




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


snake_case_ : Optional[int]    		=       logging.get_logger(__name__)







class __snake_case			(			a		):
   UpperCAmelCase__  :    Union[str, Any]          =  '''AutoTokenizer'''
   UpperCAmelCase__  :    str          =  ['''tokenizer''']

   UpperCAmelCase__  :    int          =  {
       '''semantic_prompt''': 1,
       '''coarse_prompt''': 2,
       '''fine_prompt''': 2,
   }







   def __init__(      self		:						Union[str, Any]					,						_snake_case		:						List[str]					,						_snake_case		:						List[str]=None):

        """simple docstring"""


        super().__init__(_snake_case)

        UpperCAmelCase_          =							speaker_embeddings







   @classmethod
   def lowerCamelCase						(      cls		:						Optional[Any]					,						_snake_case		:						Any					,						_snake_case		:						Tuple="speaker_embeddings_path.json"					,						**_snake_case		:						int):

        """simple docstring"""


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

        UpperCAmelCase_          =							AutoTokenizer.from_pretrained(_snake_case					,						**_snake_case)

        return cls(tokenizer=_snake_case					,						speaker_embeddings=_snake_case)







   def lowerCamelCase						(      self		:						int					,						_snake_case		:						int					,						_snake_case		:						Tuple="speaker_embeddings_path.json"					,						_snake_case		:						Any="speaker_embeddings"					,						_snake_case		:						List[Any] = False					,						**_snake_case		:						Optional[int]					,						):

        """simple docstring"""


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

             UpperCAmelCase_          =							{}

             UpperCAmelCase_          =							save_directory

             for prompt_key in self.speaker_embeddings:
                  if prompt_key != "repo_or_path":
                       UpperCAmelCase_          =							self._load_voice_preset(_snake_case)

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

                       UpperCAmelCase_          =							tmp_dict

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

        super().save_pretrained(_snake_case					,						_snake_case					,						**_snake_case)







   def lowerCamelCase						(      self		:						List[Any]					,						_snake_case		:						Tuple = None					,						**_snake_case		:						str):

        """simple docstring"""


        UpperCAmelCase_          =							self.speaker_embeddings[voice_preset]

        UpperCAmelCase_          =							{}
        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}].""")

             UpperCAmelCase_          =							get_file_from_repo(
                 self.speaker_embeddings.get('''repo_or_path'''					,						'''/''')					,						voice_preset_paths[key]					,						subfolder=kwargs.pop('''subfolder'''					,						_snake_case)					,						cache_dir=kwargs.pop('''cache_dir'''					,						_snake_case)					,						force_download=kwargs.pop('''force_download'''					,						_snake_case)					,						proxies=kwargs.pop('''proxies'''					,						_snake_case)					,						resume_download=kwargs.pop('''resume_download'''					,						_snake_case)					,						local_files_only=kwargs.pop('''local_files_only'''					,						_snake_case)					,						use_auth_token=kwargs.pop('''use_auth_token'''					,						_snake_case)					,						revision=kwargs.pop('''revision'''					,						_snake_case)					,						)
             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.""")

             UpperCAmelCase_          =							np.load(_snake_case)

        return voice_preset_dict







   def lowerCamelCase						(      self		:						str					,						_snake_case		:						List[Any] = 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		:						Any					,						_snake_case		:						str=None					,						_snake_case		:						Optional[int]=None					,						_snake_case		:						Tuple="pt"					,						_snake_case		:						Any=256					,						_snake_case		:						str=False					,						_snake_case		:						Optional[int]=True					,						_snake_case		:						List[str]=False					,						**_snake_case		:						List[Any]					,						):

        """simple docstring"""


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

             else:
                  if isinstance(_snake_case					,						_snake_case) and not voice_preset.endswith('''.npz'''):
                       UpperCAmelCase_          =							voice_preset + '''.npz'''

                  UpperCAmelCase_          =							np.load(_snake_case)

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

        UpperCAmelCase_          =							self.tokenizer(
            _snake_case					,						return_tensors=_snake_case					,						padding='''max_length'''					,						max_length=_snake_case					,						return_attention_mask=_snake_case					,						return_token_type_ids=_snake_case					,						add_special_tokens=_snake_case					,						**_snake_case					,						)

        if voice_preset is not None:
             UpperCAmelCase_          =							voice_preset

        return encoded_text










from math import isqrt
def 				UpperCAmelCase_					(							__snake_case						)     ->			list[int]:







 """simple docstring"""


 _lowercase							      =[True] * max_number
 for i in range(2     ,						isqrt(max_number - 1						) + 1						):
  if is_prime[i]:
   for j in range(i**2     ,						__snake_case     ,						__snake_case						):
    _lowercase							      =False

 return [i for i in range(2     ,						__snake_case						) if is_prime[i]]
def 				UpperCAmelCase_					(							__snake_case = 10**8						)     ->			int:







 """simple docstring"""


 _lowercase							      =calculate_prime_numbers(max_number // 2						)

 _lowercase							      =0
 _lowercase							      =0
 _lowercase							      =len(__snake_case						) - 1
 while left <= right:
  while prime_numbers[left] * prime_numbers[right] >= max_number:
   right -= 1
  semiprimes_count += right - left + 1
  left += 1

 return semiprimes_count


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

