


from math import ceil
from typing import List, Optional, Union

import numpy as np

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


__lowerCAmelCase	: Dict  				=logging.get_logger(__name__)
class        _lowercase      (			A__       ):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__				:			Union[str, Any]						=		['''audio_values''', '''audio_mask''']


				def __init__(       self			:Optional[Any]			,			lowerCAmelCase__			:str=2_048			,			lowerCAmelCase__			:str=1			,			lowerCAmelCase__			:List[Any]=[16, 16]			,			lowerCAmelCase__			:List[str]=128			,			lowerCAmelCase__			:Dict=44_100			,			lowerCAmelCase__			:Tuple=86			,			lowerCAmelCase__			:List[str]=2_048			,			lowerCAmelCase__			:Union[str, Any]=0.0			,			**lowerCAmelCase__			:int			,			)		->			str:
										super().__init__(
										    feature_size=lowerCAmelCase__			,			sampling_rate=lowerCAmelCase__			,			padding_value=lowerCAmelCase__			,			**lowerCAmelCase__			,			)

										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		spectrogram_length
										__SCREAMING_SNAKE_CASE						:     Dict					=		num_channels
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		patch_size
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		feature_size // self.patch_size[1]
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		n_fft
										__SCREAMING_SNAKE_CASE						:     int					=		sampling_rate // hop_length_to_sampling_rate
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		sampling_rate
										__SCREAMING_SNAKE_CASE						:     Tuple					=		padding_value
										__SCREAMING_SNAKE_CASE						:     Dict					=		mel_filter_bank(
										    num_frequency_bins=1 + n_fft // 2			,			num_mel_filters=lowerCAmelCase__			,			min_frequency=0.0			,			max_frequency=2_2050.0			,			sampling_rate=lowerCAmelCase__			,			norm='''slaney'''			,			mel_scale='''slaney'''			,			).T


				def 		__magic_name__(       self			:Union[str, Any]			,			lowerCAmelCase__			:np.array	)		->			np.ndarray:
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		spectrogram(
										    lowerCAmelCase__			,			window_function(self.n_fft			,			'''hann'''	)			,			frame_length=self.n_fft			,			hop_length=self.hop_length			,			power=2.0			,			mel_filters=self.mel_filters.T			,			log_mel='''dB'''			,			db_range=80.0			,			)
										__SCREAMING_SNAKE_CASE						:     Tuple					=		log_spec[:, :-1]
										__SCREAMING_SNAKE_CASE						:     Tuple					=		log_spec - 20.0
										__SCREAMING_SNAKE_CASE						:     List[str]					=		np.clip(log_spec / 40.0			,			-2.0			,			0.0	) + 1.0
										return log_spec



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

										if sampling_rate is not None:
																if sampling_rate != self.sampling_rate:
																						raise ValueError(
																						    '''This feature extractor is set to support sampling rate'''
																						    f''' of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled'''
																						    f''' 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						:     Any					=		isinstance(lowerCAmelCase__			,			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						:     str					=		is_batched_numpy or (
										    isinstance(lowerCAmelCase__			,			(list, tuple)	) and (isinstance(raw_speech[0]			,			(np.ndarray, tuple, list)	))
										)
										if is_batched:
																__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		[np.asarray([speech]			,			dtype=np.floataa	).T for speech in raw_speech]
										elif not is_batched and not isinstance(lowerCAmelCase__			,			np.ndarray	):
																__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		np.asarray(lowerCAmelCase__			,			dtype=np.floataa	)
										elif isinstance(lowerCAmelCase__			,			np.ndarray	) and raw_speech.dtype is np.dtype(np.floataa	):
																__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		raw_speech.astype(np.floataa	)
										# always return batch
										if not is_batched:
																__SCREAMING_SNAKE_CASE						:     str					=		[np.asarray([raw_speech]	).T]

										# Convert audio signals to log mel spectrograms, truncate by time axis
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		[
										    self._np_extract_fbank_features(waveform.squeeze()	).T[: self.spectrogram_length] for waveform in raw_speech
										]
										if isinstance(audio_features[0]			,			lowerCAmelCase__	):
																__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		[np.asarray(lowerCAmelCase__			,			dtype=np.floataa	) for feature in audio_features]

										# Create audio attention mask
										__SCREAMING_SNAKE_CASE						:     Tuple					=		max(
										    [ceil(feature.shape[0] / self.patch_size[0]	) * self.freq_len for feature in audio_features]	)  # The maximum number of audio patches in a batch
										if return_attention_mask:
																__SCREAMING_SNAKE_CASE						:     Dict					=		[
																    (ceil(feature.shape[0] / self.patch_size[0]	) * self.freq_len) * [1]
																    + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0]	) * self.freq_len) * [0]
																    for feature in audio_features
																]
																__SCREAMING_SNAKE_CASE						:     Optional[int]					=		np.array(lowerCAmelCase__	).astype(np.floataa	)

