '.jpx',
    '.j2c',
    '.icns',
    '.ico',
    '.im',
    '.iim',
    '.tif',
    '.tiff',
    '.jfif',
    '.jpe',
    '.jpg',
    '.jpeg',
    '.mpg',
    '.mpeg',
    '.msp',
    '.pcd',
    '.pxr',
    '.pbm',
    '.pgm',
    '.ppm',
    '.pnm',
    '.psd',
    '.bw',
    '.rgb',
    '.rgba',
    '.sgi',
    '.ras',
    '.tga',
    '.icb',
    '.vda',
    '.vst',
    '.webp',
    '.wmf',
    '.emf',
    '.xbm',
    '.xpm',
]
__lowerCAmelCase  							= IMAGE_EXTENSIONS