





'''simple docstring'''

import os
import sys
import unittest


a   	=    os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
a   	=    os.path.join(git_repo_path, "src", "transformers")

a   	=    "\n{0} = None\n"

a   	=    "\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n"


a   	=    "\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n"







class __a			( unittest.TestCase      ):





   def   UpperCAmelCase__  (       self					:      Tuple     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							find_backend("""    _import_structure[\"models.albert\"].append(\"AlbertTokenizerFast\")"""     )
      self.assertIsNone(lowerCamelCase     )

      __SCREAMING_SNAKE_CASE   				=							find_backend("""    if not is_tokenizers_available():"""     )
      self.assertEqual(lowerCamelCase					,"""tokenizers"""     )

      __SCREAMING_SNAKE_CASE   				=							find_backend("""    if not is_tensorflow_text_available():"""     )
      self.assertEqual(lowerCamelCase					,"""tensorflow_text"""     )

      __SCREAMING_SNAKE_CASE   				=							find_backend("""    if not (is_sentencepiece_available() and is_tokenizers_available()):"""     )
      self.assertEqual(lowerCamelCase					,"""sentencepiece_and_tokenizers"""     )

      __SCREAMING_SNAKE_CASE   				=							find_backend(
          """    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"""     )
      self.assertEqual(lowerCamelCase					,"""sentencepiece_and_tensorflow_text"""     )

      __SCREAMING_SNAKE_CASE   				=							find_backend(
          """    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):"""     )
      self.assertEqual(lowerCamelCase					,"""sentencepiece_and_tokenizers_and_vision"""     )





   def   UpperCAmelCase__  (       self					:      Optional[Any]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							read_init()
      # We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
      self.assertIn("""torch"""					,lowerCamelCase     )
      self.assertIn("""tensorflow_text"""					,lowerCamelCase     )
      self.assertIn("""sentencepiece_and_tokenizers"""					,lowerCamelCase     )

      # Likewise, we can't assert on the exact content of a key
      self.assertIn("""BertModel"""					,objects["""torch"""]     )
      self.assertIn("""TFBertModel"""					,objects["""tf"""]     )
      self.assertIn("""FlaxBertModel"""					,objects["""flax"""]     )
      self.assertIn("""BertModel"""					,objects["""torch"""]     )
      self.assertIn("""TFBertTokenizer"""					,objects["""tensorflow_text"""]     )
      self.assertIn("""convert_slow_tokenizer"""					,objects["""sentencepiece_and_tokenizers"""]     )





   def   UpperCAmelCase__  (       self					:      Any     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							create_dummy_object("""CONSTANT"""					,"""'torch'"""     )
      self.assertEqual(lowerCamelCase					,"""\nCONSTANT = None\n"""     )

      __SCREAMING_SNAKE_CASE   				=							create_dummy_object("""function"""					,"""'torch'"""     )
      self.assertEqual(
          lowerCamelCase					,"""\ndef function(*args, **kwargs):\n    requires_backends(function, 'torch')\n"""     )

      __SCREAMING_SNAKE_CASE   				=							"""
class FakeClass(metaclass=DummyObject):
    _backends = 'torch'

    def __init__(self, *args, **kwargs):
        requires_backends(self, 'torch')
"""
      __SCREAMING_SNAKE_CASE   				=							create_dummy_object("""FakeClass"""					,"""'torch'"""     )
      self.assertEqual(lowerCamelCase					,lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      Optional[Any]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							"""# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends


CONSTANT = None


def function(*args, **kwargs):
    requires_backends(function, [\"torch\"])


class FakeClass(metaclass=DummyObject):
    _backends = [\"torch\"]

    def __init__(self, *args, **kwargs):
        requires_backends(self, [\"torch\"])
"""
      __SCREAMING_SNAKE_CASE   				=							create_dummy_files({"""torch""": ["""CONSTANT""", """function""", """FakeClass"""]}     )
      self.assertEqual(dummy_files["""torch"""]					,lowerCamelCase     )












'''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


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

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase=100				,						__UpperCAmelCase=" "   )    ->       List[str]:

   '''simple docstring'''







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

def     __magic_name__							(				__UpperCAmelCase   )    ->       dict:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							[], []
   for title, text in zip(documents["""title"""]				,						documents["""text"""]   ):
      if text is not None:
         for passage in split_text(__UpperCAmelCase   ):
            titles.append(title if title is not None else """"""   )
            texts.append(__UpperCAmelCase   )
   return {"title": titles, "text": texts}

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )    ->       dict:

   '''simple docstring'''







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

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase				,						)    ->       Any:

   '''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
   __SCREAMING_SNAKE_CASE   				=							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
   __SCREAMING_SNAKE_CASE   				=							dataset.map(__UpperCAmelCase				,						batched=__UpperCAmelCase				,						num_proc=processing_args.num_proc   )

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

   # And finally save your dataset
   __SCREAMING_SNAKE_CASE   				=							os.path.join(rag_example_args.output_dir				,						"""my_knowledge_dataset"""   )
   dataset.save_to_disk(__UpperCAmelCase   )
   # 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
   __SCREAMING_SNAKE_CASE   				=							faiss.IndexHNSWFlat(index_hnsw_args.d				,						index_hnsw_args.m				,						faiss.METRIC_INNER_PRODUCT   )
   dataset.add_faiss_index("""embeddings"""				,						custom_index=__UpperCAmelCase   )

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







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







@dataclass
class __a			:
   __UpperCamelCase :			Optional[int]						   =						field(
       default=_snake_case, metadata={
           'help': 'The number of processes to use to split the documents into passages. Default is single process.'
       }, )
   __UpperCamelCase :			int						   =						field(
       default=16, metadata={
           'help': 'The batch size to use when computing the passages embeddings using the DPR context encoder.'
       }, )







@dataclass
class __a			:
   __UpperCamelCase :			int						   =						field(
       default=768, metadata={'help': 'The dimension of the embeddings to pass to the HNSW Faiss index.'}, )
   __UpperCamelCase :			int						   =						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)

    a   	=    HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
    a	, a	, a   	=    parser.parse_args_into_dataclasses()
    with TemporaryDirectory() as tmp_dir:
        a   	=    rag_example_args.output_dir or tmp_dir
        main(rag_example_args, processing_args, index_hnsw_args)





