




'''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


_lowerCAmelCase  		=      logging.getLogger(__name__)
torch.set_grad_enabled(False)
_lowerCAmelCase  		=      "cuda" if torch.cuda.is_available() else "cpu"






def 		_lowerCAmelCase							(   lowercase							: List[str]					,  lowercase							: str=1_0_0					,  lowercase							: Tuple=" "    )			->List[str]:
   """simple docstring"""




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






def 		_lowerCAmelCase							(   lowercase							: Optional[Any]    )			->dict:
   """simple docstring"""




   lowercase__       							=							[], []
   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							(   lowercase							: List[str]					,  lowercase							: Optional[int]					,  lowercase							: Dict    )			->dict:
   """simple docstring"""




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






def 		_lowerCAmelCase							(   lowercase							: Dict					,  lowercase							: Optional[int]					,  lowercase							: str					,  )			->int:
   """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
   lowercase__       							=							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
   lowercase__       							=							dataset.map(lowerCAmelCase_					,  batched=lowerCAmelCase_					,  num_proc=processing_args.num_proc    )

   # And compute the embeddings
   lowercase__       							=							DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name    ).to(device=lowerCAmelCase_    )
   lowercase__       							=							DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name    )
   lowercase__       							=							Features(
       {'''text''': Value('''string'''    ), '''title''': Value('''string'''    ), '''embeddings''': Sequence(Value('''float32'''    )    )}    )  # optional, save as float32 instead of float64 to save space
   lowercase__       							=							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
   lowercase__       							=							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
   lowercase__       							=							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
   lowercase__       							=							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   __A :



   """simple docstring"""







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



@dataclass
class   __A :



   """simple docstring"""







   A_												=				field(
       default=_UpperCamelCase     ,   metadata={
           'help': 'The number of processes to use to split the documents into passages. Default is single process.'
       }     ,   )
   A_												=				field(
       default=1_6     ,   metadata={
           'help': 'The batch size to use when computing the passages embeddings using the DPR context encoder.'
       }     ,   )



@dataclass
class   __A :



   """simple docstring"""







   A_												=				field(
       default=7_6_8     ,   metadata={'help': 'The dimension of the embeddings to pass to the HNSW Faiss index.'}     ,   )
   A_												=				field(
       default=1_2_8     ,   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  		=      HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
  _lowerCAmelCase		,      _lowerCAmelCase		,      _lowerCAmelCase  		=      parser.parse_args_into_dataclasses()
  with TemporaryDirectory() as tmp_dir:
    _lowerCAmelCase  		=      rag_example_args.output_dir or tmp_dir
    main(rag_example_args, processing_args, index_hnsw_args)









'''simple docstring'''

import unittest

from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


__lowerCAmelCase		     =  get_tests_dir('''fixtures/test_sentencepiece_with_bytefallback.model''')

@require_sentencepiece
@require_tokenizers
class       __magic_name__		(       _UpperCamelCase     ,		unittest.TestCase  ):
							lowerCAmelCase			:       Tuple  							=   GPTSwaTokenizer
							lowerCAmelCase			:       Tuple  							=   False
							lowerCAmelCase			:       Optional[Any]  							=   True
							lowerCAmelCase			:       Any  							=   False







							def 				__lowercase     (				self   :			Any						):
									super().setUp()

									# We have a SentencePiece fixture for testing
									_a	: int								=				GPTSwaTokenizer(_UpperCAmelCase      ,eos_token='<unk>'      ,bos_token='<unk>'      ,pad_token='<unk>'						)

									tokenizer.save_pretrained(self.tmpdirname						)







							def 				__lowercase     (				self   :			Optional[Any]      ,_UpperCAmelCase   :			int						):
									_a	: Any								=				'This is a test'
									_a	: Any								=				'This is a test'
									return input_text, output_text







							def 				__lowercase     (				self   :			str						):
									_a	: Optional[Any]								=				'<s>'
									_a	: str								=				1

									self.assertEqual(self.get_tokenizer()._convert_token_to_id(_UpperCAmelCase						)      ,_UpperCAmelCase						)
									self.assertEqual(self.get_tokenizer()._convert_id_to_token(_UpperCAmelCase						)      ,_UpperCAmelCase						)







							def 				__lowercase     (				self   :			Union[str, Any]						):
									_a	: Dict								=				list(self.get_tokenizer().get_vocab().keys()						)

