






import itertools
import random
import unittest

import numpy as np

from transformers import EncodecFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
					import torch


__snake_case     :     str       =  random.Random()







def 						_UpperCamelCase (     UpperCamelCase_							:       Optional[int]     ,							UpperCamelCase_							:       Union[str, Any]=1.0     ,							UpperCamelCase_							:       Union[str, Any]=None     ,							UpperCamelCase_							:       Optional[int]=None		)	-> Any:

						"""simple docstring"""



						if rng is None:
												lowerCAmelCase__         =    global_rng

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

						return values


@require_torch
class      __SCREAMING_SNAKE_CASE       ( unittest.TestCase):






						def __init__(						self  ,    _UpperCamelCase  ,    _UpperCamelCase=7  ,    _UpperCamelCase=4_00  ,    _UpperCamelCase=20_00  ,    _UpperCamelCase=1  ,    _UpperCamelCase=0.0  ,    _UpperCamelCase=2_40_00  ,    _UpperCamelCase=True  ,    ):






												"""simple docstring"""
												lowerCAmelCase__         =    parent
												lowerCAmelCase__         =    batch_size
												lowerCAmelCase__         =    min_seq_length
												lowerCAmelCase__         =    max_seq_length
												lowerCAmelCase__         =    (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
												lowerCAmelCase__         =    feature_size
												lowerCAmelCase__         =    padding_value
												lowerCAmelCase__         =    sampling_rate
												lowerCAmelCase__         =    return_attention_mask






						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												return {
												    "feature_size": self.feature_size,
												    "padding_value": self.padding_value,
												    "sampling_rate": self.sampling_rate,
												    "return_attention_mask": self.return_attention_mask,
												}







						def    UpperCamelCase__   (						self  ,    _UpperCamelCase=False  ,    _UpperCamelCase=False ):






												"""simple docstring"""
												def _flatten(_UpperCamelCase ):
																		return list(itertools.chain(*_UpperCamelCase ) )

												if equal_length:
																		lowerCAmelCase__         =    floats_list((self.batch_size, self.max_seq_length) )
												else:
																		# make sure that inputs increase in size
																		lowerCAmelCase__         =    [
																		    _flatten(floats_list((x, self.feature_size) ) )
																		    for x in range(self.min_seq_length  ,    self.max_seq_length  ,    self.seq_length_diff )
																		]

												if numpify:
																		lowerCAmelCase__         =    [np.asarray(_UpperCamelCase ) for x in audio_inputs]

												return audio_inputs




@require_torch
class      __SCREAMING_SNAKE_CASE       ( __lowercase		,       unittest.TestCase):
						_SCREAMING_SNAKE_CASE						:							List[Any]				=       EncodecFeatureExtractor






						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												lowerCAmelCase__         =    EnCodecFeatureExtractionTester(self )






						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												# Tests that all call wrap to encode_plus and batch_encode_plus
												lowerCAmelCase__         =    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
												# create three inputs of length 800, 1000, and 1200
												lowerCAmelCase__         =    [floats_list((1, x) )[0] for x in range(8_00  ,    14_00  ,    2_00 )]
												lowerCAmelCase__         =    [np.asarray(_UpperCamelCase ) for audio_input in audio_inputs]

												# Test not batched input
												lowerCAmelCase__         =    feat_extract(audio_inputs[0]  ,    return_tensors='np' ).input_values
												lowerCAmelCase__         =    feat_extract(np_audio_inputs[0]  ,    return_tensors='np' ).input_values
												self.assertTrue(np.allclose(_UpperCamelCase  ,    _UpperCamelCase  ,    atol=1E-3 ) )

												# Test batched
												lowerCAmelCase__         =    feat_extract(_UpperCamelCase  ,    padding=_UpperCamelCase  ,    return_tensors='np' ).input_values
												lowerCAmelCase__         =    feat_extract(_UpperCamelCase  ,    padding=_UpperCamelCase  ,    return_tensors='np' ).input_values
												for enc_seq_a, enc_seq_a in zip(_UpperCamelCase  ,    _UpperCamelCase ):
																		self.assertTrue(np.allclose(_UpperCamelCase  ,    _UpperCamelCase  ,    atol=1E-3 ) )






