




import json
import os
import shutil
import tempfile
import unittest

from transformers import is_speech_available, is_torch_available
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_torch
from transformers.utils import FEATURE_EXTRACTOR_NAME


if is_speech_available() and is_torch_available():
  from transformers import SpeechTaFeatureExtractor, SpeechTaProcessor

  from .test_feature_extraction_speechta import floats_list


__UpperCamelCase      : Union[str, Any]					      =   get_tests_dir("fixtures/test_sentencepiece_bpe_char.model")

@require_torch
class 	__lowerCAmelCase			(			unittest.TestCase				):
       def    lowerCamelCase__							(		self		:Tuple       ):







        '''simple docstring'''







        a		       =							tempfile.mkdtemp()

        a		       =							SpeechTaTokenizer(__magic_name__       )
        tokenizer.save_pretrained(self.tmpdirname       )

        a		       =							{
            """feature_size""": 1,
            """padding_value""": 0.0,
            """sampling_rate""": 1_6000,
            """do_normalize""": False,
            """num_mel_bins""": 80,
            """hop_length""": 16,
            """win_length""": 64,
            """win_function""": """hann_window""",
            """fmin""": 80,
            """fmax""": 7600,
            """mel_floor""": 1E-1_0,
            """reduction_factor""": 2,
            """return_attention_mask""": True,
        }

        a		       =							os.path.join(self.tmpdirname	,   __magic_name__       )
        with open(self.feature_extraction_file	,   """w"""	,   encoding="""utf-8"""       ) as fp:
         fp.write(json.dumps(__magic_name__       ) + """\n"""       )
       def    lowerCamelCase__							(		self		:Optional[Any]	,   **__magic_name__		:Dict       ):







        '''simple docstring'''







        return SpeechTaTokenizer.from_pretrained(self.tmpdirname	,   **__magic_name__       )
       def    lowerCamelCase__							(		self		:int	,   **__magic_name__		:List[Any]       ):







        '''simple docstring'''







        return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname	,   **__magic_name__       )
       def    lowerCamelCase__							(		self		:Optional[Any]       ):







        '''simple docstring'''







        shutil.rmtree(self.tmpdirname       )
       def    lowerCamelCase__							(		self		:List[Any]       ):







        '''simple docstring'''







        a		       =							self.get_tokenizer()
        a		       =							self.get_feature_extractor()

        a		       =							SpeechTaProcessor(tokenizer=__magic_name__	,   feature_extractor=__magic_name__       )

        processor.save_pretrained(self.tmpdirname       )
        a		       =							SpeechTaProcessor.from_pretrained(self.tmpdirname       )

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

        self.assertEqual(processor.feature_extractor.to_json_string()	,   feature_extractor.to_json_string()       )
        self.assertIsInstance(processor.feature_extractor	,   __magic_name__       )
       def    lowerCamelCase__							(		self		:str       ):







        '''simple docstring'''







        a		       =							SpeechTaProcessor(tokenizer=self.get_tokenizer()	,   feature_extractor=self.get_feature_extractor()       )
        processor.save_pretrained(self.tmpdirname       )

        a		       =							self.get_tokenizer(bos_token="""(BOS)"""	,   eos_token="""(EOS)"""       )
        a		       =							self.get_feature_extractor(do_normalize=__magic_name__	,   padding_value=1.0       )

        a		       =							SpeechTaProcessor.from_pretrained(
            self.tmpdirname	,   bos_token="""(BOS)"""	,   eos_token="""(EOS)"""	,   do_normalize=__magic_name__	,   padding_value=1.0       )

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

        self.assertEqual(processor.feature_extractor.to_json_string()	,   feature_extractor_add_kwargs.to_json_string()       )
        self.assertIsInstance(processor.feature_extractor	,   __magic_name__       )
       def    lowerCamelCase__							(		self		:Union[str, Any]       ):







        '''simple docstring'''







        a		       =							self.get_feature_extractor()
        a		       =							self.get_tokenizer()

        a		       =							SpeechTaProcessor(tokenizer=__magic_name__	,   feature_extractor=__magic_name__       )

        a		       =							floats_list((3, 1000)       )

        a		       =							feature_extractor(audio=__magic_name__	,   return_tensors="""np"""       )
        a		       =							processor(audio=__magic_name__	,   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    lowerCamelCase__							(		self		:List[Any]       ):







        '''simple docstring'''







        a		       =							self.get_feature_extractor()
        a		       =							self.get_tokenizer()

        a		       =							SpeechTaProcessor(tokenizer=__magic_name__	,   feature_extractor=__magic_name__       )

        a		       =							floats_list((3, 1000)       )

        a		       =							feature_extractor(audio_target=__magic_name__	,   return_tensors="""np"""       )
        a		       =							processor(audio_target=__magic_name__	,   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    lowerCamelCase__							(		self		:Union[str, Any]       ):







        '''simple docstring'''







        a		       =							self.get_feature_extractor()
        a		       =							self.get_tokenizer()

        a		       =							SpeechTaProcessor(tokenizer=__magic_name__	,   feature_extractor=__magic_name__       )

        a		       =							"""This is a test string"""

        a		       =							processor(text=__magic_name__       )
        a		       =							tokenizer(__magic_name__       )

        for key in encoded_tok.keys():
         self.assertListEqual(encoded_tok[key]	,   encoded_processor[key]       )
       def    lowerCamelCase__							(		self		:List[str]       ):







