



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,
)


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

def       A_   (						__a  :					str			,		__a  :					Optional[Any]=100			,		__a  :					Optional[int]=" "		):

				"""simple docstring"""
				a__					   =		text.split(_lowercase		)
				return [character.join(text[i : i + n]		).strip() for i in range(0			,		len(_lowercase		)			,		_lowercase		)]

def       A_   (						__a  :					dict		):

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

def       A_   (						__a  :					dict			,		__a  :					DPRContextEncoder			,		__a  :					DPRContextEncoderTokenizerFast		):

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

def       A_   (						__a  :					"RagExampleArguments"			,		__a  :					"ProcessingArguments"			,		__a  :					"IndexHnswArguments"			,		):

				"""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__					   =		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__					   =		dataset.map(_lowercase			,		batched=_lowercase			,		num_proc=processing_args.num_proc		)

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

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

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

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

				# Easy way to load the model
				a__					   =		RagRetriever.from_pretrained(
				    rag_example_args.rag_model_name			,		index_name="""custom"""			,		indexed_dataset=_lowercase		)
				a__					   =		RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name			,		retriever=_lowercase		)
				a__					   =		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__					   =		rag_example_args.question or '''What does Moses\' rod turn into ?'''
				a__					   =		tokenizer.question_encoder(_lowercase			,		return_tensors="""pt"""		)['''input_ids''']
				a__					   =		model.generate(_lowercase		)
				a__					   =		tokenizer.batch_decode(_lowercase			,		skip_special_tokens=_lowercase		)[0]
				logger.info("""Q: """ + question		)
				logger.info("""A: """ + generated_string		)

@dataclass
class 			__snake_case   :







							'''simple docstring'''






							UpperCamelCase__					:   str       =       field(
							    default=str(Path(_a).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=_a      ,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=_a      ,metadata={"""help""": """Path to a directory where the dataset passages and the index will be saved"""}      ,)




@dataclass
class 			__snake_case   :







							'''simple docstring'''






							UpperCamelCase__					:   Optional[int]       =       field(
							    default=_a      ,metadata={
							        """help""": """The number of processes to use to split the documents into passages. Default is single process."""
							    }      ,)
							UpperCamelCase__					:   int       =       field(
							    default=1_6      ,metadata={
							        """help""": """The batch size to use when computing the passages embeddings using the DPR context encoder."""
							    }      ,)




@dataclass
class 			__snake_case   :







							'''simple docstring'''






							UpperCamelCase__					:   int       =       field(
							    default=7_6_8      ,metadata={"""help""": """The dimension of the embeddings to pass to the HNSW Faiss index."""}      ,)
							UpperCamelCase__					:   int       =       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)

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





'''simple docstring'''



import os
import unittest

import pytest
from parameterized import parameterized

from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device


# skipping in unittest tests

__UpperCAmelCase     :Tuple												=  [(1,)]




def       _a      (		):





     '''simple docstring'''






     __UpperCAmelCase      :					Any    				=     bool(os.getenv('''RUN_SLOW'''     ,   0    )    )
     if run_slow:
          assert True
     else:
          assert False, "should have been skipped"




def       _a      (		):





     '''simple docstring'''






     __UpperCAmelCase      :					str    				=     bool(os.getenv('''RUN_SLOW'''     ,   0    )    )
     if run_slow and torch_device == "cuda":
          assert True
     else:
          assert False, "should have been skipped"



@require_torch
class       a							(						unittest.TestCase			):


     """simple docstring"""






     @slow
     @require_torch_gpu
     def 					lowerCamelCase__					(							self	:						Tuple     )		->				str:
          check_slow_torch_cuda()

     @require_torch_gpu
     @slow
     def 					lowerCamelCase__					(							self	:						Any     )		->				List[Any]:
          check_slow_torch_cuda()






     @parameterized.expand(snake_case     )
     @slow
     def 					lowerCamelCase__					(							self	:						Union[str, Any]				,					snake_case	:						int=None     )		->				Union[str, Any]:
          check_slow()




@slow
@require_torch_gpu
def       _a      (		):





     '''simple docstring'''






     check_slow_torch_cuda()




@require_torch_gpu
@slow
def       _a      (		):





     '''simple docstring'''






     check_slow_torch_cuda()




@slow
@pytest.mark.parametrize('''param'''     ,   [1]    )
def       _a      (		_lowercase   :      Union[str, Any]    ):





     '''simple docstring'''






     check_slow()




@pytest.mark.parametrize('''param'''     ,   [1]    )
@slow
def       _a      (		_lowercase   :      Dict    ):





     '''simple docstring'''






     check_slow()