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


lowerCAmelCase			:							Optional[int]     =logging.getLogger(__name__)
torch.set_grad_enabled(False)
lowerCAmelCase			:							Dict     ="cuda" if torch.cuda.is_available() else "cpu"







def 						A__  (					__A   , __A=100   , __A=" "	):







    '''simple docstring'''
    _lowerCamelCase     :		Any 						=						text.split(__A	)
    return [character.join(text[i : i + n]	).strip() for i in range(0   , len(__A	)   , __A	)]







def 						A__  (					__A	):







    '''simple docstring'''
    _lowerCamelCase     :		Optional[Any] 						=						[], []
    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 						A__  (					__A   , __A   , __A	):







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







def 						A__  (					__A   , __A   , __A   , ):







    '''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
    _lowerCamelCase     :		str 						=						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
    _lowerCamelCase     :		Dict 						=						dataset.map(__A   , batched=__A   , num_proc=processing_args.num_proc	)

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

    # And finally save your dataset
    _lowerCamelCase     :		Optional[int] 						=						os.path.join(rag_example_args.output_dir   , """my_knowledge_dataset"""	)
    dataset.save_to_disk(__A	)
    # 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
    _lowerCamelCase     :		Optional[int] 						=						faiss.IndexHNSWFlat(index_hnsw_args.d   , index_hnsw_args.m   , faiss.METRIC_INNER_PRODUCT	)
    dataset.add_faiss_index("""embeddings"""   , custom_index=__A	)

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




@dataclass
class 	__snake_case	:





      '''simple docstring'''






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





@dataclass
class 	__snake_case	:





      '''simple docstring'''






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





@dataclass
class 	__snake_case	:





      '''simple docstring'''






      _snake_case    		=       field(
          default=768 ,  metadata={'help': 'The dimension of the embeddings to pass to the HNSW Faiss index.'} ,  )
      _snake_case    		=       field(
          default=128 ,  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)

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





import unittest

from transformers.utils.backbone_utils import (
    BackboneMixin,
    get_aligned_output_features_output_indices,
    verify_out_features_out_indices,
)




class 	__snake_case	( unittest.TestCase		):





      '''simple docstring'''






      def 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any])				->Optional[Any]:


          """simple docstring"""


          _lowerCamelCase     :		Tuple 						=						["""a""", """b""", """c"""]

          # Defaults to last layer if both are None
          _lowerCamelCase						,							_lowerCamelCase     :		int 						=						get_aligned_output_features_output_indices(_UpperCamelCase    ,  _UpperCamelCase    ,  _UpperCamelCase)
          self.assertEqual(_UpperCamelCase    ,  ["""c"""])
          self.assertEqual(_UpperCamelCase    ,  [2])

          # Out indices set to match out features
          _lowerCamelCase						,							_lowerCamelCase     :		int 						=						get_aligned_output_features_output_indices(["""a""", """c"""]    ,  _UpperCamelCase    ,  _UpperCamelCase)
          self.assertEqual(_UpperCamelCase    ,  ["""a""", """c"""])
          self.assertEqual(_UpperCamelCase    ,  [0, 2])

          # Out features set to match out indices
          _lowerCamelCase						,							_lowerCamelCase     :		Tuple 						=						get_aligned_output_features_output_indices(_UpperCamelCase    ,  [0, 2]    ,  _UpperCamelCase)
          self.assertEqual(_UpperCamelCase    ,  ["""a""", """c"""])
          self.assertEqual(_UpperCamelCase    ,  [0, 2])

          # Out features selected from negative indices
          _lowerCamelCase						,							_lowerCamelCase     :		str 						=						get_aligned_output_features_output_indices(_UpperCamelCase    ,  [-3, -1]    ,  _UpperCamelCase)
          self.assertEqual(_UpperCamelCase    ,  ["""a""", """c"""])
          self.assertEqual(_UpperCamelCase    ,  [-3, -1])



      def 	_SCREAMING_SNAKE_CASE    (					self     :				int)				->int:


          """simple docstring"""


          with self.assertRaises(_UpperCamelCase):
              verify_out_features_out_indices(["""a""", """b"""]    ,  (0, 1)    ,  _UpperCamelCase)

          # Out features must be a list
          with self.assertRaises(_UpperCamelCase):
              verify_out_features_out_indices(("""a""", """b""")    ,  (0, 1)    ,  ["""a""", """b"""])

          # Out features must be a subset of stage names
          with self.assertRaises(_UpperCamelCase):
              verify_out_features_out_indices(["""a""", """b"""]    ,  (0, 1)    ,  ["""a"""])

          # Out indices must be a list or tuple
          with self.assertRaises(_UpperCamelCase):
              verify_out_features_out_indices(_UpperCamelCase    ,  0    ,  ["""a""", """b"""])

          # Out indices must be a subset of stage names
          with self.assertRaises(_UpperCamelCase):
              verify_out_features_out_indices(_UpperCamelCase    ,  (0, 1)    ,  ["""a"""])

          # Out features and out indices must be the same length
          with self.assertRaises(_UpperCamelCase):
              verify_out_features_out_indices(["""a""", """b"""]    ,  (0,)    ,  ["""a""", """b""", """c"""])

          # Out features should match out indices
          with self.assertRaises(_UpperCamelCase):
              verify_out_features_out_indices(["""a""", """b"""]    ,  (0, 2)    ,  ["""a""", """b""", """c"""])

          # Out features and out indices should be in order
          with self.assertRaises(_UpperCamelCase):
              verify_out_features_out_indices(["""b""", """a"""]    ,  (0, 1)    ,  ["""a""", """b"""])

          # Check passes with valid inputs
          verify_out_features_out_indices(["""a""", """b""", """d"""]    ,  (0, 1, -1)    ,  ["""a""", """b""", """c""", """d"""])




      def 	_SCREAMING_SNAKE_CASE    (					self     :				int)				->List[Any]:


          """simple docstring"""


          _lowerCamelCase     :		int 						=						BackboneMixin()

          _lowerCamelCase     :		Union[str, Any] 						=						["""a""", """b""", """c"""]
          _lowerCamelCase     :		Tuple 						=						["""a""", """c"""]
          _lowerCamelCase     :		List[Any] 						=						[0, 2]

          # Check that the output features and indices are set correctly
          self.assertEqual(backbone.out_features    ,  ["""a""", """c"""])
          self.assertEqual(backbone.out_indices    ,  [0, 2])

          # Check out features and indices are updated correctly
          _lowerCamelCase     :		str 						=						["""a""", """b"""]
          self.assertEqual(backbone.out_features    ,  ["""a""", """b"""])
          self.assertEqual(backbone.out_indices    ,  [0, 1])

          _lowerCamelCase     :		Optional[int] 						=						[-3, -1]
          self.assertEqual(backbone.out_features    ,  ["""a""", """c"""])
          self.assertEqual(backbone.out_indices    ,  [-3, -1])




