

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

import numpy as np

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


lowerCAmelCase							:  Optional[int]   	=						logging.get_logger(__name__)



class   __lowercase				( A__	):




			"""simple docstring"""







			_UpperCAmelCase   :							Dict										=     ['''input_features''']



			def __init__( self  :     Optional[Any]   ,						lowerCAmelCase__  :     Any=80   ,						lowerCAmelCase__  :     Union[str, Any]=1_6000   ,						lowerCAmelCase__  :     Any=160   ,						lowerCAmelCase__  :     Dict=30   ,						lowerCAmelCase__  :     str=400   ,						lowerCAmelCase__  :     List[Any]=0.0   ,						lowerCAmelCase__  :     Optional[Any]=False   ,						**lowerCAmelCase__  :     Any   ,						):
									super().__init__(
									    feature_size=_a   ,						sampling_rate=_a   ,						padding_value=_a   ,						return_attention_mask=_a   ,						**_a   ,						)
									SCREAMING_SNAKE_CASE_: str  		=       n_fft
									SCREAMING_SNAKE_CASE_: List[Any]  		=       hop_length
									SCREAMING_SNAKE_CASE_: int  		=       chunk_length
									SCREAMING_SNAKE_CASE_: int  		=       chunk_length * sampling_rate
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       self.n_samples // hop_length
									SCREAMING_SNAKE_CASE_: Tuple  		=       sampling_rate
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       mel_filter_bank(
									    num_frequency_bins=1 + n_fft // 2   ,						num_mel_filters=_a   ,						min_frequency=0.0   ,						max_frequency=8000.0   ,						sampling_rate=_a   ,						norm="slaney"   ,						mel_scale="slaney"   ,						)



			def        _SCREAMING_SNAKE_CASE			( self  :     str   ,						lowerCAmelCase__  :     np.array):
									SCREAMING_SNAKE_CASE_: Tuple  		=       spectrogram(
									    _a   ,						window_function(self.n_fft   ,						"hann")   ,						frame_length=self.n_fft   ,						hop_length=self.hop_length   ,						power=2.0   ,						mel_filters=self.mel_filters   ,						log_mel="log10"   ,						)
									SCREAMING_SNAKE_CASE_: Any  		=       log_spec[:, :-1]
									SCREAMING_SNAKE_CASE_: str  		=       np.maximum(_a   ,						log_spec.max() - 8.0)
									SCREAMING_SNAKE_CASE_: List[Any]  		=       (log_spec + 4.0) / 4.0
									return log_spec



			@staticmethod
			# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
			def        _SCREAMING_SNAKE_CASE			( lowerCAmelCase__  :     List[np.ndarray]   ,						lowerCAmelCase__  :     List[np.ndarray]   ,						lowerCAmelCase__  :     float = 0.0):






									if attention_mask is not None:
															SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       np.array(_a   ,						np.intaa)
															SCREAMING_SNAKE_CASE_: int  		=       []

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

																					normed_input_values.append(_a)
									else:
															SCREAMING_SNAKE_CASE_: List[str]  		=       [(x - x.mean()) / np.sqrt(x.var() + 1E-7) for x in input_values]

									return normed_input_values



			def __call__( self  :     str   ,						lowerCAmelCase__  :     Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]   ,						lowerCAmelCase__  :     bool = True   ,						lowerCAmelCase__  :     Optional[int] = None   ,						lowerCAmelCase__  :     Optional[Union[str, TensorType]] = None   ,						lowerCAmelCase__  :     Optional[bool] = None   ,						lowerCAmelCase__  :     Optional[str] = "max_length"   ,						lowerCAmelCase__  :     Optional[int] = None   ,						lowerCAmelCase__  :     Optional[int] = None   ,						lowerCAmelCase__  :     Optional[bool] = None   ,						**lowerCAmelCase__  :     int   ,						):






									if sampling_rate is not None:
															if sampling_rate != self.sampling_rate:
																					raise ValueError(
																					    F"The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"
																					    F" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"
																					    F" was sampled with {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.")

