


"""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,
    RagRetriever,
    RagSequenceForGeneration,
    RagTokenizer,
)


SCREAMING_SNAKE_CASE__				:  List[str]						= logging.getLogger(__name__)
torch.set_grad_enabled(False)
SCREAMING_SNAKE_CASE__				:  str						= "cuda" if torch.cuda.is_available() else "cpu"




def        A_					(					UpperCAmelCase__       ,   UpperCAmelCase__=100       ,   UpperCAmelCase__=" "				)     ->     List[str]:
   a   :	Union[str, Any]						=    text.split(UpperCAmelCase__				)
   return [character.join(text[i : i + n]				).strip() for i in range(0       ,   len(UpperCAmelCase__				)       ,   UpperCAmelCase__				)]




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




def        A_					(					UpperCAmelCase__       ,   UpperCAmelCase__       ,   UpperCAmelCase__       ,   )     ->     Optional[Any]:
   ######################################
   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
   a   :	int						=    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
   a   :	Any						=    dataset.map(UpperCAmelCase__       ,   batched=UpperCAmelCase__       ,   num_proc=processing_args.num_proc				)

   # And compute the embeddings
   a   :	List[str]						=    DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name				).to(device=UpperCAmelCase__				)
   a   :	Union[str, Any]						=    DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name				)
   a   :	Dict						=    Features(
       {'text': Value('string'				), 'title': Value('string'				), 'embeddings': Sequence(Value('float32'				)				)}				)  # optional, save as float32 instead of float64 to save space
   a   :	List[str]						=    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
   a   :	Dict						=    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
   a   :	Dict						=    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
   a   :	Optional[int]						=    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

   ######################################
   logger.info('Step 3 - Load RAG'				)
   ######################################

   # Easy way to load the model
   a   :	Dict						=    RagRetriever.from_pretrained(
       rag_example_args.rag_model_name       ,   index_name='custom'       ,   indexed_dataset=UpperCAmelCase__				)
   a   :	Union[str, Any]						=    RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name       ,   retriever=UpperCAmelCase__				)
   a   :	Union[str, Any]						=    RagTokenizer.from_pretrained(rag_example_args.rag_model_name				)

   # For distributed fine-tuning you'll need to provide the paths instead, as the dataset and the index are loaded separately.
   # retriever = RagRetriever.from_pretrained(rag_model_name, index_name="custom", passages_path=passages_path, index_path=index_path)

   ######################################
   logger.info('Step 4 - Have fun'				)
   ######################################

   a   :	Optional[int]						=    rag_example_args.question or 'What does Moses\' rod turn into ?'
   a   :	Any						=    tokenizer.question_encoder(UpperCAmelCase__       ,   return_tensors='pt'				)['input_ids']
   a   :	Any						=    model.generate(UpperCAmelCase__				)
   a   :	Union[str, Any]						=    tokenizer.batch_decode(UpperCAmelCase__       ,   skip_special_tokens=UpperCAmelCase__				)[0]
   logger.info('Q: ' + question				)
   logger.info('A: ' + generated_string				)


@dataclass
class 	A_       :





  """simple docstring"""

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



@dataclass
class 	A_       :





  """simple docstring"""

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



@dataclass
class 	A_       :





  """simple docstring"""

  lowercase		:	int   =						field(
      default=7_68      ,  metadata={"help": "The dimension of the embeddings to pass to the HNSW Faiss index."}      ,  )
  lowercase		:	int   =						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)

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









"""simple docstring"""






import re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata




def        A_					(					UpperCAmelCase__				)     ->     str:
   a   :	Optional[int]						=    min(re.search('^ +'       ,   UpperCAmelCase__				).end() if t.startswith(' '				) else 0 for t in string.splitlines()				)
   return "\n".join([line[indent_level:] for line in string.splitlines() if indent_level < len(UpperCAmelCase__				)]				)


SCREAMING_SNAKE_CASE__				:  Union[str, Any]						= "\\n---\nlanguage:\n- zh\n- en\ntask_ids:\n- sentiment-classification\n---\n# Begin of markdown\n\nSome cool dataset card\n"

SCREAMING_SNAKE_CASE__				:  List[str]						= "\\n---\n---\n# Begin of markdown\n\nSome cool dataset card\n"


SCREAMING_SNAKE_CASE__				:  Dict						= "\\n# Begin of markdown\n\nSome cool dataset card\n"


class 	A_       (     unittest.TestCase   ):





  """simple docstring"""

  def 							lowercase_     (						self						)				->					Any:
     with tempfile.TemporaryDirectory() as tmp_dir:
        a   :	Optional[Any]						=    Path(__UpperCAmelCase						) / 'README.md'
        with open(__UpperCAmelCase  ,					'w+'						) as readme_file:
           readme_file.write(__UpperCAmelCase						)
        a   :	str						=    DatasetMetadata.from_readme(__UpperCAmelCase						)
        self.assertDictEqual(__UpperCAmelCase  ,					{'language': ['zh', 'en'], 'task_ids': ['sentiment-classification']}						)

        with open(__UpperCAmelCase  ,					'w+'						) as readme_file:
           readme_file.write(__UpperCAmelCase						)
        a   :	Optional[Any]						=    DatasetMetadata.from_readme(__UpperCAmelCase						)
        self.assertDictEqual(__UpperCAmelCase  ,					{}						)

        with open(__UpperCAmelCase  ,					'w+'						) as readme_file:
           readme_file.write(__UpperCAmelCase						)
        a   :	str						=    DatasetMetadata.from_readme(__UpperCAmelCase						)
        self.assertEqual(__UpperCAmelCase  ,					{}						)






  def 							lowercase_     (						self						)				->					Tuple:
     a   :	List[str]						=    _dedent(
         '\\n            annotations_creators:\n            - found\n            language_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - question-answering\n            task_ids:\n            - open-domain-qa\n            '						)
     assert DatasetMetadata.from_yaml_string(__UpperCAmelCase						)

     a   :	Optional[Any]						=    _dedent(
         '\\n            annotations_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - question-answering\n            task_ids:\n            - open-domain-qa\n            task_ids:\n            - open-domain-qa\n            '						)
     with self.assertRaises(__UpperCAmelCase						):
        DatasetMetadata.from_yaml_string(__UpperCAmelCase						)

     a   :	List[Any]						=    _dedent(
         '\\n            annotations_creators:\n            - found\n            language_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - text-classification\n            task_ids:\n            - multi-class-classification\n            paperswithcode_id:\n            - squad\n            configs:\n            - en\n            train-eval-index:\n            - config: en\n              task: text-classification\n              task_id: multi_class_classification\n              splits:\n                train_split: train\n                eval_split: test\n              col_mapping:\n                text: text\n                label: target\n              metrics:\n                - type: accuracy\n                  name: Accuracy\n            extra_gated_prompt: |\n              By clicking on “Access repository” below, you also agree to ImageNet Terms of Access:\n              [RESEARCHER_FULLNAME] (the "Researcher") has requested permission to use the ImageNet database (the "Database") at Princeton University and Stanford University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions:\n              1. Researcher shall use the Database only for non-commercial research and educational purposes.\n            extra_gated_fields:\n              Company: text\n              Country: text\n              I agree to use this model for non-commerical use ONLY: checkbox\n            '						)
     assert DatasetMetadata.from_yaml_string(__UpperCAmelCase						)





