






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


lowerCamelCase : int						   =       logging.get_logger(__name__)

class 		lowerCAmelCase     ( UpperCamelCase__			):

					'''simple docstring'''

					_A						:			str									=      ["""input_values""", """attention_mask"""]



					def __init__(  self				:	str		,     __a				:	str = 1		,     __a				:	Optional[int] = 16000		,     __a				:	Optional[Any] = 0.0		,     __a				:	List[Any] = False		,     __a				:	Union[str, Any] = 80		,     __a				:	int = 16		,     __a				:	Optional[int] = 64		,     __a				:	Tuple = "hann_window"		,     __a				:	Any = 1.0		,     __a				:	List[str] = 80		,     __a				:	List[str] = 7600		,     __a				:	Any = 1E-10		,     __a				:	Optional[int] = 2		,     __a				:	Tuple = True		,     **__a				:	Union[str, Any]		,     )			->  Union[str, Any]:


												"""simple docstring"""

												super().__init__(feature_size=__a		,     sampling_rate=__a		,     padding_value=__a		,     **__a							)
												__lowercase      :							Any				=     do_normalize
												__lowercase      :							Any				=     return_attention_mask

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

												__lowercase      :							List[str]				=     win_length * sampling_rate // 1000
												__lowercase      :							Tuple				=     hop_length * sampling_rate // 1000
												__lowercase      :							Tuple				=     optimal_fft_length(self.sample_size							)
												__lowercase      :							List[Any]				=     (self.n_fft // 2) + 1

												__lowercase      :							Optional[Any]				=     window_function(window_length=self.sample_size		,     name=self.win_function		,     periodic=__a							)

												__lowercase      :							Any				=     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"""		,     __a		,     )
												if reduction_factor != 2.0:
																			warnings.warn(
																			    """The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers"""		,     __a		,     )



					@staticmethod
					# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
					def       lowerCAmelCase				(  __a				:	Optional[int]		,     __a				:	str		,     __a				:	Tuple = 0.0							)			->  List[np.ndarray]:


												"""simple docstring"""

												if attention_mask is not None:
																			__lowercase      :							Optional[Any]				=     np.array(__a		,     np.intaa							)
																			__lowercase      :							Tuple				=     []

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

																										normed_input_values.append(__a							)
												else:
																			__lowercase      :							Optional[int]				=     [(x - x.mean()) / np.sqrt(x.var() + 1E-7							) for x in input_values]

												return normed_input_values



					def       lowerCAmelCase				(  self				:	Dict		,     __a				:	Dict		,     )			->  np.ndarray:


												"""simple docstring"""

												__lowercase      :							Any				=     spectrogram(
												    __a		,     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				:	List[Any]		,     __a				:	int = None		,     __a				:	Optional[int] = None		,     __a				:	List[str] = False		,     __a				:	List[Any] = None		,     __a				:	Any = False		,     __a				:	str = None		,     __a				:	Optional[int] = None		,     __a				:	Optional[Any] = None		,     __a				:	Union[str, Any] = None		,     **__a				:	Any		,     )			->  BatchFeature:


												"""simple docstring"""

												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:
																			__lowercase      :							str				=     self._process_audio(
																			    __a		,     __a		,     __a		,     __a		,     __a		,     __a		,     __a		,     __a		,     **__a		,     )
												else:
																			__lowercase      :							List[str]				=     None

												if audio_target is not None:
																			__lowercase      :							Dict				=     self._process_audio(
																			    __a		,     __a		,     __a		,     __a		,     __a		,     __a		,     __a		,     __a		,     **__a		,     )

																			if inputs is None:
																										return inputs_target
																			else:
																										__lowercase      :							int				=     inputs_target["""input_values"""]
																										__lowercase      :							int				=     inputs_target.get("""attention_mask"""							)
																										if decoder_attention_mask is not None:
																																	__lowercase      :							Union[str, Any]				=     decoder_attention_mask

												return inputs



					def       lowerCAmelCase				(  self				:	Any		,     __a				:	str		,     __a				:	Tuple = False		,     __a				:	List[Any] = False		,     __a				:	str = None		,     __a				:	Tuple = False		,     __a				:	int = None		,     __a				:	Optional[int] = None		,     __a				:	str = None		,     **__a				:	Any		,     )			->  BatchFeature:


