




"""simple docstring"""
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


a_				:    str										=     logging.get_logger(__name__)

class __lowercase(      _UpperCAmelCase				):




					'''simple docstring'''


					__a						:						Any     		= ['''audio_values''', '''audio_mask''']
					def __init__(    self						,	__a=2048						,	__a=1						,	__a=[16, 16]						,	__a=128						,	__a=44100						,	__a=86						,	__a=2048						,	__a=0.0						,	**__a						,	):
								super().__init__(
								    feature_size=A_						,	sampling_rate=A_						,	padding_value=A_						,	**A_						,	)

								__lowerCamelCase					:	Tuple	       =				spectrogram_length
								__lowerCamelCase					:	Optional[Any]	       =				num_channels
								__lowerCamelCase					:	Any	       =				patch_size
								__lowerCamelCase					:	int	       =				feature_size // self.patch_size[1]
								__lowerCamelCase					:	Optional[int]	       =				n_fft
								__lowerCamelCase					:	List[str]	       =				sampling_rate // hop_length_to_sampling_rate
								__lowerCamelCase					:	int	       =				sampling_rate
								__lowerCamelCase					:	int	       =				padding_value
								__lowerCamelCase					:	int	       =				mel_filter_bank(
								    num_frequency_bins=1 + n_fft // 2						,	num_mel_filters=A_						,	min_frequency=0.0						,	max_frequency=22050.0						,	sampling_rate=A_						,	norm='slaney'						,	mel_scale='slaney'						,	).T
					def     snake_case_				(    self						,	__a     ):
								__lowerCamelCase					:	str	       =				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.T						,	log_mel='dB'						,	db_range=80.0						,	)
								__lowerCamelCase					:	int	       =				log_spec[:, :-1]
								__lowerCamelCase					:	str	       =				log_spec - 20.0
								__lowerCamelCase					:	Optional[int]	       =				np.clip(log_spec / 40.0						,	-2.0						,	0.0     ) + 1.0
								return log_spec





					def __call__(    self						,	__a						,	__a = None						,	__a = True						,	__a = None						,	__a = False						,	__a = False						,	**__a						,	):

								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.'     )

								__lowerCamelCase					:	Tuple	       =				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}'''     )
								__lowerCamelCase					:	List[str]	       =				is_batched_numpy or (
								    isinstance(A_						,	(list, tuple)     ) and (isinstance(raw_speech[0]						,	(np.ndarray, tuple, list)     ))
								)
								if is_batched:
											__lowerCamelCase					:	List[str]	       =				[np.asarray([speech]						,	dtype=np.floataa     ).T for speech in raw_speech]
								elif not is_batched and not isinstance(A_						,	np.ndarray     ):
											__lowerCamelCase					:	List[str]	       =				np.asarray(A_						,	dtype=np.floataa     )
								elif isinstance(A_						,	np.ndarray     ) and raw_speech.dtype is np.dtype(np.floataa     ):
											__lowerCamelCase					:	Union[str, Any]	       =				raw_speech.astype(np.floataa     )
								# always return batch
								if not is_batched:
											__lowerCamelCase					:	Dict	       =				[np.asarray([raw_speech]     ).T]

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

								# Create audio attention mask
								__lowerCamelCase					:	Dict	       =				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:
											__lowerCamelCase					:	Optional[int]	       =				[
											    (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
											]
											__lowerCamelCase					:	Tuple	       =				np.array(A_     ).astype(np.floataa     )

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

								# return as BatchFeature
								if return_attention_mask:
											__lowerCamelCase					:	Any	       =				{'audio_values': padded_audio_features, 'audio_mask': audio_mask}
								else:
											__lowerCamelCase					:	Union[str, Any]	       =				{'audio_values': padded_audio_features}

								__lowerCamelCase					:	int	       =				BatchFeature(data=A_						,	tensor_type=A_     )
								return encoded_inputs









"""simple docstring"""
from cva import destroyAllWindows, imread, imshow, waitKey





def 						UpperCAmelCase				(			A__:    Tuple     )  ->     Union[str, Any]:
			# getting number of pixels in the image
			__lowerCamelCase       ,      __lowerCamelCase					:	Optional[Any]	       =				img.shape[0], img.shape[1]

			# converting each pixel's color to its negative
			for i in range(A__     ):
						for j in range(A__     ):
									__lowerCamelCase					:	Optional[Any]	       =				[255, 255, 255] - img[i][j]

			return img


if __name__ == "__main__":
					# read original image
					a_				:    Dict										=     imread('''image_data/lena.jpg''', 1)

					# convert to its negative
					a_				:    str										=     convert_to_negative(img)

					# show result image
					imshow('''negative of original image''', img)
					waitKey(0)
					destroyAllWindows()



