



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    (				snake_case     ,						snake_case=1_00     ,						snake_case=" "      ):





					"""simple docstring"""

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


def 		_UpperCAmelCase    (				snake_case      ):





					"""simple docstring"""

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


def 		_UpperCAmelCase    (				snake_case     ,						snake_case     ,						snake_case     ,						snake_case     ,						snake_case     ,						snake_case      ):





					"""simple docstring"""

					_lowerCAmelCase      =   load_dataset(
					    """csv"""     ,						data_files=[csv_path]     ,						split="""train"""     ,						delimiter="""\t"""     ,						column_names=["""title""", """text"""]      )
					_lowerCAmelCase      =   kb_dataset.map(
					    snake_case     ,						batched=snake_case     ,						num_proc=1      )  # if you want you can load already splitted csv.
					_lowerCAmelCase      =   [kb_dataset.shard(snake_case     ,						snake_case     ,						contiguous=snake_case      ) for i in range(snake_case      )]
					_lowerCAmelCase      =   kb_list[process_num]

					_lowerCAmelCase      =   """data_""" + str(snake_case      )
					_lowerCAmelCase      =   os.path.join(snake_case     ,						snake_case      )

					_lowerCAmelCase      =   DPRContextEncoderTokenizerFast.from_pretrained("""facebook/dpr-ctx_encoder-multiset-base"""      )
					_lowerCAmelCase      =   ctx_encoder.to(device=snake_case      )

					def embed(
					    snake_case     ,						snake_case     ,						snake_case     ,						snake_case      ) -> dict:
										_lowerCAmelCase      =   ctx_tokenizer(
										    documents["""title"""]     ,						documents["""text"""]     ,						truncation=snake_case     ,						padding="""longest"""     ,						return_tensors="""pt"""      )["""input_ids"""]
										_lowerCAmelCase      =   ctx_encoder(input_ids.to(device=snake_case      )     ,						return_dict=snake_case      ).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(snake_case     ,						ctx_encoder=snake_case     ,						ctx_tokenizer=snake_case     ,						device=snake_case      )     ,						batched=snake_case     ,						batch_size=16     ,						features=snake_case     ,						)
					dataset.save_to_disk(snake_case      )


def 		_UpperCAmelCase    (				snake_case     ,						snake_case      ):





					"""simple docstring"""

					_lowerCAmelCase      =   []

					for shard_address in glob(str(snake_case      ) + """/*/"""      ):
										data_shard_list.append(load_from_disk(snake_case      )      )

					_lowerCAmelCase      =   concatenate_datasets(snake_case      )
					faiss.omp_set_num_threads(96      )

					_lowerCAmelCase      =   faiss.IndexHNSWFlat(7_68     ,						1_28     ,						faiss.METRIC_INNER_PRODUCT      )
					concat.add_faiss_index("""embeddings"""     ,						custom_index=snake_case      )
					concat.get_index("""embeddings"""      ).save(
					    snake_case      )  # since we load the index in to memory,we can directly update the index in the disk








from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


A__     				=	logging.get_logger(__name__)





class  __lowerCAmelCase			(					lowerCamelCase__	):
				__lowerCamelCase			=    ['''input_values''', '''attention_mask''']




				def __init__(							self						,    _snake_case=1						,    _snake_case=16000						,    _snake_case=128						,    _snake_case=1024						,    _snake_case=0.0						,    _snake_case=True						,    _snake_case=-4.267_7393						,    _snake_case=4.568_9974						,    _snake_case=False						,    **_snake_case						,    ):



									"""simple docstring"""





									super().__init__(feature_size=_snake_case						,    sampling_rate=_snake_case						,    padding_value=_snake_case						,    **_snake_case		)
									_lowerCAmelCase      =   num_mel_bins
									_lowerCAmelCase      =   max_length
									_lowerCAmelCase      =   do_normalize
									_lowerCAmelCase      =   mean
									_lowerCAmelCase      =   std
									_lowerCAmelCase      =   return_attention_mask




				def 							snake_case       (							self						,    _snake_case						,    _snake_case						,    ):



