
"""simple docstring"""

import warnings
from typing import Any, Dict, List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


_A						=			logging.get_logger(__name__)






class 	_lowercase       (    a__		):
	lowercase_				=     ['input_values', 'attention_mask']




	def __init__(			self		,			UpperCAmelCase_ = 1		,			UpperCAmelCase_ = 16000		,			UpperCAmelCase_ = 0.0		,			UpperCAmelCase_ = False		,			UpperCAmelCase_ = 80		,			UpperCAmelCase_ = 16		,			UpperCAmelCase_ = 64		,			UpperCAmelCase_ = "hann_window"		,			UpperCAmelCase_ = 1.0		,			UpperCAmelCase_ = 80		,			UpperCAmelCase_ = 7600		,			UpperCAmelCase_ = 1E-1_0		,			UpperCAmelCase_ = 2		,			UpperCAmelCase_ = True		,			**UpperCAmelCase_		,			)		->							Optional[int]:
			super().__init__(feature_size=UpperCAmelCase_		,			sampling_rate=UpperCAmelCase_		,			padding_value=UpperCAmelCase_		,			**UpperCAmelCase_ )
			lowerCamelCase					:      Optional[int]          =   do_normalize
			lowerCamelCase					:      Dict          =   return_attention_mask

			lowerCamelCase					:      Any          =   num_mel_bins
			lowerCamelCase					:      str          =   hop_length
			lowerCamelCase					:      int          =   win_length
			lowerCamelCase					:      Optional[int]          =   win_function
			lowerCamelCase					:      List[str]          =   frame_signal_scale
			lowerCamelCase					:      Optional[int]          =   fmin
			lowerCamelCase					:      Optional[Any]          =   fmax
			lowerCamelCase					:      Any          =   mel_floor
			lowerCamelCase					:      Any          =   reduction_factor

			lowerCamelCase					:      Union[str, Any]          =   win_length * sampling_rate // 1000
			lowerCamelCase					:      Optional[Any]          =   hop_length * sampling_rate // 1000
			lowerCamelCase					:      int          =   optimal_fft_length(self.sample_size )
			lowerCamelCase					:      int          =   (self.n_fft // 2) + 1

			lowerCamelCase					:      Tuple          =   window_function(window_length=self.sample_size		,			name=self.win_function		,			periodic=UpperCAmelCase_ )

			lowerCamelCase					:      str          =   mel_filter_bank(
			    num_frequency_bins=self.n_freqs		,			num_mel_filters=self.num_mel_bins		,			min_frequency=self.fmin		,			max_frequency=self.fmax		,			sampling_rate=self.sampling_rate		,			norm='slaney'		,			mel_scale='slaney'		,			)

			if frame_signal_scale != 1.0:
					warnings.warn(
					    'The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers'		,			UpperCAmelCase_		,			)
			if reduction_factor != 2.0:
					warnings.warn(
					    'The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers'		,			UpperCAmelCase_		,			)




	@staticmethod
	# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
	def       _UpperCamelCase   (			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_ = 0.0 )		->							Any:
			if attention_mask is not None:
					lowerCamelCase					:      Optional[int]          =   np.array(UpperCAmelCase_		,			np.intaa )
					lowerCamelCase					:      str          =   []

					for vector, length in zip(UpperCAmelCase_		,			attention_mask.sum(-1 ) ):
							lowerCamelCase					:      Tuple          =   (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7 )
							if length < normed_slice.shape[0]:
									lowerCamelCase					:      str          =   padding_value

							normed_input_values.append(UpperCAmelCase_ )
			else:
					lowerCamelCase					:      Dict          =   [(x - x.mean()) / np.sqrt(x.var() + 1E-7 ) for x in input_values]

			return normed_input_values




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			)		->							str:
			lowerCamelCase					:      str          =   spectrogram(
			    UpperCAmelCase_		,			window=self.window		,			frame_length=self.sample_size		,			hop_length=self.sample_stride		,			fft_length=self.n_fft		,			mel_filters=self.mel_filters		,			mel_floor=self.mel_floor		,			log_mel='log10'		,			)
			return log_mel_spec.T




	def __call__(			self		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = False		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = False		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = None		,			**UpperCAmelCase_		,			)		->							Dict:
			if audio is None and audio_target is None:
					raise ValueError('You must provide either `audio` or `audio_target` values.' )

