
import os
import sys

from . import rsa_key_generator as rkg

__lowerCAmelCase      :					List[str]   	=       128
__lowerCAmelCase      :					Union[str, Any]   	=       256

def a__   (	A_,					A_ = DEFAULT_BLOCK_SIZE ):






  '''simple docstring'''





  __magic_name__    					=	message.encode("""ascii""" )

  __magic_name__    					=	[]
  for block_start in range(0,					len(lowercase__ ),					lowercase__ ):
    __magic_name__    					=	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__   (	A_,					A_,					A_ = DEFAULT_BLOCK_SIZE ):






  '''simple docstring'''





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

def a__   (	A_,					A_,					A_ = DEFAULT_BLOCK_SIZE ):






  '''simple docstring'''





  __magic_name__    					=	[]
  __magic_name__					,   __magic_name__    					=	key

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

def a__   (	A_,					A_,					A_,					A_ = DEFAULT_BLOCK_SIZE,					):






  '''simple docstring'''





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

def a__   (	A_ ):






  '''simple docstring'''





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

def a__   (	A_,					A_,					A_,					A_ = DEFAULT_BLOCK_SIZE,					):






  '''simple docstring'''





  __magic_name__					,   __magic_name__					,   __magic_name__    					=	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__ ) )

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

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

def a__   (	A_,					A_ ):






  '''simple docstring'''





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

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

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

def a__   (	):






  '''simple docstring'''





  __magic_name__    					=	"""encrypted_file.txt"""
  __magic_name__    					=	input(R"""Encrypt\Decrypt [e\d]: """ )

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

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

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

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

  elif mode == "decrypt":
    __magic_name__    					=	"""rsa_privkey.txt"""
    print(f'''Reading from {filename} and decrypting...''' )
    __magic_name__    					=	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()



import random
import shutil
import tempfile
import unittest

import numpy as np

from transformers import TaTokenizer, TaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torch
from transformers.utils.import_utils import is_speech_available, is_torch_available


if is_torch_available():
    pass

if is_speech_available():
    from transformers import EncodecFeatureExtractor, MusicgenProcessor


__lowerCAmelCase      :					Dict   	=       random.Random()

def a__   (	A_,					A_=1.0,					A_=None,					A_=None ):






  '''simple docstring'''





  if rng is None:
    __magic_name__    					=	global_rng

  __magic_name__    					=	[]
  for batch_idx in range(shape[0] ):
    values.append([] )
    for _ in range(shape[1] ):
      values[-1].append(rng.random() * scale )

  return values





@require_torch
@require_sentencepiece
class 	UpperCAmelCase_      (			unittest.TestCase					):


  '''simple docstring'''







  def 							_lowercase							(			self      :			Union[str, Any]   )      ->     List[str]:







    """simple docstring"""






    __magic_name__    					=	"""facebook/musicgen-small"""
    __magic_name__    					=	tempfile.mkdtemp()
  def 							_lowercase							(			self      :			Tuple      ,							**UpperCamelCase__      :			str   )      ->     Any:







    """simple docstring"""






    return TaTokenizer.from_pretrained(self.checkpoint      ,							**UpperCamelCase__   )
  def 							_lowercase							(			self      :			Optional[int]      ,							**UpperCamelCase__      :			List[str]   )      ->     List[str]:







    """simple docstring"""






    return EncodecFeatureExtractor.from_pretrained(self.checkpoint      ,							**UpperCamelCase__   )
  def 							_lowercase							(			self      :			Union[str, Any]   )      ->     List[Any]:







    """simple docstring"""






    shutil.rmtree(self.tmpdirname   )
  def 							_lowercase							(			self      :			Optional[Any]   )      ->     Optional[int]:







    """simple docstring"""






    __magic_name__    					=	self.get_tokenizer()
    __magic_name__    					=	self.get_feature_extractor()

    __magic_name__    					=	MusicgenProcessor(tokenizer=UpperCamelCase__      ,							feature_extractor=UpperCamelCase__   )

    processor.save_pretrained(self.tmpdirname   )
    __magic_name__    					=	MusicgenProcessor.from_pretrained(self.tmpdirname   )

    self.assertEqual(processor.tokenizer.get_vocab()      ,							tokenizer.get_vocab()   )
    self.assertIsInstance(processor.tokenizer      ,							UpperCamelCase__   )

    self.assertEqual(processor.feature_extractor.to_json_string()      ,							feature_extractor.to_json_string()   )
    self.assertIsInstance(processor.feature_extractor      ,							UpperCamelCase__   )
  def 							_lowercase							(			self      :			Optional[Any]   )      ->     List[str]:







    """simple docstring"""






    __magic_name__    					=	MusicgenProcessor(tokenizer=self.get_tokenizer()      ,							feature_extractor=self.get_feature_extractor()   )
    processor.save_pretrained(self.tmpdirname   )

