"""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 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE=100						,	_SCREAMING_SNAKE_CASE=" "  )      ->Tuple:
  a__:	str     				=      text.split(SCREAMING_SNAKE_CASE__  )
  return [character.join(text[i : i + n]  ).strip() for i in range(0						,	len(SCREAMING_SNAKE_CASE__  )						,	SCREAMING_SNAKE_CASE__  )]







def 		__a  (      _SCREAMING_SNAKE_CASE  )      ->List[str]:
  a__				,       a__:	int     				=      [], []
  for title, text in zip(documents['title']						,	documents['text']  ):
    if text is not None:
      for passage in split_text(SCREAMING_SNAKE_CASE__  ):
        titles.append(title if title is not None else ''  )
        texts.append(SCREAMING_SNAKE_CASE__  )
  return {"title": titles, "text": texts}







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )      ->List[str]:
  a__:	Optional[int]     				=      load_dataset(
      'csv'						,	data_files=[csv_path]						,	split='train'						,	delimiter='\t'						,	column_names=['title', 'text']  )
  a__:	Tuple     				=      kb_dataset.map(
      SCREAMING_SNAKE_CASE__						,	batched=SCREAMING_SNAKE_CASE__						,	num_proc=1  )  # if you want you can load already splitted csv.
  a__:	Optional[int]     				=      [kb_dataset.shard(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	contiguous=SCREAMING_SNAKE_CASE__  ) for i in range(SCREAMING_SNAKE_CASE__  )]
  a__:	List[Any]     				=      kb_list[process_num]

  a__:	Optional[int]     				=      'data_' + str(SCREAMING_SNAKE_CASE__  )
  a__:	List[Any]     				=      os.path.join(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__  )

  a__:	Tuple     				=      DPRContextEncoderTokenizerFast.from_pretrained('facebook/dpr-ctx_encoder-multiset-base'  )
  a__:	Tuple     				=      ctx_encoder.to(device=SCREAMING_SNAKE_CASE__  )

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

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

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







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )      ->Dict:
  a__:	List[str]     				=      []

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

  a__:	Tuple     				=      concatenate_datasets(SCREAMING_SNAKE_CASE__  )
  faiss.omp_set_num_threads(96  )

  a__:	List[Any]     				=      faiss.IndexHNSWFlat(768						,	128						,	faiss.METRIC_INNER_PRODUCT  )
  concat.add_faiss_index('embeddings'						,	custom_index=SCREAMING_SNAKE_CASE__  )
  concat.get_index('embeddings'  ).save(
      SCREAMING_SNAKE_CASE__  )  # since we load the index in to memory,we can directly update the index in the disk






import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import EfficientNetImageProcessor






class        UpperCAmelCase__			(   unittest.TestCase						):



    """simple docstring"""






    def __init__(     self   ,  A_   ,  A_=13   ,  A_=3   ,  A_=18   ,  A_=30   ,  A_=400   ,  A_=True   ,  A_=None   ,  A_=True   ,  A_=[0.5, 0.5, 0.5]   ,  A_=[0.5, 0.5, 0.5]   ,  )    ->			int:
         __UpperCamelCase       				=size if size is not None else {'height': 18, 'width': 18}
         __UpperCamelCase       				=parent
         __UpperCamelCase       				=batch_size
         __UpperCamelCase       				=num_channels
         __UpperCamelCase       				=image_size
         __UpperCamelCase       				=min_resolution
         __UpperCamelCase       				=max_resolution
         __UpperCamelCase       				=do_resize
         __UpperCamelCase       				=size
         __UpperCamelCase       				=do_normalize
         __UpperCamelCase       				=image_mean
         __UpperCamelCase       				=image_std






    def _a   (     self       )    ->			Optional[Any]:
         return {
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_normalize": self.do_normalize,
             "do_resize": self.do_resize,
             "size": self.size,
         }






@require_torch
@require_vision
class        UpperCAmelCase__			(   A_  ,    unittest.TestCase						):



    """simple docstring"""






    UpperCAmelCase__		:						List[Any]    =    EfficientNetImageProcessor if is_vision_available() else None






    def _a   (     self       )    ->			List[str]:
         __UpperCamelCase       				=EfficientNetImageProcessorTester(self       )