									"""simple docstring"""





									_lowerCAmelCase      =   torch.from_numpy(_snake_case		).unsqueeze(0		)
									_lowerCAmelCase      =   ta_kaldi.fbank(
									    _snake_case						,    htk_compat=_snake_case						,    sample_frequency=self.sampling_rate						,    use_energy=_snake_case						,    window_type="""hanning"""						,    num_mel_bins=self.num_mel_bins						,    dither=0.0						,    frame_shift=10						,    )

									_lowerCAmelCase      =   fbank.shape[0]
									_lowerCAmelCase      =   max_length - n_frames

									# pad or truncate, depending on difference
									if difference > 0:
														_lowerCAmelCase      =   torch.nn.ZeroPadad((0, 0, 0, difference)		)
														_lowerCAmelCase      =   pad_module(_snake_case		)
									elif difference < 0:
														_lowerCAmelCase      =   fbank[0:max_length, :]

									_lowerCAmelCase      =   fbank.numpy()

									return fbank




				def 							snake_case       (							self						,    _snake_case		):



									"""simple docstring"""





									return (input_values - (self.mean)) / (self.std * 2)




				def __call__(							self						,    _snake_case						,    _snake_case = None						,    _snake_case = None						,    **_snake_case						,    ):



									"""simple docstring"""





									if sampling_rate is not None:
														if sampling_rate != self.sampling_rate:
																			raise ValueError(
																			    F'The model corresponding to this feature extractor: {self} was trained using a sampling rate of'
																			    F' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'
																			    F' {self.sampling_rate} and not {sampling_rate}.'		)
									else:
														logger.warning(
														    """It is strongly recommended to pass the `sampling_rate` argument to this function. """
														    """Failing to do so can result in silent errors that might be hard to debug."""		)

									_lowerCAmelCase      =   isinstance(_snake_case						,    np.ndarray		) and len(raw_speech.shape		) > 1
									if is_batched_numpy and len(raw_speech.shape		) > 2:
														raise ValueError(F'Only mono-channel audio is supported for input to {self}'		)
									_lowerCAmelCase      =   is_batched_numpy or (
									    isinstance(_snake_case						,    (list, tuple)		) and (isinstance(raw_speech[0]						,    (np.ndarray, tuple, list)		))
									)

									if is_batched:
														_lowerCAmelCase      =   [np.asarray(_snake_case						,    dtype=np.floataa		) for speech in raw_speech]
									elif not is_batched and not isinstance(_snake_case						,    np.ndarray		):
														_lowerCAmelCase      =   np.asarray(_snake_case						,    dtype=np.floataa		)
									elif isinstance(_snake_case						,    np.ndarray		) and raw_speech.dtype is np.dtype(np.floataa		):
														_lowerCAmelCase      =   raw_speech.astype(np.floataa		)

									# always return batch
									if not is_batched:
														_lowerCAmelCase      =   [raw_speech]

									# extract fbank features and pad/truncate to max_length
									_lowerCAmelCase      =   [self._extract_fbank_features(_snake_case						,    max_length=self.max_length		) for waveform in raw_speech]

									# convert into BatchFeature
									_lowerCAmelCase      =   BatchFeature({"""input_values""": features}		)

									# make sure list is in array format
									_lowerCAmelCase      =   padded_inputs.get("""input_values"""		)
									if isinstance(input_values[0]						,    _snake_case		):
														_lowerCAmelCase      =   [np.asarray(_snake_case						,    dtype=np.floataa		) for feature in input_values]

									# normalization
									if self.do_normalize:
														_lowerCAmelCase      =   [self.normalize(_snake_case		) for feature in input_values]

									if return_tensors is not None:
														_lowerCAmelCase      =   padded_inputs.convert_to_tensors(_snake_case		)

									return padded_inputs



