"""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       :			str   	=logging.get_logger(__name__)





class 					_A		(			lowerCAmelCase      ):
      snake_case__     :  str				    =      'AutoTokenizer'
      snake_case__     :  Any				    =      ['tokenizer']

      snake_case__     :  Tuple				    =      {
          'semantic_prompt': 1,
          'coarse_prompt': 2,
          'fine_prompt': 2,
      }

      def __init__(		self						,			__lowerCAmelCase						,			__lowerCAmelCase=None		):







            """simple docstring"""



            super().__init__(__lowerCAmelCase		)

            lowercase								=      speaker_embeddings

      @classmethod
      def 							A__			(		cls						,			__lowerCAmelCase						,			__lowerCAmelCase="speaker_embeddings_path.json"						,			**__lowerCAmelCase		):







            """simple docstring"""



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

            lowercase								=      AutoTokenizer.from_pretrained(__lowerCAmelCase						,			**__lowerCAmelCase		)

            return cls(tokenizer=__lowerCAmelCase						,			speaker_embeddings=__lowerCAmelCase		)

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase="speaker_embeddings_path.json"						,			__lowerCAmelCase="speaker_embeddings"						,			__lowerCAmelCase = False						,			**__lowerCAmelCase						,			):







            """simple docstring"""



            if self.speaker_embeddings is not None:
                  os.makedirs(os.path.join(__lowerCAmelCase						,			__lowerCAmelCase						,			"""v2"""		)						,			exist_ok=__lowerCAmelCase		)

                  lowercase								=      {}

                  lowercase								=      save_directory

                  for prompt_key in self.speaker_embeddings:
                        if prompt_key != "repo_or_path":
                              lowercase								=      self._load_voice_preset(__lowerCAmelCase		)

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

                              lowercase								=      tmp_dict

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

            super().save_pretrained(__lowerCAmelCase						,			__lowerCAmelCase						,			**__lowerCAmelCase		)

      def 							A__			(		self						,			__lowerCAmelCase = None						,			**__lowerCAmelCase		):







            """simple docstring"""



            lowercase								=      self.speaker_embeddings[voice_preset]

            lowercase								=      {}
            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}].'		)

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

                  lowercase								=      np.load(__lowerCAmelCase		)

            return voice_preset_dict

      def 							A__			(		self						,			__lowerCAmelCase = None		):







            """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						,			__lowerCAmelCase=None						,			__lowerCAmelCase=None						,			__lowerCAmelCase="pt"						,			__lowerCAmelCase=256						,			__lowerCAmelCase=False						,			__lowerCAmelCase=True						,			__lowerCAmelCase=False						,			**__lowerCAmelCase						,			):







            """simple docstring"""



            if voice_preset is not None and not isinstance(__lowerCAmelCase						,			__lowerCAmelCase		):
                  if (
                      isinstance(__lowerCAmelCase						,			__lowerCAmelCase		)
                      and self.speaker_embeddings is not None
                      and voice_preset in self.speaker_embeddings
                  ):
                        lowercase								=      self._load_voice_preset(__lowerCAmelCase		)

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

                        lowercase								=      np.load(__lowerCAmelCase		)

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

            lowercase								=      self.tokenizer(
                __lowerCAmelCase						,			return_tensors=__lowerCAmelCase						,			padding="""max_length"""						,			max_length=__lowerCAmelCase						,			return_attention_mask=__lowerCAmelCase						,			return_token_type_ids=__lowerCAmelCase						,			add_special_tokens=__lowerCAmelCase						,			**__lowerCAmelCase						,			)

            if voice_preset is not None:
                  lowercase								=      voice_preset

            return encoded_text




"""simple docstring"""






import math


def      UpperCAmelCase__			(							lowerCAmelCase__	:int						)					->       bool:
      '''simple docstring'''


      lowercase								=      math.loga(math.sqrt(4 * positive_integer + 1						) / 2 + 1 / 2						)

      return exponent == int(lowerCAmelCase__						)


def      UpperCAmelCase__			(							lowerCAmelCase__	:float = 1 / 1_2_3_4_5						)					->       int:
      '''simple docstring'''


      lowercase								=      0
      lowercase								=      0

      lowercase								=      3
      while True:
            lowercase								=      (integer**2 - 1) / 4
            # if candidate is an integer, then there is a partition for k
            if partition_candidate == int(lowerCAmelCase__						):
                  lowercase								=      int(lowerCAmelCase__						)
                  total_partitions += 1
                  if check_partition_perfect(lowerCAmelCase__						):
                        perfect_partitions += 1
            if perfect_partitions > 0:
                  if perfect_partitions / total_partitions < max_proportion:
                        return int(lowerCAmelCase__						)
            integer += 1


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