        '''simple docstring'''







        a		       =							self.get_feature_extractor()
        a		       =							self.get_tokenizer()

        a		       =							SpeechTaProcessor(tokenizer=__magic_name__	,   feature_extractor=__magic_name__       )

        a		       =							"""This is a test string"""

        a		       =							processor(text_target=__magic_name__       )
        a		       =							tokenizer(__magic_name__       )

        for key in encoded_tok.keys():
         self.assertListEqual(encoded_tok[key]	,   encoded_processor[key]       )
       def    lowerCamelCase__							(		self		:Union[str, Any]       ):







        '''simple docstring'''







        a		       =							self.get_feature_extractor()
        a		       =							self.get_tokenizer()

        a		       =							SpeechTaProcessor(tokenizer=__magic_name__	,   feature_extractor=__magic_name__       )

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

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

        self.assertListEqual(__magic_name__	,   __magic_name__       )





       def    lowerCamelCase__							(		self		:Any       ):







        '''simple docstring'''







        a		       =							self.get_feature_extractor()
        a		       =							self.get_tokenizer()

        a		       =							SpeechTaProcessor(tokenizer=__magic_name__	,   feature_extractor=__magic_name__       )

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







__UpperCamelCase      : Union[str, Any]					      =   {
    "A": ("ABCDEFGHIJKLM", "NOPQRSTUVWXYZ"),
    "B": ("ABCDEFGHIJKLM", "NOPQRSTUVWXYZ"),
    "C": ("ABCDEFGHIJKLM", "ZNOPQRSTUVWXY"),
    "D": ("ABCDEFGHIJKLM", "ZNOPQRSTUVWXY"),
    "E": ("ABCDEFGHIJKLM", "YZNOPQRSTUVWX"),
    "F": ("ABCDEFGHIJKLM", "YZNOPQRSTUVWX"),
    "G": ("ABCDEFGHIJKLM", "XYZNOPQRSTUVW"),
    "H": ("ABCDEFGHIJKLM", "XYZNOPQRSTUVW"),
    "I": ("ABCDEFGHIJKLM", "WXYZNOPQRSTUV"),
    "J": ("ABCDEFGHIJKLM", "WXYZNOPQRSTUV"),
    "K": ("ABCDEFGHIJKLM", "VWXYZNOPQRSTU"),
    "L": ("ABCDEFGHIJKLM", "VWXYZNOPQRSTU"),
    "M": ("ABCDEFGHIJKLM", "UVWXYZNOPQRST"),
    "N": ("ABCDEFGHIJKLM", "UVWXYZNOPQRST"),
    "O": ("ABCDEFGHIJKLM", "TUVWXYZNOPQRS"),
    "P": ("ABCDEFGHIJKLM", "TUVWXYZNOPQRS"),
    "Q": ("ABCDEFGHIJKLM", "STUVWXYZNOPQR"),
    "R": ("ABCDEFGHIJKLM", "STUVWXYZNOPQR"),
    "S": ("ABCDEFGHIJKLM", "RSTUVWXYZNOPQ"),
    "T": ("ABCDEFGHIJKLM", "RSTUVWXYZNOPQ"),
    "U": ("ABCDEFGHIJKLM", "QRSTUVWXYZNOP"),
    "V": ("ABCDEFGHIJKLM", "QRSTUVWXYZNOP"),
    "W": ("ABCDEFGHIJKLM", "PQRSTUVWXYZNO"),
    "X": ("ABCDEFGHIJKLM", "PQRSTUVWXYZNO"),
    "Y": ("ABCDEFGHIJKLM", "OPQRSTUVWXYZN"),
    "Z": ("ABCDEFGHIJKLM", "OPQRSTUVWXYZN"),
}





def 	__A	(			__lowerCamelCase					)   -> list[tuple[str, str]]:
	return [alphabet[char] for char in key.upper()]





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase					)   -> str:
	a		       =							""""""
	a		       =							0
	a		       =							generate_table(__lowerCamelCase					)
	for char in words.upper():
		cipher += get_opponent(table[count]							,    __lowerCamelCase					)
		a		       =							(count + 1) % len(__lowerCamelCase					)
	return cipher





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase					)   -> str:
	return encrypt(__lowerCamelCase							,    __lowerCamelCase					)





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase					)   -> tuple[int, int]:
	a		       =							0 if char in table[0] else 1
	a		       =							table[row].index(__lowerCamelCase					)
	return row, col





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase					)   -> str:
	a       ,				a		       =							get_position(__lowerCamelCase							,    char.upper()					)
	if row == 1:
		return table[0][col]
	else:
		return table[1][col] if row == 0 else char


if __name__ == "__main__":
		import doctest

		doctest.testmod()  # Fist ensure that all our tests are passing...
		__UpperCamelCase      : int					      =   input("Enter key: ").strip()
		__UpperCamelCase      : int					      =   input("Enter text to encrypt: ").strip()
		__UpperCamelCase      : Optional[Any]					      =   encrypt(key, text)

		print(F'Encrypted: {cipher_text}')
		print(F'Decrypted with key: {decrypt(key, cipher_text)}')

