

"""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"""


import os

a					:List[str]    = {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1_000}




def  _lowercase					(    __lowerCAmelCase							)       ->				int:
      SCREAMING_SNAKE_CASE__   :      Any      		=	0

      SCREAMING_SNAKE_CASE__   :      Dict      		=	0
      while index < len(__lowerCAmelCase							) - 1:
            SCREAMING_SNAKE_CASE__   :      List[Any]      		=	SYMBOLS[numerals[index]]
            SCREAMING_SNAKE_CASE__   :      Dict      		=	SYMBOLS[numerals[index + 1]]
            if current_value < next_value:
                  total_value -= current_value
            else:
                  total_value += current_value
            index += 1
      total_value += SYMBOLS[numerals[index]]

      return total_value




def  _lowercase					(    __lowerCAmelCase							)       ->				str:
      SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	""""""

      SCREAMING_SNAKE_CASE__   :      int      		=	num // 1000
      numerals += m_count * "M"
      num %= 1000

      SCREAMING_SNAKE_CASE__   :      List[str]      		=	num // 100
      if c_count == 9:
            numerals += "CM"
            c_count -= 9
      elif c_count == 4:
            numerals += "CD"
            c_count -= 4
      if c_count >= 5:
            numerals += "D"
            c_count -= 5
      numerals += c_count * "C"
      num %= 100

      SCREAMING_SNAKE_CASE__   :      List[Any]      		=	num // 10
      if x_count == 9:
            numerals += "XC"
            x_count -= 9
      elif x_count == 4:
            numerals += "XL"
            x_count -= 4
      if x_count >= 5:
            numerals += "L"
            x_count -= 5
      numerals += x_count * "X"
      num %= 10

      if num == 9:
            numerals += "IX"
            num -= 9
      elif num == 4:
            numerals += "IV"
            num -= 4
      if num >= 5:
            numerals += "V"
            num -= 5
      numerals += num * "I"

      return numerals




def  _lowercase					(    __lowerCAmelCase = "/p089_roman.txt"							)       ->				int:
      SCREAMING_SNAKE_CASE__   :      int      		=	0

      with open(os.path.dirname(__lowerCAmelCase							) + roman_numerals_filename							) as filea:
            SCREAMING_SNAKE_CASE__   :      str      		=	filea.readlines()

      for line in lines:
            SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	line.strip()
            SCREAMING_SNAKE_CASE__   :      Dict      		=	parse_roman_numerals(__lowerCAmelCase							)
            SCREAMING_SNAKE_CASE__   :      str      		=	generate_roman_numerals(__lowerCAmelCase							)
            savings += len(__lowerCAmelCase							) - len(__lowerCAmelCase							)

      return savings


if __name__ == "__main__":
  print(f'{solution() = }')




