




"""simple docstring"""




import itertools
from dataclasses import dataclass
from io import StringIO
from typing import Optional

import pyarrow as pa

import datasets
from datasets.features.features import require_storage_cast
from datasets.table import table_cast


_snake_case					   =     datasets.utils.logging.get_logger(__name__)






@dataclass
class  UpperCamelCase    (			datasets.BuilderConfig   ):
 UpperCamelCase      : Optional[datasets.Features]    =      None
 UpperCamelCase      : str    =      "utf-8"
 UpperCamelCase      : Optional[str]    =      None
 UpperCamelCase      : int    =      10 << 20  # 10MB
 UpperCamelCase      : bool    =      False
 UpperCamelCase      : str    =      "line"







class  UpperCamelCase    (			datasets.ArrowBasedBuilder   ):
 UpperCamelCase      : str    =      TextConfig


 def _lowercase			(  self	:		Optional[Any]							)      -> List[Any]:
   return datasets.DatasetInfo(features=self.config.features							)


 def _lowercase			(  self	:		int							,		UpperCAmelCase__	:		Tuple							)      -> Any:
   if not self.config.data_files:
     raise ValueError(f"""At least one data file must be specified, but got data_files={self.config.data_files}"""							)
   _a						: Any    =			dl_manager.download_and_extract(self.config.data_files							)
   if isinstance(UpperCAmelCase__							,		(str, list, tuple)							):
     _a						: List[Any]    =			data_files
     if isinstance(UpperCAmelCase__							,		UpperCAmelCase__							):
       _a						: Optional[int]    =			[files]
     _a						: List[str]    =			[dl_manager.iter_files(UpperCAmelCase__							) for file in files]
     return [datasets.SplitGenerator(name=datasets.Split.TRAIN							,		gen_kwargs={"""files""": files}							)]
   _a						: List[str]    =			[]
   for split_name, files in data_files.items():
     if isinstance(UpperCAmelCase__							,		UpperCAmelCase__							):
       _a						: Any    =			[files]
     _a						: Any    =			[dl_manager.iter_files(UpperCAmelCase__							) for file in files]
     splits.append(datasets.SplitGenerator(name=UpperCAmelCase__							,		gen_kwargs={"""files""": files}							)							)
   return splits


 def _lowercase			(  self	:		Optional[int]							,		UpperCAmelCase__	:		pa.Table							)      -> pa.Table:
   if self.config.features is not None:
     _a						: int    =			self.config.features.arrow_schema
     if all(not require_storage_cast(UpperCAmelCase__							) for feature in self.config.features.values()							):
       # cheaper cast
       _a						: str    =			pa_table.cast(UpperCAmelCase__							)
     else:
       # more expensive cast; allows str <-> int/float or str to Audio for example
       _a						: Any    =			table_cast(UpperCAmelCase__							,		UpperCAmelCase__							)
     return pa_table
   else:
     return pa_table.cast(pa.schema({"""text""": pa.string()}							)							)







