


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


UpperCAmelCase_   :				int						      = logging.getLogger(__name__)
torch.set_grad_enabled(False)
UpperCAmelCase_   :				str						      = 'cuda' if torch.cuda.is_available() else 'cpu'







def  SCREAMING_SNAKE_CASE_		(    __A				:    str						,					__A				:    Union[str, Any]=1_00						,					__A				:    List[Any]=" "       )			->	List[str]:

       """simple docstring"""

       a_      :	List[str]							=						text.split(__A       )
       return [character.join(text[i : i + n]       ).strip() for i in range(0						,					len(__A       )						,					__A       )]







def  SCREAMING_SNAKE_CASE_		(    __A				:    dict       )			->	dict:

       """simple docstring"""

       a_							,	a_      :	List[str]							=						[], []
       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  SCREAMING_SNAKE_CASE_		(    __A				:    dict						,					__A				:    DPRContextEncoder						,					__A				:    DPRContextEncoderTokenizerFast       )			->	dict:

       """simple docstring"""

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







def  SCREAMING_SNAKE_CASE_		(    __A				:    "RagExampleArguments"						,					__A				:    "ProcessingArguments"						,					__A				:    "IndexHnswArguments"						,					)			->	Tuple:

       """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_      :	Optional[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_      :	Dict							=						dataset.map(__A						,					batched=__A						,					num_proc=processing_args.num_proc       )

       # And compute the embeddings
       a_      :	Optional[int]							=						DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name       ).to(device=__A       )
       a_      :	Any							=						DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name       )
       a_      :	List[Any]							=						Features(
           {'text': Value('string'       ), 'title': Value('string'       ), 'embeddings': Sequence(Value('float32'       )       )}       )  # optional, save as float32 instead of float64 to save space
       a_      :	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
       a_      :	str							=						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
       a_      :	str							=						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
       a_      :	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 				SCREAMING_SNAKE_CASE__						:
      snake_case__       :  str       =		field(
          default=str(Path(lowercase__    ).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__       :  Optional[str]       =		field(
          default=lowercase__		,					metadata={'''help''': '''Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'''}		,					)
      snake_case__       :  str       =		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__       :  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\''''
              )
          }		,					)
      snake_case__       :  Optional[str]       =		field(
          default=str(Path(lowercase__    ).parent / '''test_run''' / '''dummy-kb'''    )		,					metadata={'''help''': '''Path to a directory where the dataset passages and the index will be saved'''}		,					)
@dataclass
class 				SCREAMING_SNAKE_CASE__						:
      snake_case__       :  Optional[int]       =		field(
          default=lowercase__		,					metadata={
              '''help''': '''The number of processes to use to split the documents into passages. Default is single process.'''
          }		,					)
      snake_case__       :  int       =		field(
          default=16		,					metadata={
              '''help''': '''The batch size to use when computing the passages embeddings using the DPR context encoder.'''
          }		,					)
@dataclass
class 				SCREAMING_SNAKE_CASE__						:
      snake_case__       :  int       =		field(
          default=768		,					metadata={'''help''': '''The dimension of the embeddings to pass to the HNSW Faiss index.'''}		,					)
      snake_case__       :  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)

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






# This code is adapted from OpenAI's release
# https://github.com/openai/human-eval/blob/master/human_eval/execution.py

import contextlib
import faulthandler
import io
import multiprocessing
import os
import platform
import signal
import tempfile







def  SCREAMING_SNAKE_CASE_		(    __A				:    List[Any]						,					__A				:    List[Any]						,					__A				:    List[Any]						,					__A				:    Optional[int]       )			->	Dict:

       """simple docstring"""

       a_      :	Optional[Any]							=						multiprocessing.Manager()
       a_      :	Tuple							=						manager.list()

       a_      :	Optional[int]							=						multiprocessing.Process(target=__A						,					args=(check_program, result, timeout)       )
       p.start()
       p.join(timeout=timeout + 1       )
       if p.is_alive():
              p.kill()

       if not result:
              result.append('timed out'       )

       return {
           "task_id": task_id,
           "passed": result[0] == "passed",
           "result": result[0],
           "completion_id": completion_id,
       }







def  SCREAMING_SNAKE_CASE_		(    __A				:    Tuple						,					__A				:    Optional[Any]						,					__A				:    Optional[Any]       )			->	Dict:

       """simple docstring"""

       with create_tempdir():
              # These system calls are needed when cleaning up tempdir.
              import os
              import shutil

              a_      :	Any							=						shutil.rmtree
              a_      :	List[str]							=						os.rmdir
              a_      :	List[Any]							=						os.chdir

              # Disable functionalities that can make destructive changes to the test.
              reliability_guard()

              # Run program.
              try:
                     a_      :	Any							=						{}
                     with swallow_io():
                            with time_limit(__A       ):
                                   exec(__A						,					__A       )
                     result.append('passed'       )
              except TimeoutException:
                     result.append('timed out'       )
              except BaseException as e:
                     result.append(F"""failed: {e}"""       )

              # Needed for cleaning up.
              a_      :	Tuple							=						rmtree
              a_      :	Any							=						rmdir
              a_      :	Optional[int]							=						chdir







