
"""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


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





def __A						(   a_ :							str		,		a_ :							Optional[Any]=1_00		,		a_ :							Dict=" "     )->							List[str]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Optional[Any]				= text.split(a_     )
		return [character.join(text[i : i + n]     ).strip() for i in range(0		,		len(a_     )		,		a_     )]





def __A						(   a_ :							dict     )->							dict:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     Any				= [], []
		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 __A						(   a_ :							dict		,		a_ :							DPRContextEncoder		,		a_ :							DPRContextEncoderTokenizerFast     )->							dict:




		'''simple docstring'''






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





def __A						(   a_ :							"RagExampleArguments"		,		a_ :							"ProcessingArguments"		,		a_ :							"IndexHnswArguments"		,		)->							Optional[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
		SCREAMING_SNAKE_CASE       :     Union[str, Any]				= 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
		SCREAMING_SNAKE_CASE       :     int				= dataset.map(a_		,		batched=a_		,		num_proc=processing_args.num_proc     )

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

	UpperCamelCase				=  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'"""}		,						)
	UpperCamelCase				=  field(
	    default=_UpperCAmelCase		,						metadata={"""help""": """Question that is passed as input to RAG. Default is 'What does Moses' rod turn into ?'."""}		,						)
	UpperCamelCase				=  field(
	    default="""facebook/rag-sequence-nq"""		,						metadata={"""help""": """The RAG model to use. Either 'facebook/rag-sequence-nq' or 'facebook/rag-token-nq'"""}		,						)
	UpperCamelCase				=  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				=  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 				lowercase__:
	'''simple docstring'''

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





@dataclass
class 				lowercase__:
	'''simple docstring'''

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

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







"""simple docstring"""






import json
import os
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCamelCase__ :       List[str]						     =			logging.get_logger(__name__)

lowerCamelCase__ :       Optional[int]						     =			{"vocab_file": "vocab.json"}

lowerCamelCase__ :       Dict						     =			{
    "vocab_file": {
        "mgp-str": "https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json",
    }
}

lowerCamelCase__ :       Optional[Any]						     =			{"mgp-str": 27}




class 				lowercase__(				_UpperCAmelCase   ):
	'''simple docstring'''

	UpperCamelCase				=  VOCAB_FILES_NAMES
	UpperCamelCase				=  PRETRAINED_VOCAB_FILES_MAP
	UpperCamelCase				=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES





	def __init__(						self    :int		, lowerCamelCase_    :Optional[int]		, lowerCamelCase_    :Optional[int]="[GO]"		, lowerCamelCase_    :int="[GO]"		, lowerCamelCase_    :str="[s]"		, lowerCamelCase_    :Dict="[GO]"		, **lowerCamelCase_    :List[str]					)							->			Tuple:







			'''simple docstring'''




			super().__init__(
			    unk_token=lowerCamelCase_		, bos_token=lowerCamelCase_		, eos_token=lowerCamelCase_		, pad_token=lowerCamelCase_		, **lowerCamelCase_		, )

			with open(lowerCamelCase_		, encoding='''utf-8'''					) as vocab_handle:
					SCREAMING_SNAKE_CASE       :     int				= json.load(lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= {v: k for k, v in self.vocab.items()}





	@property
	def   __lowerCAmelCase (						self    :int					)							->			Dict:







			'''simple docstring'''




			return len(self.vocab					)





	def   __lowerCAmelCase (						self    :List[str]					)							->			Dict:







			'''simple docstring'''




			return dict(self.vocab		, **self.added_tokens_encoder					)





	def   __lowerCAmelCase (						self    :List[str]		, lowerCamelCase_    :Optional[Any]					)							->			List[str]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= []
			for s in text:
					char_tokens.extend(lowerCamelCase_					)
			return char_tokens





	def   __lowerCAmelCase (						self    :Dict		, lowerCamelCase_    :Tuple					)							->			Optional[int]:







			'''simple docstring'''




			return self.vocab.get(lowerCamelCase_		, self.vocab.get(self.unk_token					)					)





	def   __lowerCAmelCase (						self    :List[str]		, lowerCamelCase_    :Dict					)							->			Optional[int]:







			'''simple docstring'''




			return self.decoder.get(lowerCamelCase_					)







	def   __lowerCAmelCase (						self    :Optional[int]		, lowerCamelCase_    :str		, lowerCamelCase_    :Optional[str] = None					)							->			Tuple[str]:







			'''simple docstring'''




			if not os.path.isdir(lowerCamelCase_					):
					logger.error('''Vocabulary path ({}) should be a directory'''.format(lowerCamelCase_					)					)
					return
			SCREAMING_SNAKE_CASE       :     List[Any]				= os.path.join(
			    lowerCamelCase_		, (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']					)

			with open(lowerCamelCase_		, '''w'''		, encoding='''utf-8'''					) as f:
					f.write(json.dumps(self.vocab		, indent=2		, sort_keys=lowerCamelCase_		, ensure_ascii=lowerCamelCase_					) + '''\n'''					)

			return (vocab_file,)