									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       isinstance(_a   ,						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}")
									SCREAMING_SNAKE_CASE_: List[Any]  		=       is_batched_numpy or (
									    isinstance(_a   ,						(list, tuple)) and (isinstance(raw_speech[0]   ,						(np.ndarray, tuple, list)))
									)

									if is_batched:
															SCREAMING_SNAKE_CASE_: str  		=       [np.asarray([speech]   ,						dtype=np.floataa).T for speech in raw_speech]
									elif not is_batched and not isinstance(_a   ,						np.ndarray):
															SCREAMING_SNAKE_CASE_: Optional[Any]  		=       np.asarray(_a   ,						dtype=np.floataa)
									elif isinstance(_a   ,						np.ndarray) and raw_speech.dtype is np.dtype(np.floataa):
															SCREAMING_SNAKE_CASE_: Tuple  		=       raw_speech.astype(np.floataa)

									# always return batch
									if not is_batched:
															SCREAMING_SNAKE_CASE_: Optional[Any]  		=       [np.asarray([raw_speech]).T]

									SCREAMING_SNAKE_CASE_: List[str]  		=       BatchFeature({"input_features": raw_speech})

									# convert into correct format for padding

									SCREAMING_SNAKE_CASE_: Optional[int]  		=       self.pad(
									    _a   ,						padding=_a   ,						max_length=max_length if max_length else self.n_samples   ,						truncation=_a   ,						pad_to_multiple_of=_a   ,						return_attention_mask=return_attention_mask or do_normalize   ,						)

									# zero-mean and unit-variance normalization
									if do_normalize:
															SCREAMING_SNAKE_CASE_: List[str]  		=       self.zero_mean_unit_var_norm(
															    padded_inputs["input_features"]   ,						attention_mask=padded_inputs["attention_mask"]   ,						padding_value=self.padding_value   ,						)
															SCREAMING_SNAKE_CASE_: List[str]  		=       np.stack(padded_inputs["input_features"]   ,						axis=0)

									# make sure list is in array format
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       padded_inputs.get("input_features").transpose(2   ,						0   ,						1)

									SCREAMING_SNAKE_CASE_: List[str]  		=       [self._np_extract_fbank_features(_a) for waveform in input_features[0]]

									if isinstance(input_features[0]   ,						_a):
															SCREAMING_SNAKE_CASE_: List[str]  		=       [np.asarray(_a   ,						dtype=np.floataa) for feature in input_features]
									else:
															SCREAMING_SNAKE_CASE_: int  		=       input_features

									if return_attention_mask:
															# rescale from sample (48000) to feature (3000)
															SCREAMING_SNAKE_CASE_: List[str]  		=       padded_inputs["attention_mask"][:, :: self.hop_length]

									if return_tensors is not None:
															SCREAMING_SNAKE_CASE_: List[str]  		=       padded_inputs.convert_to_tensors(_a)

									return padded_inputs




			def        _SCREAMING_SNAKE_CASE			( self  :     int):
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       copy.deepcopy(self.__dict__)
									SCREAMING_SNAKE_CASE_: Any  		=       self.__class__.__name__
									if "mel_filters" in output:
															del output["mel_filters"]
									return output









'''simple docstring'''
class 							A__    :





  def __init__(		self      :	Union[str, Any]							,					_a      :	int )	->    None:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =size
       _SCREAMING_SNAKE_CASE							    =[0] * size
       _SCREAMING_SNAKE_CASE							    =[0] * size





  @staticmethod
  def 						A  (		_a      :	int )	->    int:





       '''simple docstring'''






       return index | (index + 1)





  @staticmethod
  def 						A  (		_a      :	int )	->    int:





       '''simple docstring'''






       return (index & (index + 1)) - 1





  def 						A  (		self      :	Tuple							,					_a      :	int							,					_a      :	int )	->    None:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =value
       while index < self.size:
            _SCREAMING_SNAKE_CASE							    =self.get_prev(_a ) + 1
            if current_left_border == index:
                 _SCREAMING_SNAKE_CASE							    =value
            else:
                 _SCREAMING_SNAKE_CASE							    =max(_a							,					_a							,					_a )
            _SCREAMING_SNAKE_CASE							    =self.get_next(_a )





  def 						A  (		self      :	int							,					_a      :	int							,					_a      :	int )	->    int:





       '''simple docstring'''






       right -= 1  # Because of right is exclusive
       _SCREAMING_SNAKE_CASE							    =0
       while left <= right:
            _SCREAMING_SNAKE_CASE							    =self.get_prev(_a )
            if left <= current_left:
                 _SCREAMING_SNAKE_CASE							    =max(_a							,					self.tree[right] )
                 _SCREAMING_SNAKE_CASE							    =current_left
            else:
                 _SCREAMING_SNAKE_CASE							    =max(_a							,					self.arr[right] )
                 right -= 1
       return result


if __name__ == "__main__":
     import doctest

     doctest.testmod()
