





import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np
import torch

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


snake_case 				=       logging.get_logger(__name__)





class 				A_				(      UpperCAmelCase__	):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_      : List[str]      =  ["input_features", "is_longer"]







       def __init__(    self	:	List[str]						,__A	:	Optional[Any]=64						,__A	:	int=4_8000						,__A	:	str=480						,__A	:	str=10						,__A	:	List[str]=1024						,__A	:	Any=0.0						,__A	:	int=False						,__A	:	float = 0						,__A	:	float = 1_4000						,__A	:	int = None						,__A	:	str = "fusion"						,__A	:	str = "repeatpad"						,**__A	:	Optional[Any]						,)						->					str:
           super().__init__(
               feature_size=lowerCamelCase__						,sampling_rate=lowerCamelCase__						,padding_value=lowerCamelCase__						,return_attention_mask=lowerCamelCase__						,**lowerCamelCase__						,)
           _lowercase							= top_db
           _lowercase							= truncation
           _lowercase							= padding
           _lowercase							= fft_window_size
           _lowercase							= (fft_window_size >> 1) + 1
           _lowercase							= hop_length
           _lowercase							= max_length_s
           _lowercase							= max_length_s * sampling_rate
           _lowercase							= sampling_rate
           _lowercase							= frequency_min
           _lowercase							= frequency_max
           _lowercase							= mel_filter_bank(
               num_frequency_bins=self.nb_frequency_bins						,num_mel_filters=lowerCamelCase__						,min_frequency=lowerCamelCase__						,max_frequency=lowerCamelCase__						,sampling_rate=lowerCamelCase__						,norm=lowerCamelCase__						,mel_scale='htk'						,)
           _lowercase							= mel_filter_bank(
               num_frequency_bins=self.nb_frequency_bins						,num_mel_filters=lowerCamelCase__						,min_frequency=lowerCamelCase__						,max_frequency=lowerCamelCase__						,sampling_rate=lowerCamelCase__						,norm='slaney'						,mel_scale='slaney'						,)







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







       def    __UpperCAmelCase (    self	:	Optional[int]						,__A	:	np.array						,__A	:	Optional[np.array] = None  )						->					np.ndarray:
           _lowercase							= spectrogram(
               lowerCamelCase__						,window_function(self.fft_window_size						,'hann'  )						,frame_length=self.fft_window_size						,hop_length=self.hop_length						,power=2.0						,mel_filters=lowerCamelCase__						,log_mel='dB'						,)
           return log_mel_spectrogram.T







       def    __UpperCAmelCase (    self	:	int						,__A	:	List[Any]						,__A	:	Any						,__A	:	str  )						->					Optional[int]:
           _lowercase							= np.array_split(list(range(0						,total_frames - chunk_frames + 1  )  )						,3  )
           if len(ranges[1]  ) == 0:
               # if the audio is too short, we just use the first chunk
               _lowercase							= [0]
           if len(ranges[2]  ) == 0:
               # if the audio is too short, we just use the first chunk
               _lowercase							= [0]
           # randomly choose index for each part
           _lowercase							= np.random.choice(ranges[0]  )
           _lowercase							= np.random.choice(ranges[1]  )
           _lowercase							= np.random.choice(ranges[2]  )

           _lowercase							= mel[idx_front : idx_front + chunk_frames, :]
           _lowercase							= mel[idx_middle : idx_middle + chunk_frames, :]
           _lowercase							= mel[idx_back : idx_back + chunk_frames, :]

