




"""simple docstring"""




import string

import numpy



def     a__ (				snake_case__       , snake_case__				)						->					int:
			return b if a == 0 else greatest_common_divisor(b % a       , snake_case__				)






class     __magic_name__							:






				'''simple docstring'''
				__UpperCamelCase			  =						string.ascii_uppercase + string.digits
				# This cipher takes alphanumerics into account
				# i.e. a total of 36 characters

				# take x and return x % len(key_string)
				__UpperCamelCase			  =						numpy.vectorize(lambda UpperCAmelCase__				: x % 36				)

				__UpperCamelCase			  =						numpy.vectorize(UpperCAmelCase__				)





				def __init__(       self		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					self.modulus(_a )  # mod36 calc's on the encrypt key
							self.check_determinant()  # validate the determinant of the encryption key
							lowerCamelCase			      =					encrypt_key.shape[0]





				def    _lowerCAmelCase				(       self		, _a ):





							"""simple docstring"""
							return self.key_string.index(_a )





				def    _lowerCAmelCase				(       self		, _a ):





							"""simple docstring"""
							return self.key_string[round(_a )]





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					round(numpy.linalg.det(self.encrypt_key ) )

							if det < 0:
										lowerCamelCase			      =					det % len(self.key_string )

							lowerCamelCase			      =					len(self.key_string )
							if greatest_common_divisor(_a		, len(self.key_string ) ) != 1:
										lowerCamelCase			      =					(
										    f'determinant modular {req_l} of encryption key({det}) '
										    f'is not co prime w.r.t {req_l}.\nTry another key.'
										)
										raise ValueError(_a )





				def    _lowerCAmelCase				(       self		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					[char for char in text.upper() if char in self.key_string]

							lowerCamelCase			      =					chars[-1]
							while len(_a ) % self.break_key != 0:
										chars.append(_a )

							return "".join(_a )





				def    _lowerCAmelCase				(       self		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					self.process_text(text.upper() )
							lowerCamelCase			      =					""""""

							for i in range(0		, len(_a ) - self.break_key + 1		, self.break_key ):
										lowerCamelCase			      =					text[i : i + self.break_key]
										lowerCamelCase			      =					[self.replace_letters(_a ) for char in batch]
										lowerCamelCase			      =					numpy.array([vec] ).T
										lowerCamelCase			      =					self.modulus(self.encrypt_key.dot(_a ) ).T.tolist()[
										    0
										]
										lowerCamelCase			      =					"""""".join(
										    self.replace_digits(_a ) for num in batch_encrypted )
										encrypted += encrypted_batch

							return encrypted





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					round(numpy.linalg.det(self.encrypt_key ) )

							if det < 0:
										lowerCamelCase			      =					det % len(self.key_string )
							lowerCamelCase			      =					None
							for i in range(len(self.key_string ) ):
										if (det * i) % len(self.key_string ) == 1:
													lowerCamelCase			      =					i
													break

							lowerCamelCase			      =					(
							    det_inv
							    * numpy.linalg.det(self.encrypt_key )
							    * numpy.linalg.inv(self.encrypt_key )
							)

							return self.to_int(self.modulus(_a ) )





				def    _lowerCAmelCase				(       self		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					self.make_decrypt_key()
							lowerCamelCase			      =					self.process_text(text.upper() )
							lowerCamelCase			      =					""""""

							for i in range(0		, len(_a ) - self.break_key + 1		, self.break_key ):
										lowerCamelCase			      =					text[i : i + self.break_key]
										lowerCamelCase			      =					[self.replace_letters(_a ) for char in batch]
										lowerCamelCase			      =					numpy.array([vec] ).T
										lowerCamelCase			      =					self.modulus(decrypt_key.dot(_a ) ).T.tolist()[0]
										lowerCamelCase			      =					"""""".join(
										    self.replace_digits(_a ) for num in batch_decrypted )
										decrypted += decrypted_batch

