


'''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 ):

 '''simple docstring'''

 def 		UpperCamelCase__  (			self      ):
       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 		UpperCamelCase__  (			self	,   **_UpperCAmelCase      ):
       return AutoTokenizer.from_pretrained(self.checkpoint	,   **__a      )



 def 		UpperCamelCase__  (			self      ):
       shutil.rmtree(self.tmpdirname      )



 def 		UpperCamelCase__  (			self      ):
       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 		UpperCamelCase__  (			self      ):
       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 		UpperCamelCase__  (			self      ):
       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 		UpperCamelCase__  (			self      ):
       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()      )



def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE = 3      ,      SCREAMING_SNAKE_CASE = 7      ,      SCREAMING_SNAKE_CASE = 100_0000      )->						int:




      """simple docstring"""

      snake_case_ =   0
      snake_case_ =   1

      for current_denominator in range(1      ,      limit + 1      ):
            snake_case_ =   current_denominator * numerator // denominator
            if current_denominator % denominator == 0:
                  current_numerator -= 1
            if current_numerator * max_denominator > current_denominator * max_numerator:
                  snake_case_ =   current_numerator
                  snake_case_ =   current_denominator
      return max_numerator


if __name__ == "__main__":
   print(solution(numerator=3, denominator=7, limit=100_0000))