						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												lowerCAmelCase__         =    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
												lowerCAmelCase__         =    np.random.rand(1_00 ).astype(np.floataa )
												lowerCAmelCase__         =    np_audio_inputs.tolist()

												for inputs in [py_audio_inputs, np_audio_inputs]:
																		lowerCAmelCase__         =    feature_extractor.pad([{'input_values': inputs}]  ,    return_tensors='np' )
																		self.assertTrue(np_processed.input_values.dtype == np.floataa )
																		lowerCAmelCase__         =    feature_extractor.pad([{'input_values': inputs}]  ,    return_tensors='pt' )
																		self.assertTrue(pt_processed.input_values.dtype == torch.floataa )






						def    UpperCamelCase__   (						self  ,    _UpperCamelCase ):






												"""simple docstring"""
												from datasets import load_dataset

												lowerCAmelCase__         =    load_dataset('hf-internal-testing/librispeech_asr_dummy'  ,    'clean'  ,    split='validation' )
												# automatic decoding with librispeech
												lowerCAmelCase__         =    ds.sort('id' ).select(range(_UpperCamelCase ) )[:num_samples]['audio']

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






						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												# fmt: off
												lowerCAmelCase__         =    torch.tensor(
												    [2.3804E-03, 2.0752E-03, 1.9836E-03, 2.1057E-03, 1.6174E-03,
												     3.0518E-04, 9.1553E-05, 3.3569E-04, 9.7656E-04, 1.8311E-03,
												     2.0142E-03, 2.1057E-03, 1.7395E-03, 4.5776E-04, -3.9673E-04,
												     4.5776E-04, 1.0071E-03, 9.1553E-05, 4.8828E-04, 1.1597E-03,
												     7.3242E-04, 9.4604E-04, 1.8005E-03, 1.8311E-03, 8.8501E-04,
												     4.2725E-04, 4.8828E-04, 7.3242E-04, 1.0986E-03, 2.1057E-03] )
												# fmt: on
												lowerCAmelCase__         =    self._load_datasamples(1 )
												lowerCAmelCase__         =    EncodecFeatureExtractor()
												lowerCAmelCase__         =    feature_extractor(_UpperCamelCase  ,    return_tensors='pt' ).input_values
												self.assertEquals(input_values.shape  ,    (1, 1, 9_36_80) )
												self.assertTrue(torch.allclose(input_values[0, 0, :30]  ,    _UpperCamelCase  ,    atol=1E-6 ) )






						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												# fmt: off
												lowerCAmelCase__         =    torch.tensor(
												    [2.3804E-03, 2.0752E-03, 1.9836E-03, 2.1057E-03, 1.6174E-03,
												     3.0518E-04, 9.1553E-05, 3.3569E-04, 9.7656E-04, 1.8311E-03,
												     2.0142E-03, 2.1057E-03, 1.7395E-03, 4.5776E-04, -3.9673E-04,
												     4.5776E-04, 1.0071E-03, 9.1553E-05, 4.8828E-04, 1.1597E-03,
												     7.3242E-04, 9.4604E-04, 1.8005E-03, 1.8311E-03, 8.8501E-04,
												     4.2725E-04, 4.8828E-04, 7.3242E-04, 1.0986E-03, 2.1057E-03] )
												# fmt: on
												lowerCAmelCase__         =    self._load_datasamples(1 )
												lowerCAmelCase__         =    [np.tile(input_audio[0][None]  ,    reps=(2, 1) )]
												input_audio[0][1] *= 0.5
												lowerCAmelCase__         =    EncodecFeatureExtractor(feature_size=2 )
												lowerCAmelCase__         =    feature_extractor(_UpperCamelCase  ,    return_tensors='pt' ).input_values
												self.assertEquals(input_values.shape  ,    (1, 2, 9_36_80) )
												self.assertTrue(torch.allclose(input_values[0, 0, :30]  ,    _UpperCamelCase  ,    atol=1E-6 ) )
												self.assertTrue(torch.allclose(input_values[0, 1, :30]  ,    EXPECTED_INPUT_VALUES * 0.5  ,    atol=1E-6 ) )







