"""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


lowercase__   			=      random.Random()







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE=1.0						,	_SCREAMING_SNAKE_CASE=None						,	_SCREAMING_SNAKE_CASE=None  )      ->Any:
  if rng is None:
    a__:	List[str]     				=      global_rng

  a__:	Tuple     				=      []
  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   __snake_case			(	unittest.TestCase							):



      def __init__(		self       ,     lowercase       ,     lowercase=7       ,     lowercase=4_00       ,     lowercase=20_00       ,     lowercase=1       ,     lowercase=0.0       ,     lowercase=2_40_00       ,     lowercase=True       ,     )      ->     List[str]:





        '''simple docstring'''





        a__:	Any     				=      parent
        a__:	Any     				=      batch_size
        a__:	List[str]     				=      min_seq_length
        a__:	Dict     				=      max_seq_length
        a__:	List[str]     				=      (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
        a__:	List[str]     				=      feature_size
        a__:	List[Any]     				=      padding_value
        a__:	Any     				=      sampling_rate
        a__:	str     				=      return_attention_mask



      def     lowerCamelCase_  (		self)      ->     Dict:





        '''simple docstring'''





        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       ,     lowercase=False       ,     lowercase=False)      ->     Union[str, Any]:





        '''simple docstring'''





        def _flatten(lowercase):
          return list(itertools.chain(*lowercase))

        if equal_length:
          a__:	List[str]     				=      floats_list((self.batch_size, self.max_seq_length))
        else:
          # make sure that inputs increase in size
          a__:	List[Any]     				=      [
              _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:
          a__:	List[str]     				=      [np.asarray(lowercase) for x in audio_inputs]

        return audio_inputs







@require_torch
class   __snake_case			(	__lowerCAmelCase			, unittest.TestCase							):
      a__								=						EncodecFeatureExtractor



      def     lowerCamelCase_  (		self)      ->     Dict:





        '''simple docstring'''





        a__:	Optional[int]     				=      EnCodecFeatureExtractionTester(self)



      def     lowerCamelCase_  (		self)      ->     str:





        '''simple docstring'''





        a__:	Union[str, Any]     				=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
        # create three inputs of length 800, 1000, and 1200
        a__:	Tuple     				=      [floats_list((1, x))[0] for x in range(8_00       ,     14_00       ,     2_00)]
        a__:	Dict     				=      [np.asarray(lowercase) for audio_input in audio_inputs]

        # Test not batched input
        a__:	List[Any]     				=      feat_extract(audio_inputs[0]       ,     return_tensors='np').input_values
        a__:	Optional[int]     				=      feat_extract(np_audio_inputs[0]       ,     return_tensors='np').input_values
        self.assertTrue(np.allclose(lowercase       ,     lowercase       ,     atol=1e-3))

        # Test batched
        a__:	Optional[int]     				=      feat_extract(lowercase       ,     padding=lowercase       ,     return_tensors='np').input_values
        a__:	Optional[int]     				=      feat_extract(lowercase       ,     padding=lowercase       ,     return_tensors='np').input_values
        for enc_seq_a, enc_seq_a in zip(lowercase       ,     lowercase):
          self.assertTrue(np.allclose(lowercase       ,     lowercase       ,     atol=1e-3))



      def     lowerCamelCase_  (		self)      ->     Optional[int]:





        '''simple docstring'''





        a__:	Optional[Any]     				=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
        a__:	Optional[Any]     				=      np.random.rand(1_00).astype(np.floataa)
        a__:	int     				=      np_audio_inputs.tolist()

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



      def     lowerCamelCase_  (		self       ,     lowercase)      ->     Tuple:





        '''simple docstring'''





        from datasets import load_dataset

        a__:	Any     				=      load_dataset('hf-internal-testing/librispeech_asr_dummy'       ,     'clean'       ,     split='validation')
        # automatic decoding with librispeech
        a__:	Dict     				=      ds.sort('id').select(range(lowercase))[:num_samples]['audio']

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



      def     lowerCamelCase_  (		self)      ->     int:





