





import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow






@require_torch
class 							__lowercase			(     unittest.TestCase       ):






      def 			_a						(		self)     ->       int:
       __snake_case         =			'''ylacombe/bark-small'''
       __snake_case         =			tempfile.mkdtemp()
       __snake_case         =			'''en_speaker_1'''
       __snake_case         =			'''This is a test string'''
       __snake_case         =			'''speaker_embeddings_path.json'''
       __snake_case         =			'''speaker_embeddings'''






      def 			_a						(		self			,							**lowercase_)     ->       Optional[int]:
       return AutoTokenizer.from_pretrained(self.checkpoint			,							**__SCREAMING_SNAKE_CASE)






      def 			_a						(		self)     ->       str:
       shutil.rmtree(self.tmpdirname)






      def 			_a						(		self)     ->       Union[str, Any]:
       __snake_case         =			self.get_tokenizer()

       __snake_case         =			BarkProcessor(tokenizer=__SCREAMING_SNAKE_CASE)

       processor.save_pretrained(self.tmpdirname)
       __snake_case         =			BarkProcessor.from_pretrained(self.tmpdirname)

       self.assertEqual(processor.tokenizer.get_vocab()			,							tokenizer.get_vocab())






      @slow
      def 			_a						(		self)     ->       Optional[int]:
       __snake_case         =			BarkProcessor.from_pretrained(
           pretrained_processor_name_or_path=self.checkpoint			,							speaker_embeddings_dict_path=self.speaker_embeddings_dict_path			,							)
       processor.save_pretrained(
           self.tmpdirname			,							speaker_embeddings_dict_path=self.speaker_embeddings_dict_path			,							speaker_embeddings_directory=self.speaker_embeddings_directory			,							)

       __snake_case         =			self.get_tokenizer(bos_token='(BOS)'			,							eos_token='(EOS)')

       __snake_case         =			BarkProcessor.from_pretrained(
           self.tmpdirname			,							self.speaker_embeddings_dict_path			,							bos_token='(BOS)'			,							eos_token='(EOS)'			,							)

       self.assertEqual(processor.tokenizer.get_vocab()			,							tokenizer_add_kwargs.get_vocab())






      def 			_a						(		self)     ->       List[str]:
       __snake_case         =			BarkProcessor.from_pretrained(
           pretrained_processor_name_or_path=self.checkpoint			,							speaker_embeddings_dict_path=self.speaker_embeddings_dict_path			,							)

       __snake_case         =			3_5
       __snake_case         =			2
       __snake_case         =			8

       __snake_case         =			{
           '''semantic_prompt''': np.ones(__SCREAMING_SNAKE_CASE),
           '''coarse_prompt''': np.ones((nb_codebooks_coarse, seq_len)),
           '''fine_prompt''': np.ones((nb_codebooks_total, seq_len)),
       }

       # test providing already loaded voice_preset
       __snake_case         =			processor(text=self.input_string			,							voice_preset=__SCREAMING_SNAKE_CASE)

       __snake_case         =			inputs['''history_prompt''']
       for key in voice_preset:
        self.assertListEqual(voice_preset[key].tolist()			,							processed_voice_preset.get(__SCREAMING_SNAKE_CASE			,							np.array([])).tolist())

       # test loading voice preset from npz file
       __snake_case         =			os.path.join(self.tmpdirname			,							'file.npz')
       np.savez(__SCREAMING_SNAKE_CASE			,							**__SCREAMING_SNAKE_CASE)
       __snake_case         =			processor(text=self.input_string			,							voice_preset=__SCREAMING_SNAKE_CASE)
       __snake_case         =			inputs['''history_prompt''']

       for key in voice_preset:
        self.assertListEqual(voice_preset[key].tolist()			,							processed_voice_preset.get(__SCREAMING_SNAKE_CASE			,							np.array([])).tolist())

       # test loading voice preset from the hub
       __snake_case         =			processor(text=self.input_string			,							voice_preset=self.voice_preset)







      def 			_a						(		self)     ->       Dict:
       __snake_case         =			self.get_tokenizer()

       __snake_case         =			BarkProcessor(tokenizer=__SCREAMING_SNAKE_CASE)

       __snake_case         =			processor(text=self.input_string)

       __snake_case         =			tokenizer(
           self.input_string			,							padding='max_length'			,							max_length=2_5_6			,							add_special_tokens=__SCREAMING_SNAKE_CASE			,							return_attention_mask=__SCREAMING_SNAKE_CASE			,							return_token_type_ids=__SCREAMING_SNAKE_CASE			,							)

       for key in encoded_tok.keys():
        self.assertListEqual(encoded_tok[key]			,							encoded_processor[key].squeeze().tolist())









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





def 		_UpperCAmelCase (				__A				: str    ,		__A				: Optional[Any]=1_00    ,		__A				: int=" "   ):
				a_		:	Optional[int]									=   text.split(__A   )
				return [character.join(text[i : i + n]   ).strip() for i in range(0    ,		len(__A   )    ,		__A   )]





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





def 		_UpperCAmelCase (				__A				: "RagExampleArguments"    ,		__A				: "ProcessingArguments"    ,		__A				: "IndexHnswArguments"    ,		):
				######################################
				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_		:	List[str]									=   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_		:	str									=   DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name   ).to(device=__A   )
				a_		:	Tuple									=   DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name   )
				a_		:	Union[str, Any]									=   Features(
				    {'''text''': Value('''string'''   ), '''title''': Value('''string'''   ), '''embeddings''': Sequence(Value('''float32'''   )   )}   )  # optional, save as float32 instead of float64 to save space
				a_		:	Union[str, Any]									=   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_		:	Tuple									=   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_		:	Dict									=   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_		:	Optional[Any]									=   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__	=					field(
			    default=str(Path(SCREAMING_SNAKE_CASE_							).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__	=					field(
			    default=SCREAMING_SNAKE_CASE_						, metadata={"help": "Question that is passed as input to RAG. Default is 'What does Moses' rod turn into ?'."}						, )
			snake_case__	=					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__	=					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__	=					field(
			    default=str(Path(SCREAMING_SNAKE_CASE_							).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__	=					field(
			    default=SCREAMING_SNAKE_CASE_						, metadata={
			        "help": "The number of processes to use to split the documents into passages. Default is single process."
			    }						, )
			snake_case__	=					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__	=					field(
			    default=768						, metadata={"help": "The dimension of the embeddings to pass to the HNSW Faiss index."}						, )
			snake_case__	=					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)

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




