


"""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


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





class        __A     (snake_case__):





							'''simple docstring'''

							__lowercase:		Dict							      =    ["""input_values""", """attention_mask"""]



							def __init__(				self							:      List[Any]		,       UpperCAmelCase_							:      int = 1		,       UpperCAmelCase_							:      int = 16_000		,       UpperCAmelCase_							:      float = 0.0		,       UpperCAmelCase_							:      bool = False		,       UpperCAmelCase_							:      int = 80		,       UpperCAmelCase_							:      int = 16		,       UpperCAmelCase_							:      int = 64		,       UpperCAmelCase_							:      str = "hann_window"		,       UpperCAmelCase_							:      float = 1.0		,       UpperCAmelCase_							:      float = 80		,       UpperCAmelCase_							:      float = 7_600		,       UpperCAmelCase_							:      float = 1E-10		,       UpperCAmelCase_							:      int = 2		,       UpperCAmelCase_							:      bool = True		,       **UpperCAmelCase_							:      str		,       )					->str:



														"""simple docstring"""



														super().__init__(feature_size=UpperCAmelCase_		,       sampling_rate=UpperCAmelCase_		,       padding_value=UpperCAmelCase_		,       **UpperCAmelCase_       )
														snake_case_           =		do_normalize
														snake_case_           =		return_attention_mask

														snake_case_           =		num_mel_bins
														snake_case_           =		hop_length
														snake_case_           =		win_length
														snake_case_           =		win_function
														snake_case_           =		frame_signal_scale
														snake_case_           =		fmin
														snake_case_           =		fmax
														snake_case_           =		mel_floor
														snake_case_           =		reduction_factor

														snake_case_           =		win_length * sampling_rate // 1_000
														snake_case_           =		hop_length * sampling_rate // 1_000
														snake_case_           =		optimal_fft_length(self.sample_size       )
														snake_case_           =		(self.n_fft // 2) + 1

														snake_case_           =		window_function(window_length=self.sample_size		,       name=self.win_function		,       periodic=UpperCAmelCase_       )

														snake_case_           =		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 		lowerCAmelCase       (				UpperCAmelCase_							:      List[np.ndarray]		,       UpperCAmelCase_							:      List[np.ndarray]		,       UpperCAmelCase_							:      float = 0.0       )					->List[np.ndarray]:



														"""simple docstring"""



														if attention_mask is not None:
																					snake_case_           =		np.array(UpperCAmelCase_		,       np.intaa       )
																					snake_case_           =		[]

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

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

														return normed_input_values



							def 		lowerCAmelCase       (				self							:      Tuple		,       UpperCAmelCase_							:      np.ndarray		,       )					->np.ndarray:



														"""simple docstring"""



														snake_case_           =		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							:      int		,       UpperCAmelCase_							:      Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None		,       UpperCAmelCase_							:      Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None		,       UpperCAmelCase_							:      Union[bool, str, PaddingStrategy] = False		,       UpperCAmelCase_							:      Optional[int] = None		,       UpperCAmelCase_							:      bool = False		,       UpperCAmelCase_							:      Optional[int] = None		,       UpperCAmelCase_							:      Optional[bool] = None		,       UpperCAmelCase_							:      Optional[Union[str, TensorType]] = None		,       UpperCAmelCase_							:      Optional[int] = None		,       **UpperCAmelCase_							:      Optional[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:
																					snake_case_           =		self._process_audio(
																					    UpperCAmelCase_		,       UpperCAmelCase_		,       UpperCAmelCase_		,       UpperCAmelCase_		,       UpperCAmelCase_		,       UpperCAmelCase_		,       UpperCAmelCase_		,       UpperCAmelCase_		,       **UpperCAmelCase_		,       )
														else:
																					snake_case_           =		None

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

																					if inputs is None:
																												return inputs_target
																					else:
																												snake_case_           =		inputs_target["""input_values"""]
																												snake_case_           =		inputs_target.get("""attention_mask"""       )
																												if decoder_attention_mask is not None:
																																			snake_case_           =		decoder_attention_mask

														return inputs



							def 		lowerCAmelCase       (				self							:      Optional[int]		,       UpperCAmelCase_							:      Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]		,       UpperCAmelCase_							:      bool = False		,       UpperCAmelCase_							:      Union[bool, str, PaddingStrategy] = False		,       UpperCAmelCase_							:      Optional[int] = None		,       UpperCAmelCase_							:      bool = False		,       UpperCAmelCase_							:      Optional[int] = None		,       UpperCAmelCase_							:      Optional[bool] = None		,       UpperCAmelCase_							:      Optional[Union[str, TensorType]] = None		,       **UpperCAmelCase_							:      Optional[int]		,       )					->BatchFeature:



														"""simple docstring"""



														snake_case_           =		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}"""       )
														snake_case_           =		is_batched_numpy or (
														    isinstance(UpperCAmelCase_		,       (list, tuple)       ) and (isinstance(speech[0]		,       (np.ndarray, tuple, list)       ))
														)

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

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

														# needed to make pad() work on spectrogram inputs
														snake_case_           =		self.feature_size

														# convert into correct format for padding
														if is_target:
																					snake_case_           =		[self._extract_mel_features(UpperCAmelCase_       ) for waveform in speech]
																					snake_case_           =		BatchFeature({"""input_values""": features}       )
																					snake_case_           =		self.num_mel_bins
														else:
																					snake_case_           =		BatchFeature({"""input_values""": speech}       )

														snake_case_           =		self.pad(
														    UpperCAmelCase_		,       padding=UpperCAmelCase_		,       max_length=UpperCAmelCase_		,       truncation=UpperCAmelCase_		,       pad_to_multiple_of=UpperCAmelCase_		,       return_attention_mask=UpperCAmelCase_		,       **UpperCAmelCase_		,       )

														snake_case_           =		feature_size_hack

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

														# convert attention_mask to correct format
														snake_case_           =		padded_inputs.get("""attention_mask"""       )
														if attention_mask is not None:
																					snake_case_           =		[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:
																					snake_case_           =		(
																					    attention_mask
																					    if self._get_padding_strategies(UpperCAmelCase_		,       max_length=UpperCAmelCase_       ) is not PaddingStrategy.DO_NOT_PAD
																					    else None
																					)
																					snake_case_           =		self.zero_mean_unit_var_norm(
																					    padded_inputs["""input_values"""]		,       attention_mask=UpperCAmelCase_		,       padding_value=self.padding_value       )

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

														return padded_inputs



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



														"""simple docstring"""



														snake_case_           =		super().to_dict()

														# Don't serialize these as they are derived from the other properties.
														snake_case_           =		["""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"""
from __future__ import annotations







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				list[int]:
							snake_case_           =		0
							snake_case_           =		len(_SCREAMING_SNAKE_CASE				) - 1

							while i < j:
														if nums[i] + nums[j] == target:
																					return [i, j]
														elif nums[i] + nums[j] < target:
																					snake_case_           =		i + 1
														else:
																					snake_case_           =		j - 1

							return []


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				print(f"""{two_pointer([2, 7, 11, 15], 9) = }""")