			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 audio 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.' )

			if audio is not None:
					lowerCamelCase					:      int          =   self._process_audio(
					    UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_		,			**UpperCAmelCase_		,			)
			else:
					lowerCamelCase					:      List[str]          =   None

			if audio_target is not None:
					lowerCamelCase					:      Dict          =   self._process_audio(
					    UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_		,			**UpperCAmelCase_		,			)

					if inputs is None:
							return inputs_target
					else:
							lowerCamelCase					:      List[str]          =   inputs_target["input_values"]
							lowerCamelCase					:      int          =   inputs_target.get('attention_mask' )
							if decoder_attention_mask is not None:
									lowerCamelCase					:      Tuple          =   decoder_attention_mask

			return inputs




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_ = False		,			UpperCAmelCase_ = False		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = False		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = None		,			UpperCAmelCase_ = None		,			**UpperCAmelCase_		,			)		->							List[Any]:
			lowerCamelCase					:      Union[str, Any]          =   isinstance(UpperCAmelCase_		,			np.ndarray ) and len(speech.shape ) > 1
			if is_batched_numpy and len(speech.shape ) > 2:
					raise ValueError(F"""Only mono-channel audio is supported for input to {self}""" )
			lowerCamelCase					:      Any          =   is_batched_numpy or (
			    isinstance(UpperCAmelCase_		,			(list, tuple) ) and (isinstance(speech[0]		,			(np.ndarray, tuple, list) ))
			)

			if is_batched:
					lowerCamelCase					:      Dict          =   [np.asarray(UpperCAmelCase_		,			dtype=np.floataa ) for speech in speech]
			elif not is_batched and not isinstance(UpperCAmelCase_		,			np.ndarray ):
					lowerCamelCase					:      List[Any]          =   np.asarray(UpperCAmelCase_		,			dtype=np.floataa )
			elif isinstance(UpperCAmelCase_		,			np.ndarray ) and speech.dtype is np.dtype(np.floataa ):
					lowerCamelCase					:      Any          =   speech.astype(np.floataa )

			# always return batch
			if not is_batched:
					lowerCamelCase					:      Tuple          =   [speech]

			# needed to make pad() work on spectrogram inputs
			lowerCamelCase					:      List[Any]          =   self.feature_size

			# convert into correct format for padding
			if is_target:
					lowerCamelCase					:      Tuple          =   [self._extract_mel_features(UpperCAmelCase_ ) for waveform in speech]
					lowerCamelCase					:      List[Any]          =   BatchFeature({'input_values': features} )
					lowerCamelCase					:      Union[str, Any]          =   self.num_mel_bins
			else:
					lowerCamelCase					:      Dict          =   BatchFeature({'input_values': speech} )

			lowerCamelCase					:      List[str]          =   self.pad(
			    UpperCAmelCase_		,			padding=UpperCAmelCase_		,			max_length=UpperCAmelCase_		,			truncation=UpperCAmelCase_		,			pad_to_multiple_of=UpperCAmelCase_		,			return_attention_mask=UpperCAmelCase_		,			**UpperCAmelCase_		,			)

			lowerCamelCase					:      int          =   feature_size_hack

			# convert input values to correct format
			lowerCamelCase					:      Any          =   padded_inputs["input_values"]
			if not isinstance(input_values[0]		,			np.ndarray ):
					lowerCamelCase					:      List[Any]          =   [np.asarray(UpperCAmelCase_		,			dtype=np.floataa ) for array in input_values]
			elif (
			    not isinstance(UpperCAmelCase_		,			np.ndarray )
			    and isinstance(input_values[0]		,			np.ndarray )
			    and input_values[0].dtype is np.dtype(np.floataa )
			):
					lowerCamelCase					:      Tuple          =   [array.astype(np.floataa ) for array in input_values]
			elif isinstance(UpperCAmelCase_		,			np.ndarray ) and input_values.dtype is np.dtype(np.floataa ):
					lowerCamelCase					:      Union[str, Any]          =   input_values.astype(np.floataa )

			# convert attention_mask to correct format
			lowerCamelCase					:      Tuple          =   padded_inputs.get('attention_mask' )
			if attention_mask is not None:
					lowerCamelCase					:      str          =   [np.asarray(UpperCAmelCase_		,			dtype=np.intaa ) for array in attention_mask]

			# zero-mean and unit-variance normalization
			if not is_target and self.do_normalize:
					lowerCamelCase					:      Optional[int]          =   (
					    attention_mask
					    if self._get_padding_strategies(UpperCAmelCase_		,			max_length=UpperCAmelCase_ ) is not PaddingStrategy.DO_NOT_PAD
					    else None
					)
					lowerCamelCase					:      Tuple          =   self.zero_mean_unit_var_norm(
					    padded_inputs['input_values']		,			attention_mask=UpperCAmelCase_		,			padding_value=self.padding_value )

			if return_tensors is not None:
					lowerCamelCase					:      int          =   padded_inputs.convert_to_tensors(UpperCAmelCase_ )

			return padded_inputs




	def       _UpperCamelCase   (			self )		->							List[Any]:
			lowerCamelCase					:      Dict          =   super().to_dict()