           _lowercase							= torch.tensor(mel[None, None, :]  )
           _lowercase							= torch.nn.functional.interpolate(
               lowerCamelCase__						,size=[chunk_frames, 64]						,mode='bilinear'						,align_corners=lowerCamelCase__  )
           _lowercase							= mel_shrink[0][0].numpy()
           _lowercase							= np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]						,axis=0  )
           return mel_fusion







       def    __UpperCAmelCase (    self	:	Optional[Any]						,__A	:	np.array						,__A	:	Optional[int]						,__A	:	str						,__A	:	str  )						->					np.array:







           if waveform.shape[0] > max_length:
               if truncation == "rand_trunc":
                   _lowercase							= True
                   # random crop to max_length (for compatibility) -> this should be handled by self.pad
                   _lowercase							= len(lowerCamelCase__  ) - max_length
                   _lowercase							= np.random.randint(0						,overflow + 1  )
                   _lowercase							= waveform[idx : idx + max_length]
                   _lowercase							= self._np_extract_fbank_features(lowerCamelCase__						,self.mel_filters_slaney  )[None, :]
               elif truncation == "fusion":
                   _lowercase							= self._np_extract_fbank_features(lowerCamelCase__						,self.mel_filters  )
                   _lowercase							= max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
                   _lowercase							= mel.shape[0]
                   if chunk_frames == total_frames:
                       # there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
                       # In this case, we just use the whole audio.
                       _lowercase							= np.stack([mel, mel, mel, mel]						,axis=0  )
                       _lowercase							= False
                   else:
                       _lowercase							= self._random_mel_fusion(lowerCamelCase__						,lowerCamelCase__						,lowerCamelCase__  )
                       _lowercase							= True
               else:
                   raise NotImplementedError(F"""data_truncating {truncation} not implemented"""  )

           else:
               _lowercase							= False
               # only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
               if waveform.shape[0] < max_length:
                   if padding == "repeat":
                       _lowercase							= int(max_length / len(lowerCamelCase__  )  )
                       _lowercase							= np.stack(np.tile(lowerCamelCase__						,n_repeat + 1  )  )[:max_length]
                   if padding == "repeatpad":
                       _lowercase							= int(max_length / len(lowerCamelCase__  )  )
                       _lowercase							= np.stack(np.tile(lowerCamelCase__						,lowerCamelCase__  )  )
                   _lowercase							= np.pad(lowerCamelCase__						,(0, max_length - waveform.shape[0])						,mode='constant'						,constant_values=0  )

               if truncation == "fusion":
                   _lowercase							= self._np_extract_fbank_features(lowerCamelCase__						,self.mel_filters  )
                   _lowercase							= np.stack([input_mel, input_mel, input_mel, input_mel]						,axis=0  )
               else:
                   _lowercase							= self._np_extract_fbank_features(lowerCamelCase__						,self.mel_filters_slaney  )[None, :]

           return input_mel, longer







       def __call__(    self	:	str						,__A	:	Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]						,__A	:	str = None						,__A	:	Optional[str] = None						,__A	:	Optional[int] = None						,__A	:	Optional[int] = None						,__A	:	Optional[Union[str, TensorType]] = None						,**__A	:	str						,)						->					BatchFeature:
           _lowercase							= truncation if truncation is not None else self.truncation
           _lowercase							= padding if padding else self.padding

           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.'  )

           _lowercase							= 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}"""  )
           _lowercase							= is_batched_numpy or (
               isinstance(lowerCamelCase__						,(list, tuple)  ) and (isinstance(raw_speech[0]						,(np.ndarray, tuple, list)  ))
           )

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

           # always return batch
           if not is_batched:
               _lowercase							= [np.asarray(lowerCamelCase__  )]

           # convert to mel spectrogram, truncate and pad if needed.
           _lowercase							= [
               self._get_input_mel(lowerCamelCase__						,max_length if max_length else self.nb_max_samples						,lowerCamelCase__						,lowerCamelCase__  )
               for waveform in raw_speech
           ]

           _lowercase							= []
           _lowercase							= []
           for mel, longer in padded_inputs:
               input_mel.append(lowerCamelCase__  )
               is_longer.append(lowerCamelCase__  )

           if truncation == "fusion" and sum(lowerCamelCase__  ) == 0:
               # if no audio is longer than 10s, then randomly select one audio to be longer
               _lowercase							= np.random.randint(0						,len(lowerCamelCase__  )  )
               _lowercase							= True

           if isinstance(input_mel[0]						,lowerCamelCase__  ):
               _lowercase							= [np.asarray(lowerCamelCase__						,dtype=np.floataa  ) for feature in input_mel]

