from __future__ import annotations







def     A__	(       __A ,			__A						):







    '''simple docstring'''
    _lowerCamelCase    ,				_lowerCamelCase    ,				_lowerCamelCase  :		Any			    =    """""", """""", """"""
    _lowerCamelCase  :		Any			    =    []

    for character in message_part:
        tmp.append(character_to_number[character]						)

    for each in tmp:
        one += each[0]
        two += each[1]
        three += each[2]

    return one + two + three







def     A__	(       __A ,			__A						):







    '''simple docstring'''
    _lowerCamelCase    ,				_lowerCamelCase  :		Union[str, Any]			    =    """""", """"""
    _lowerCamelCase  :		Optional[Any]			    =    []

    for character in message_part:
        this_part += character_to_number[character]

    for digit in this_part:
        tmp += digit
        if len(snake_case_						) == len(snake_case_						):
            result.append(snake_case_						)
            _lowerCamelCase  :		Optional[int]			    =    """"""

    return result[0], result[1], result[2]







def     A__	(       __A ,			__A						):







    '''simple docstring'''
    # Validate message and alphabet, set to upper and remove spaces
    _lowerCamelCase  :		List[Any]			    =    alphabet.replace(""" """ ,			""""""						).upper()
    _lowerCamelCase  :		str			    =    message.replace(""" """ ,			""""""						).upper()

    # Check length and characters
    if len(snake_case_						) != 27:
        raise KeyError("""Length of alphabet has to be 27."""						)
    for each in message:
        if each not in alphabet:
            raise ValueError("""Each message character has to be included in alphabet!"""						)

    # Generate dictionares
    _lowerCamelCase  :		Union[str, Any]			    =    (
        """111""",
        """112""",
        """113""",
        """121""",
        """122""",
        """123""",
        """131""",
        """132""",
        """133""",
        """211""",
        """212""",
        """213""",
        """221""",
        """222""",
        """223""",
        """231""",
        """232""",
        """233""",
        """311""",
        """312""",
        """313""",
        """321""",
        """322""",
        """323""",
        """331""",
        """332""",
        """333""",
    )
    _lowerCamelCase  :		Dict			    =    {}
    _lowerCamelCase  :		int			    =    {}
    for letter, number in zip(snake_case_ ,			snake_case_						):
        _lowerCamelCase  :		List[Any]			    =    number
        _lowerCamelCase  :		List[Any]			    =    letter

    return message, alphabet, character_to_number, number_to_character







def     A__	(       __A ,			__A = "ABCDEFGHIJKLMNOPQRSTUVWXYZ." ,			__A = 5						):







    '''simple docstring'''
    _lowerCamelCase    ,				_lowerCamelCase    ,				_lowerCamelCase    ,				_lowerCamelCase  :		Tuple			    =    __prepare(
        snake_case_ ,			snake_case_						)
    _lowerCamelCase    ,				_lowerCamelCase  :		str			    =    """""", """"""

    for i in range(0 ,			len(snake_case_						) + 1 ,			snake_case_						):
        encrypted_numeric += __encrypt_part(
            message[i : i + period] ,			snake_case_						)

    for i in range(0 ,			len(snake_case_						) ,			3						):
        encrypted += number_to_character[encrypted_numeric[i : i + 3]]

    return encrypted







def     A__	(       __A ,			__A = "ABCDEFGHIJKLMNOPQRSTUVWXYZ." ,			__A = 5						):







    '''simple docstring'''
    _lowerCamelCase    ,				_lowerCamelCase    ,				_lowerCamelCase    ,				_lowerCamelCase  :		List[str]			    =    __prepare(
        snake_case_ ,			snake_case_						)
    _lowerCamelCase  :		Any			    =    []
    _lowerCamelCase  :		List[str]			    =    """"""

    for i in range(0 ,			len(snake_case_						) + 1 ,			snake_case_						):
        _lowerCamelCase    ,				_lowerCamelCase    ,				_lowerCamelCase  :		List[str]			    =    __decrypt_part(message[i : i + period] ,			snake_case_						)

        for j in range(0 ,			len(snake_case_						)						):
            decrypted_numeric.append(a[j] + b[j] + c[j]						)

    for each in decrypted_numeric:
        decrypted += number_to_character[each]

    return decrypted


if __name__ == "__main__":
    lowerCAmelCase      :				Dict     			='DEFEND THE EAST WALL OF THE CASTLE.'
    lowerCAmelCase      :				Optional[int]     			=encrypt_message(msg, "EPSDUCVWYM.ZLKXNBTFGORIJHAQ")
    lowerCAmelCase      :				str     			=decrypt_message(encrypted, "EPSDUCVWYM.ZLKXNBTFGORIJHAQ")
    print(F"""Encrypted: {encrypted}\nDecrypted: {decrypted}""")











import json
import os
import shutil
import tempfile
import unittest

from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaProcessor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_wavaveca import floats_list



class  SCREAMING_SNAKE_CASE_  ( unittest.TestCase			):


