'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional

import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset

from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast, HfArgumentParser


lowercase__  :      Tuple        =					logging.getLogger(__name__)
torch.set_grad_enabled(False)
lowercase__  :      int        =					'cuda' if torch.cuda.is_available() else 'cpu'



def  __lowerCamelCase       (      _UpperCamelCase  :  str							,    _UpperCamelCase  :  List[Any]=100							,    _UpperCamelCase  :  Union[str, Any]=" "					):


      '''simple docstring'''


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



def  __lowerCamelCase       (      _UpperCamelCase  :  dict					):


      '''simple docstring'''


      UpperCAmelCase_            =  [], []
      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       (      _UpperCamelCase  :  dict							,    _UpperCamelCase  :  DPRContextEncoder							,    _UpperCamelCase  :  DPRContextEncoderTokenizerFast					):


      '''simple docstring'''


      UpperCAmelCase_            =  ctx_tokenizer(
          documents['''title''']							,    documents['''text''']							,    truncation=_lowerCamelCase							,    padding='''longest'''							,    return_tensors='''pt'''					)["input_ids"]
      UpperCAmelCase_            =  ctx_encoder(input_ids.to(device=_lowerCamelCase					)							,    return_dict=_lowerCamelCase					).pooler_output
      return {"embeddings": embeddings.detach().cpu().numpy()}



def  __lowerCamelCase       (      _UpperCamelCase  :  "RagExampleArguments"							,    _UpperCamelCase  :  "ProcessingArguments"							,    _UpperCamelCase  :  "IndexHnswArguments"							,    ):


      '''simple docstring'''


      logger.info('''Step 1 - Create the dataset'''					)
      ######################################

      # The dataset needed for RAG must have three columns:
      # - title (string): title of the document
      # - text (string): text of a passage of the document
      # - embeddings (array of dimension d): DPR representation of the passage
      # Let's say you have documents in tab-separated csv files with columns "title" and "text"
      assert os.path.isfile(rag_example_args.csv_path					), "Please provide a valid path to a csv file"

      # You can load a Dataset object this way
      UpperCAmelCase_            =  load_dataset(
          '''csv'''							,    data_files=[rag_example_args.csv_path]							,    split='''train'''							,    delimiter='''\t'''							,    column_names=['''title''', '''text''']					)

      # More info about loading csv files in the documentation: https://huggingface.co/docs/datasets/loading_datasets.html?highlight=csv#csv-files

      # Then split the documents into passages of 100 words
      UpperCAmelCase_            =  dataset.map(_lowerCamelCase							,    batched=_lowerCamelCase							,    num_proc=processing_args.num_proc					)

      # And compute the embeddings
      UpperCAmelCase_            =  DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name					).to(device=_lowerCamelCase					)
      UpperCAmelCase_            =  DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name					)
      UpperCAmelCase_            =  Features(
          {'''text''': Value('''string'''					), '''title''': Value('''string'''					), '''embeddings''': Sequence(Value('''float32'''					)					)}					)  # optional, save as float32 instead of float64 to save space
      UpperCAmelCase_            =  dataset.map(
          partial(_lowerCamelCase							,    ctx_encoder=_lowerCamelCase							,    ctx_tokenizer=_lowerCamelCase					)							,    batched=_lowerCamelCase							,    batch_size=processing_args.batch_size							,    features=_lowerCamelCase							,    )

      # And finally save your dataset
      UpperCAmelCase_            =  os.path.join(rag_example_args.output_dir							,    '''my_knowledge_dataset'''					)
      dataset.save_to_disk(_lowerCamelCase					)
      # from datasets import load_from_disk
      # dataset = load_from_disk(passages_path)  # to reload the dataset

      ######################################
      logger.info('''Step 2 - Index the dataset'''					)
      ######################################

      # Let's use the Faiss implementation of HNSW for fast approximate nearest neighbor search
      UpperCAmelCase_            =  faiss.IndexHNSWFlat(index_hnsw_args.d							,    index_hnsw_args.m							,    faiss.METRIC_INNER_PRODUCT					)
      dataset.add_faiss_index('''embeddings'''							,    custom_index=_lowerCamelCase					)