    __magic_name__    					=	self.get_tokenizer(bos_token="""(BOS)"""      ,							eos_token="""(EOS)"""   )
    __magic_name__    					=	self.get_feature_extractor(do_normalize=UpperCamelCase__      ,							padding_value=1.0   )

    __magic_name__    					=	MusicgenProcessor.from_pretrained(
        self.tmpdirname      ,							bos_token="""(BOS)"""      ,							eos_token="""(EOS)"""      ,							do_normalize=UpperCamelCase__      ,							padding_value=1.0   )

    self.assertEqual(processor.tokenizer.get_vocab()      ,							tokenizer_add_kwargs.get_vocab()   )
    self.assertIsInstance(processor.tokenizer      ,							UpperCamelCase__   )

    self.assertEqual(processor.feature_extractor.to_json_string()      ,							feature_extractor_add_kwargs.to_json_string()   )
    self.assertIsInstance(processor.feature_extractor      ,							UpperCamelCase__   )
  def 							_lowercase							(			self      :			Dict   )      ->     Optional[Any]:







    """simple docstring"""






    __magic_name__    					=	self.get_feature_extractor()
    __magic_name__    					=	self.get_tokenizer()

    __magic_name__    					=	MusicgenProcessor(tokenizer=UpperCamelCase__      ,							feature_extractor=UpperCamelCase__   )

    __magic_name__    					=	floats_list((3, 1000)   )

    __magic_name__    					=	feature_extractor(UpperCamelCase__      ,							return_tensors="""np"""   )
    __magic_name__    					=	processor(UpperCamelCase__      ,							return_tensors="""np"""   )

    for key in input_feat_extract.keys():
      self.assertAlmostEqual(input_feat_extract[key].sum()      ,							input_processor[key].sum()      ,							delta=1E-2   )
  def 							_lowercase							(			self      :			Optional[Any]   )      ->     str:







    """simple docstring"""






    __magic_name__    					=	self.get_feature_extractor()
    __magic_name__    					=	self.get_tokenizer()

    __magic_name__    					=	MusicgenProcessor(tokenizer=UpperCamelCase__      ,							feature_extractor=UpperCamelCase__   )

    __magic_name__    					=	"""This is a test string"""

    __magic_name__    					=	processor(text=UpperCamelCase__   )

    __magic_name__    					=	tokenizer(UpperCamelCase__   )

    for key in encoded_tok.keys():
      self.assertListEqual(encoded_tok[key]      ,							encoded_processor[key]   )
  def 							_lowercase							(			self      :			Dict   )      ->     int:







    """simple docstring"""






    __magic_name__    					=	self.get_feature_extractor()
    __magic_name__    					=	self.get_tokenizer()

    __magic_name__    					=	MusicgenProcessor(tokenizer=UpperCamelCase__      ,							feature_extractor=UpperCamelCase__   )

    __magic_name__    					=	[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

    __magic_name__    					=	processor.batch_decode(sequences=UpperCamelCase__   )
    __magic_name__    					=	tokenizer.batch_decode(UpperCamelCase__   )

    self.assertListEqual(UpperCamelCase__      ,							UpperCamelCase__   )
  def 							_lowercase							(			self      :			Tuple   )      ->     int:







    """simple docstring"""






    __magic_name__    					=	self.get_feature_extractor()
    __magic_name__    					=	self.get_tokenizer()

    __magic_name__    					=	MusicgenProcessor(tokenizer=UpperCamelCase__      ,							feature_extractor=UpperCamelCase__   )

    self.assertListEqual(
        processor.model_input_names      ,							feature_extractor.model_input_names      ,							msg="""`processor` and `feature_extractor` model input names do not match"""      ,							)





  def 							_lowercase							(			self      :			List[str]   )      ->     int:







    """simple docstring"""






    __magic_name__    					=	self.get_feature_extractor(padding_side="""left"""   )
    __magic_name__    					=	self.get_tokenizer()

    __magic_name__    					=	MusicgenProcessor(tokenizer=UpperCamelCase__      ,							feature_extractor=UpperCamelCase__   )

    __magic_name__    					=	[floats_list((1, x)   )[0] for x in range(5      ,							20      ,							5   )]
    __magic_name__    					=	processor(UpperCamelCase__   ).padding_mask

    __magic_name__    					=	np.asarray(floats_list((3, 20)   )   )[:, None, :]
    __magic_name__    					=	processor.batch_decode(UpperCamelCase__      ,							padding_mask=UpperCamelCase__   )

    self.assertIsInstance(UpperCamelCase__      ,							UpperCamelCase__   )

    for audio in decoded_audios:
      self.assertIsInstance(UpperCamelCase__      ,							np.ndarray   )

    self.assertTrue(decoded_audios[0].shape == (1, 10)   )
    self.assertTrue(decoded_audios[1].shape == (1, 15)   )
    self.assertTrue(decoded_audios[2].shape == (1, 20)   )