						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												lowerCAmelCase__         =    self._load_datasamples(2 )
												# would be easier if the stride was like
												lowerCAmelCase__         =    EncodecFeatureExtractor(feature_size=1  ,    chunk_length_s=1  ,    overlap=0.01 )

												# pad and trunc raise an error ?
												with self.assertRaisesRegex(
												    _UpperCamelCase  ,    '^Both padding and truncation were set. Make sure you only set one.$'  ,    ):
																		lowerCAmelCase__         =    feature_extractor(
																		    _UpperCamelCase  ,    padding='max_length'  ,    truncation=_UpperCamelCase  ,    return_tensors='pt' ).input_values

												# truncate to chunk
												lowerCAmelCase__         =    feature_extractor(_UpperCamelCase  ,    truncation=_UpperCamelCase  ,    return_tensors='pt' ).input_values
												self.assertEquals(truncated_outputs.shape  ,    (2, 1, 7_15_20) )  # 2 chunks

												# force truncate to max_length
												lowerCAmelCase__         =    feature_extractor(
												    _UpperCamelCase  ,    truncation=_UpperCamelCase  ,    max_length=4_80_00  ,    return_tensors='pt' ).input_values
												self.assertEquals(truncated_outputs.shape  ,    (2, 1, 4_80_00) )

												# pad to chunk
												lowerCAmelCase__         =    feature_extractor(_UpperCamelCase  ,    padding=_UpperCamelCase  ,    return_tensors='pt' ).input_values
												self.assertEquals(padded_outputs.shape  ,    (2, 1, 9_52_80) )

												# pad to chunk
												lowerCAmelCase__         =    feature_extractor(_UpperCamelCase  ,    return_tensors='pt' ).input_values
												self.assertEquals(truncated_outputs.shape  ,    (2, 1, 9_52_80) )

												# force pad to max length
												lowerCAmelCase__         =    feature_extractor(
												    _UpperCamelCase  ,    padding='max_length'  ,    max_length=10_00_00  ,    return_tensors='pt' ).input_values
												self.assertEquals(truncated_outputs.shape  ,    (2, 1, 10_00_00) )

												# force no pad
												with self.assertRaisesRegex(
												    _UpperCamelCase  ,    '^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'  ,    ):
																		lowerCAmelCase__         =    feature_extractor(_UpperCamelCase  ,    padding=_UpperCamelCase  ,    return_tensors='pt' ).input_values

												lowerCAmelCase__         =    feature_extractor(input_audio[0]  ,    padding=_UpperCamelCase  ,    return_tensors='pt' ).input_values
												self.assertEquals(truncated_outputs.shape  ,    (1, 1, 9_36_80) )

												# no pad if no chunk_length_s
												lowerCAmelCase__         =    None
												with self.assertRaisesRegex(
												    _UpperCamelCase  ,    '^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'  ,    ):
																		lowerCAmelCase__         =    feature_extractor(_UpperCamelCase  ,    padding=_UpperCamelCase  ,    return_tensors='pt' ).input_values

												lowerCAmelCase__         =    feature_extractor(input_audio[0]  ,    padding=_UpperCamelCase  ,    return_tensors='pt' ).input_values
												self.assertEquals(truncated_outputs.shape  ,    (1, 1, 9_36_80) )

												# no pad if no overlap
												lowerCAmelCase__         =    2
												lowerCAmelCase__         =    None
												with self.assertRaisesRegex(
												    _UpperCamelCase  ,    '^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'  ,    ):
																		lowerCAmelCase__         =    feature_extractor(_UpperCamelCase  ,    padding=_UpperCamelCase  ,    return_tensors='pt' ).input_values

												lowerCAmelCase__         =    feature_extractor(input_audio[0]  ,    padding=_UpperCamelCase  ,    return_tensors='pt' ).input_values
												self.assertEquals(truncated_outputs.shape  ,    (1, 1, 9_36_80) )













import os
from functools import partial
from glob import glob

import faiss
from datasets import Features, Sequence, Value, concatenate_datasets, load_dataset, load_from_disk

from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast







def 						_UpperCamelCase (     UpperCamelCase_							:       Dict     ,							UpperCamelCase_							:       Union[str, Any]=100     ,							UpperCamelCase_							:       Optional[int]=" "		)	-> int:

