
'''simple docstring'''

import shutil
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SpeechaTextTokenizer, is_speech_available
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, require_torchaudio
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_speech_to_text import floats_list


if is_speech_available():
   from transformers import SpeechaTextFeatureExtractor, SpeechaTextProcessor


__UpperCAmelCase												=   get_tests_dir('fixtures/test_sentencepiece.model')


@require_torch
@require_torchaudio
@require_sentencepiece
class 		_a     (							unittest.TestCase  ):







   """simple docstring"""




   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  List[str]								=		tempfile.mkdtemp()

    SCREAMING_SNAKE_CASE  :  Optional[int]								=		['<s>', '<pad>', '</s>', '<unk>', '▁This', '▁is', '▁a', '▁t', 'est']
    SCREAMING_SNAKE_CASE  :  Any								=		dict(zip(__SCREAMING_SNAKE_CASE    ,range(len(__SCREAMING_SNAKE_CASE      )      )      )      )
    SCREAMING_SNAKE_CASE  :  List[Any]								=		Path(self.tmpdirname      )
    save_json(__SCREAMING_SNAKE_CASE    ,save_dir / VOCAB_FILES_NAMES['vocab_file']      )
    if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
     copyfile(__SCREAMING_SNAKE_CASE    ,save_dir / VOCAB_FILES_NAMES['spm_file']      )

    SCREAMING_SNAKE_CASE  :  Any								=		SpeechaTextTokenizer.from_pretrained(self.tmpdirname      )
    tokenizer.save_pretrained(self.tmpdirname      )

    SCREAMING_SNAKE_CASE  :  int								=		{
        'feature_size': 24,
        'num_mel_bins': 24,
        'padding_value': 0.0,
        'sampling_rate': 16000,
        'return_attention_mask': False,
        'do_normalize': True,
    }
    save_json(__SCREAMING_SNAKE_CASE    ,save_dir / FEATURE_EXTRACTOR_NAME      )






   def 					__a			(       self    ,**__SCREAMING_SNAKE_CASE      ):
    return SpeechaTextTokenizer.from_pretrained(self.tmpdirname    ,**__SCREAMING_SNAKE_CASE      )






   def 					__a			(       self    ,**__SCREAMING_SNAKE_CASE      ):
    return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname    ,**__SCREAMING_SNAKE_CASE      )






   def 					__a			(       self      ):
    shutil.rmtree(self.tmpdirname      )






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		self.get_tokenizer()
    SCREAMING_SNAKE_CASE  :  int								=		self.get_feature_extractor()

    SCREAMING_SNAKE_CASE  :  int								=		SpeechaTextProcessor(tokenizer=__SCREAMING_SNAKE_CASE    ,feature_extractor=__SCREAMING_SNAKE_CASE      )

    processor.save_pretrained(self.tmpdirname      )
    SCREAMING_SNAKE_CASE  :  List[str]								=		SpeechaTextProcessor.from_pretrained(self.tmpdirname      )

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

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






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		SpeechaTextProcessor(
        tokenizer=self.get_tokenizer()    ,feature_extractor=self.get_feature_extractor()      )
    processor.save_pretrained(self.tmpdirname      )

    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		self.get_tokenizer(bos_token='(BOS)'    ,eos_token='(EOS)'      )
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		self.get_feature_extractor(do_normalize=__SCREAMING_SNAKE_CASE    ,padding_value=1.0      )

    SCREAMING_SNAKE_CASE  :  Dict								=		SpeechaTextProcessor.from_pretrained(
        self.tmpdirname    ,bos_token='(BOS)'    ,eos_token='(EOS)'    ,do_normalize=__SCREAMING_SNAKE_CASE    ,padding_value=1.0      )

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

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






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  List[str]								=		self.get_feature_extractor()
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		self.get_tokenizer()

    SCREAMING_SNAKE_CASE  :  Any								=		SpeechaTextProcessor(tokenizer=__SCREAMING_SNAKE_CASE    ,feature_extractor=__SCREAMING_SNAKE_CASE      )

    SCREAMING_SNAKE_CASE  :  Dict								=		floats_list((3, 1000)      )

    SCREAMING_SNAKE_CASE  :  int								=		feature_extractor(__SCREAMING_SNAKE_CASE    ,return_tensors='np'      )
    SCREAMING_SNAKE_CASE  :  int								=		processor(__SCREAMING_SNAKE_CASE    ,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      ):
    SCREAMING_SNAKE_CASE  :  List[str]								=		self.get_feature_extractor()
    SCREAMING_SNAKE_CASE  :  List[str]								=		self.get_tokenizer()