    @property
    def _a   (     self       )    ->			Optional[Any]:
         return self.image_processor_tester.prepare_image_processor_dict()






    def _a   (     self       )    ->			str:
         __UpperCamelCase       				=self.image_processing_class(**self.image_processor_dict       )
         self.assertTrue(hasattr(A_   ,  'image_mean'       )       )
         self.assertTrue(hasattr(A_   ,  'image_std'       )       )
         self.assertTrue(hasattr(A_   ,  'do_normalize'       )       )
         self.assertTrue(hasattr(A_   ,  'do_resize'       )       )
         self.assertTrue(hasattr(A_   ,  'size'       )       )






    def _a   (     self       )    ->			Dict:
         __UpperCamelCase       				=self.image_processing_class.from_dict(self.image_processor_dict       )
         self.assertEqual(image_processor.size   ,  {'height': 18, 'width': 18}       )

         __UpperCamelCase       				=self.image_processing_class.from_dict(self.image_processor_dict   ,  size=42       )
         self.assertEqual(image_processor.size   ,  {'height': 42, 'width': 42}       )






    def _a   (     self       )    ->			List[str]:
         # Initialize image_processing
         __UpperCamelCase       				=self.image_processing_class(**self.image_processor_dict       )
         # create random PIL images
         __UpperCamelCase       				=prepare_image_inputs(self.image_processor_tester   ,  equal_resolution=A_       )
         for image in image_inputs:
              self.assertIsInstance(A_   ,  Image.Image       )

         # Test not batched input
         __UpperCamelCase       				=image_processing(image_inputs[0]   ,  return_tensors='pt'       ).pixel_values
         self.assertEqual(
             encoded_images.shape   ,  (
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size['height'],
                 self.image_processor_tester.size['width'],
             )   ,  )

         # Test batched
         __UpperCamelCase       				=image_processing(A_   ,  return_tensors='pt'       ).pixel_values
         self.assertEqual(
             encoded_images.shape   ,  (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size['height'],
                 self.image_processor_tester.size['width'],
             )   ,  )






    def _a   (     self       )    ->			List[str]:
         # Initialize image_processing
         __UpperCamelCase       				=self.image_processing_class(**self.image_processor_dict       )
         # create random numpy tensors
         __UpperCamelCase       				=prepare_image_inputs(self.image_processor_tester   ,  equal_resolution=A_   ,  numpify=A_       )
         for image in image_inputs:
              self.assertIsInstance(A_   ,  np.ndarray       )

         # Test not batched input
         __UpperCamelCase       				=image_processing(image_inputs[0]   ,  return_tensors='pt'       ).pixel_values
         self.assertEqual(
             encoded_images.shape   ,  (
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size['height'],
                 self.image_processor_tester.size['width'],
             )   ,  )

         # Test batched
         __UpperCamelCase       				=image_processing(A_   ,  return_tensors='pt'       ).pixel_values
         self.assertEqual(
             encoded_images.shape   ,  (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size['height'],
                 self.image_processor_tester.size['width'],
             )   ,  )






    def _a   (     self       )    ->			Dict:
         # Initialize image_processing
         __UpperCamelCase       				=self.image_processing_class(**self.image_processor_dict       )
         # create random PyTorch tensors
         __UpperCamelCase       				=prepare_image_inputs(self.image_processor_tester   ,  equal_resolution=A_   ,  torchify=A_       )
         for image in image_inputs:
              self.assertIsInstance(A_   ,  torch.Tensor       )

         # Test not batched input
         __UpperCamelCase       				=image_processing(image_inputs[0]   ,  return_tensors='pt'       ).pixel_values
         self.assertEqual(
             encoded_images.shape   ,  (
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size['height'],
                 self.image_processor_tester.size['width'],
             )   ,  )

         # Test batched
         __UpperCamelCase       				=image_processing(A_   ,  return_tensors='pt'       ).pixel_values
         self.assertEqual(
             encoded_images.shape   ,  (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size['height'],
                 self.image_processor_tester.size['width'],
             )   ,  )