           # is_longer is a list of bool
           _lowercase							= [[longer] for longer in is_longer]

           _lowercase							= {"input_features": input_mel, "is_longer": is_longer}
           _lowercase							= BatchFeature(lowerCamelCase__  )

           if return_tensors is not None:
               _lowercase							= input_features.convert_to_tensors(lowerCamelCase__  )

           return input_features






import string

import numpy

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





class 				A_				:







       """simple docstring"""





       SCREAMING_SNAKE_CASE_      : Dict      =  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)
       SCREAMING_SNAKE_CASE_      : Dict      =  numpy.vectorize(lambda UpperCAmelCase	: x % 3_6	)

       SCREAMING_SNAKE_CASE_      : List[Any]      =  numpy.vectorize(UpperCAmelCase	)







       def __init__(    self	:	Optional[Any]						,__A	:	numpy.ndarray  )						->					None:
         _lowercase							= self.modulus(__A  )  # mod36 calc's on the encrypt key
         self.check_determinant()  # validate the determinant of the encryption key
         _lowercase							= encrypt_key.shape[0]







       def    __UpperCAmelCase (    self	:	Tuple						,__A	:	str  )						->					int:
         return self.key_string.index(__A  )







       def    __UpperCAmelCase (    self	:	Optional[int]						,__A	:	int  )						->					str:
         return self.key_string[round(__A  )]







       def    __UpperCAmelCase (    self	:	str  )						->					None:
         _lowercase							= round(numpy.linalg.det(self.encrypt_key  )  )

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

         _lowercase							= len(self.key_string  )
         if greatest_common_divisor(__A						,len(self.key_string  )  ) != 1:
           _lowercase							= (
               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    __UpperCAmelCase (    self	:	Any						,__A	:	str  )						->					str:
         _lowercase							= [char for char in text.upper() if char in self.key_string]

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

         return "".join(__A  )







       def    __UpperCAmelCase (    self	:	Optional[int]						,__A	:	str  )						->					str:
         _lowercase							= self.process_text(text.upper()  )
         _lowercase							= ''

         for i in range(0						,len(__A  ) - self.break_key + 1						,self.break_key  ):
           _lowercase							= text[i : i + self.break_key]
           _lowercase							= [self.replace_letters(__A  ) for char in batch]
           _lowercase							= numpy.array([vec]  ).T
           _lowercase							= self.modulus(self.encrypt_key.dot(__A  )  ).T.tolist()[
               0
           ]
           _lowercase							= ''.join(
               self.replace_digits(__A  ) for num in batch_encrypted  )
           encrypted += encrypted_batch

         return encrypted







       def    __UpperCAmelCase (    self	:	List[Any]  )						->					numpy.ndarray:
         _lowercase							= round(numpy.linalg.det(self.encrypt_key  )  )

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

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

         return self.to_int(self.modulus(__A  )  )







       def    __UpperCAmelCase (    self	:	Tuple						,__A	:	str  )						->					str:
         _lowercase							= self.make_decrypt_key()
         _lowercase							= self.process_text(text.upper()  )
         _lowercase							= ''

         for i in range(0						,len(__A  ) - self.break_key + 1						,self.break_key  ):
           _lowercase							= text[i : i + self.break_key]
           _lowercase							= [self.replace_letters(__A  ) for char in batch]
           _lowercase							= numpy.array([vec]  ).T
           _lowercase							= self.modulus(decrypt_key.dot(__A  )  ).T.tolist()[0]
           _lowercase							= ''.join(
               self.replace_digits(__A  ) for num in batch_decrypted  )
           decrypted += decrypted_batch

         return decrypted

def 							SCREAMING_SNAKE_CASE__       (  )      ->						None:
  _lowercase							= int(input('Enter the order of the encryption key: '     )     )
  _lowercase							= []

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

  _lowercase							= HillCipher(numpy.array(snake_case__     )     )

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