

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 			__lowerCAmelCase					( unittest.TestCase							):




  def   A__					(    self )  ->			Union[str, Any]:
        '''simple docstring'''






        _lowercase     	='<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(' ' )
        _lowercase     	=dict(zip(lowerCamelCase__							,       range(len(lowerCamelCase__ ) ) ) )

        _lowercase     	={
            'pad_token': '<pad>',
            'unk_token': '<unk>',
            'bos_token': '<s>',
            'eos_token': '</s>',
        }
        _lowercase     	={
            'feature_size': 1,
            'padding_value': 0.0,
            'sampling_rate': 16_000,
            'return_attention_mask': False,
            'do_normalize': True,
        }

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

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




  def   A__					(    self							,       **lowerCAmelCase )  ->			int:
        '''simple docstring'''






        _lowercase     	=self.add_kwargs_tokens_map.copy()
        kwargs.update(lowerCamelCase__ )
        return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname							,       **lowerCamelCase__ )




  def   A__					(    self							,       **lowerCAmelCase )  ->			Dict:
        '''simple docstring'''






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




  def   A__					(    self )  ->			Dict:
        '''simple docstring'''






        shutil.rmtree(self.tmpdirname )




  def   A__					(    self )  ->			str:
        '''simple docstring'''






        _lowercase     	=self.get_tokenizer()
        _lowercase     	=self.get_feature_extractor()

        _lowercase     	=WavaVecaProcessor(tokenizer=lowerCamelCase__							,       feature_extractor=lowerCamelCase__ )

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

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

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




  def   A__					(    self )  ->			Tuple:
        '''simple docstring'''






        _lowercase     	=WavaVecaProcessor(tokenizer=self.get_tokenizer()							,       feature_extractor=self.get_feature_extractor() )
        processor.save_pretrained(self.tmpdirname )

        _lowercase     	=self.get_tokenizer(bos_token='(BOS)'							,       eos_token='(EOS)' )
        _lowercase     	=self.get_feature_extractor(do_normalize=lowerCamelCase__							,       padding_value=1.0 )

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

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

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




  def   A__					(    self )  ->			List[str]:
        '''simple docstring'''






        _lowercase     	=self.get_feature_extractor()
        _lowercase     	=self.get_tokenizer()

        _lowercase     	=WavaVecaProcessor(tokenizer=lowerCamelCase__							,       feature_extractor=lowerCamelCase__ )

        _lowercase     	=floats_list((3, 1_000) )

        _lowercase     	=feature_extractor(lowerCamelCase__							,       return_tensors='np' )
        _lowercase     	=processor(lowerCamelCase__							,       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'''






        _lowercase     	=self.get_feature_extractor()
        _lowercase     	=self.get_tokenizer()

        _lowercase     	=WavaVecaProcessor(tokenizer=lowerCamelCase__							,       feature_extractor=lowerCamelCase__ )

        _lowercase     	='This is a test string'

        _lowercase     	=processor(text=lowerCamelCase__ )

        _lowercase     	=tokenizer(lowerCamelCase__ )

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




  def   A__					(    self )  ->			List[Any]:
        '''simple docstring'''






        _lowercase     	=self.get_feature_extractor()
        _lowercase     	=self.get_tokenizer()

        _lowercase     	=WavaVecaProcessor(tokenizer=lowerCamelCase__							,       feature_extractor=lowerCamelCase__ )

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

        _lowercase     	=processor.batch_decode(lowerCamelCase__ )
        _lowercase     	=tokenizer.batch_decode(lowerCamelCase__ )

        self.assertListEqual(lowerCamelCase__							,       lowerCamelCase__ )




  def   A__					(    self )  ->			List[str]:
        '''simple docstring'''






        _lowercase     	=self.get_feature_extractor()
        _lowercase     	=self.get_tokenizer()

        _lowercase     	=WavaVecaProcessor(tokenizer=lowerCamelCase__							,       feature_extractor=lowerCamelCase__ )

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





from __future__ import annotations

import random
import string





class 			__lowerCAmelCase					:




  def __init__(    self							,       lowerCAmelCase = None )  ->			None:
        '''simple docstring'''






        _lowercase     	=passcode or self.__passcode_creator()
        _lowercase     	=self.__make_key_list()
        _lowercase     	=self.__make_shift_key()




  def __str__(    self )  ->			str:
        '''simple docstring'''






        return "".join(self.__passcode )




  def   A__					(    self							,       lowerCAmelCase )  ->			list[int]:
        '''simple docstring'''






        for i in range(1							,       len(lowerCAmelCase )							,       2 ):
              iterlist[i] *= -1
        return iterlist




  def   A__					(    self )  ->			list[str]:
        '''simple docstring'''






        _lowercase     	=string.ascii_letters + string.digits
        _lowercase     	=[random.choice(lowerCAmelCase ) for _ in range(random.randint(10							,       20 ) )]
        return password




  def   A__					(    self )  ->			list[str]:
        '''simple docstring'''






        _lowercase     	=(
            string.ascii_letters + string.digits + string.punctuation + ' \t\n'
        )

        _lowercase     	=[]

        # creates points known as breakpoints to break the key_list_options at those
        # points and pivot each substring
        _lowercase     	=sorted(set(self.__passcode ) )
        _lowercase     	=[]

        # algorithm for creating a new shuffled list, keys_l, out of key_list_options
        for i in key_list_options:
              temp_list.extend(lowerCAmelCase )

              # checking breakpoints at which to pivot temporary sublist and add it into
              # keys_l
              if i in breakpoints or i == key_list_options[-1]:
                    keys_l.extend(temp_list[::-1] )
                    temp_list.clear()

        # returning a shuffled keys_l to prevent brute force guessing of shift key
        return keys_l




  def   A__					(    self )  ->			int:
        '''simple docstring'''






        _lowercase     	=sum(self.__neg_pos([ord(lowerCAmelCase ) for x in self.__passcode] ) )
        return num if num > 0 else len(self.__passcode )




  def   A__					(    self							,       lowerCAmelCase )  ->			str:
        '''simple docstring'''






        _lowercase     	=''

        # decoding shift like Caesar cipher algorithm implementing negative shift or
        # reverse shift or left shift
        for i in encoded_message:
              _lowercase     	=self.__key_list.index(lowerCAmelCase )
              decoded_message += self.__key_list[
                  (position - self.__shift_key) % -len(self.__key_list )
              ]

        return decoded_message




  def   A__					(    self							,       lowerCAmelCase )  ->			str:
        '''simple docstring'''






        _lowercase     	=''

        # encoding shift like Caesar cipher algorithm implementing positive shift or
        # forward shift or right shift
        for i in plaintext:
              _lowercase     	=self.__key_list.index(lowerCAmelCase )
              encoded_message += self.__key_list[
                  (position + self.__shift_key) % len(self.__key_list )
              ]

        return encoded_message
def 					a				(      A__     :       str = "Hello, this is a modified Caesar cipher"					)   ->				str:





      """simple docstring"""



      _lowercase     	=ShuffledShiftCipher()
      return cipa.decrypt(cipa.encrypt(A__					)					)


if __name__ == "__main__":
  import doctest

  doctest.testmod()