@contextlib.contextmanager
def  SCREAMING_SNAKE_CASE_		(    __A				:    List[str]       )			->	str:

       """simple docstring"""

       def signal_handler(__A				:    List[str]						,					__A				:    Dict       ):
              raise TimeoutException('Timed out!'       )

       signal.setitimer(signal.ITIMER_REAL						,					__A       )
       signal.signal(signal.SIGALRM						,					__A       )
       try:
              yield
       finally:
              signal.setitimer(signal.ITIMER_REAL						,					0       )







@contextlib.contextmanager
def  SCREAMING_SNAKE_CASE_		(    )			->	Dict:

       """simple docstring"""

       a_      :	int							=						WriteOnlyStringIO()
       with contextlib.redirect_stdout(__A       ):
              with contextlib.redirect_stderr(__A       ):
                     with redirect_stdin(__A       ):
                            yield







@contextlib.contextmanager
def  SCREAMING_SNAKE_CASE_		(    )			->	Optional[int]:

       """simple docstring"""

       with tempfile.TemporaryDirectory() as dirname:
              with chdir(__A       ):
                     yield dirname
class 				SCREAMING_SNAKE_CASE__						(       lowercase__    ):
      pass
class 				SCREAMING_SNAKE_CASE__						(       io.StringIO    ):
      def  SCREAMING_SNAKE_CASE							( self       : List[Any]  ,							*SCREAMING_SNAKE_CASE__       : List[str]  ,							**SCREAMING_SNAKE_CASE__       : List[Any]  )	->    int:
             raise OSError
      def  SCREAMING_SNAKE_CASE							( self       : Tuple  ,							*SCREAMING_SNAKE_CASE__       : Tuple  ,							**SCREAMING_SNAKE_CASE__       : Dict  )	->    List[str]:
             raise OSError
      def  SCREAMING_SNAKE_CASE							( self       : int  ,							*SCREAMING_SNAKE_CASE__       : Dict  ,							**SCREAMING_SNAKE_CASE__       : List[Any]  )	->    Optional[Any]:
             raise OSError

      def  SCREAMING_SNAKE_CASE							( self       : Any  ,							*SCREAMING_SNAKE_CASE__       : Union[str, Any]  ,							**SCREAMING_SNAKE_CASE__       : List[Any]  )	->    List[Any]:
             return False
class 				SCREAMING_SNAKE_CASE__						(       contextlib._RedirectStream    ):  # type: ignore
      snake_case__       :  Optional[int]       =		'''stdin'''







@contextlib.contextmanager
def  SCREAMING_SNAKE_CASE_		(    __A				:    Tuple       )			->	Union[str, Any]:

       """simple docstring"""

       if root == ".":
              yield
              return
       a_      :	str							=						os.getcwd()
       os.chdir(__A       )
       try:
              yield
       except BaseException as exc:
              raise exc
       finally:
              os.chdir(__A       )







def  SCREAMING_SNAKE_CASE_		(    __A				:    Dict=None       )			->	Dict:

       """simple docstring"""

       if maximum_memory_bytes is not None:
              import resource

              resource.setrlimit(resource.RLIMIT_AS						,					(maximum_memory_bytes, maximum_memory_bytes)       )
              resource.setrlimit(resource.RLIMIT_DATA						,					(maximum_memory_bytes, maximum_memory_bytes)       )
              if not platform.uname().system == "Darwin":
                     resource.setrlimit(resource.RLIMIT_STACK						,					(maximum_memory_bytes, maximum_memory_bytes)       )

       faulthandler.disable()

       import builtins

       a_      :	str							=						None
       a_      :	str							=						None

       import os

       a_      :	Optional[int]							=						'1'

       a_      :	Any							=						None
       a_      :	int							=						None
       a_      :	int							=						None
       a_      :	Tuple							=						None
       a_      :	Dict							=						None
       a_      :	Any							=						None
       a_      :	Dict							=						None
       a_      :	Dict							=						None
       a_      :	List[str]							=						None
       a_      :	Optional[Any]							=						None
       a_      :	Tuple							=						None
       a_      :	Union[str, Any]							=						None
       a_      :	Dict							=						None
       a_      :	Dict							=						None
       a_      :	int							=						None
       a_      :	str							=						None
       a_      :	Union[str, Any]							=						None
       a_      :	str							=						None
       a_      :	Optional[int]							=						None
       a_      :	List[Any]							=						None
       a_      :	Union[str, Any]							=						None
       a_      :	Dict							=						None
       a_      :	Tuple							=						None
       a_      :	List[str]							=						None
       a_      :	str							=						None
       a_      :	str							=						None
       a_      :	str							=						None

       import shutil

       a_      :	Dict							=						None
       a_      :	Tuple							=						None
       a_      :	str							=						None

       import subprocess

       a_      :	str							=						None  # type: ignore

       a_      :	List[Any]							=						None

       import sys

       a_      :	Optional[int]							=						None
       a_      :	int							=						None
       a_      :	Any							=						None
       a_      :	str							=						None
       a_      :	str							=						None


