


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     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_=100      ,       lowercase_=" "      )		->     List[str]:




						'''simple docstring'''







						__UpperCAmelCase			:							Optional[int]	    =	text.split(lowercase_      )
						return [character.join(text[i : i + n]      ).strip() for i in range(0      ,       len(lowercase_      )      ,       lowercase_      )]




def     __SCREAMING_SNAKE_CASE							(       lowercase_      )		->     dict:




						'''simple docstring'''







						__UpperCAmelCase    ,      __UpperCAmelCase			:							int	    =	[], []
						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     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_      ,       lowercase_      )		->     dict:




						'''simple docstring'''







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




def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_      ,       lowercase_      ,       )		->     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
						__UpperCAmelCase			:							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
						__UpperCAmelCase			:							Dict	    =	dataset.map(lowercase_      ,       batched=lowercase_      ,       num_proc=processing_args.num_proc      )

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




@dataclass
class 							lowerCamelCase   :
	_lowerCAmelCase     :		str										=						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\''''}							,						)
	_lowerCAmelCase     :		Optional[str]										=						field(
	    default=_UpperCamelCase							,						metadata={'''help''': '''Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'''}							,						)
	_lowerCAmelCase     :		str										=						field(
	    default='''facebook/rag-sequence-nq'''							,						metadata={'''help''': '''The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''''}							,						)
	_lowerCAmelCase     :		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\''''
	        )
	    }							,						)
	_lowerCAmelCase     :		Optional[str]										=						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 							lowerCamelCase   :
	_lowerCAmelCase     :		Optional[int]										=						field(
	    default=_UpperCamelCase							,						metadata={
	        '''help''': '''The number of processes to use to split the documents into passages. Default is single process.'''
	    }							,						)
	_lowerCAmelCase     :		int										=						field(
	    default=1_6							,						metadata={
	        '''help''': '''The batch size to use when computing the passages embeddings using the DPR context encoder.'''
	    }							,						)






@dataclass
class 							lowerCamelCase   :
	_lowerCAmelCase     :		int										=						field(
	    default=7_6_8							,						metadata={'''help''': '''The dimension of the embeddings to pass to the HNSW Faiss index.'''}							,						)
	_lowerCAmelCase     :		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)

				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)





import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
				import torch

if is_datasets_available():
				from datasets import load_dataset

lowerCAmelCase       		=    random.Random()




def     __SCREAMING_SNAKE_CASE							(       lowercase_      ,       lowercase_=1.0      ,       lowercase_=None      ,       lowercase_=None      )		->     Union[str, Any]:




						'''simple docstring'''







						if rng is None:
												__UpperCAmelCase			:							str	    =	global_rng

						__UpperCAmelCase			:							List[Any]	    =	[]
						for batch_idx in range(shape[0]      ):
												values.append([]      )
												for _ in range(shape[1]      ):
																		values[-1].append(rng.random() * scale      )

						return values




class 							lowerCamelCase   (  unittest.TestCase ):


	def __init__(    self				,     lowercase__				,     lowercase__=7				,     lowercase__=4_0_0				,     lowercase__=2_0_0_0				,     lowercase__=2_0_4_8				,     lowercase__=1_2_8				,     lowercase__=1				,     lowercase__=5_1_2				,     lowercase__=3_0				,     lowercase__=4_4_1_0_0				,     ):
							__UpperCAmelCase			:							Optional[Any]	    =	parent
							__UpperCAmelCase			:							Any	    =	batch_size
							__UpperCAmelCase			:							int	    =	min_seq_length
							__UpperCAmelCase			:							List[str]	    =	max_seq_length
							__UpperCAmelCase			:							Dict	    =	(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
							__UpperCAmelCase			:							Any	    =	spectrogram_length
							__UpperCAmelCase			:							List[Any]	    =	feature_size
							__UpperCAmelCase			:							Union[str, Any]	    =	num_audio_channels
							__UpperCAmelCase			:							Optional[int]	    =	hop_length
							__UpperCAmelCase			:							Tuple	    =	chunk_length
							__UpperCAmelCase			:							Any	    =	sampling_rate


	def  A(    self):
							return {
							    "spectrogram_length": self.spectrogram_length,
							    "feature_size": self.feature_size,
							    "num_audio_channels": self.num_audio_channels,
							    "hop_length": self.hop_length,
							    "chunk_length": self.chunk_length,
							    "sampling_rate": self.sampling_rate,
							}


	def  A(    self				,     lowercase__=False				,     lowercase__=False):
							def _flatten(lowercase__):
													return list(itertools.chain(*lowercase__))

							if equal_length:
													__UpperCAmelCase			:							str	    =	[floats_list((self.max_seq_length, self.feature_size)) for _ in range(self.batch_size)]
							else:
													# make sure that inputs increase in size
													__UpperCAmelCase			:							List[str]	    =	[
													    floats_list((x, self.feature_size))
													    for x in range(self.min_seq_length				,     self.max_seq_length				,     self.seq_length_diff)
													]
							if numpify:
													__UpperCAmelCase			:							List[str]	    =	[np.asarray(lowercase__) for x in speech_inputs]
							return speech_inputs






