


from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


snake_case				:    List[Any]       				=     logging.get_logger(__name__)







class 		_snake_case  (      _snake_case							):
 SCREAMING_SNAKE_CASE__      =       ['input_values', 'padding_mask']




 def __init__(		self					,  _lowerCamelCase = 1					,  _lowerCamelCase = 2_4000					,  _lowerCamelCase = 0.0					,  _lowerCamelCase = None					,  _lowerCamelCase = None					,  **_lowerCamelCase					,  ):
       super().__init__(feature_size=_lowerCamelCase					,  sampling_rate=_lowerCamelCase					,  padding_value=_lowerCamelCase					,  **_lowerCamelCase    )
       a						:Optional[int] 							=       chunk_length_s
       a						:Union[str, Any] 							=       overlap




 @property
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       if self.chunk_length_s is None:
             return None
       else:
             return int(self.chunk_length_s * self.sampling_rate    )




 @property
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       if self.chunk_length_s is None or self.overlap is None:
             return None
       else:
             return max(1					,  int((1.0 - self.overlap) * self.chunk_length    )    )




 def __call__(		self					,  _lowerCamelCase					,  _lowerCamelCase = None					,  _lowerCamelCase = False					,  _lowerCamelCase = None					,  _lowerCamelCase = None					,  _lowerCamelCase = None					,  ):
       if sampling_rate is not None:
             if sampling_rate != self.sampling_rate:
                   raise ValueError(
                       F'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
                       F''' {self.sampling_rate}. Please make sure that the provided audio input was sampled with'''
                       F''' {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.'''    )

       if padding and truncation:
             raise ValueError('''Both padding and truncation were set. Make sure you only set one.'''    )
       elif padding is None:
             # by default let's pad the inputs
             a						:Optional[Any] 							=       True

       a						:Dict 							=       bool(
           isinstance(_lowerCamelCase					,  (list, tuple)    ) and (isinstance(raw_audio[0]					,  (np.ndarray, tuple, list)    ))    )

       if is_batched:
             a						:Dict 							=       [np.asarray(_lowerCamelCase					,  dtype=np.floataa    ).T for audio in raw_audio]
       elif not is_batched and not isinstance(_lowerCamelCase					,  np.ndarray    ):
             a						:Union[str, Any] 							=       np.asarray(_lowerCamelCase					,  dtype=np.floataa    )
       elif isinstance(_lowerCamelCase					,  np.ndarray    ) and raw_audio.dtype is np.dtype(np.floataa    ):
             a						:List[Any] 							=       raw_audio.astype(np.floataa    )

       # always return batch
       if not is_batched:
             a						:Tuple 							=       [np.asarray(_lowerCamelCase    ).T]

       # verify inputs are valid
       for idx, example in enumerate(_lowerCamelCase    ):
             if example.ndim > 2:
                   raise ValueError(F'''Expected input shape (channels, length) but got shape {example.shape}'''    )
             if self.feature_size == 1 and example.ndim != 1:
                   raise ValueError(F'''Expected mono audio but example has {example.shape[-1]} channels'''    )
             if self.feature_size == 2 and example.shape[-1] != 2:
                   raise ValueError(F'''Expected stereo audio but example has {example.shape[-1]} channels'''    )

       a						:Tuple 							=       None
       a						:Dict 							=       BatchFeature({'''input_values''': raw_audio}    )
       if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
             if truncation:
                   a						:Dict 							=       min(array.shape[0] for array in raw_audio    )
                   a						:List[Any] 							=       int(np.floor(max_length / self.chunk_stride    )    )
                   a						:Optional[Any] 							=       (nb_step - 1) * self.chunk_stride + self.chunk_length
             elif padding:
                   a						:str 							=       max(array.shape[0] for array in raw_audio    )
                   a						:Union[str, Any] 							=       int(np.ceil(max_length / self.chunk_stride    )    )
                   a						:List[Any] 							=       (nb_step - 1) * self.chunk_stride + self.chunk_length
                   a						:List[Any] 							=       '''max_length'''
             else:
                   a						:str 							=       input_values

