






"""simple docstring"""





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							=  logging.get_logger(__name__)



class       __A		(   SCREAMING_SNAKE_CASE_			):
					UpperCAmelCase__								=				["input_features"]

					def __init__(			self  :      str     ,		__snake_case  :      List[Any]=8_0     ,		__snake_case  :      Dict=1_6_0_0_0     ,		__snake_case  :      Union[str, Any]=1_6_0     ,		__snake_case  :      Optional[int]=3_0     ,		__snake_case  :      List[str]=4_0_0     ,		__snake_case  :      Any=0.0     ,		__snake_case  :      int=False     ,		**__snake_case  :      Optional[Any]     ,		)			->							Optional[Any]:
											super().__init__(
											    feature_size=__snake_case     ,		sampling_rate=__snake_case     ,		padding_value=__snake_case     ,		return_attention_mask=__snake_case     ,		**__snake_case     ,		)
											__magic_name__:	List[str]							=       n_fft
											__magic_name__:	Dict							=       hop_length
											__magic_name__:	Optional[Any]							=       chunk_length
											__magic_name__:	List[Any]							=       chunk_length * sampling_rate
											__magic_name__:	List[Any]							=       self.n_samples // hop_length
											__magic_name__:	List[str]							=       sampling_rate
											__magic_name__:	str							=       mel_filter_bank(
											    num_frequency_bins=1 + n_fft // 2     ,		num_mel_filters=__snake_case     ,		min_frequency=0.0     ,		max_frequency=8000.0     ,		sampling_rate=__snake_case     ,		norm="""slaney"""     ,		mel_scale="""slaney"""     ,		)

					def   lowerCamelCase__   (			self  :      str     ,		__snake_case  :      np.array					)			->							np.ndarray:
											__magic_name__:	Tuple							=       spectrogram(
											    __snake_case     ,		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"""     ,		)
											__magic_name__:	Optional[Any]							=       log_spec[:, :-1]
											__magic_name__:	int							=       np.maximum(__snake_case     ,		log_spec.max() - 8.0					)
											__magic_name__:	Union[str, 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   lowerCamelCase__   (			__snake_case  :      List[np.ndarray]     ,		__snake_case  :      List[np.ndarray]     ,		__snake_case  :      float = 0.0					)			->							List[np.ndarray]:
											if attention_mask is not None:
																	__magic_name__:	str							=       np.array(__snake_case     ,		np.intaa					)
																	__magic_name__:	Dict							=       []

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

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

											return normed_input_values

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

											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."""					)

											__magic_name__:	Dict							=       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}'					)
											__magic_name__:	Optional[Any]							=       is_batched_numpy or (
											    isinstance(__snake_case     ,		(list, tuple)					) and (isinstance(raw_speech[0]     ,		(np.ndarray, tuple, list)					))
											)

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

											# always return batch
											if not is_batched:
																	__magic_name__:	str							=       [np.asarray([raw_speech]					).T]

											__magic_name__:	str							=       BatchFeature({"""input_features""": raw_speech}					)

											# convert into correct format for padding

											__magic_name__:	Optional[Any]							=       self.pad(
											    __snake_case     ,		padding=__snake_case     ,		max_length=max_length if max_length else self.n_samples     ,		truncation=__snake_case     ,		pad_to_multiple_of=__snake_case     ,		return_attention_mask=return_attention_mask or do_normalize     ,		)

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

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

											__magic_name__:	Union[str, Any]							=       [self._np_extract_fbank_features(__snake_case					) for waveform in input_features[0]]

											if isinstance(input_features[0]     ,		__snake_case					):
																	__magic_name__:	Dict							=       [np.asarray(__snake_case     ,		dtype=np.floataa					) for feature in input_features]
											else:
																	__magic_name__:	List[str]							=       input_features

											if return_attention_mask:
																	# rescale from sample (48000) to feature (3000)
																	__magic_name__:	Optional[Any]							=       padded_inputs["""attention_mask"""][:, :: self.hop_length]

											if return_tensors is not None:
																	__magic_name__:	Optional[int]							=       padded_inputs.convert_to_tensors(__snake_case					)

											return padded_inputs






					def   lowerCamelCase__   (			self  :      List[str]					)			->							Dict[str, Any]:
											__magic_name__:	Tuple							=       copy.deepcopy(self.__dict__					)
											__magic_name__:	Union[str, Any]							=       self.__class__.__name__
											if "mel_filters" in output:
																	del output["mel_filters"]
											return output









UpperCAmelCase_		:					Tuple          =  0  # The first color of the flag.
UpperCAmelCase_		:					Any          =  1  # The second color of the flag.
UpperCAmelCase_		:					str          =  2  # The third color of the flag.
UpperCAmelCase_		:					Tuple          =  (red, white, blue)




def 		lowerCAmelCase_ (						lowerCamelCase ):
				if not sequence:
								return []
				if len(lowerCamelCase ) == 1:
								return list(lowerCamelCase )
				__magic_name__						:						int										=0
				__magic_name__						:						str										=len(lowerCamelCase ) - 1
				__magic_name__						:						Optional[Any]										=0
				while mid <= high:
								if sequence[mid] == colors[0]:
												__magic_name__					,			__magic_name__						:						Tuple										=sequence[mid], sequence[low]
												low += 1
												mid += 1
								elif sequence[mid] == colors[1]:
												mid += 1
								elif sequence[mid] == colors[2]:
												__magic_name__					,			__magic_name__						:						Optional[Any]										=sequence[high], sequence[mid]
												high -= 1
								else:
												__magic_name__						:						Optional[int]										=F"The elements inside the sequence must contains only {colors} values"
												raise ValueError(lowerCamelCase )
				return sequence


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							UpperCAmelCase_		:					List[Any]          =  input("Enter numbers separated by commas:\n").strip()
							UpperCAmelCase_		:					Optional[int]          =  [int(item.strip()) for item in user_input.split(",")]
							print(F"""{dutch_national_flag_sort(unsorted)}""")




