
"""simple docstring"""


import itertools
import random
import unittest

import numpy as np

from transformers import EncodecFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
			import torch


__A 		= random.Random()



def 					_lowerCamelCase(__UpperCamelCase					,				__UpperCamelCase=1.0					,				__UpperCamelCase=None					,				__UpperCamelCase=None )       ->				Tuple:
	if rng is None:
		_lowerCAmelCase			=global_rng

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

	return values







@require_torch
class 	lowerCamelCase__      (      unittest.TestCase       ):





			'''simple docstring'''



			def __init__(							self  ,  __UpperCAmelCase  ,  __UpperCAmelCase=7  ,  __UpperCAmelCase=4_00  ,  __UpperCAmelCase=20_00  ,  __UpperCAmelCase=1  ,  __UpperCAmelCase=0.0  ,  __UpperCAmelCase=2_40_00  ,  __UpperCAmelCase=True  ,  )				->  Tuple:
				_lowerCAmelCase			=parent
				_lowerCAmelCase			=batch_size
				_lowerCAmelCase			=min_seq_length
				_lowerCAmelCase			=max_seq_length
				_lowerCAmelCase			=(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
				_lowerCAmelCase			=feature_size
				_lowerCAmelCase			=padding_value
				_lowerCAmelCase			=sampling_rate
				_lowerCAmelCase			=return_attention_mask



			def       _lowerCAmelCase	(							self						)				->  str:
				return {
				    "feature_size": self.feature_size,
				    "padding_value": self.padding_value,
				    "sampling_rate": self.sampling_rate,
				    "return_attention_mask": self.return_attention_mask,
				}



			def       _lowerCAmelCase	(							self  ,  __UpperCAmelCase=False  ,  __UpperCAmelCase=False						)				->  Union[str, Any]:
				def _flatten(__UpperCAmelCase						):
					return list(itertools.chain(*__UpperCAmelCase						)						)

				if equal_length:
					_lowerCAmelCase			=floats_list((self.batch_size, self.max_seq_length)						)
				else:
					# make sure that inputs increase in size
					_lowerCAmelCase			=[
					    _flatten(floats_list((x, self.feature_size)						)						)
					    for x in range(self.min_seq_length  ,  self.max_seq_length  ,  self.seq_length_diff						)
					]

				if numpify:
					_lowerCAmelCase			=[np.asarray(__UpperCAmelCase						) for x in audio_inputs]

				return audio_inputs







@require_torch
class 	lowerCamelCase__      (      __magic_name__				,			unittest.TestCase       ):





			'''simple docstring'''
			lowerCamelCase						      =	EncodecFeatureExtractor



			def       _lowerCAmelCase	(							self						)				->  Any:
				_lowerCAmelCase			=EnCodecFeatureExtractionTester(self						)



			def       _lowerCAmelCase	(							self						)				->  str:
				# Tests that all call wrap to encode_plus and batch_encode_plus
				_lowerCAmelCase			=self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
				# create three inputs of length 800, 1000, and 1200
				_lowerCAmelCase			=[floats_list((1, x)						)[0] for x in range(8_00  ,  14_00  ,  2_00						)]
				_lowerCAmelCase			=[np.asarray(__UpperCAmelCase						) for audio_input in audio_inputs]

				# Test not batched input
				_lowerCAmelCase			=feat_extract(audio_inputs[0]  ,  return_tensors="""np"""						).input_values
				_lowerCAmelCase			=feat_extract(np_audio_inputs[0]  ,  return_tensors="""np"""						).input_values
				self.assertTrue(np.allclose(__UpperCAmelCase  ,  __UpperCAmelCase  ,  atol=1e-3						)						)