      # And save the index
      UpperCAmelCase_            =  os.path.join(rag_example_args.output_dir							,    '''my_knowledge_dataset_hnsw_index.faiss'''					)
      dataset.get_index('''embeddings'''					).save(_lowerCamelCase					)
      # dataset.load_faiss_index("embeddings", index_path)  # to reload the index







@dataclass
class lowerCamelCase							:




  '''simple docstring'''



  lowerCAmelCase__										=	field(
      default=str(Path(__lowercase					).parent / '''test_run''' / '''dummy-kb''' / '''my_knowledge_dataset.csv'''					)     ,		metadata={'''help''': '''Path to a tab-separated csv file with columns \'title\' and \'text\''''}     ,		)
  lowerCAmelCase__										=	field(
      default=__lowercase     ,		metadata={'''help''': '''Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'''}     ,		)
  lowerCAmelCase__										=	field(
      default='''facebook/rag-sequence-nq'''     ,		metadata={'''help''': '''The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''''}     ,		)
  lowerCAmelCase__										=	field(
      default='''facebook/dpr-ctx_encoder-multiset-base'''     ,		metadata={
          '''help''': (
              '''The DPR context encoder model to use. Either \'facebook/dpr-ctx_encoder-single-nq-base\' or'''
              ''' \'facebook/dpr-ctx_encoder-multiset-base\''''
          )
      }     ,		)
  lowerCAmelCase__										=	field(
      default=str(Path(__lowercase					).parent / '''test_run''' / '''dummy-kb'''					)     ,		metadata={'''help''': '''Path to a directory where the dataset passages and the index will be saved'''}     ,		)







@dataclass
class lowerCamelCase							:




  '''simple docstring'''



  lowerCAmelCase__										=	field(
      default=__lowercase     ,		metadata={
          '''help''': '''The number of processes to use to split the documents into passages. Default is single process.'''
      }     ,		)
  lowerCAmelCase__										=	field(
      default=16     ,		metadata={
          '''help''': '''The batch size to use when computing the passages embeddings using the DPR context encoder.'''
      }     ,		)







@dataclass
class lowerCamelCase							:




  '''simple docstring'''



  lowerCAmelCase__										=	field(
      default=7_68     ,		metadata={'''help''': '''The dimension of the embeddings to pass to the HNSW Faiss index.'''}     ,		)
  lowerCAmelCase__										=	field(
      default=1_28     ,		metadata={
          '''help''': (
              '''The number of bi-directional links created for every new element during the HNSW index construction.'''
          )
      }     ,		)


if __name__ == "__main__":
      logging.basicConfig(level=logging.WARNING)
      logger.setLevel(logging.INFO)

      lowercase__  :      List[Any]        =					HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
      lowercase__  :      Tuple        =					parser.parse_args_into_dataclasses()
      with TemporaryDirectory() as tmp_dir:
            lowercase__  :      Any        =					rag_example_args.output_dir or tmp_dir
            main(rag_example_args, processing_args, index_hnsw_args)


'''simple docstring'''
import unittest
from typing import Dict, List, Optional, Union

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 BridgeTowerImageProcessor







class lowerCamelCase							(	unittest.TestCase					):




  '''simple docstring'''



  def __init__(      self			:       int  ,							UpperCAmelCase__			:       Dict  ,							UpperCAmelCase__			:       bool = True  ,							UpperCAmelCase__			:       Dict[str, int] = None  ,							UpperCAmelCase__			:       int = 32  ,							UpperCAmelCase__			:       bool = True  ,							UpperCAmelCase__			:       Union[int, float] = 1 / 255  ,							UpperCAmelCase__			:       bool = True  ,							UpperCAmelCase__			:       bool = True  ,							UpperCAmelCase__			:       Optional[Union[float, List[float]]] = [0.4814_5466, 0.457_8275, 0.4082_1073]  ,							UpperCAmelCase__			:       Optional[Union[float, List[float]]] = [0.2686_2954, 0.2613_0258, 0.2757_7711]  ,							UpperCAmelCase__			:       bool = True  ,							UpperCAmelCase__			:       Union[str, Any]=7  ,							UpperCAmelCase__			:       Optional[int]=30  ,							UpperCAmelCase__			:       Union[str, Any]=400  ,							UpperCAmelCase__			:       List[Any]=3  ,							)				->Dict:
        UpperCAmelCase_            =  parent
        UpperCAmelCase_            =  do_resize
        UpperCAmelCase_            =  size if size is not None else {'''shortest_edge''': 288}
        UpperCAmelCase_            =  size_divisor
        UpperCAmelCase_            =  do_rescale
        UpperCAmelCase_            =  rescale_factor
        UpperCAmelCase_            =  do_normalize
        UpperCAmelCase_            =  do_center_crop
        UpperCAmelCase_            =  image_mean
        UpperCAmelCase_            =  image_std
        UpperCAmelCase_            =  do_pad
        UpperCAmelCase_            =  batch_size
        UpperCAmelCase_            =  num_channels
        UpperCAmelCase_            =  min_resolution
        UpperCAmelCase_            =  max_resolution






