


'''simple docstring'''







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 		_snake_case     (					unittest.TestCase		):






						'''simple docstring'''







						def lowerCAmelCase__			(	self:						Dict  )				->						int:
										__magic_name__					:       Any    							=							"ylacombe/bark-small"
										__magic_name__					:       str    							=							tempfile.mkdtemp()
										__magic_name__					:       str    							=							"en_speaker_1"
										__magic_name__					:       Tuple    							=							"This is a test string"
										__magic_name__					:       Union[str, Any]    							=							"speaker_embeddings_path.json"
										__magic_name__					:       Any    							=							"speaker_embeddings"







						def lowerCAmelCase__			(	self:						Optional[Any]   ,				**__UpperCamelCase:						List[str]  )				->						Tuple:
										return AutoTokenizer.from_pretrained(self.checkpoint   ,				**snake_case__  )







						def lowerCAmelCase__			(	self:						Dict  )				->						Any:
										shutil.rmtree(self.tmpdirname  )







						def lowerCAmelCase__			(	self:						Union[str, Any]  )				->						Optional[int]:
										__magic_name__					:       Optional[Any]    							=							self.get_tokenizer()

										__magic_name__					:       Optional[Any]    							=							BarkProcessor(tokenizer=snake_case__  )

										processor.save_pretrained(self.tmpdirname  )
										__magic_name__					:       Any    							=							BarkProcessor.from_pretrained(self.tmpdirname  )

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







						@slow
						def lowerCAmelCase__			(	self:						Optional[Any]  )				->						Any:
										__magic_name__					:       Tuple    							=							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   ,				)

										__magic_name__					:       Optional[int]    							=							self.get_tokenizer(bos_token="(BOS)"   ,				eos_token="(EOS)"  )

										__magic_name__					:       int    							=							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 lowerCAmelCase__			(	self:						Optional[Any]  )				->						Any:
										__magic_name__					:       Any    							=							BarkProcessor.from_pretrained(
										    pretrained_processor_name_or_path=self.checkpoint   ,				speaker_embeddings_dict_path=self.speaker_embeddings_dict_path   ,				)

										__magic_name__					:       List[Any]    							=							35
										__magic_name__					:       Tuple    							=							2
										__magic_name__					:       Dict    							=							8

										__magic_name__					:       List[str]    							=							{
										    "semantic_prompt": np.ones(snake_case__  ),
										    "coarse_prompt": np.ones((nb_codebooks_coarse, seq_len)  ),
										    "fine_prompt": np.ones((nb_codebooks_total, seq_len)  ),
										}

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

										__magic_name__					:       List[Any]    							=							inputs["history_prompt"]
										for key in voice_preset:
														self.assertListEqual(voice_preset[key].tolist()   ,				processed_voice_preset.get(snake_case__   ,				np.array([]  )  ).tolist()  )

										# test loading voice preset from npz file
										__magic_name__					:       Any    							=							os.path.join(self.tmpdirname   ,				"file.npz"  )
										np.savez(snake_case__   ,				**snake_case__  )
										__magic_name__					:       List[str]    							=							processor(text=self.input_string   ,				voice_preset=snake_case__  )
										__magic_name__					:       Union[str, Any]    							=							inputs["history_prompt"]

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

										# test loading voice preset from the hub
										__magic_name__					:       int    							=							processor(text=self.input_string   ,				voice_preset=self.voice_preset  )







						def lowerCAmelCase__			(	self:						Any  )				->						List[str]:
										__magic_name__					:       Union[str, Any]    							=							self.get_tokenizer()

										__magic_name__					:       Optional[Any]    							=							BarkProcessor(tokenizer=snake_case__  )

										__magic_name__					:       Dict    							=							processor(text=self.input_string  )

										__magic_name__					:       int    							=							tokenizer(
										    self.input_string   ,				padding="max_length"   ,				max_length=256   ,				add_special_tokens=snake_case__   ,				return_attention_mask=snake_case__   ,				return_token_type_ids=snake_case__   ,				)

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

import random






class     UpperCAmelCase						:



   @staticmethod
   def 							_SCREAMING_SNAKE_CASE  (snake_case__						:   str					)						->      tuple[list[int], list[int]]:


          '''simple docstring'''


          snake_case			:  int     							= [ord(snake_case__					) for i in text]
          snake_case			:  Optional[int]     							= []
          snake_case			:  int     							= []
          for i in plain:
                 snake_case			:  List[Any]     							= random.randint(1       ,		3_00					)
                 snake_case			:  List[Any]     							= (i + k) * k
                 cipher.append(snake_case__					)
                 key.append(snake_case__					)
          return cipher, key



   @staticmethod
   def 							_SCREAMING_SNAKE_CASE  (snake_case__						:   list[int]       ,		snake_case__						:   list[int]					)						->      str:


          '''simple docstring'''


          snake_case			:  int     							= []
          for i in range(len(snake_case__					)					):
                 snake_case			:  List[str]     							= int((cipher[i] - (key[i]) ** 2) / key[i]					)
                 plain.append(chr(snake_case__					)					)
          return "".join(snake_case__					)


if __name__ == "__main__":
   __lowerCamelCase,  __lowerCamelCase				=			Onepad().encrypt("""Hello""")
   print(c, k)
   print(Onepad().decrypt(c, k))





