

'''simple docstring'''







import json
import os
from typing import Optional

import numpy as np

from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer


__lowerCamelCase		       =    logging.get_logger(__name__)


class 					A__      (      _snake_case			):
    lowercase						=		"AutoTokenizer"
    lowercase						=		["tokenizer"]

    lowercase						=		{
        "semantic_prompt": 1,
        "coarse_prompt": 2,
        "fine_prompt": 2,
    }



    def __init__(					self		,				UpperCamelCase__		,				UpperCamelCase__=None       )						->      Tuple:





           '''simple docstring'''
           super().__init__(UpperCamelCase__       )

           A_						=      speaker_embeddings



    @classmethod
    def 					snake_case_			(					cls		,				UpperCamelCase__		,				UpperCamelCase__="speaker_embeddings_path.json"		,				**UpperCamelCase__       )						->      Optional[Any]:





           '''simple docstring'''

           if speaker_embeddings_dict_path is not None:
                  A_						=      get_file_from_repo(
                      UpperCamelCase__		,				UpperCamelCase__		,				subfolder=kwargs.pop("""subfolder"""		,				UpperCamelCase__       )		,				cache_dir=kwargs.pop("""cache_dir"""		,				UpperCamelCase__       )		,				force_download=kwargs.pop("""force_download"""		,				UpperCamelCase__       )		,				proxies=kwargs.pop("""proxies"""		,				UpperCamelCase__       )		,				resume_download=kwargs.pop("""resume_download"""		,				UpperCamelCase__       )		,				local_files_only=kwargs.pop("""local_files_only"""		,				UpperCamelCase__       )		,				use_auth_token=kwargs.pop("""use_auth_token"""		,				UpperCamelCase__       )		,				revision=kwargs.pop("""revision"""		,				UpperCamelCase__       )		,				)
                  if speaker_embeddings_path is None:
                         logger.warning(
                             f'''`{os.path.join(UpperCamelCase__		,				UpperCamelCase__       )}` does not exists
                    , no preloaded speaker embeddings will be used - Make sure to provide a correct path to the json
                    dictionnary if wanted, otherwise set `speaker_embeddings_dict_path=None`.'''       )
                         A_						=      None
                  else:
                         with open(UpperCamelCase__       ) as speaker_embeddings_json:
                                A_						=      json.load(UpperCamelCase__       )
           else:
                  A_						=      None

           A_						=      AutoTokenizer.from_pretrained(UpperCamelCase__		,				**UpperCamelCase__       )

           return cls(tokenizer=UpperCamelCase__		,				speaker_embeddings=UpperCamelCase__       )



    def 					snake_case_			(					self		,				UpperCamelCase__		,				UpperCamelCase__="speaker_embeddings_path.json"		,				UpperCamelCase__="speaker_embeddings"		,				UpperCamelCase__ = False		,				**UpperCamelCase__		,				)						->      Optional[int]:





           '''simple docstring'''
           if self.speaker_embeddings is not None:
                  os.makedirs(os.path.join(UpperCamelCase__		,				UpperCamelCase__		,				"""v2"""       )		,				exist_ok=UpperCamelCase__       )

                  A_						=      {}

                  A_						=      save_directory

                  for prompt_key in self.speaker_embeddings:
                         if prompt_key != "repo_or_path":
                                A_						=      self._load_voice_preset(UpperCamelCase__       )

                                A_						=      {}
                                for key in self.speaker_embeddings[prompt_key]:
                                       np.save(
                                           os.path.join(
                                               embeddings_dict["""repo_or_path"""]		,				UpperCamelCase__		,				f'''{prompt_key}_{key}'''       )		,				voice_preset[key]		,				allow_pickle=UpperCamelCase__		,				)
                                       A_						=      os.path.join(UpperCamelCase__		,				f'''{prompt_key}_{key}.npy'''       )

                                A_						=      tmp_dict

                  with open(os.path.join(UpperCamelCase__		,				UpperCamelCase__       )		,				"""w"""       ) as fp:
                         json.dump(UpperCamelCase__		,				UpperCamelCase__       )

           super().save_pretrained(UpperCamelCase__		,				UpperCamelCase__		,				**UpperCamelCase__       )



    def 					snake_case_			(					self		,				UpperCamelCase__ = None		,				**UpperCamelCase__       )						->      Any:





           '''simple docstring'''
           A_						=      self.speaker_embeddings[voice_preset]

           A_						=      {}
           for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
                  if key not in voice_preset_paths:
                         raise ValueError(
                             f'''Voice preset unrecognized, missing {key} as a key in self.speaker_embeddings[{voice_preset}].'''       )