    SCREAMING_SNAKE_CASE  :  str								=		SpeechaTextProcessor(tokenizer=__SCREAMING_SNAKE_CASE    ,feature_extractor=__SCREAMING_SNAKE_CASE      )

    SCREAMING_SNAKE_CASE  :  Any								=		'This is a test string'

    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		processor(text=__SCREAMING_SNAKE_CASE      )

    SCREAMING_SNAKE_CASE  :  Any								=		tokenizer(__SCREAMING_SNAKE_CASE      )

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






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  int								=		self.get_feature_extractor()
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		self.get_tokenizer()

    SCREAMING_SNAKE_CASE  :  List[str]								=		SpeechaTextProcessor(tokenizer=__SCREAMING_SNAKE_CASE    ,feature_extractor=__SCREAMING_SNAKE_CASE      )

    SCREAMING_SNAKE_CASE  :  Optional[int]								=		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

    SCREAMING_SNAKE_CASE  :  Tuple								=		processor.batch_decode(__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  Any								=		tokenizer.batch_decode(__SCREAMING_SNAKE_CASE      )

    self.assertListEqual(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      )






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  int								=		self.get_feature_extractor()
    SCREAMING_SNAKE_CASE  :  List[Any]								=		self.get_tokenizer()

    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		SpeechaTextProcessor(tokenizer=__SCREAMING_SNAKE_CASE    ,feature_extractor=__SCREAMING_SNAKE_CASE      )

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




'''simple docstring'''

import random
import sys

__UpperCAmelCase												=   'ABCDEFGHIJKLMNOPQRSTUVWXYZ'



def        SCREAMING_SNAKE_CASE_  (	)       ->	None:
 SCREAMING_SNAKE_CASE  :  Dict								=		input('Enter message: '							)
 SCREAMING_SNAKE_CASE  :  Dict								=		'LFWOAYUISVKMNXPBDCRJTQEGHZ'
 SCREAMING_SNAKE_CASE  :  Tuple								=		input('Encrypt/Decrypt [e/d]: '							)

 check_valid_key(snake_case_							)

 if resp.lower().startswith('e'							):
  SCREAMING_SNAKE_CASE  :  str								=		'encrypt'
  SCREAMING_SNAKE_CASE  :  Optional[Any]								=		encrypt_message(snake_case_   ,     snake_case_							)
 elif resp.lower().startswith('d'							):
  SCREAMING_SNAKE_CASE  :  List[Any]								=		'decrypt'
  SCREAMING_SNAKE_CASE  :  List[str]								=		decrypt_message(snake_case_   ,     snake_case_							)

 print(f"""\n{mode.title()}ion: \n{translated}"""							)



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	str							)       ->	None:
 SCREAMING_SNAKE_CASE  :  int								=		list(snake_case_							)
 SCREAMING_SNAKE_CASE  :  Optional[Any]								=		list(snake_case_							)
 key_list.sort()
 letters_list.sort()

 if key_list != letters_list:
  sys.exit('Error in the key or symbol set.'							)



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	str   ,     snake_case_				:	str							)       ->	str:
 return translate_message(snake_case_   ,     snake_case_   ,     'encrypt'							)



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	str   ,     snake_case_				:	str							)       ->	str:
 return translate_message(snake_case_   ,     snake_case_   ,     'decrypt'							)



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	str   ,     snake_case_				:	str   ,     snake_case_				:	str							)       ->	str:
 SCREAMING_SNAKE_CASE  :  Any								=		''
 SCREAMING_SNAKE_CASE  :  Optional[Any]								=		LETTERS
 SCREAMING_SNAKE_CASE  :  List[Any]								=		key

 if mode == "decrypt":
  SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE  :  List[Any]								=		chars_b, chars_a

 for symbol in message:
  if symbol.upper() in chars_a:
   SCREAMING_SNAKE_CASE  :  Optional[Any]								=		chars_a.find(symbol.upper()							)
   if symbol.isupper():
    translated += chars_b[sym_index].upper()
   else:
    translated += chars_b[sym_index].lower()
  else:
   translated += symbol

 return translated



def        SCREAMING_SNAKE_CASE_  (	)       ->	str:
 SCREAMING_SNAKE_CASE  :  Dict								=		list(snake_case_							)
 random.shuffle(snake_case_							)
 return "".join(snake_case_							)


if __name__ == "__main__":
   main()