@require_torch
@require_torchaudio
class 							lowerCamelCase   (  _UpperCamelCase							,						unittest.TestCase ):
	_lowerCAmelCase     :		Optional[int]										=						TvltFeatureExtractor


	def  A(    self):
							__UpperCAmelCase			:							Dict	    =	TvltFeatureExtractionTester(self)


	def  A(    self):
							__UpperCAmelCase			:							Optional[int]	    =	self.feature_extraction_class(**self.feat_extract_dict)
							self.assertTrue(hasattr(lowercase__				,     '''spectrogram_length'''))
							self.assertTrue(hasattr(lowercase__				,     '''feature_size'''))
							self.assertTrue(hasattr(lowercase__				,     '''num_audio_channels'''))
							self.assertTrue(hasattr(lowercase__				,     '''hop_length'''))
							self.assertTrue(hasattr(lowercase__				,     '''chunk_length'''))
							self.assertTrue(hasattr(lowercase__				,     '''sampling_rate'''))


	def  A(    self):
							__UpperCAmelCase			:							str	    =	self.feature_extraction_class(**self.feat_extract_dict)

							with tempfile.TemporaryDirectory() as tmpdirname:
													__UpperCAmelCase			:							str	    =	feat_extract_first.save_pretrained(lowercase__)[0]
													check_json_file_has_correct_format(lowercase__)
													__UpperCAmelCase			:							Optional[int]	    =	self.feature_extraction_class.from_pretrained(lowercase__)

							__UpperCAmelCase			:							List[Any]	    =	feat_extract_first.to_dict()
							__UpperCAmelCase			:							Union[str, Any]	    =	feat_extract_second.to_dict()
							__UpperCAmelCase			:							Union[str, Any]	    =	dict_first.pop('''mel_filters''')
							__UpperCAmelCase			:							Union[str, Any]	    =	dict_second.pop('''mel_filters''')
							self.assertTrue(np.allclose(lowercase__				,     lowercase__))
							self.assertEqual(lowercase__				,     lowercase__)


	def  A(    self):
							__UpperCAmelCase			:							Optional[int]	    =	self.feature_extraction_class(**self.feat_extract_dict)

							with tempfile.TemporaryDirectory() as tmpdirname:
													__UpperCAmelCase			:							Union[str, Any]	    =	os.path.join(lowercase__				,     '''feat_extract.json''')
													feat_extract_first.to_json_file(lowercase__)
													__UpperCAmelCase			:							str	    =	self.feature_extraction_class.from_json_file(lowercase__)

							__UpperCAmelCase			:							Any	    =	feat_extract_first.to_dict()
							__UpperCAmelCase			:							Union[str, Any]	    =	feat_extract_second.to_dict()
							__UpperCAmelCase			:							Tuple	    =	dict_first.pop('''mel_filters''')
							__UpperCAmelCase			:							List[str]	    =	dict_second.pop('''mel_filters''')
							self.assertTrue(np.allclose(lowercase__				,     lowercase__))
							self.assertEqual(lowercase__				,     lowercase__)


	def  A(    self):
							# Initialize feature_extractor
							__UpperCAmelCase			:							Optional[int]	    =	self.feature_extraction_class(**self.feat_extract_dict)

							# create three inputs of length 800, 1000, and 1200
							__UpperCAmelCase			:							Optional[int]	    =	[floats_list((1, x))[0] for x in range(8_0_0				,     1_4_0_0				,     2_0_0)]
							__UpperCAmelCase			:							int	    =	[np.asarray(lowercase__) for speech_input in speech_inputs]

							# Test not batched input
							__UpperCAmelCase			:							Dict	    =	feature_extractor(np_speech_inputs[0]				,     return_tensors='''np'''				,     sampling_rate=4_4_1_0_0).audio_values

							self.assertTrue(encoded_audios.ndim == 4)
							self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size)
							self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length)
							self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels)

							# Test batched
							__UpperCAmelCase			:							List[str]	    =	feature_extractor(lowercase__				,     return_tensors='''np'''				,     sampling_rate=4_4_1_0_0).audio_values

							self.assertTrue(encoded_audios.ndim == 4)
							self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size)
							self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length)
							self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels)

							# Test audio masking
							__UpperCAmelCase			:							Tuple	    =	feature_extractor(
							    lowercase__				,     return_tensors='''np'''				,     sampling_rate=4_4_1_0_0				,     mask_audio=lowercase__).audio_values

							self.assertTrue(encoded_audios.ndim == 4)
							self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size)
							self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length)
							self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels)

							# Test 2-D numpy arrays are batched.
							__UpperCAmelCase			:							Any	    =	[floats_list((1, x))[0] for x in (8_0_0, 8_0_0, 8_0_0)]
							__UpperCAmelCase			:							Optional[Any]	    =	np.asarray(lowercase__)
							__UpperCAmelCase			:							Tuple	    =	feature_extractor(lowercase__				,     return_tensors='''np'''				,     sampling_rate=4_4_1_0_0).audio_values
							self.assertTrue(encoded_audios.ndim == 4)
							self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size)
							self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length)
							self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels)


	def  A(    self				,     lowercase__):
							__UpperCAmelCase			:							Optional[int]	    =	load_dataset('''hf-internal-testing/librispeech_asr_dummy'''				,     '''clean'''				,     split='''validation''')
							# automatic decoding with librispeech
							__UpperCAmelCase			:							int	    =	ds.sort('''id''').select(range(lowercase__))[:num_samples]['''audio''']

							return [x["array"] for x in speech_samples]


	def  A(    self):
							__UpperCAmelCase			:							Optional[Any]	    =	self._load_datasamples(1)
							__UpperCAmelCase			:							Tuple	    =	TvltFeatureExtractor()
							__UpperCAmelCase			:							Tuple	    =	feature_extractor(lowercase__				,     return_tensors='''pt''').audio_values

							self.assertEquals(audio_values.shape				,     (1, 1, 1_9_2, 1_2_8))

							__UpperCAmelCase			:							int	    =	torch.tensor([[-0.3_0_3_2, -0.2_7_0_8], [-0.4_4_3_4, -0.4_0_0_7]])
							self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]				,     lowercase__				,     atol=1e-4))

