

"""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      __a							(unittest.TestCase):






     '''simple docstring'''

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


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      str      		=	"""ylacombe/bark-small"""
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	tempfile.mkdtemp()
           SCREAMING_SNAKE_CASE__   :      Dict      		=	"""en_speaker_1"""
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	"""This is a test string"""
           SCREAMING_SNAKE_CASE__   :      str      		=	"""speaker_embeddings_path.json"""
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	"""speaker_embeddings"""

     def 					_a			(						self	,    **_a  )  ->      List[str]:


           """simple docstring"""


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

     def 					_a			(						self  )  ->      int:


           """simple docstring"""


           shutil.rmtree(self.tmpdirname  )

     def 					_a			(						self  )  ->      str:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      str      		=	self.get_tokenizer()

           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	BarkProcessor(tokenizer=_a  )

           processor.save_pretrained(self.tmpdirname  )
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	BarkProcessor.from_pretrained(self.tmpdirname  )

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

     @slow
     def 					_a			(						self  )  ->      Union[str, Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[str]      		=	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__   :      Tuple      		=	self.get_tokenizer(bos_token="""(BOS)"""	,    eos_token="""(EOS)"""  )

           SCREAMING_SNAKE_CASE__   :      str      		=	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 					_a			(						self  )  ->      Any:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	BarkProcessor.from_pretrained(
               pretrained_processor_name_or_path=self.checkpoint	,    speaker_embeddings_dict_path=self.speaker_embeddings_dict_path	,    )

           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	35
           SCREAMING_SNAKE_CASE__   :      Any      		=	2
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	8

           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	{
               """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
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	processor(text=self.input_string	,    voice_preset=_a  )

           SCREAMING_SNAKE_CASE__   :      Any      		=	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
           SCREAMING_SNAKE_CASE__   :      str      		=	os.path.join(self.tmpdirname	,    """file.npz"""  )
           np.savez(_a	,    **_a  )
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	processor(text=self.input_string	,    voice_preset=_a  )
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	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
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	processor(text=self.input_string	,    voice_preset=self.voice_preset  )





     def 					_a			(						self  )  ->      Dict:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[str]      		=	self.get_tokenizer()

           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	BarkProcessor(tokenizer=_a  )

           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	processor(text=self.input_string  )

           SCREAMING_SNAKE_CASE__   :      Dict      		=	tokenizer(
               self.input_string	,    padding="""max_length"""	,    max_length=256	,    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"""


a					:List[str]    = [
    (1_000, "M"),
    (900, "CM"),
    (500, "D"),
    (400, "CD"),
    (100, "C"),
    (90, "XC"),
    (50, "L"),
    (40, "XL"),
    (10, "X"),
    (9, "IX"),
    (5, "V"),
    (4, "IV"),
    (1, "I"),
]




def  _lowercase					(    __lowerCAmelCase							)       ->				int:
      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	{"""I""": 1, """V""": 5, """X""": 10, """L""": 50, """C""": 100, """D""": 500, """M""": 1000}
      SCREAMING_SNAKE_CASE__   :      List[Any]      		=	0
      SCREAMING_SNAKE_CASE__   :      List[str]      		=	0
      while place < len(__lowerCAmelCase							):
            if (place + 1 < len(__lowerCAmelCase							)) and (vals[roman[place]] < vals[roman[place + 1]]):
                  total += vals[roman[place + 1]] - vals[roman[place]]
                  place += 2
            else:
                  total += vals[roman[place]]
                  place += 1
      return total




def  _lowercase					(    __lowerCAmelCase							)       ->				str:
      SCREAMING_SNAKE_CASE__   :      Any      		=	[]
      for arabic, roman in ROMAN:
            ((SCREAMING_SNAKE_CASE__)		,		(SCREAMING_SNAKE_CASE__))   :      List[str]      		=	divmod(__lowerCAmelCase  ,					__lowerCAmelCase							)
            result.append(roman * factor							)
            if number == 0:
                  break
      return "".join(__lowerCAmelCase							)


if __name__ == "__main__":
  import doctest

  doctest.testmod()




