


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			:						List[Any]        =  logging.getLogger(__name__)
torch.set_grad_enabled(False)
A			:						Any        =  'cuda' if torch.cuda.is_available() else 'cpu'






def 					UpperCamelCase   (      __magic_name__	:				str       ,       __magic_name__	:				str=100       ,       __magic_name__	:				Tuple=" "	)  ->    List[str]:
		"""simple docstring"""




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






def 					UpperCamelCase   (      __magic_name__	:				dict	)  ->    dict:
		"""simple docstring"""




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






def 					UpperCamelCase   (      __magic_name__	:				dict       ,       __magic_name__	:				DPRContextEncoder       ,       __magic_name__	:				DPRContextEncoderTokenizerFast	)  ->    dict:
		"""simple docstring"""




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






def 					UpperCamelCase   (      __magic_name__	:				"RagExampleArguments"       ,       __magic_name__	:				"ProcessingArguments"       ,       __magic_name__	:				"IndexHnswArguments"       ,       )  ->    int:
		"""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
		lowercase__	     =						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
		lowercase__	     =						dataset.map(__magic_name__       ,       batched=__magic_name__       ,       num_proc=processing_args.num_proc	)

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

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

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



@dataclass
class 				A       :
					'''simple docstring'''






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






@dataclass
class 				A       :
					'''simple docstring'''






					A__     =					field(
					    default=UpperCAmelCase__					,    metadata={
					        '''help''': '''The number of processes to use to split the documents into passages. Default is single process.'''
					    }					,    )
					A__     =					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'''






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

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





from typing import Optional

from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader



class 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






					def __init__(self       :		Any ,   _UpperCAmelCase       :		NestedDataStructureLike[PathLike] ,   _UpperCAmelCase       :		Optional[NamedSplit] = None ,   _UpperCAmelCase       :		Optional[Features] = None ,   _UpperCAmelCase       :		str = None ,   _UpperCAmelCase       :		bool = False ,   _UpperCAmelCase       :		bool = False ,   _UpperCAmelCase       :		Optional[int] = None ,   **_UpperCAmelCase       :		Optional[int] ,   )		->  List[str]:




							"""simple docstring"""


							super().__init__(
							    _UpperCAmelCase ,   split=_UpperCAmelCase ,   features=_UpperCAmelCase ,   cache_dir=_UpperCAmelCase ,   keep_in_memory=_UpperCAmelCase ,   streaming=_UpperCAmelCase ,   num_proc=_UpperCAmelCase ,   **_UpperCAmelCase ,   )
							lowercase__	     =						path_or_paths if isinstance(_UpperCAmelCase ,   _UpperCAmelCase						) else {self.split: path_or_paths}
							lowercase__	     =						Text(
							    cache_dir=_UpperCAmelCase ,   data_files=_UpperCAmelCase ,   features=_UpperCAmelCase ,   **_UpperCAmelCase ,   )


					def     lowerCamelCase__	(self       :		Tuple						)		->  Any:




							"""simple docstring"""


							if self.streaming:
									lowercase__	     =						self.builder.as_streaming_dataset(split=self.split						)
							# Build regular (map-style) dataset
							else:
									lowercase__	     =						None
									lowercase__	     =						None
									lowercase__	     =						None
									lowercase__	     =						None

									self.builder.download_and_prepare(
									    download_config=_UpperCAmelCase ,   download_mode=_UpperCAmelCase ,   verification_mode=_UpperCAmelCase ,   base_path=_UpperCAmelCase ,   num_proc=self.num_proc ,   )
									lowercase__	     =						self.builder.as_dataset(
									    split=self.split ,   verification_mode=_UpperCAmelCase ,   in_memory=self.keep_in_memory						)
							return dataset