                  A_						=      get_file_from_repo(
                      self.speaker_embeddings.get("""repo_or_path"""		,				"""/"""       )		,				voice_preset_paths[key]		,				subfolder=kwargs.pop("""subfolder"""		,				UpperCamelCase__       )		,				cache_dir=kwargs.pop("""cache_dir"""		,				UpperCamelCase__       )		,				force_download=kwargs.pop("""force_download"""		,				UpperCamelCase__       )		,				proxies=kwargs.pop("""proxies"""		,				UpperCamelCase__       )		,				resume_download=kwargs.pop("""resume_download"""		,				UpperCamelCase__       )		,				local_files_only=kwargs.pop("""local_files_only"""		,				UpperCamelCase__       )		,				use_auth_token=kwargs.pop("""use_auth_token"""		,				UpperCamelCase__       )		,				revision=kwargs.pop("""revision"""		,				UpperCamelCase__       )		,				)
                  if path is None:
                         raise ValueError(
                             f'''`{os.path.join(self.speaker_embeddings.get("repo_or_path"		,				"/"       )		,				voice_preset_paths[key]       )}` does not exists
                    , no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}
                    embeddings.'''       )

                  A_						=      np.load(UpperCamelCase__       )

           return voice_preset_dict



    def 					snake_case_			(					self		,				UpperCamelCase__ = None       )						->      int:





           '''simple docstring'''
           for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
                  if key not in voice_preset:
                         raise ValueError(f'''Voice preset unrecognized, missing {key} as a key.'''       )

                  if not isinstance(voice_preset[key]		,				np.ndarray       ):
                         raise ValueError(f'''{key} voice preset must be a {str(self.preset_shape[key]       )}D ndarray.'''       )

                  if len(voice_preset[key].shape       ) != self.preset_shape[key]:
                         raise ValueError(f'''{key} voice preset must be a {str(self.preset_shape[key]       )}D ndarray.'''       )







    def __call__(					self		,				UpperCamelCase__=None		,				UpperCamelCase__=None		,				UpperCamelCase__="pt"		,				UpperCamelCase__=256		,				UpperCamelCase__=False		,				UpperCamelCase__=True		,				UpperCamelCase__=False		,				**UpperCamelCase__		,				)						->      List[Any]:





           '''simple docstring'''
           if voice_preset is not None and not isinstance(UpperCamelCase__		,				UpperCamelCase__       ):
                  if (
                      isinstance(UpperCamelCase__		,				UpperCamelCase__       )
                      and self.speaker_embeddings is not None
                      and voice_preset in self.speaker_embeddings
                  ):
                         A_						=      self._load_voice_preset(UpperCamelCase__       )

                  else:
                         if isinstance(UpperCamelCase__		,				UpperCamelCase__       ) and not voice_preset.endswith(""".npz"""       ):
                                A_						=      voice_preset + """.npz"""

                         A_						=      np.load(UpperCamelCase__       )

           if voice_preset is not None:
                  self._validate_voice_preset_dict(UpperCamelCase__		,				**UpperCamelCase__       )
                  A_						=      BatchFeature(data=UpperCamelCase__		,				tensor_type=UpperCamelCase__       )

           A_						=      self.tokenizer(
               UpperCamelCase__		,				return_tensors=UpperCamelCase__		,				padding="""max_length"""		,				max_length=UpperCamelCase__		,				return_attention_mask=UpperCamelCase__		,				return_token_type_ids=UpperCamelCase__		,				add_special_tokens=UpperCamelCase__		,				**UpperCamelCase__		,				)

           if voice_preset is not None:
                  A_						=      voice_preset

           return encoded_text



'''simple docstring'''







import os

__lowerCamelCase		       =    {'''I''': 1, '''V''': 5, '''X''': 10, '''L''': 50, '''C''': 100, '''D''': 500, '''M''': 1000}
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			int:
       A_						=      0

       A_						=      0
       while index < len(UpperCAmelCase__				) - 1:
              A_						=      SYMBOLS[numerals[index]]
              A_						=      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       UpperCAmelCase__    (      UpperCAmelCase__				)			->			str:
       A_						=      """"""

       A_						=      num // 10_00
       numerals += m_count * "M"
       num %= 10_00

       A_						=      num // 1_00
       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 %= 1_00

       A_						=      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       UpperCAmelCase__    (      UpperCAmelCase__ = "/p089_roman.txt"				)			->			int:
       A_						=      0

       with open(os.path.dirname(UpperCAmelCase__				) + roman_numerals_filename				) as filea:
              A_						=      filea.readlines()

       for line in lines:
              A_						=      line.strip()
              A_						=      parse_roman_numerals(UpperCAmelCase__				)
              A_						=      generate_roman_numerals(UpperCAmelCase__				)
              savings += len(UpperCAmelCase__				) - len(UpperCAmelCase__				)

       return savings


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