

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   ):

    def UpperCamelCase			(			self :      Dict    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			'ylacombe/bark-small'
     SCREAMING_SNAKE_CASE								=			tempfile.mkdtemp()
     SCREAMING_SNAKE_CASE								=			'en_speaker_1'
     SCREAMING_SNAKE_CASE								=			'This is a test string'
     SCREAMING_SNAKE_CASE								=			'speaker_embeddings_path.json'
     SCREAMING_SNAKE_CASE								=			'speaker_embeddings'

    def UpperCamelCase			(			self :      int							,    **snake_case__ :      Any    ):

     """simple docstring"""






     return AutoTokenizer.from_pretrained(self.checkpoint							,    **snake_case__    )

    def UpperCamelCase			(			self :      Any    ):

     """simple docstring"""






     shutil.rmtree(self.tmpdirname    )

    def UpperCamelCase			(			self :      List[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.get_tokenizer()

     SCREAMING_SNAKE_CASE								=			BarkProcessor(tokenizer=snake_case__    )

     processor.save_pretrained(self.tmpdirname    )
     SCREAMING_SNAKE_CASE								=			BarkProcessor.from_pretrained(self.tmpdirname    )

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

    @slow
    def UpperCamelCase			(			self :      str    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			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							,    )

     SCREAMING_SNAKE_CASE								=			self.get_tokenizer(bos_token='(BOS)'							,    eos_token='(EOS)'    )

     SCREAMING_SNAKE_CASE								=			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 UpperCamelCase			(			self :      Union[str, Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			BarkProcessor.from_pretrained(
         pretrained_processor_name_or_path=self.checkpoint							,    speaker_embeddings_dict_path=self.speaker_embeddings_dict_path							,    )

     SCREAMING_SNAKE_CASE								=			3_5
     SCREAMING_SNAKE_CASE								=			2
     SCREAMING_SNAKE_CASE								=			8

     SCREAMING_SNAKE_CASE								=			{
         '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
     SCREAMING_SNAKE_CASE								=			processor(text=self.input_string							,    voice_preset=snake_case__    )

     SCREAMING_SNAKE_CASE								=			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
     SCREAMING_SNAKE_CASE								=			os.path.join(self.tmpdirname							,    'file.npz'    )
     np.savez(snake_case__							,    **snake_case__    )
     SCREAMING_SNAKE_CASE								=			processor(text=self.input_string							,    voice_preset=snake_case__    )
     SCREAMING_SNAKE_CASE								=			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
     SCREAMING_SNAKE_CASE								=			processor(text=self.input_string							,    voice_preset=self.voice_preset    )




    def UpperCamelCase			(			self :      Union[str, Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.get_tokenizer()

     SCREAMING_SNAKE_CASE								=			BarkProcessor(tokenizer=snake_case__    )

     SCREAMING_SNAKE_CASE								=			processor(text=self.input_string    )

     SCREAMING_SNAKE_CASE								=			tokenizer(
         self.input_string							,    padding='max_length'							,    max_length=2_5_6							,    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()    )




def        __lowerCAmelCase    (       _UpperCamelCase		:  int = 10_00	)		->							int:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			2**power
 SCREAMING_SNAKE_CASE								=			str(_UpperCamelCase	)
 SCREAMING_SNAKE_CASE								=			list(_UpperCamelCase	)
 SCREAMING_SNAKE_CASE								=			0

 for i in list_num:
  sum_of_num += int(_UpperCamelCase	)

 return sum_of_num


if __name__ == "__main__":
      a_    :						List[str]    		=      int(input("Enter the power of 2: ").strip())
      print("2 ^ ", power, " = ", 2**power)
      a_    :						int    		=      solution(power)
      print("Sum of the digits is: ", result)