        '''simple docstring'''





        a__:	Union[str, Any]     				=      torch.tensor(
            [2.3_804e-03, 2.0_752e-03, 1.9_836e-03, 2.1_057e-03, 1.6_174e-03,
             3.0_518e-04, 9.1_553e-05, 3.3_569e-04, 9.7_656e-04, 1.8_311e-03,
             2.0_142e-03, 2.1_057e-03, 1.7_395e-03, 4.5_776e-04, -3.9_673e-04,
             4.5_776e-04, 1.0_071e-03, 9.1_553e-05, 4.8_828e-04, 1.1_597e-03,
             7.3_242e-04, 9.4_604e-04, 1.8_005e-03, 1.8_311e-03, 8.8_501e-04,
             4.2_725e-04, 4.8_828e-04, 7.3_242e-04, 1.0_986e-03, 2.1_057e-03])
        # fmt: on
        a__:	str     				=      self._load_datasamples(1)
        a__:	int     				=      EncodecFeatureExtractor()
        a__:	List[Any]     				=      feature_extractor(lowercase       ,     return_tensors='pt').input_values
        self.assertEquals(input_values.shape       ,     (1, 1, 9_36_80))
        self.assertTrue(torch.allclose(input_values[0, 0, :30]       ,     lowercase       ,     atol=1e-6))



      def     lowerCamelCase_  (		self)      ->     Tuple:





        '''simple docstring'''





        a__:	int     				=      torch.tensor(
            [2.3_804e-03, 2.0_752e-03, 1.9_836e-03, 2.1_057e-03, 1.6_174e-03,
             3.0_518e-04, 9.1_553e-05, 3.3_569e-04, 9.7_656e-04, 1.8_311e-03,
             2.0_142e-03, 2.1_057e-03, 1.7_395e-03, 4.5_776e-04, -3.9_673e-04,
             4.5_776e-04, 1.0_071e-03, 9.1_553e-05, 4.8_828e-04, 1.1_597e-03,
             7.3_242e-04, 9.4_604e-04, 1.8_005e-03, 1.8_311e-03, 8.8_501e-04,
             4.2_725e-04, 4.8_828e-04, 7.3_242e-04, 1.0_986e-03, 2.1_057e-03])
        # fmt: on
        a__:	List[Any]     				=      self._load_datasamples(1)
        a__:	Optional[int]     				=      [np.tile(input_audio[0][None]       ,     reps=(2, 1))]
        input_audio[0][1] *= 0.5
        a__:	Optional[Any]     				=      EncodecFeatureExtractor(feature_size=2)
        a__:	List[Any]     				=      feature_extractor(lowercase       ,     return_tensors='pt').input_values
        self.assertEquals(input_values.shape       ,     (1, 2, 9_36_80))
        self.assertTrue(torch.allclose(input_values[0, 0, :30]       ,     lowercase       ,     atol=1e-6))
        self.assertTrue(torch.allclose(input_values[0, 1, :30]       ,     EXPECTED_INPUT_VALUES * 0.5       ,     atol=1e-6))



      def     lowerCamelCase_  (		self)      ->     Any:





        '''simple docstring'''





        a__:	Any     				=      self._load_datasamples(2)
        # would be easier if the stride was like
        a__:	Any     				=      EncodecFeatureExtractor(feature_size=1       ,     chunk_length_s=1       ,     overlap=0.01)

        # pad and trunc raise an error ?
        with self.assertRaisesRegex(
            lowercase       ,     '^Both padding and truncation were set. Make sure you only set one.$'       ,     ):
          a__:	Optional[Any]     				=      feature_extractor(
              lowercase       ,     padding='max_length'       ,     truncation=lowercase       ,     return_tensors='pt').input_values

        # truncate to chunk
        a__:	Union[str, Any]     				=      feature_extractor(lowercase       ,     truncation=lowercase       ,     return_tensors='pt').input_values
        self.assertEquals(truncated_outputs.shape       ,     (2, 1, 7_15_20))  # 2 chunks

        # force truncate to max_length
        a__:	List[str]     				=      feature_extractor(
            lowercase       ,     truncation=lowercase       ,     max_length=4_80_00       ,     return_tensors='pt').input_values
        self.assertEquals(truncated_outputs.shape       ,     (2, 1, 4_80_00))

        # pad to chunk
        a__:	List[Any]     				=      feature_extractor(lowercase       ,     padding=lowercase       ,     return_tensors='pt').input_values
        self.assertEquals(padded_outputs.shape       ,     (2, 1, 9_52_80))

        # pad to chunk
        a__:	int     				=      feature_extractor(lowercase       ,     return_tensors='pt').input_values
        self.assertEquals(truncated_outputs.shape       ,     (2, 1, 9_52_80))

        # force pad to max length
        a__:	int     				=      feature_extractor(
            lowercase       ,     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(
            lowercase       ,     '^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'       ,     ):
          a__:	Union[str, Any]     				=      feature_extractor(lowercase       ,     padding=lowercase       ,     return_tensors='pt').input_values

        a__:	str     				=      feature_extractor(input_audio[0]       ,     padding=lowercase       ,     return_tensors='pt').input_values
        self.assertEquals(truncated_outputs.shape       ,     (1, 1, 9_36_80))