 def _lowercase			(  self	:		List[Any]							,		UpperCAmelCase__	:		int							)      -> Optional[Any]:
   _a						: int    =			list(self.config.features							) if self.config.features is not None else ["""text"""]
   for file_idx, file in enumerate(itertools.chain.from_iterable(UpperCAmelCase__							)							):
     # open in text mode, by default translates universal newlines ("\n", "\r\n" and "\r") into "\n"
     with open(UpperCAmelCase__							,		encoding=self.config.encoding							,		errors=self.config.errors							) as f:
       if self.config.sample_by == "line":
         _a						: Optional[int]    =			0
         while True:
           _a						: Any    =			f.read(self.config.chunksize							)
           if not batch:
             break
           batch += f.readline()  # finish current line
           # StringIO.readlines, by default splits only on "\n" (and keeps line breaks)
           _a						: List[str]    =			StringIO(UpperCAmelCase__							).readlines()
           if not self.config.keep_linebreaks:
             _a						: Optional[Any]    =			[line.rstrip("""\n"""							) for line in batch]
           _a						: List[str]    =			pa.Table.from_arrays([pa.array(UpperCAmelCase__							)]							,		names=UpperCAmelCase__							)
           # Uncomment for debugging (will print the Arrow table size and elements)
           # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
           # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
           yield (file_idx, batch_idx), self._cast_table(UpperCAmelCase__							)
           batch_idx += 1
       elif self.config.sample_by == "paragraph":
         _a						: Optional[Any]    =			0
         _a						: Union[str, Any]    =			""""""
         while True:
           _a						: str    =			f.read(self.config.chunksize							)
           if not new_batch:
             break
           batch += new_batch
           batch += f.readline()  # finish current line
           _a						: str    =			batch.split("""\n\n"""							)
           _a						: Any    =			pa.Table.from_arrays(
               [pa.array([example for example in batch[:-1] if example]							)]							,		names=UpperCAmelCase__							)
           # Uncomment for debugging (will print the Arrow table size and elements)
           # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
           # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
           yield (file_idx, batch_idx), self._cast_table(UpperCAmelCase__							)
           batch_idx += 1
           _a						: Dict    =			batch[-1]
         if batch:
           _a						: Dict    =			pa.Table.from_arrays([pa.array([batch]							)]							,		names=UpperCAmelCase__							)
           yield (file_idx, batch_idx), self._cast_table(UpperCAmelCase__							)
       elif self.config.sample_by == "document":
         _a						: List[Any]    =			f.read()
         _a						: int    =			pa.Table.from_arrays([pa.array([text]							)]							,		names=UpperCAmelCase__							)
         yield file_idx, self._cast_table(UpperCAmelCase__							)









"""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,
)


_snake_case					   =     logging.getLogger(__name__)
torch.set_grad_enabled(False)
_snake_case					   =     'cuda' if torch.cuda.is_available() else 'cpu'

def     lowerCAmelCase__  (  UpperCamelCase__  ,     UpperCamelCase__=1_0_0  ,     UpperCamelCase__=" "       ):



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

def     lowerCAmelCase__  (  UpperCamelCase__       ):



  '''simple docstring'''
  _a   ,						_a						: Optional[int]    =			[], []
  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     lowerCAmelCase__  (  UpperCamelCase__  ,     UpperCamelCase__  ,     UpperCamelCase__       ):



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

def     lowerCAmelCase__  (  UpperCamelCase__  ,     UpperCamelCase__  ,     UpperCamelCase__  ,     ):



  '''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
  _a						: Dict    =			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						: Optional[Any]    =			DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name       ).to(device=UpperCamelCase__       )
  _a						: str    =			DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name       )
  _a						: int    =			Features(
      {"""text""": Value("""string"""       ), """title""": Value("""string"""       ), """embeddings""": Sequence(Value("""float32"""       )       )}       )  # optional, save as float32 instead of float64 to save space
  _a						: 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						: List[Any]    =			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						: List[str]    =			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						: List[str]    =			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						: str    =			RagRetriever.from_pretrained(
      rag_example_args.rag_model_name  ,     index_name="""custom"""  ,     indexed_dataset=UpperCamelCase__       )
  _a						: Any    =			RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name  ,     retriever=UpperCamelCase__       )
  _a						: List[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[Any]    =			rag_example_args.question or """What does Moses' rod turn into ?"""
  _a						: int    =			tokenizer.question_encoder(UpperCamelCase__  ,     return_tensors="""pt"""       )["""input_ids"""]
  _a						: Union[str, Any]    =			model.generate(UpperCamelCase__       )
  _a						: Any    =			tokenizer.batch_decode(UpperCamelCase__  ,     skip_special_tokens=UpperCamelCase__       )[0]
  logger.info("""Q: """ + question       )
  logger.info("""A: """ + generated_string       )






@dataclass
class  UpperCamelCase    :
 UpperCamelCase      : str    =      field(
     default=str(Path(snake_case_   ).parent / '''test_data''' / '''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=snake_case_	,  metadata={'''help''': '''Path to a directory where the dataset passages and the index will be saved'''}	,  )







@dataclass
class  UpperCamelCase    :
 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  UpperCamelCase    :
 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)

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

