





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  _SCREAMING_SNAKE_CASE(       __SCREAMING_SNAKE_CASE  ):
       SCREAMING_SNAKE_CASE_       :				List[str]     						=  '''AutoTokenizer'''
       SCREAMING_SNAKE_CASE_       :				Optional[int]     						=  ['''tokenizer''']

       SCREAMING_SNAKE_CASE_       :				Optional[int]     						=  {
           '''semantic_prompt''': 1,
           '''coarse_prompt''': 2,
           '''fine_prompt''': 2,
       }




       def __init__(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__=None							)      ->				int:





             """simple docstring"""






             super().__init__(UpperCamelCase__							)

             __SCREAMING_SNAKE_CASE       :int		    =  speaker_embeddings




       @classmethod
       def    _UpperCamelCase					(						cls		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__="speaker_embeddings_path.json"		,**SCREAMING_SNAKE_CASE__							)      ->				int:





             """simple docstring"""






             if speaker_embeddings_dict_path is not None:
                   __SCREAMING_SNAKE_CASE       :List[Any]		    =  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\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`.'''							)
                         __SCREAMING_SNAKE_CASE       :Tuple		    =  None
                   else:
                         with open(UpperCamelCase__							) as speaker_embeddings_json:
                               __SCREAMING_SNAKE_CASE       :Dict		    =  json.load(UpperCamelCase__							)
             else:
                   __SCREAMING_SNAKE_CASE       :str		    =  None

             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  AutoTokenizer.from_pretrained(UpperCamelCase__		,**UpperCamelCase__							)

             return cls(tokenizer=UpperCamelCase__		,speaker_embeddings=UpperCamelCase__							)




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__="speaker_embeddings_path.json"		,SCREAMING_SNAKE_CASE__="speaker_embeddings"		,SCREAMING_SNAKE_CASE__ = False		,**SCREAMING_SNAKE_CASE__		,)      ->				Any:





             """simple docstring"""






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

                   __SCREAMING_SNAKE_CASE       :List[Any]		    =  {}

                   __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  save_directory

                   for prompt_key in self.speaker_embeddings:
                         if prompt_key != "repo_or_path":
                               __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  self._load_voice_preset(UpperCamelCase__							)

                               __SCREAMING_SNAKE_CASE       :Optional[int]		    =  {}
                               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__		,)
                                     __SCREAMING_SNAKE_CASE       :Dict		    =  os.path.join(UpperCamelCase__		,f'''{prompt_key}_{key}.npy'''							)

                               __SCREAMING_SNAKE_CASE       :List[str]		    =  tmp_dict

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

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




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__ = None		,**SCREAMING_SNAKE_CASE__							)      ->				Union[str, Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :List[str]		    =  self.speaker_embeddings[voice_preset]

             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  {}
             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}].'''							)

                   __SCREAMING_SNAKE_CASE       :Optional[int]		    =  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\n                    , no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}\n                    embeddings.'''							)

                   __SCREAMING_SNAKE_CASE       :Tuple		    =  np.load(UpperCamelCase__							)

             return voice_preset_dict




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__ = None							)      ->				str:





             """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		,SCREAMING_SNAKE_CASE__=None		,SCREAMING_SNAKE_CASE__=None		,SCREAMING_SNAKE_CASE__="pt"		,SCREAMING_SNAKE_CASE__=2_56		,SCREAMING_SNAKE_CASE__=False		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__=False		,**SCREAMING_SNAKE_CASE__		,)      ->				Optional[int]:





             """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
                   ):
                         __SCREAMING_SNAKE_CASE       :str		    =  self._load_voice_preset(UpperCamelCase__							)

                   else:
                         if isinstance(UpperCamelCase__		,UpperCamelCase__							) and not voice_preset.endswith('''.npz'''							):
                               __SCREAMING_SNAKE_CASE       :int		    =  voice_preset + '''.npz'''

                         __SCREAMING_SNAKE_CASE       :Tuple		    =  np.load(UpperCamelCase__							)

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

             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  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:
                   __SCREAMING_SNAKE_CASE       :Optional[int]		    =  voice_preset

             return encoded_text






"""simple docstring"""
import math
import unittest



def 					__lowerCamelCase       (							a_					:		int							)      ->				bool:
      assert isinstance(a_			,       a_							) and (
          number >= 0
      ), "'number' must been an int and positive"

      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(a_							) + 1							)			,       6							):
            if number % i == 0 or number % (i + 2) == 0:
                  return False
      return True






class  _SCREAMING_SNAKE_CASE(       unittest.TestCase  ):




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





             """simple docstring"""






             self.assertTrue(is_prime(2							)							)
             self.assertTrue(is_prime(3							)							)
             self.assertTrue(is_prime(5							)							)
             self.assertTrue(is_prime(7							)							)
             self.assertTrue(is_prime(11							)							)
             self.assertTrue(is_prime(13							)							)
             self.assertTrue(is_prime(17							)							)
             self.assertTrue(is_prime(19							)							)
             self.assertTrue(is_prime(23							)							)
             self.assertTrue(is_prime(29							)							)




       def    _UpperCamelCase					(						self							)      ->				Tuple:





             """simple docstring"""






             with self.assertRaises(SCREAMING_SNAKE_CASE__							):
                   is_prime(-19							)
             self.assertFalse(
                 is_prime(0							)		,'''Zero doesn\'t have any positive factors, primes must have exactly two.'''		,)
             self.assertFalse(
                 is_prime(1							)		,'''One only has 1 positive factor, primes must have exactly two.'''		,)
             self.assertFalse(is_prime(2 * 2							)							)
             self.assertFalse(is_prime(2 * 3							)							)
             self.assertFalse(is_prime(3 * 3							)							)
             self.assertFalse(is_prime(3 * 5							)							)
             self.assertFalse(is_prime(3 * 5 * 7							)							)


if __name__ == "__main__":
  unittest.main()