




'''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      lowerCAmelCase (			unittest.TestCase):


       def    lowerCAmelCase   ( self	)   ->	Union[str, Any]:



            '''simple docstring'''




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


       def    lowerCAmelCase   ( self     ,	**__SCREAMING_SNAKE_CASE	)   ->	str:



            '''simple docstring'''




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


       def    lowerCAmelCase   ( self	)   ->	Any:



            '''simple docstring'''




            shutil.rmtree(self.tmpdirname	)


       def    lowerCAmelCase   ( self	)   ->	Union[str, Any]:



            '''simple docstring'''




            __snake_case            =	self.get_tokenizer()

            __snake_case            =	BarkProcessor(tokenizer=__SCREAMING_SNAKE_CASE	)

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

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


       @slow
       def    lowerCAmelCase   ( self	)   ->	Dict:



            '''simple docstring'''




            __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     ,	)

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

            __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    lowerCAmelCase   ( self	)   ->	Optional[Any]:



            '''simple docstring'''




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

            __snake_case            =	35
            __snake_case            =	2
            __snake_case            =	8

            __snake_case            =	{
                '''semantic_prompt''': np.ones(__SCREAMING_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
            __snake_case            =	processor(text=self.input_string     ,	voice_preset=__SCREAMING_SNAKE_CASE	)

            __snake_case            =	inputs['''history_prompt''']
            for key in voice_preset:
                 self.assertListEqual(voice_preset[key].tolist()     ,	processed_voice_preset.get(__SCREAMING_SNAKE_CASE     ,	np.array([]	)	).tolist()	)

            # test loading voice preset from npz file
            __snake_case            =	os.path.join(self.tmpdirname     ,	'''file.npz'''	)
            np.savez(__SCREAMING_SNAKE_CASE     ,	**__SCREAMING_SNAKE_CASE	)
            __snake_case            =	processor(text=self.input_string     ,	voice_preset=__SCREAMING_SNAKE_CASE	)
            __snake_case            =	inputs['''history_prompt''']

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

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




       def    lowerCAmelCase   ( self	)   ->	Any:



            '''simple docstring'''




            __snake_case            =	self.get_tokenizer()

            __snake_case            =	BarkProcessor(tokenizer=__SCREAMING_SNAKE_CASE	)

            __snake_case            =	processor(text=self.input_string	)

            __snake_case            =	tokenizer(
                self.input_string     ,	padding='''max_length'''     ,	max_length=256     ,	add_special_tokens=__SCREAMING_SNAKE_CASE     ,	return_attention_mask=__SCREAMING_SNAKE_CASE     ,	return_token_type_ids=__SCREAMING_SNAKE_CASE     ,	)

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












'''simple docstring'''



def       _UpperCamelCase			(_lowerCamelCase				:  int						)-> int:




     '''simple docstring'''





     __snake_case            =	[[0 for _ in range(_lowerCamelCase						)] for _ in range(m + 1						)]
     for i in range(m + 1						):
          __snake_case            =	1

     for n in range(m + 1						):
          for k in range(1 ,						_lowerCamelCase						):
               memo[n][k] += memo[n][k - 1]
               if n - k > 0:
                    memo[n][k] += memo[n - k - 1][k]

     return memo[m][m - 1]


if __name__ == "__main__":
     import sys

     if len(sys.argv) == 1:
          try:
               UpperCAmelCase_							:	List[str]	     =		int(input('''Enter a number: ''').strip())
               print(partition(n))
          except ValueError:
               print('''Please enter a number.''')
     else:
          try:
               UpperCAmelCase_							:	Union[str, Any]	     =		int(sys.argv[1])
               print(partition(n))
          except ValueError:
               print('''Please pass a number.''')






