




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


lowercase__				:      Optional[Any]	   = logging.get_logger(__name__)




class       a__	( UpperCamelCase__				):
     a		:      Tuple		 =     """AutoTokenizer"""
     a		:      Any		 =     ["""tokenizer"""]

     a		:      Dict		 =     {
         """semantic_prompt""": 1,
         """coarse_prompt""": 2,
         """fine_prompt""": 2,
     }







     def __init__(     self  ,		A  ,		A=None			)  ->   Union[str, Any]:



          '''simple docstring'''



          super().__init__(A			)

          a     =				speaker_embeddings







     @classmethod
     def       lowerCAmelCase_    (     cls  ,		A  ,		A="speaker_embeddings_path.json"  ,		**A			)  ->   Union[str, Any]:



          '''simple docstring'''



          if speaker_embeddings_dict_path is not None:
               a     =				get_file_from_repo(
                   A  ,		A  ,		subfolder=kwargs.pop("subfolder"  ,		A			)  ,		cache_dir=kwargs.pop("cache_dir"  ,		A			)  ,		force_download=kwargs.pop("force_download"  ,		A			)  ,		proxies=kwargs.pop("proxies"  ,		A			)  ,		resume_download=kwargs.pop("resume_download"  ,		A			)  ,		local_files_only=kwargs.pop("local_files_only"  ,		A			)  ,		use_auth_token=kwargs.pop("use_auth_token"  ,		A			)  ,		revision=kwargs.pop("revision"  ,		A			)  ,		)
               if speaker_embeddings_path is None:
                    logger.warning(
                        F'''`{os.path.join(A  ,		A			)}` 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(A			) as speaker_embeddings_json:
                         a     =				json.load(A			)
          else:
               a     =				None

          a     =				AutoTokenizer.from_pretrained(A  ,		**A			)

          return cls(tokenizer=A  ,		speaker_embeddings=A			)







     def       lowerCAmelCase_    (     self  ,		A  ,		A="speaker_embeddings_path.json"  ,		A="speaker_embeddings"  ,		A = False  ,		**A  ,		)  ->   int:



          '''simple docstring'''



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

               a     =				{}

               a     =				save_directory

               for prompt_key in self.speaker_embeddings:
                    if prompt_key != "repo_or_path":
                         a     =				self._load_voice_preset(A			)

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

                         a     =				tmp_dict

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

          super().save_pretrained(A  ,		A  ,		**A			)







     def       lowerCAmelCase_    (     self  ,		A = None  ,		**A			)  ->   List[str]:



          '''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"  ,		A			)  ,		cache_dir=kwargs.pop("cache_dir"  ,		A			)  ,		force_download=kwargs.pop("force_download"  ,		A			)  ,		proxies=kwargs.pop("proxies"  ,		A			)  ,		resume_download=kwargs.pop("resume_download"  ,		A			)  ,		local_files_only=kwargs.pop("local_files_only"  ,		A			)  ,		use_auth_token=kwargs.pop("use_auth_token"  ,		A			)  ,		revision=kwargs.pop("revision"  ,		A			)  ,		)
               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(A			)

          return voice_preset_dict







     def       lowerCAmelCase_    (     self  ,		A = None			)  ->   Optional[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  ,		A=None  ,		A=None  ,		A="pt"  ,		A=256  ,		A=False  ,		A=True  ,		A=False  ,		**A  ,		)  ->   Optional[int]:



          '''simple docstring'''



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

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

                    a     =				np.load(A			)

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

          a     =				self.tokenizer(
              A  ,		return_tensors=A  ,		padding="max_length"  ,		max_length=A  ,		return_attention_mask=A  ,		return_token_type_ids=A  ,		add_special_tokens=A  ,		**A  ,		)

          if voice_preset is not None:
               a     =				voice_preset

          return encoded_text







import math

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					bool:

     if 1 < number < 4:
          # 2 and 3 are primes
          return True
     elif number < 2 or number % 2 == 0 or number % 3 == 0:
          # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
          return False

     # All primes number are in format of 6k +/- 1
     for i in range(5		, int(math.sqrt(__UpperCamelCase) + 1)		, 6):
          if number % i == 0 or number % (i + 2) == 0:
               return False
     return True

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase = 0.1)      ->					int:
     a     =				3
     a     =				3

     while primes / (2 * j - 1) >= ratio:
          for i in range(j * j + j + 1		, (j + 2) * (j + 2)		, j + 1):
               primes += is_prime(__UpperCamelCase)
          j += 2
     return j


if __name__ == "__main__":
   import doctest

   doctest.testmod()

