
'''simple docstring'''



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


__A       =      logging.get_logger(__name__)





class      A							(    __UpperCAmelCase			):
			lowerCamelCase							:							List[Any]    						=					["""input_values""", """attention_mask"""]



			def __init__(   self    ,       lowerCamelCase__=1    ,       lowerCamelCase__=16_000    ,       lowerCamelCase__=0.0    ,       lowerCamelCase__=False    ,       lowerCamelCase__=True    ,       **lowerCamelCase__    ,       )     ->						Union[str, Any]:




									'''simple docstring'''



									super().__init__(feature_size=lowerCamelCase__    ,       sampling_rate=lowerCamelCase__    ,       padding_value=lowerCamelCase__    ,       **lowerCamelCase__    )
									lowercase__								=    return_attention_mask
									lowercase__								=    do_normalize



			@staticmethod
			def A__	(   lowerCamelCase__    ,       lowerCamelCase__    ,       lowerCamelCase__ = 0.0    )     ->						List[np.ndarray]:




									'''simple docstring'''



									if attention_mask is not None:
															lowercase__								=    np.array(lowerCamelCase__    ,       np.intaa    )
															lowercase__								=    []

															for vector, length in zip(lowerCamelCase__    ,       attention_mask.sum(-1    )    ):
																					lowercase__								=    (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7    )
																					if length < normed_slice.shape[0]:
																											lowercase__								=    padding_value

																					normed_input_values.append(lowerCamelCase__    )
									else:
															lowercase__								=    [(x - x.mean()) / np.sqrt(x.var() + 1e-7    ) for x in input_values]

									return normed_input_values



			def __call__(   self    ,       lowerCamelCase__    ,       lowerCamelCase__ = False    ,       lowerCamelCase__ = None    ,       lowerCamelCase__ = False    ,       lowerCamelCase__ = None    ,       lowerCamelCase__ = None    ,       lowerCamelCase__ = None    ,       lowerCamelCase__ = None    ,       **lowerCamelCase__    ,       )     ->						BatchFeature:




									'''simple docstring'''



									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 `raw_speech` 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."""    )

									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)    ))
									)

									# always return batch
									if not is_batched:
															lowercase__								=    [raw_speech]

									# convert into correct format for padding
									lowercase__								=    BatchFeature({"""input_values""": raw_speech}    )

									lowercase__								=    self.pad(
									    lowerCamelCase__    ,       padding=lowerCamelCase__    ,       max_length=lowerCamelCase__    ,       truncation=lowerCamelCase__    ,       pad_to_multiple_of=lowerCamelCase__    ,       return_attention_mask=lowerCamelCase__    ,       )

									# convert input values to correct format
									lowercase__								=    padded_inputs["""input_values"""]
									if not isinstance(input_values[0]    ,       np.ndarray    ):
															lowercase__								=    [np.asarray(lowerCamelCase__    ,       dtype=np.floataa    ) for array in input_values]
									elif (
									    not isinstance(lowerCamelCase__    ,       np.ndarray    )
									    and isinstance(input_values[0]    ,       np.ndarray    )
									    and input_values[0].dtype is np.dtype(np.floataa    )
									):
															lowercase__								=    [array.astype(np.floataa    ) for array in input_values]
									elif isinstance(lowerCamelCase__    ,       np.ndarray    ) and input_values.dtype is np.dtype(np.floataa    ):
															lowercase__								=    input_values.astype(np.floataa    )

									# convert attention_mask to correct format
									lowercase__								=    padded_inputs.get("""attention_mask"""    )
									if attention_mask is not None:
															lowercase__								=    [np.asarray(lowerCamelCase__    ,       dtype=np.intaa    ) for array in attention_mask]

									# zero-mean and unit-variance normalization
									if self.do_normalize:
															lowercase__								=    (
															    attention_mask
															    if self._get_padding_strategies(lowerCamelCase__    ,       max_length=lowerCamelCase__    ) is not PaddingStrategy.DO_NOT_PAD
															    else None
															)
															lowercase__								=    self.zero_mean_unit_var_norm(
															    padded_inputs["""input_values"""]    ,       attention_mask=lowerCamelCase__    ,       padding_value=self.padding_value    )

									if return_tensors is not None:
															lowercase__								=    padded_inputs.convert_to_tensors(lowerCamelCase__    )

									return padded_inputs








'''simple docstring'''



import os
import sys

from . import rsa_key_generator as rkg

__A       =      128
__A       =      256





def 	_A						(							lowercase__						,						lowercase__ = DEFAULT_BLOCK_SIZE		):
						lowercase__								=    message.encode("""ascii"""		)

						lowercase__								=    []
						for block_start in range(0						,						len(lowercase__		)						,						lowercase__		):
												lowercase__								=    0
												for i in range(lowercase__						,						min(block_start + block_size						,						len(lowercase__		)		)		):
																		block_int += message_bytes[i] * (BYTE_SIZE ** (i % block_size))
												block_ints.append(lowercase__		)
						return block_ints