			# Don't serialize these as they are derived from the other properties.
			lowerCamelCase					:      List[Any]          =   ["window", "mel_filters", "sample_size", "sample_stride", "n_fft", "n_freqs"]
			for name in names:
					if name in output:
							del output[name]

			return output








"""simple docstring"""

import logging
import random

import ray

from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex


_A						=			logging.getLogger(__name__)






class 	_lowercase       :




	def __init__(			self )		->							Tuple:
			lowerCamelCase					:      Dict          =   False




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_ )		->							Any:
			if not self.initialized:
					lowerCamelCase					:      Optional[int]          =   RagRetriever(
					    UpperCAmelCase_		,			question_encoder_tokenizer=UpperCAmelCase_		,			generator_tokenizer=UpperCAmelCase_		,			index=UpperCAmelCase_		,			init_retrieval=UpperCAmelCase_		,			)
					lowerCamelCase					:      Union[str, Any]          =   True




	def       _UpperCamelCase   (			self )		->							Union[str, Any]:
			self.retriever.index.init_index()




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_ )		->							str:
			lowerCamelCase     ,   lowerCamelCase					:      int          =   self.retriever._main_retrieve(UpperCAmelCase_		,			UpperCAmelCase_ )
			return doc_ids, retrieved_doc_embeds






class 	_lowercase       (    __UpperCAmelCase		):




	def __init__(			self		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_=None )		->							Dict:
			if index is not None and index.is_initialized() and len(UpperCAmelCase_ ) > 0:
					raise ValueError(
					    'When using Ray for distributed fine-tuning, '
					    'you\'ll need to provide the paths instead, '
					    'as the dataset and the index are loaded '
					    'separately. More info in examples/rag/use_own_knowledge_dataset.py ' )
			super().__init__(
			    UpperCAmelCase_		,			question_encoder_tokenizer=UpperCAmelCase_		,			generator_tokenizer=UpperCAmelCase_		,			index=UpperCAmelCase_		,			init_retrieval=UpperCAmelCase_		,			)
			lowerCamelCase					:      List[str]          =   retrieval_workers
			if len(self.retrieval_workers ) > 0:
					ray.get(
					    [
					        worker.create_rag_retriever.remote(UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_ )
					        for worker in self.retrieval_workers
					    ] )




	def       _UpperCamelCase   (			self )		->							Dict:
			logger.info('initializing retrieval' )

			if len(self.retrieval_workers ) > 0:
					ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers] )
			else:
					# Non-distributed training. Load index into this same process.
					self.index.init_index()




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_ )		->							List[Any]:
			if len(self.retrieval_workers ) > 0:
					# Select a random retrieval actor.
					lowerCamelCase					:      Tuple          =   self.retrieval_workers[random.randint(0		,			len(self.retrieval_workers ) - 1 )]
					lowerCamelCase     ,   lowerCamelCase					:      Tuple          =   ray.get(random_worker.retrieve.remote(UpperCAmelCase_		,			UpperCAmelCase_ ) )
			else:
					lowerCamelCase     ,   lowerCamelCase					:      int          =   self._main_retrieve(UpperCAmelCase_		,			UpperCAmelCase_ )
			return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(UpperCAmelCase_ )




	@classmethod
	def       _UpperCamelCase   (			cls		,			UpperCAmelCase_		,			UpperCAmelCase_=None		,			**UpperCAmelCase_ )		->							Dict:
			return super(UpperCAmelCase_		,			cls ).get_tokenizers(UpperCAmelCase_		,			UpperCAmelCase_		,			**UpperCAmelCase_ )




	@classmethod
	def       _UpperCamelCase   (			cls		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_=None		,			**UpperCAmelCase_ )		->							Dict:
			lowerCamelCase					:      str          =   kwargs.pop('config'		,			UpperCAmelCase_ ) or RagConfig.from_pretrained(UpperCAmelCase_		,			**UpperCAmelCase_ )
			lowerCamelCase					:      Any          =   RagTokenizer.from_pretrained(UpperCAmelCase_		,			config=UpperCAmelCase_ )
			lowerCamelCase					:      Any          =   rag_tokenizer.question_encoder
			lowerCamelCase					:      Union[str, Any]          =   rag_tokenizer.generator
			if indexed_dataset is not None:
					lowerCamelCase					:      int          =   'custom'
					lowerCamelCase					:      int          =   CustomHFIndex(config.retrieval_vector_size		,			UpperCAmelCase_ )
			else:
					lowerCamelCase					:      Optional[Any]          =   cls._build_index(UpperCAmelCase_ )
			return cls(
			    UpperCAmelCase_		,			question_encoder_tokenizer=UpperCAmelCase_		,			generator_tokenizer=UpperCAmelCase_		,			retrieval_workers=UpperCAmelCase_		,			index=UpperCAmelCase_		,			)






