


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 		__magic_name__						(unittest.TestCase				):





     '''simple docstring'''



     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]   ):
       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 	SCREAMING_SNAKE_CASE__						(					self:Any     ,		**_a:List[Any]   ):
       return AutoTokenizer.from_pretrained(self.checkpoint     ,		**_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Any   ):
       shutil.rmtree(self.tmpdirname   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]   ):
       snake_case__						= self.get_tokenizer()

       snake_case__						= BarkProcessor(tokenizer=_a   )

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

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

     @slow
     def 	SCREAMING_SNAKE_CASE__						(					self:str   ):
       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 	SCREAMING_SNAKE_CASE__						(					self:Optional[int]   ):
       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(_a   ),
           '''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=_a   )

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

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

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

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







     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[int]   ):
       snake_case__						= self.get_tokenizer()

       snake_case__						= BarkProcessor(tokenizer=_a   )

       snake_case__						= processor(text=self.input_string   )

       snake_case__						= tokenizer(
           self.input_string     ,		padding='''max_length'''     ,		max_length=2_56     ,		add_special_tokens=_a     ,		return_attention_mask=_a     ,		return_token_type_ids=_a     ,		)

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





"""simple docstring"""
def 				_snake_case      (   __snake_case							:      str						, __snake_case							:      str   ):




   """simple docstring"""







   _lowerCamelCase	:					str   =		len(__snake_case   )
   _lowerCamelCase	:					Union[str, Any]   =		len(__snake_case   )
   _lowerCamelCase	:					int   =		[[False for _ in range(m + 1   )] for _ in range(n + 1   )]
   _lowerCamelCase	:					Union[str, Any]   =		True
   for i in range(__snake_case   ):
      for j in range(m + 1   ):
         if dp[i][j]:
            if j < m and a[i].upper() == b[j]:
               _lowerCamelCase	:					Tuple   =		True
            if a[i].islower():
               _lowerCamelCase	:					Tuple   =		True
   return dp[n][m]


if __name__ == "__main__":
 import doctest

 doctest.testmod()