				# Test batched
				_lowerCAmelCase			=feat_extract(__UpperCAmelCase  ,  padding=__UpperCAmelCase  ,  return_tensors="""np"""						).input_values
				_lowerCAmelCase			=feat_extract(__UpperCAmelCase  ,  padding=__UpperCAmelCase  ,  return_tensors="""np"""						).input_values
				for enc_seq_a, enc_seq_a in zip(__UpperCAmelCase  ,  __UpperCAmelCase						):
					self.assertTrue(np.allclose(__UpperCAmelCase  ,  __UpperCAmelCase  ,  atol=1e-3						)						)



			def       _lowerCAmelCase	(							self						)				->  int:
				_lowerCAmelCase			=self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
				_lowerCAmelCase			=np.random.rand(1_00						).astype(np.floataa						)
				_lowerCAmelCase			=np_audio_inputs.tolist()

				for inputs in [py_audio_inputs, np_audio_inputs]:
					_lowerCAmelCase			=feature_extractor.pad([{"""input_values""": inputs}]  ,  return_tensors="""np"""						)
					self.assertTrue(np_processed.input_values.dtype == np.floataa						)
					_lowerCAmelCase			=feature_extractor.pad([{"""input_values""": inputs}]  ,  return_tensors="""pt"""						)
					self.assertTrue(pt_processed.input_values.dtype == torch.floataa						)



			def       _lowerCAmelCase	(							self  ,  __UpperCAmelCase						)				->  Any:
				from datasets import load_dataset

				_lowerCAmelCase			=load_dataset("""hf-internal-testing/librispeech_asr_dummy"""  ,  """clean"""  ,  split="""validation"""						)
				# automatic decoding with librispeech
				_lowerCAmelCase			=ds.sort("""id"""						).select(range(__UpperCAmelCase						)						)[:num_samples]["""audio"""]

				return [x["array"] for x in audio_samples]



			def       _lowerCAmelCase	(							self						)				->  Tuple:
				# fmt: off
				_lowerCAmelCase			=torch.tensor(
				    [2.38_04e-03, 2.07_52e-03, 1.98_36e-03, 2.10_57e-03, 1.61_74e-03,
				     3.05_18e-04, 9.15_53e-05, 3.35_69e-04, 9.76_56e-04, 1.83_11e-03,
				     2.01_42e-03, 2.10_57e-03, 1.73_95e-03, 4.57_76e-04, -3.96_73e-04,
				     4.57_76e-04, 1.00_71e-03, 9.15_53e-05, 4.88_28e-04, 1.15_97e-03,
				     7.32_42e-04, 9.46_04e-04, 1.80_05e-03, 1.83_11e-03, 8.85_01e-04,
				     4.27_25e-04, 4.88_28e-04, 7.32_42e-04, 1.09_86e-03, 2.10_57e-03]						)
				# fmt: on
				_lowerCAmelCase			=self._load_datasamples(1						)
				_lowerCAmelCase			=EncodecFeatureExtractor()
				_lowerCAmelCase			=feature_extractor(__UpperCAmelCase  ,  return_tensors="""pt"""						).input_values
				self.assertEquals(input_values.shape  ,  (1, 1, 9_36_80)						)
				self.assertTrue(torch.allclose(input_values[0, 0, :30]  ,  __UpperCAmelCase  ,  atol=1e-6						)						)