        # normal padding on batch
       if padded_inputs is None:
             a						:Any 							=       self.pad(
                 _lowerCamelCase					,  max_length=_lowerCamelCase					,  truncation=_lowerCamelCase					,  padding=_lowerCamelCase					,  return_attention_mask=_lowerCamelCase					,  )
             if padding:
                   a						:Tuple 							=       padded_inputs.pop('''attention_mask'''    )

       a						:Union[str, Any] 							=       []
       for example in padded_inputs.pop('''input_values'''    ):
             if self.feature_size == 1:
                   a						:Optional[int] 							=       example[..., None]
             input_values.append(example.T    )

       a						:int 							=       input_values
       if return_tensors is not None:
             a						:Any 							=       padded_inputs.convert_to_tensors(_lowerCamelCase    )

       return padded_inputs









import string

import numpy
def 				__lowerCamelCase						( UpperCAmelCase_						:		int ,  UpperCAmelCase_						:		int						):




      """simple docstring"""





      return b if a == 0 else greatest_common_divisor(b % a ,  UpperCAmelCase_						)







class 		_snake_case  :
 SCREAMING_SNAKE_CASE__      =       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__      =       numpy.vectorize(lambda _snake_case							: x % 36							)

 SCREAMING_SNAKE_CASE__      =       numpy.vectorize(_snake_case							)




 def __init__(		self					,  _lowerCamelCase    ):
       a						:List[Any] 							=       self.modulus(_lowerCamelCase    )  # mod36 calc's on the encrypt key
       self.check_determinant()  # validate the determinant of the encryption key
       a						:int 							=       encrypt_key.shape[0]




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase    ):
       return self.key_string.index(_lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase    ):
       return self.key_string[round(_lowerCamelCase    )]




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:str 							=       round(numpy.linalg.det(self.encrypt_key    )    )

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

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




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase    ):
       a						:Optional[Any] 							=       [char for char in text.upper() if char in self.key_string]

       a						:List[str] 							=       chars[-1]
       while len(_lowerCamelCase    ) % self.break_key != 0:
             chars.append(_lowerCamelCase    )

       return "".join(_lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase    ):
       a						:Dict 							=       self.process_text(text.upper()    )
       a						:List[str] 							=       ''''''

       for i in range(0					,  len(_lowerCamelCase    ) - self.break_key + 1					,  self.break_key    ):
             a						:int 							=       text[i : i + self.break_key]
             a						:Optional[int] 							=       [self.replace_letters(_lowerCamelCase    ) for char in batch]
             a						:Union[str, Any] 							=       numpy.array([vec]    ).T
             a						:str 							=       self.modulus(self.encrypt_key.dot(_lowerCamelCase    )    ).T.tolist()[
                 0
             ]
             a						:List[Any] 							=       ''''''.join(
                 self.replace_digits(_lowerCamelCase    ) for num in batch_encrypted    )
             encrypted += encrypted_batch

       return encrypted




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:List[Any] 							=       round(numpy.linalg.det(self.encrypt_key    )    )

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

       a						:List[str] 							=       (
           det_inv
           * numpy.linalg.det(self.encrypt_key    )
           * numpy.linalg.inv(self.encrypt_key    )
       )

       return self.to_int(self.modulus(_lowerCamelCase    )    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase    ):
       a						:List[Any] 							=       self.make_decrypt_key()
       a						:str 							=       self.process_text(text.upper()    )
       a						:List[Any] 							=       ''''''

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

       return decrypted







def 				__lowerCamelCase						( ):




      """simple docstring"""





      a						:Tuple 							=       int(input('''Enter the order of the encryption key: '''						)						)
      a						:Dict 							=       []

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

      a						:Any 							=       HillCipher(numpy.array(UpperCAmelCase_						)						)

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


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     main()





