




"""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				(			lowerCamelCase   ,      lowerCamelCase=100   ,      lowerCamelCase=" "		):
						'''simple docstring'''


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

def 		lowerCAmelCase				(			lowerCamelCase		):
						'''simple docstring'''


						snake_case__      ,						snake_case__							:       Dict				      =       [], []
						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 		lowerCAmelCase				(			lowerCamelCase   ,      lowerCamelCase   ,      lowerCamelCase   ,      lowerCamelCase   ,      lowerCamelCase   ,      lowerCamelCase		):
						'''simple docstring'''


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

						snake_case__							:       Optional[Any]				      =       '''data_''' + str(lowerCamelCase		)
						snake_case__							:       Optional[Any]				      =       os.path.join(lowerCamelCase   ,      lowerCamelCase		)

						snake_case__							:       List[str]				      =       DPRContextEncoderTokenizerFast.from_pretrained('''facebook/dpr-ctx_encoder-multiset-base'''		)
						snake_case__							:       List[str]				      =       ctx_encoder.to(device=lowerCamelCase		)

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

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

						snake_case__							:       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 		lowerCAmelCase				(			lowerCamelCase   ,      lowerCamelCase		):
						'''simple docstring'''


						snake_case__							:       Optional[Any]				      =       []

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

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

						snake_case__							:       List[Any]				      =       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








"""simple docstring"""





import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_multi_gpu,
    require_single_gpu,
    test_sync,
)
from accelerate.utils import patch_environment

class    __lowercase							(      unittest.TestCase						):


				'''simple docstring'''


				def    _lowerCamelCase       ( self :  Any   ):
										snake_case__							:       Dict				      =       inspect.getfile(accelerate.test_utils   )
										snake_case__							:       Tuple				      =       os.path.sep.join(mod_file.split(os.path.sep   )[:-1] + ['''scripts''', '''test_sync.py''']   )

				@require_cpu
				def    _lowerCamelCase       ( self :  Optional[Any]   ):
										debug_launcher(test_sync.main   ,num_processes=1   )

				@require_cpu
				def    _lowerCamelCase       ( self :  List[str]   ):
										debug_launcher(test_sync.main   )

				@require_single_gpu
				def    _lowerCamelCase       ( self :  Union[str, Any]   ):
										test_sync.main()





				@require_multi_gpu
				def    _lowerCamelCase       ( self :  List[str]   ):
										print(F'''Found {torch.cuda.device_count()} devices.'''   )
										snake_case__							:       Dict				      =       ['''torchrun''', F'''--nproc_per_node={torch.cuda.device_count()}''', self.test_file_path]
										with patch_environment(omp_num_threads=1   ):
																execute_subprocess_async(SCREAMING_SNAKE_CASE_   ,env=os.environ.copy()   )