  def  lowerCAmelCase__ (      self			:       Tuple  )				->List[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,
            "size_divisor": self.size_divisor,
        }






  def  lowerCAmelCase__ (      self			:       Optional[int]  ,							UpperCAmelCase__			:       List[str]  ,							UpperCAmelCase__			:       Dict=False  )				->Any:
        if not batched:
              UpperCAmelCase_            =  self.size['''shortest_edge''']
              UpperCAmelCase_            =  image_inputs[0]
              if isinstance(UpperCAmelCase__  ,							Image.Image  ):
                    UpperCAmelCase_      ,		UpperCAmelCase_            =  image.size
              else:
                    UpperCAmelCase_      ,		UpperCAmelCase_            =  image.shape[1], image.shape[2]
              UpperCAmelCase_            =  size / min(UpperCAmelCase__  ,							UpperCAmelCase__  )
              if h < w:
                    UpperCAmelCase_      ,		UpperCAmelCase_            =  size, scale * w
              else:
                    UpperCAmelCase_      ,		UpperCAmelCase_            =  scale * h, size

              UpperCAmelCase_            =  int((1333 / 800) * size  )
              if max(UpperCAmelCase__  ,							UpperCAmelCase__  ) > max_size:
                    UpperCAmelCase_            =  max_size / max(UpperCAmelCase__  ,							UpperCAmelCase__  )
                    UpperCAmelCase_            =  newh * scale
                    UpperCAmelCase_            =  neww * scale

              UpperCAmelCase_      ,		UpperCAmelCase_            =  int(newh + 0.5  ), int(neww + 0.5  )
              UpperCAmelCase_      ,		UpperCAmelCase_            =  (
                  newh // self.size_divisor * self.size_divisor,
                  neww // self.size_divisor * self.size_divisor,
              )

        else:
              UpperCAmelCase_            =  []
              for image in image_inputs:
                    UpperCAmelCase_      ,		UpperCAmelCase_            =  self.get_expected_values([image]  )
                    expected_values.append((expected_height, expected_width)  )
              UpperCAmelCase_            =  max(UpperCAmelCase__  ,							key=lambda UpperCAmelCase__  : item[0]  )[0]
              UpperCAmelCase_            =  max(UpperCAmelCase__  ,							key=lambda UpperCAmelCase__  : item[1]  )[1]

        return expected_height, expected_width







@require_torch
@require_vision
class lowerCamelCase							(	lowerCamelCase     ,		unittest.TestCase					):




  '''simple docstring'''



  lowerCAmelCase__										=	BridgeTowerImageProcessor if is_vision_available() else None






  def  lowerCAmelCase__ (      self			:       Optional[int]  )				->str:
        UpperCAmelCase_            =  BridgeTowerImageProcessingTester(self  )






  @property
  def  lowerCAmelCase__ (      self			:       List[str]  )				->Dict:
        return self.image_processor_tester.prepare_image_processor_dict()






  def  lowerCAmelCase__ (      self			:       Optional[int]  )				->int:
        UpperCAmelCase_            =  self.image_processing_class(**self.image_processor_dict  )
        self.assertTrue(hasattr(UpperCAmelCase__  ,							'''image_mean'''  )  )
        self.assertTrue(hasattr(UpperCAmelCase__  ,							'''image_std'''  )  )
        self.assertTrue(hasattr(UpperCAmelCase__  ,							'''do_normalize'''  )  )
        self.assertTrue(hasattr(UpperCAmelCase__  ,							'''do_resize'''  )  )
        self.assertTrue(hasattr(UpperCAmelCase__  ,							'''size'''  )  )
        self.assertTrue(hasattr(UpperCAmelCase__  ,							'''size_divisor'''  )  )