										# convert into correct format for padding
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
										__SCREAMING_SNAKE_CASE						:     List[str]					=		np.ones([len(lowerCAmelCase__	), 1, max_time_len, self.feature_size]	).astype(np.floataa	)
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		padded_audio_features * self.padding_value
										for i in range(len(lowerCAmelCase__	)	):
																__SCREAMING_SNAKE_CASE						:     Dict					=		audio_features[i]
																__SCREAMING_SNAKE_CASE						:     str					=		feature

										# return as BatchFeature
										if return_attention_mask:
																__SCREAMING_SNAKE_CASE						:     Dict					=		{'''audio_values''': padded_audio_features, '''audio_mask''': audio_mask}
										else:
																__SCREAMING_SNAKE_CASE						:     List[str]					=		{'''audio_values''': padded_audio_features}

										__SCREAMING_SNAKE_CASE						:     Any					=		BatchFeature(data=lowerCAmelCase__			,			tensor_type=lowerCAmelCase__	)
										return encoded_inputs




from __future__ import annotations

import bisect


def        _UpperCamelCase							(      lowercase__    ,       lowercase__    ,       lowercase__ = 0    ,       lowercase__ = -1      ):
						if hi < 0:
												__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		len(lowercase__      )

						while lo < hi:
												__SCREAMING_SNAKE_CASE						:     Any					=		lo + (hi - lo) // 2
												if sorted_collection[mid] < item:
																		__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		mid + 1
												else:
																		__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		mid

						return lo


def        _UpperCamelCase							(      lowercase__    ,       lowercase__    ,       lowercase__ = 0    ,       lowercase__ = -1      ):
						if hi < 0:
												__SCREAMING_SNAKE_CASE						:     List[Any]					=		len(lowercase__      )

						while lo < hi:
												__SCREAMING_SNAKE_CASE						:     Optional[int]					=		lo + (hi - lo) // 2
												if sorted_collection[mid] <= item:
																		__SCREAMING_SNAKE_CASE						:     Any					=		mid + 1
												else:
																		__SCREAMING_SNAKE_CASE						:     Optional[int]					=		mid

						return lo


def        _UpperCamelCase							(      lowercase__    ,       lowercase__    ,       lowercase__ = 0    ,       lowercase__ = -1      ):
						sorted_collection.insert(bisect_left(lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__      )    ,       lowercase__      )


def        _UpperCamelCase							(      lowercase__    ,       lowercase__    ,       lowercase__ = 0    ,       lowercase__ = -1      ):
						sorted_collection.insert(bisect_right(lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__      )    ,       lowercase__      )


def        _UpperCamelCase							(      lowercase__    ,       lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     Any					=		0
						__SCREAMING_SNAKE_CASE						:     List[Any]					=		len(lowercase__      ) - 1

						while left <= right:
												__SCREAMING_SNAKE_CASE						:     str					=		left + (right - left) // 2
												__SCREAMING_SNAKE_CASE						:     List[str]					=		sorted_collection[midpoint]
												if current_item == item:
																		return midpoint
												elif item < current_item:
																		__SCREAMING_SNAKE_CASE						:     int					=		midpoint - 1
												else:
																		__SCREAMING_SNAKE_CASE						:     Dict					=		midpoint + 1
						return None


def        _UpperCamelCase							(      lowercase__    ,       lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		bisect.bisect_left(lowercase__    ,       lowercase__      )
						if index != len(lowercase__      ) and sorted_collection[index] == item:
												return index
						return None


def        _UpperCamelCase							(      lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__      ):
						if right < left:
												return None

						__SCREAMING_SNAKE_CASE						:     int					=		left + (right - left) // 2

						if sorted_collection[midpoint] == item:
												return midpoint
						elif sorted_collection[midpoint] > item:
												return binary_search_by_recursion(lowercase__    ,       lowercase__    ,       lowercase__    ,       midpoint - 1      )
						else:
												return binary_search_by_recursion(lowercase__    ,       lowercase__    ,       midpoint + 1    ,       lowercase__      )


if __name__ == "__main__":
						__lowerCAmelCase	: Dict  				=input('Enter numbers separated by comma:\n').strip()
						__lowerCAmelCase	: str  				=sorted(int(item) for item in user_input.split(','))
						__lowerCAmelCase	: Tuple  				=int(input('Enter a single number to be found in the list:\n'))
						__lowerCAmelCase	: Tuple  				=binary_search(collection, target)
						if result is None:
												print(f"""{target} was not found in {collection}.""")
						else:
												print(f"""{target} was found at position {result} in {collection}.""")