			def       _lowerCAmelCase	(							self						)				->  Any:
				# fmt: off
				_lowerCAmelCase			=torch.tensor(
				    [2.38_04e-03, 2.07_52e-03, 1.98_36e-03, 2.10_57e-03, 1.61_74e-03,
				     3.05_18e-04, 9.15_53e-05, 3.35_69e-04, 9.76_56e-04, 1.83_11e-03,
				     2.01_42e-03, 2.10_57e-03, 1.73_95e-03, 4.57_76e-04, -3.96_73e-04,
				     4.57_76e-04, 1.00_71e-03, 9.15_53e-05, 4.88_28e-04, 1.15_97e-03,
				     7.32_42e-04, 9.46_04e-04, 1.80_05e-03, 1.83_11e-03, 8.85_01e-04,
				     4.27_25e-04, 4.88_28e-04, 7.32_42e-04, 1.09_86e-03, 2.10_57e-03]						)
				# fmt: on
				_lowerCAmelCase			=self._load_datasamples(1						)
				_lowerCAmelCase			=[np.tile(input_audio[0][None]  ,  reps=(2, 1)						)]
				input_audio[0][1] *= 0.5
				_lowerCAmelCase			=EncodecFeatureExtractor(feature_size=2						)
				_lowerCAmelCase			=feature_extractor(__UpperCAmelCase  ,  return_tensors="""pt"""						).input_values
				self.assertEquals(input_values.shape  ,  (1, 2, 9_36_80)						)
				self.assertTrue(torch.allclose(input_values[0, 0, :30]  ,  __UpperCAmelCase  ,  atol=1e-6						)						)
				self.assertTrue(torch.allclose(input_values[0, 1, :30]  ,  EXPECTED_INPUT_VALUES * 0.5  ,  atol=1e-6						)						)



			def       _lowerCAmelCase	(							self						)				->  Tuple:
				_lowerCAmelCase			=self._load_datasamples(2						)
				# would be easier if the stride was like
				_lowerCAmelCase			=EncodecFeatureExtractor(feature_size=1  ,  chunk_length_s=1  ,  overlap=0.0_1						)

				# pad and trunc raise an error ?
				with self.assertRaisesRegex(
				    __UpperCAmelCase  ,  """^Both padding and truncation were set. Make sure you only set one.$"""  ,  ):
					_lowerCAmelCase			=feature_extractor(
					    __UpperCAmelCase  ,  padding="""max_length"""  ,  truncation=__UpperCAmelCase  ,  return_tensors="""pt"""						).input_values

				# truncate to chunk
				_lowerCAmelCase			=feature_extractor(__UpperCAmelCase  ,  truncation=__UpperCAmelCase  ,  return_tensors="""pt"""						).input_values
				self.assertEquals(truncated_outputs.shape  ,  (2, 1, 7_15_20)						)  # 2 chunks

				# force truncate to max_length
				_lowerCAmelCase			=feature_extractor(
				    __UpperCAmelCase  ,  truncation=__UpperCAmelCase  ,  max_length=4_80_00  ,  return_tensors="""pt"""						).input_values
				self.assertEquals(truncated_outputs.shape  ,  (2, 1, 4_80_00)						)

				# pad to chunk
				_lowerCAmelCase			=feature_extractor(__UpperCAmelCase  ,  padding=__UpperCAmelCase  ,  return_tensors="""pt"""						).input_values
				self.assertEquals(padded_outputs.shape  ,  (2, 1, 9_52_80)						)

				# pad to chunk
				_lowerCAmelCase			=feature_extractor(__UpperCAmelCase  ,  return_tensors="""pt"""						).input_values
				self.assertEquals(truncated_outputs.shape  ,  (2, 1, 9_52_80)						)

				# force pad to max length
				_lowerCAmelCase			=feature_extractor(
				    __UpperCAmelCase  ,  padding="""max_length"""  ,  max_length=10_00_00  ,  return_tensors="""pt"""						).input_values
				self.assertEquals(truncated_outputs.shape  ,  (2, 1, 10_00_00)						)

				# force no pad
				with self.assertRaisesRegex(
				    __UpperCAmelCase  ,  """^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"""  ,  ):
					_lowerCAmelCase			=feature_extractor(__UpperCAmelCase  ,  padding=__UpperCAmelCase  ,  return_tensors="""pt"""						).input_values

				_lowerCAmelCase			=feature_extractor(input_audio[0]  ,  padding=__UpperCAmelCase  ,  return_tensors="""pt"""						).input_values
				self.assertEquals(truncated_outputs.shape  ,  (1, 1, 9_36_80)						)