						"""simple docstring"""



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







def 						_UpperCamelCase (     UpperCamelCase_							:       List[str]		)	-> Any:

						"""simple docstring"""



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







def 						_UpperCamelCase (     UpperCamelCase_							:       Union[str, Any]     ,							UpperCamelCase_							:       int     ,							UpperCamelCase_							:       Optional[int]     ,							UpperCamelCase_							:       Tuple     ,							UpperCamelCase_							:       str     ,							UpperCamelCase_							:       Tuple		)	-> Optional[Any]:

						"""simple docstring"""



						lowerCAmelCase__         =    load_dataset(
						    'csv'     ,							data_files=[csv_path]     ,							split='train'     ,							delimiter='\t'     ,							column_names=['title', 'text']		)
						lowerCAmelCase__         =    kb_dataset.map(
						    UpperCamelCase_     ,							batched=UpperCamelCase_     ,							num_proc=1		)  # if you want you can load already splitted csv.
						lowerCAmelCase__         =    [kb_dataset.shard(UpperCamelCase_     ,							UpperCamelCase_     ,							contiguous=UpperCamelCase_		) for i in range(UpperCamelCase_		)]
						lowerCAmelCase__         =    kb_list[process_num]

						lowerCAmelCase__         =    'data_' + str(UpperCamelCase_		)
						lowerCAmelCase__         =    os.path.join(UpperCamelCase_     ,							UpperCamelCase_		)

						lowerCAmelCase__         =    DPRContextEncoderTokenizerFast.from_pretrained('facebook/dpr-ctx_encoder-multiset-base'		)
						lowerCAmelCase__         =    ctx_encoder.to(device=UpperCamelCase_		)

						def embed(
						    UpperCamelCase_							:       dict     ,							UpperCamelCase_							:       DPRContextEncoder     ,							UpperCamelCase_							:       DPRContextEncoderTokenizerFast     ,							UpperCamelCase_							:       Tuple		) -> dict:
												lowerCAmelCase__         =    ctx_tokenizer(
												    documents['title']     ,							documents['text']     ,							truncation=UpperCamelCase_     ,							padding='longest'     ,							return_tensors='pt'		)['input_ids']
												lowerCAmelCase__         =    ctx_encoder(input_ids.to(device=UpperCamelCase_		)     ,							return_dict=UpperCamelCase_		).pooler_output
												return {"embeddings": embeddings.detach().cpu().numpy()}

						lowerCAmelCase__         =    Features(
						    {'text': Value('string'		), 'title': Value('string'		), 'embeddings': Sequence(Value('float32'		)		)}		)  # optional, save as float32 instead of float64 to save space

						lowerCAmelCase__         =    data_shrad.map(
						    partial(UpperCamelCase_     ,							ctx_encoder=UpperCamelCase_     ,							ctx_tokenizer=UpperCamelCase_     ,							device=UpperCamelCase_		)     ,							batched=UpperCamelCase_     ,							batch_size=16     ,							features=UpperCamelCase_     ,							)
						dataset.save_to_disk(UpperCamelCase_		)







def 						_UpperCamelCase (     UpperCamelCase_							:       List[Any]     ,							UpperCamelCase_							:       Dict		)	-> Optional[Any]:

						"""simple docstring"""



						lowerCAmelCase__         =    []

						for shard_address in glob(str(UpperCamelCase_		) + '/*/'		):
												data_shard_list.append(load_from_disk(UpperCamelCase_		)		)

						lowerCAmelCase__         =    concatenate_datasets(UpperCamelCase_		)
						faiss.omp_set_num_threads(96		)

						lowerCAmelCase__         =    faiss.IndexHNSWFlat(768     ,							128     ,							faiss.METRIC_INNER_PRODUCT		)
						concat.add_faiss_index('embeddings'     ,							custom_index=UpperCamelCase_		)
						concat.get_index('embeddings'		).save(
						    UpperCamelCase_		)  # since we load the index in to memory,we can directly update the index in the disk