  def  lowerCAmelCase__ (      self			:       Optional[int]  )				->Union[str, Any]:
        pass






  def  lowerCAmelCase__ (      self			:       List[str]  )				->Union[str, Any]:
        # Initialize image processor
        UpperCAmelCase_            =  self.image_processing_class(**self.image_processor_dict  )
        # create random PIL images
        UpperCAmelCase_            =  prepare_image_inputs(self.image_processor_tester  ,							equal_resolution=UpperCAmelCase__  )
        for image in image_inputs:
              self.assertIsInstance(UpperCAmelCase__  ,							Image.Image  )

        # Test not batched input
        UpperCAmelCase_            =  image_processing(image_inputs[0]  ,							return_tensors='''pt'''  ).pixel_values

        UpperCAmelCase_      ,		UpperCAmelCase_            =  self.image_processor_tester.get_expected_values(UpperCAmelCase__  )
        self.assertEqual(
            encoded_images.shape  ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,							)

        # Test batched
        UpperCAmelCase_            =  image_processing(UpperCAmelCase__  ,							return_tensors='''pt'''  ).pixel_values

        UpperCAmelCase_      ,		UpperCAmelCase_            =  self.image_processor_tester.get_expected_values(UpperCAmelCase__  ,							batched=UpperCAmelCase__  )
        self.assertEqual(
            encoded_images.shape  ,							(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )  ,							)






  def  lowerCAmelCase__ (      self			:       Any  )				->Optional[int]:
        # Initialize image processor
        UpperCAmelCase_            =  self.image_processing_class(**self.image_processor_dict  )
        # create random numpy tensors
        UpperCAmelCase_            =  prepare_image_inputs(self.image_processor_tester  ,							equal_resolution=UpperCAmelCase__  ,							numpify=UpperCAmelCase__  )
        for image in image_inputs:
              self.assertIsInstance(UpperCAmelCase__  ,							np.ndarray  )

        # Test not batched input
        UpperCAmelCase_            =  image_processing(image_inputs[0]  ,							return_tensors='''pt'''  ).pixel_values

        UpperCAmelCase_      ,		UpperCAmelCase_            =  self.image_processor_tester.get_expected_values(UpperCAmelCase__  )
        self.assertEqual(
            encoded_images.shape  ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,							)

        # Test batched
        UpperCAmelCase_            =  image_processing(UpperCAmelCase__  ,							return_tensors='''pt'''  ).pixel_values

        UpperCAmelCase_      ,		UpperCAmelCase_            =  self.image_processor_tester.get_expected_values(UpperCAmelCase__  ,							batched=UpperCAmelCase__  )
        self.assertEqual(
            encoded_images.shape  ,							(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )  ,							)






  def  lowerCAmelCase__ (      self			:       int  )				->List[str]:
        # Initialize image processor
        UpperCAmelCase_            =  self.image_processing_class(**self.image_processor_dict  )
        # create random PyTorch tensors
        UpperCAmelCase_            =  prepare_image_inputs(self.image_processor_tester  ,							equal_resolution=UpperCAmelCase__  ,							torchify=UpperCAmelCase__  )
        for image in image_inputs:
              self.assertIsInstance(UpperCAmelCase__  ,							torch.Tensor  )

        # Test not batched input
        UpperCAmelCase_            =  image_processing(image_inputs[0]  ,							return_tensors='''pt'''  ).pixel_values

        UpperCAmelCase_      ,		UpperCAmelCase_            =  self.image_processor_tester.get_expected_values(UpperCAmelCase__  )
        self.assertEqual(
            encoded_images.shape  ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,							)

        # Test batched
        UpperCAmelCase_            =  image_processing(UpperCAmelCase__  ,							return_tensors='''pt'''  ).pixel_values

        UpperCAmelCase_      ,		UpperCAmelCase_            =  self.image_processor_tester.get_expected_values(UpperCAmelCase__  ,							batched=UpperCAmelCase__  )
        self.assertEqual(
            encoded_images.shape  ,							(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )  ,							)