				# no pad if no chunk_length_s
				_lowerCAmelCase			=None
				with self.assertRaisesRegex(
				    __UpperCAmelCase  ,  """^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"""  ,  ):
					_lowerCAmelCase			=feature_extractor(__UpperCAmelCase  ,  padding=__UpperCAmelCase  ,  return_tensors="""pt"""						).input_values

				_lowerCAmelCase			=feature_extractor(input_audio[0]  ,  padding=__UpperCAmelCase  ,  return_tensors="""pt"""						).input_values
				self.assertEquals(truncated_outputs.shape  ,  (1, 1, 9_36_80)						)

				# no pad if no overlap
				_lowerCAmelCase			=2
				_lowerCAmelCase			=None
				with self.assertRaisesRegex(
				    __UpperCAmelCase  ,  """^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"""  ,  ):
					_lowerCAmelCase			=feature_extractor(__UpperCAmelCase  ,  padding=__UpperCAmelCase  ,  return_tensors="""pt"""						).input_values

				_lowerCAmelCase			=feature_extractor(input_audio[0]  ,  padding=__UpperCAmelCase  ,  return_tensors="""pt"""						).input_values
				self.assertEquals(truncated_outputs.shape  ,  (1, 1, 9_36_80)						)





"""simple docstring"""


import os
import sys

from . import rsa_key_generator as rkg

__A 		= 128
__A 		= 256



def 					_lowerCamelCase(__UpperCamelCase					,				__UpperCamelCase = DEFAULT_BLOCK_SIZE )       ->				list[int]:
	_lowerCAmelCase			=message.encode("""ascii""" )

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



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



def 					_lowerCamelCase(__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase = DEFAULT_BLOCK_SIZE )       ->				list[int]:
	_lowerCAmelCase			=[]
	_lowerCAmelCase		,							_lowerCAmelCase			=key

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



def 					_lowerCamelCase(__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase = DEFAULT_BLOCK_SIZE					,				)       ->				str:
	_lowerCAmelCase			=[]
	_lowerCAmelCase		,							_lowerCAmelCase			=key
	for block in encrypted_blocks:
		decrypted_blocks.append(pow(__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase ) )
	return get_text_from_blocks(__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase )



def 					_lowerCamelCase(__UpperCamelCase )       ->				tuple[int, int, int]:
	with open(__UpperCamelCase ) as fo:
		_lowerCAmelCase			=fo.read()
	_lowerCAmelCase		,							_lowerCAmelCase		,							_lowerCAmelCase			=content.split(""",""" )
	return (int(__UpperCamelCase ), int(__UpperCamelCase ), int(__UpperCamelCase ))



def 					_lowerCamelCase(__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase = DEFAULT_BLOCK_SIZE					,				)       ->				str:
	_lowerCAmelCase		,							_lowerCAmelCase		,							_lowerCAmelCase			=read_key_file(__UpperCamelCase )
	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					,				__UpperCamelCase ) )

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

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



def 					_lowerCamelCase(__UpperCamelCase					,				__UpperCamelCase )       ->				str:
	_lowerCAmelCase		,							_lowerCAmelCase		,							_lowerCAmelCase			=read_key_file(__UpperCamelCase )
	with open(__UpperCamelCase ) as fo:
		_lowerCAmelCase			=fo.read()
	_lowerCAmelCase		,							_lowerCAmelCase		,							_lowerCAmelCase			=content.split("""_""" )
	_lowerCAmelCase			=int(__UpperCamelCase )
	_lowerCAmelCase			=int(__UpperCamelCase )

	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					,				__UpperCamelCase ) )

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

	return decrypt_message(__UpperCamelCase					,				__UpperCamelCase					,				(n, d)					,				__UpperCamelCase )



def 					_lowerCamelCase()       ->				None:
	_lowerCAmelCase			="""encrypted_file.txt"""
	_lowerCAmelCase			=input(R"""Encrypt\Decrypt [e\d]: """ )

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

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

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

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

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

		print("""\nDecryption:""" )
		print(__UpperCamelCase )


if __name__ == "__main__":
			main()