									self.assertEqual(vocab_keys[0]      ,'<unk>'						)
									self.assertEqual(vocab_keys[1]      ,'<s>'						)
									self.assertEqual(vocab_keys[-1]      ,'j'						)
									self.assertEqual(len(_UpperCAmelCase						)      ,2000						)







							def 				__lowercase     (				self   :			Optional[int]						):
									self.assertEqual(self.get_tokenizer().vocab_size      ,2000						)







							def 				__lowercase     (				self   :			List[Any]						):
									_a	: Union[str, Any]								=				GPTSwaTokenizer(_UpperCAmelCase						)

									_a	: str								=				tokenizer.tokenize('This is a test'						)
									self.assertListEqual(_UpperCAmelCase      ,['▁This', '▁is', '▁a', '▁t', 'est']						)

									self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase						)      ,[465, 287, 265, 631, 842]						)

									_a	: Any								=				tokenizer.tokenize('I was born in 92000, and this is falsé.'						)
									# fmt: off
									self.assertListEqual(
									    _UpperCAmelCase      ,['▁I', '▁was', '▁bor', 'n', '▁in', '▁', '<0x39>', '2', '0', '0', '0', ',', '▁and', '▁this', '▁is', '▁f', 'al', 's', '<0xC3>', '<0xA9>', '.']      ,)
									# fmt: on

									_a	: Optional[Any]								=				tokenizer.convert_tokens_to_ids(_UpperCAmelCase						)
									self.assertListEqual(
									    _UpperCAmelCase      ,[262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260]      ,)

									_a	: List[str]								=				tokenizer.convert_ids_to_tokens(_UpperCAmelCase						)
									# fmt: off
									self.assertListEqual(
									    _UpperCAmelCase      ,['▁I', '▁was', '▁bor', 'n', '▁in', '▁', '<0x39>', '2', '0', '0', '0', ',', '▁and', '▁this', '▁is', '▁f', 'al', 's', '<0xC3>', '<0xA9>', '.']						)
									# fmt: on







							def 				__lowercase     (				self   :			str						):
									_a	: List[Any]								=				GPTSwaTokenizer(_UpperCAmelCase						)
									_a	: Optional[Any]								=				['This is a test', 'I was born in 92000, and this is falsé.']
									_a	: Dict								=				[
									    [465, 287, 265, 631, 842],
									    [262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260],
									]

									# Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
									for text, expected_ids in zip(_UpperCAmelCase      ,_UpperCAmelCase						):
											self.assertListEqual(tokenizer.encode_fast(_UpperCAmelCase						)      ,_UpperCAmelCase						)

									# Test that decode_fast returns the input text
									for text, token_ids in zip(_UpperCAmelCase      ,_UpperCAmelCase						):
											self.assertEqual(tokenizer.decode_fast(_UpperCAmelCase						)      ,_UpperCAmelCase						)







							@slow
							def 				__lowercase     (				self   :			Optional[Any]						):
									_a	: Optional[int]								=				[
									    '<|python|>def fibonacci(n)\n    if n < 0:\n        print(\'Incorrect input\')',
									    'Hey there, how are you doing this fine day?',
									    'This is a text with a trailing spaces followed by a dot     .',
									    'Häj sväjs lillebrör! =)',
									    'Det är inget fel på Mr. Cool',
									]

									# fmt: off
									_a	: Tuple								=				{'input_ids': [[63423, 5, 6811, 14954, 282, 816, 3821, 63466, 63425, 63462, 18, 63978, 678, 301, 1320, 63423, 63455, 63458, 18, 63982, 4246, 3940, 1901, 47789, 5547, 18994], [19630, 1100, 63446, 1342, 633, 544, 4488, 593, 5102, 2416, 63495, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1652, 428, 268, 1936, 515, 268, 58593, 22413, 9106, 546, 268, 33213, 63979, 698, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [55130, 63450, 924, 63449, 2249, 4062, 1558, 318, 63504, 21498, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [509, 377, 2827, 2559, 332, 6575, 63443, 26801, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}
									# fmt: on
									self.tokenizer_integration_test_util(
									    expected_encoding=_UpperCAmelCase      ,model_name='AI-Sweden/gpt-sw3-126m'      ,sequences=_UpperCAmelCase      ,)