  """simple docstring"""





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





       '''simple docstring'''







       __magic_name__				=       '''<pad> <s> </s> <unk> | E T A O N I H S R D L U M W C F G Y P B V K \' X J Q Z'''.split(''' '''						)
       __magic_name__				=       dict(zip(A	,  range(len(A						)						)						)						)

       __magic_name__				=       {
           '''pad_token''': '''<pad>''',
           '''unk_token''': '''<unk>''',
           '''bos_token''': '''<s>''',
           '''eos_token''': '''</s>''',
       }
       __magic_name__				=       {
           '''feature_size''': 1,
           '''padding_value''': 0.0,
           '''sampling_rate''': 1_60_00,
           '''return_attention_mask''': False,
           '''do_normalize''': True,
       }

       __magic_name__				=       tempfile.mkdtemp()
       __magic_name__				=       os.path.join(self.tmpdirname	,  VOCAB_FILES_NAMES['''vocab_file''']						)
       __magic_name__				=       os.path.join(self.tmpdirname	,  A						)
       with open(self.vocab_file	,  '''w'''	,  encoding='''utf-8'''						) as fp:
            fp.write(json.dumps(A						) + '''\n'''						)

       with open(self.feature_extraction_file	,  '''w'''	,  encoding='''utf-8'''						) as fp:
            fp.write(json.dumps(A						) + '''\n'''						)





  def 			__A      (  self	,  **A						)		->   str:





       '''simple docstring'''







       __magic_name__				=       self.add_kwargs_tokens_map.copy()
       kwargs.update(A						)
       return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname	,  **A						)





  def 			__A      (  self	,  **A						)		->   List[Any]:





       '''simple docstring'''







       return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname	,  **A						)





  def 			__A      (  self						)		->   List[Any]:





       '''simple docstring'''







       shutil.rmtree(self.tmpdirname						)





  def 			__A      (  self						)		->   List[Any]:





       '''simple docstring'''







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

       __magic_name__				=       WavaVecaProcessor(tokenizer=A	,  feature_extractor=A						)

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

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

       self.assertEqual(processor.feature_extractor.to_json_string()	,  feature_extractor.to_json_string()						)
       self.assertIsInstance(processor.feature_extractor	,  A						)





  def 			__A      (  self						)		->   Dict:





       '''simple docstring'''







       __magic_name__				=       WavaVecaProcessor(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=A	,  padding_value=1.0						)

       __magic_name__				=       WavaVecaProcessor.from_pretrained(
           self.tmpdirname	,  bos_token='''(BOS)'''	,  eos_token='''(EOS)'''	,  do_normalize=A	,  padding_value=1.0						)

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

       self.assertEqual(processor.feature_extractor.to_json_string()	,  feature_extractor_add_kwargs.to_json_string()						)
       self.assertIsInstance(processor.feature_extractor	,  A						)





  def 			__A      (  self						)		->   Dict:





       '''simple docstring'''







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

       __magic_name__				=       WavaVecaProcessor(tokenizer=A	,  feature_extractor=A						)

       __magic_name__				=       floats_list((3, 10_00)						)

       __magic_name__				=       feature_extractor(A	,  return_tensors='''np'''						)
       __magic_name__				=       processor(A	,  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 			__A      (  self						)		->   Optional[Any]:





       '''simple docstring'''







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

       __magic_name__				=       WavaVecaProcessor(tokenizer=A	,  feature_extractor=A						)

       __magic_name__				=       '''This is a test string'''

       __magic_name__				=       processor(text=A						)

       __magic_name__				=       tokenizer(A						)

       for key in encoded_tok.keys():
            self.assertListEqual(encoded_tok[key]	,  encoded_processor[key]						)





  def 			__A      (  self						)		->   Tuple:





       '''simple docstring'''







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

       __magic_name__				=       WavaVecaProcessor(tokenizer=A	,  feature_extractor=A						)

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

       __magic_name__				=       processor.batch_decode(A						)
       __magic_name__				=       tokenizer.batch_decode(A						)

       self.assertListEqual(A	,  A						)







  def 			__A      (  self						)		->   Dict:





       '''simple docstring'''







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

       __magic_name__				=       WavaVecaProcessor(tokenizer=A	,  feature_extractor=A						)

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