												"""simple docstring"""

												__lowercase      :							List[Any]				=     isinstance(__a		,     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}"							)
												__lowercase      :							int				=     is_batched_numpy or (
												    isinstance(__a		,     (list, tuple)							) and (isinstance(speech[0]		,     (np.ndarray, tuple, list)							))
												)

												if is_batched:
																			__lowercase      :							str				=     [np.asarray(__a		,     dtype=np.floataa							) for speech in speech]
												elif not is_batched and not isinstance(__a		,     np.ndarray							):
																			__lowercase      :							Optional[Any]				=     np.asarray(__a		,     dtype=np.floataa							)
												elif isinstance(__a		,     np.ndarray							) and speech.dtype is np.dtype(np.floataa							):
																			__lowercase      :							Optional[Any]				=     speech.astype(np.floataa							)

												# always return batch
												if not is_batched:
																			__lowercase      :							Dict				=     [speech]

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

												# convert into correct format for padding
												if is_target:
																			__lowercase      :							Tuple				=     [self._extract_mel_features(__a							) for waveform in speech]
																			__lowercase      :							Optional[int]				=     BatchFeature({"""input_values""": features}							)
																			__lowercase      :							Dict				=     self.num_mel_bins
												else:
																			__lowercase      :							Union[str, Any]				=     BatchFeature({"""input_values""": speech}							)

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

												__lowercase      :							Dict				=     feature_size_hack

												# convert input values to correct format
												__lowercase      :							str				=     padded_inputs["""input_values"""]
												if not isinstance(input_values[0]		,     np.ndarray							):
																			__lowercase      :							str				=     [np.asarray(__a		,     dtype=np.floataa							) for array in input_values]
												elif (
												    not isinstance(__a		,     np.ndarray							)
												    and isinstance(input_values[0]		,     np.ndarray							)
												    and input_values[0].dtype is np.dtype(np.floataa							)
												):
																			__lowercase      :							int				=     [array.astype(np.floataa							) for array in input_values]
												elif isinstance(__a		,     np.ndarray							) and input_values.dtype is np.dtype(np.floataa							):
																			__lowercase      :							Dict				=     input_values.astype(np.floataa							)

												# convert attention_mask to correct format
												__lowercase      :							Union[str, Any]				=     padded_inputs.get("""attention_mask"""							)
												if attention_mask is not None:
																			__lowercase      :							Tuple				=     [np.asarray(__a		,     dtype=np.intaa							) for array in attention_mask]

												# zero-mean and unit-variance normalization
												if not is_target and self.do_normalize:
																			__lowercase      :							Optional[int]				=     (
																			    attention_mask
																			    if self._get_padding_strategies(__a		,     max_length=__a							) is not PaddingStrategy.DO_NOT_PAD
																			    else None
																			)
																			__lowercase      :							Union[str, Any]				=     self.zero_mean_unit_var_norm(
																			    padded_inputs["""input_values"""]		,     attention_mask=__a		,     padding_value=self.padding_value							)

												if return_tensors is not None:
																			__lowercase      :							Tuple				=     padded_inputs.convert_to_tensors(__a							)

												return padded_inputs



					def       lowerCAmelCase				(  self				:	Optional[int]							)			->  Dict[str, Any]:


												"""simple docstring"""

												__lowercase      :							Dict				=     super().to_dict()

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

												return output












from __future__ import annotations
def       snake_case_		(     lowerCAmelCase_       :						list[int]      ):
							if not nums:
														return 0
							__lowercase      :							Tuple				=     nums[0]
							__lowercase      :							Tuple				=     0
							for num in nums[1:]:
														__lowercase			,			__lowercase      :							List[str]				=     (
														    max_excluding + num,
														    max(lowerCAmelCase_       ,       lowerCAmelCase_      ),
														)
							return max(lowerCAmelCase_       ,       lowerCAmelCase_      )


if __name__ == "__main__":
	import doctest

	doctest.testmod()