




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__					:  List[Any]    ,							UpperCamelCase__					:  Optional[int]=100    ,							UpperCamelCase__					:  Tuple=" "						):
       _A				:	List[Any]       =			text.split(_lowerCamelCase						)
       return [character.join(text[i : i + n]						).strip() for i in range(0    ,							len(_lowerCamelCase						)    ,							_lowerCamelCase						)]
def 							_UpperCAmelCase    (UpperCamelCase__					:  List[str]						):
       _A				:	str       =			[], []
       for title, text in zip(documents["title"]    ,							documents["text"]						):
              if text is not None:
                     for passage in split_text(_lowerCamelCase						):
                            titles.append(title if title is not None else ""						)
                            texts.append(_lowerCamelCase						)
       return {"title": titles, "text": texts}
def 							_UpperCAmelCase    (UpperCamelCase__					:  Tuple    ,							UpperCamelCase__					:  Union[str, Any]    ,							UpperCamelCase__					:  Optional[int]    ,							UpperCamelCase__					:  Any    ,							UpperCamelCase__					:  List[Any]    ,							UpperCamelCase__					:  Any						):
       _A				:	int       =			load_dataset(
           "csv"    ,							data_files=[csv_path]    ,							split="train"    ,							delimiter="\t"    ,							column_names=["title", "text"]						)
       _A				:	Dict       =			kb_dataset.map(
           _lowerCamelCase    ,							batched=_lowerCamelCase    ,							num_proc=1						)  # if you want you can load already splitted csv.
       _A				:	Optional[int]       =			[kb_dataset.shard(_lowerCamelCase    ,							_lowerCamelCase    ,							contiguous=_lowerCamelCase						) for i in range(_lowerCamelCase						)]
       _A				:	Union[str, Any]       =			kb_list[process_num]

       _A				:	Dict       =			"""data_""" + str(_lowerCamelCase						)
       _A				:	Union[str, Any]       =			os.path.join(_lowerCamelCase    ,							_lowerCamelCase						)

       _A				:	Any       =			DPRContextEncoderTokenizerFast.from_pretrained("facebook/dpr-ctx_encoder-multiset-base"						)
       _A				:	int       =			ctx_encoder.to(device=_lowerCamelCase						)

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

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

       _A				:	List[str]       =			data_shrad.map(
           partial(_lowerCamelCase    ,							ctx_encoder=_lowerCamelCase    ,							ctx_tokenizer=_lowerCamelCase    ,							device=_lowerCamelCase						)    ,							batched=_lowerCamelCase    ,							batch_size=16    ,							features=_lowerCamelCase    ,							)
       dataset.save_to_disk(_lowerCamelCase						)






def 							_UpperCAmelCase    (UpperCamelCase__					:  str    ,							UpperCamelCase__					:  Union[str, Any]						):
       _A				:	Any       =			[]

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

       _A				:	List[Any]       =			concatenate_datasets(_lowerCamelCase						)
       faiss.omp_set_num_threads(96						)

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




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'''
     __SCREAMING_SNAKE_CASE        =			None
     __SCREAMING_SNAKE_CASE        =			None







class 					lowerCAmelCase__	(    folder_based_builder.FolderBasedBuilder):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE        =			datasets.Image()
     __SCREAMING_SNAKE_CASE        =			"image"
     __SCREAMING_SNAKE_CASE        =			ImageFolderConfig
     __SCREAMING_SNAKE_CASE        =			42  # definition at the bottom of the script
     __SCREAMING_SNAKE_CASE        =			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