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

        a__:	Tuple     				=      feature_extractor(input_audio[0]       ,     padding=lowercase       ,     return_tensors='pt').input_values
        self.assertEquals(truncated_outputs.shape       ,     (1, 1, 9_36_80))

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

        a__:	Any     				=      feature_extractor(input_audio[0]       ,     padding=lowercase       ,     return_tensors='pt').input_values
        self.assertEquals(truncated_outputs.shape       ,     (1, 1, 9_36_80))



"""simple docstring"""
import random
import sys

from . import cryptomath_module as cryptomath

lowercase__   			=      (
    r' !"#$%&\'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`'
    r'abcdefghijklmnopqrstuvwxyz{|}~'
)







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )      ->None:
  if mode == "encrypt":
    if key_a == 1:
      sys.exit(
          'The affine cipher becomes weak when key '
          'A is set to 1. Choose different key'  )
    if key_b == 0:
      sys.exit(
          'The affine cipher becomes weak when key '
          'B is set to 0. Choose different key'  )
  if key_a < 0 or key_b < 0 or key_b > len(_SCREAMING_SNAKE_CASE  ) - 1:
    sys.exit(
        'Key A must be greater than 0 and key B must '
        F'be between 0 and {len(_SCREAMING_SNAKE_CASE  ) - 1}.'  )
  if cryptomath.gcd(_SCREAMING_SNAKE_CASE						,	len(_SCREAMING_SNAKE_CASE  )  ) != 1:
    sys.exit(
        F'Key A {key_a} and the symbol set size {len(_SCREAMING_SNAKE_CASE  )} '
        'are not relatively prime. Choose a different key.'  )







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )      ->str:
  a__				,       a__:	Optional[Any]     				=      divmod(_SCREAMING_SNAKE_CASE						,	len(_SCREAMING_SNAKE_CASE  )  )
  check_keys(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	'encrypt'  )
  a__:	Dict     				=      ''
  for symbol in message:
    if symbol in SYMBOLS:
      a__:	int     				=      SYMBOLS.find(_SCREAMING_SNAKE_CASE  )
      cipher_text += SYMBOLS[(sym_index * key_a + key_b) % len(_SCREAMING_SNAKE_CASE  )]
    else:
      cipher_text += symbol
  return cipher_text







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )      ->str:
  a__				,       a__:	List[str]     				=      divmod(_SCREAMING_SNAKE_CASE						,	len(_SCREAMING_SNAKE_CASE  )  )
  check_keys(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	'decrypt'  )
  a__:	List[Any]     				=      ''
  a__:	str     				=      cryptomath.find_mod_inverse(_SCREAMING_SNAKE_CASE						,	len(_SCREAMING_SNAKE_CASE  )  )
  for symbol in message:
    if symbol in SYMBOLS:
      a__:	List[str]     				=      SYMBOLS.find(_SCREAMING_SNAKE_CASE  )
      plain_text += SYMBOLS[
          (sym_index - key_b) * mod_inverse_of_key_a % len(_SCREAMING_SNAKE_CASE  )
      ]
    else:
      plain_text += symbol
  return plain_text







def 		__a  (      )      ->int:
  while True:
    a__:	int     				=      random.randint(2						,	len(_SCREAMING_SNAKE_CASE  )  )
    a__:	List[Any]     				=      random.randint(2						,	len(_SCREAMING_SNAKE_CASE  )  )
    if cryptomath.gcd(_SCREAMING_SNAKE_CASE						,	len(_SCREAMING_SNAKE_CASE  )  ) == 1 and key_b % len(_SCREAMING_SNAKE_CASE  ) != 0:
      return key_b * len(_SCREAMING_SNAKE_CASE  ) + key_b







def 		__a  (      )      ->None:
  a__:	Union[str, Any]     				=      input('Enter message: '  ).strip()
  a__:	List[Any]     				=      int(input('Enter key [2000 - 9000]: '  ).strip()  )
  a__:	List[Any]     				=      input('Encrypt/Decrypt [E/D]: '  ).strip().lower()

  if mode.startswith('e'  ):
    a__:	int     				=      'encrypt'
    a__:	List[str]     				=      encrypt_message(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )
  elif mode.startswith('d'  ):
    a__:	Dict     				=      'decrypt'
    a__:	Any     				=      decrypt_message(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )
  print(F'\n{mode.title()}ed text: \n{translated}'  )


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 # main()