							return decrypted




def     a__ (				)						->					None:
			lowerCamelCase			      =					int(input("""Enter the order of the encryption key: """				)				)
			lowerCamelCase			      =					[]

			print("""Enter each row of the encryption key with space separated integers"""				)
			for _ in range(snake_case__				):
						lowerCamelCase			      =					[int(snake_case__				) for x in input().split()]
						hill_matrix.append(snake_case__				)

			lowerCamelCase			      =					HillCipher(numpy.array(snake_case__				)				)

			print("""Would you like to encrypt or decrypt some text? (1 or 2)"""				)
			lowerCamelCase			      =					input("""\n1. Encrypt\n2. Decrypt\n"""				)
			if option == "1":
						lowerCamelCase			      =					input("""What text would you like to encrypt?: """				)
						print("""Your encrypted text is:"""				)
						print(hc.encrypt(snake_case__				)				)
			elif option == "2":
						lowerCamelCase			      =					input("""What text would you like to decrypt?: """				)
						print("""Your decrypted text is:"""				)
						print(hc.decrypt(snake_case__				)				)


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					main()









"""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


lowerCAmelCase							:			List[str]     							=							logging.get_logger(__name__)






class     __magic_name__							(       UpperCAmelCase__				):






				'''simple docstring'''
				__UpperCamelCase			  =						["audio_values", "audio_mask"]





				def __init__(       self		, _a=2_048		, _a=1		, _a=[16, 16]		, _a=128		, _a=44_100		, _a=86		, _a=2_048		, _a=0.0		, **_a		, ):





							"""simple docstring"""
							super().__init__(
							    feature_size=_a		, sampling_rate=_a		, padding_value=_a		, **_a		, )

							lowerCamelCase			      =					spectrogram_length
							lowerCamelCase			      =					num_channels
							lowerCamelCase			      =					patch_size
							lowerCamelCase			      =					feature_size // self.patch_size[1]
							lowerCamelCase			      =					n_fft
							lowerCamelCase			      =					sampling_rate // hop_length_to_sampling_rate
							lowerCamelCase			      =					sampling_rate
							lowerCamelCase			      =					padding_value
							lowerCamelCase			      =					mel_filter_bank(
							    num_frequency_bins=1 + n_fft // 2		, num_mel_filters=_a		, min_frequency=0.0		, max_frequency=22_050.0		, sampling_rate=_a		, norm="""slaney"""		, mel_scale="""slaney"""		, ).T





				def    _lowerCAmelCase				(       self		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					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			      =					log_spec[:, :-1]
							lowerCamelCase			      =					log_spec - 20.0
							lowerCamelCase			      =					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		, ):





							"""simple docstring"""

							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			      =					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			      =					is_batched_numpy or (
							    isinstance(_a		, (list, tuple) ) and (isinstance(raw_speech[0]		, (np.ndarray, tuple, list) ))
							)
							if is_batched:
										lowerCamelCase			      =					[np.asarray([speech]		, dtype=np.floataa ).T for speech in raw_speech]
							elif not is_batched and not isinstance(_a		, np.ndarray ):
										lowerCamelCase			      =					np.asarray(_a		, dtype=np.floataa )
							elif isinstance(_a		, np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
										lowerCamelCase			      =					raw_speech.astype(np.floataa )
							# always return batch
							if not is_batched:
										lowerCamelCase			      =					[np.asarray([raw_speech] ).T]

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

							# Create audio attention mask
							lowerCamelCase			      =					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			      =					[
										    (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			      =					np.array(_a ).astype(np.floataa )

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

							# return as BatchFeature
							if return_attention_mask:
										lowerCamelCase			      =					{"""audio_values""": padded_audio_features, """audio_mask""": audio_mask}
							else:
										lowerCamelCase			      =					{"""audio_values""": padded_audio_features}

							lowerCamelCase			      =					BatchFeature(data=_a		, tensor_type=_a )
							return encoded_inputs