def 	_A						(							lowercase__						,						lowercase__						,						lowercase__ = DEFAULT_BLOCK_SIZE		):
						lowercase__								=    []
						for block_int in block_ints:
												lowercase__								=    []
												for i in range(block_size - 1						,						-1						,						-1		):
																		if len(lowercase__		) + i < message_length:
																								lowercase__								=    block_int // (BYTE_SIZE**i)
																								lowercase__								=    block_int % (BYTE_SIZE**i)
																								block_message.insert(0						,						chr(lowercase__		)		)
												message.extend(lowercase__		)
						return "".join(lowercase__		)





def 	_A						(							lowercase__						,						lowercase__						,						lowercase__ = DEFAULT_BLOCK_SIZE		):
						lowercase__								=    []
						lowercase__	,  lowercase__								=    key

						for block in get_blocks_from_text(lowercase__						,						lowercase__		):
												encrypted_blocks.append(pow(lowercase__						,						lowercase__						,						lowercase__		)		)
						return encrypted_blocks





def 	_A						(							lowercase__						,						lowercase__						,						lowercase__						,						lowercase__ = DEFAULT_BLOCK_SIZE						,						):
						lowercase__								=    []
						lowercase__	,  lowercase__								=    key
						for block in encrypted_blocks:
												decrypted_blocks.append(pow(lowercase__						,						lowercase__						,						lowercase__		)		)
						return get_text_from_blocks(lowercase__						,						lowercase__						,						lowercase__		)





def 	_A						(							lowercase__		):
						with open(lowercase__		) as fo:
												lowercase__								=    fo.read()
						lowercase__	,  lowercase__	,  lowercase__								=    content.split(""","""		)
						return (int(lowercase__		), int(lowercase__		), int(lowercase__		))





def 	_A						(							lowercase__						,						lowercase__						,						lowercase__						,						lowercase__ = DEFAULT_BLOCK_SIZE						,						):
						lowercase__	,  lowercase__	,  lowercase__								=    read_key_file(lowercase__		)
						if key_size < block_size * 8:
												sys.exit(
												    """ERROR: Block size is {} bits and key size is {} bits. The RSA cipher """
												    """requires the block size to be equal to or greater than the key size. """
												    """Either decrease the block size or use different keys.""".format(
												        block_size * 8						,						lowercase__		)		)

						lowercase__								=    [str(lowercase__		) for i in encrypt_message(lowercase__						,						(n, e)						,						lowercase__		)]

						lowercase__								=    """,""".join(lowercase__		)
						lowercase__								=    f'''{len(lowercase__		)}_{block_size}_{encrypted_content}'''
						with open(lowercase__						,						"""w"""		) as fo:
												fo.write(lowercase__		)
						return encrypted_content





def 	_A						(							lowercase__						,						lowercase__		):
						lowercase__	,  lowercase__	,  lowercase__								=    read_key_file(lowercase__		)
						with open(lowercase__		) as fo:
												lowercase__								=    fo.read()
						lowercase__	,  lowercase__	,  lowercase__								=    content.split("""_"""		)
						lowercase__								=    int(lowercase__		)
						lowercase__								=    int(lowercase__		)

						if key_size < block_size * 8:
												sys.exit(
												    """ERROR: Block size is {} bits and key size is {} bits. The RSA cipher """
												    """requires the block size to be equal to or greater than the key size. """
												    """Did you specify the correct key file and encrypted file?""".format(
												        block_size * 8						,						lowercase__		)		)

						lowercase__								=    []
						for block in encrypted_message.split(""","""		):
												encrypted_blocks.append(int(lowercase__		)		)

						return decrypt_message(lowercase__						,						lowercase__						,						(n, d)						,						lowercase__		)





def 	_A						(							):
						lowercase__								=    """encrypted_file.txt"""
						lowercase__								=    input(r"""Encrypt\Decrypt [e\d]: """		)

						if response.lower().startswith("""e"""		):
												lowercase__								=    """encrypt"""
						elif response.lower().startswith("""d"""		):
												lowercase__								=    """decrypt"""

						if mode == "encrypt":
												if not os.path.exists("""rsa_pubkey.txt"""		):
																		rkg.make_key_files("""rsa"""						,						1024		)

												lowercase__								=    input("""\nEnter message: """		)
												lowercase__								=    """rsa_pubkey.txt"""
												print(f'''Encrypting and writing to {filename}...'''		)
												lowercase__								=    encrypt_and_write_to_file(lowercase__						,						lowercase__						,						lowercase__		)

												print("""\nEncrypted text:"""		)
												print(lowercase__		)

						elif mode == "decrypt":
												lowercase__								=    """rsa_privkey.txt"""
												print(f'''Reading from {filename} and decrypting...'''		)
												lowercase__								=    read_from_file_and_decrypt(lowercase__						,						lowercase__		)
												print("""writing decryption to rsa_decryption.txt..."""		)
												with open("""rsa_decryption.txt"""						,						"""w"""		) as dec:
																		dec.write(lowercase__		)

												print("""\nDecryption:"""		)
												print(lowercase__		)


if __name__ == "__main__":
				main()






