

import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow

@require_torch
class 							UpperCAmelCase_						(     unittest.TestCase  ):
							"""simple docstring"""







							def 							__lowercase							(     self			)       ->       List[str]:
												_a      :       Optional[Any]   =						'''ylacombe/bark-small'''
												_a      :       Optional[int]   =						tempfile.mkdtemp()
												_a      :       Optional[Any]   =						'''en_speaker_1'''
												_a      :       Dict   =						'''This is a test string'''
												_a      :       List[str]   =						'''speaker_embeddings_path.json'''
												_a      :       int   =						'''speaker_embeddings'''


							def 							__lowercase							(     self			,				**_a			)       ->       Union[str, Any]:
												return AutoTokenizer.from_pretrained(self.checkpoint			,				**__lowercase			)


							def 							__lowercase							(     self			)       ->       int:
												shutil.rmtree(self.tmpdirname			)


							def 							__lowercase							(     self			)       ->       Any:
												_a      :       Tuple   =						self.get_tokenizer()

												_a      :       str   =						BarkProcessor(tokenizer=__lowercase			)

												processor.save_pretrained(self.tmpdirname			)
												_a      :       Tuple   =						BarkProcessor.from_pretrained(self.tmpdirname			)

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


							@slow
							def 							__lowercase							(     self			)       ->       str:
												_a      :       Optional[int]   =						BarkProcessor.from_pretrained(
												    pretrained_processor_name_or_path=self.checkpoint			,				speaker_embeddings_dict_path=self.speaker_embeddings_dict_path			,				)
												processor.save_pretrained(
												    self.tmpdirname			,				speaker_embeddings_dict_path=self.speaker_embeddings_dict_path			,				speaker_embeddings_directory=self.speaker_embeddings_directory			,				)

												_a      :       Union[str, Any]   =						self.get_tokenizer(bos_token='''(BOS)'''			,				eos_token='''(EOS)'''			)

												_a      :       Dict   =						BarkProcessor.from_pretrained(
												    self.tmpdirname			,				self.speaker_embeddings_dict_path			,				bos_token='''(BOS)'''			,				eos_token='''(EOS)'''			,				)

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


							def 							__lowercase							(     self			)       ->       Any:
												_a      :       Optional[Any]   =						BarkProcessor.from_pretrained(
												    pretrained_processor_name_or_path=self.checkpoint			,				speaker_embeddings_dict_path=self.speaker_embeddings_dict_path			,				)

												_a      :       List[Any]   =						3_5
												_a      :       Dict   =						2
												_a      :       Optional[Any]   =						8

												_a      :       Any   =						{
												    '''semantic_prompt''': np.ones(__lowercase			),
												    '''coarse_prompt''': np.ones((nb_codebooks_coarse, seq_len)			),
												    '''fine_prompt''': np.ones((nb_codebooks_total, seq_len)			),
												}

												# test providing already loaded voice_preset
												_a      :       str   =						processor(text=self.input_string			,				voice_preset=__lowercase			)

												_a      :       List[str]   =						inputs['''history_prompt''']
												for key in voice_preset:
																	self.assertListEqual(voice_preset[key].tolist()			,				processed_voice_preset.get(__lowercase			,				np.array([]			)			).tolist()			)

												# test loading voice preset from npz file
												_a      :       Union[str, Any]   =						os.path.join(self.tmpdirname			,				'''file.npz'''			)
												np.savez(__lowercase			,				**__lowercase			)
												_a      :       Union[str, Any]   =						processor(text=self.input_string			,				voice_preset=__lowercase			)
												_a      :       Optional[int]   =						inputs['''history_prompt''']

												for key in voice_preset:
																	self.assertListEqual(voice_preset[key].tolist()			,				processed_voice_preset.get(__lowercase			,				np.array([]			)			).tolist()			)

												# test loading voice preset from the hub
												_a      :       List[Any]   =						processor(text=self.input_string			,				voice_preset=self.voice_preset			)




							def 							__lowercase							(     self			)       ->       int:
												_a      :       str   =						self.get_tokenizer()

												_a      :       List[Any]   =						BarkProcessor(tokenizer=__lowercase			)

												_a      :       List[Any]   =						processor(text=self.input_string			)

												_a      :       Tuple   =						tokenizer(
												    self.input_string			,				padding='''max_length'''			,				max_length=2_5_6			,				add_special_tokens=__lowercase			,				return_attention_mask=__lowercase			,				return_token_type_ids=__lowercase			,				)

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






'''simple docstring'''

import string

def 					__UpperCamelCase				(						lowercase__    :	str			):






     '''simple docstring'''





     for key in range(len(string.ascii_uppercase			)			):
          __lowercase      		=''
          for symbol in message:
               if symbol in string.ascii_uppercase:
                    __lowercase      		=string.ascii_uppercase.find(lowercase__			)
                    __lowercase      		=num - key
                    if num < 0:
                         __lowercase      		=num + len(string.ascii_uppercase			)
                    __lowercase      		=translated + string.ascii_uppercase[num]
               else:
                    __lowercase      		=translated + symbol
          print(F'''Decryption using Key #{key}: {translated}'''			)

def 					__UpperCamelCase				(						):






     '''simple docstring'''





     __lowercase      		=input('Encrypted message: '			)
     __lowercase      		=message.upper()
     decrypt(lowercase__			)


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       main()


