



"""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


a_			   =					logging.get_logger(__name__)







class    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						_A		:							Dict							       =		"""AutoTokenizer"""
						_A		:							List[str]							       =		["""tokenizer"""]

						_A		:							str							       =		{
						    """semantic_prompt""": 1,
						    """coarse_prompt""": 2,
						    """fine_prompt""": 2,
						}







						def __init__(self   ,		lowercase__   ,		lowercase__=None       ):
											super().__init__(__A       )

											snake_case_					:		Optional[Any] 					=					speaker_embeddings







						@classmethod
						def 						__UpperCamelCase							(cls   ,		lowercase__   ,		lowercase__="speaker_embeddings_path.json"   ,		**lowercase__       ):

											if speaker_embeddings_dict_path is not None:
																snake_case_					:		int 					=					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\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`.'       )
																					snake_case_					:		Dict 					=					None
																else:
																					with open(__A       ) as speaker_embeddings_json:
																										snake_case_					:		Union[str, Any] 					=					json.load(__A       )
											else:
																snake_case_					:		Optional[int] 					=					None

											snake_case_					:		Any 					=					AutoTokenizer.from_pretrained(__A   ,		**__A       )

											return cls(tokenizer=__A   ,		speaker_embeddings=__A       )







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__="speaker_embeddings_path.json"   ,		lowercase__="speaker_embeddings"   ,		lowercase__ = False   ,		**lowercase__   ,		):
											if self.speaker_embeddings is not None:
																os.makedirs(os.path.join(__A   ,		__A   ,		"""v2"""       )   ,		exist_ok=__A       )

																snake_case_					:		Dict 					=					{}

																snake_case_					:		Any 					=					save_directory

																for prompt_key in self.speaker_embeddings:
																					if prompt_key != "repo_or_path":
																										snake_case_					:		Union[str, Any] 					=					self._load_voice_preset(__A       )

																										snake_case_					:		List[str] 					=					{}
																										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   ,		)
																															snake_case_					:		List[str] 					=					os.path.join(__A   ,		f'{prompt_key}_{key}.npy'       )

																										snake_case_					:		Any 					=					tmp_dict

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

											super().save_pretrained(__A   ,		__A   ,		**__A       )







						def 						__UpperCamelCase							(self   ,		lowercase__ = None   ,		**lowercase__       ):
											snake_case_					:		Optional[int] 					=					self.speaker_embeddings[voice_preset]

											snake_case_					:		int 					=					{}
											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}].'       )

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

																snake_case_					:		int 					=					np.load(__A       )

											return voice_preset_dict







						def 						__UpperCamelCase							(self   ,		lowercase__ = None       ):
											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   ,		lowercase__=None   ,		lowercase__=None   ,		lowercase__="pt"   ,		lowercase__=2_56   ,		lowercase__=False   ,		lowercase__=True   ,		lowercase__=False   ,		**lowercase__   ,		):
											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
																):
																					snake_case_					:		Optional[Any] 					=					self._load_voice_preset(__A       )

																else:
																					if isinstance(__A   ,		__A       ) and not voice_preset.endswith(""".npz"""       ):
																										snake_case_					:		List[str] 					=					voice_preset + ".npz"

																					snake_case_					:		List[Any] 					=					np.load(__A       )

											if voice_preset is not None:
																self._validate_voice_preset_dict(__A   ,		**__A       )
																snake_case_					:		List[str] 					=					BatchFeature(data=__A   ,		tensor_type=__A       )

											snake_case_					:		Dict 					=					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:
																snake_case_					:		Any 					=					voice_preset

											return encoded_text










"""simple docstring"""

import random







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  int			):



					"""simple docstring"""





					snake_case_					:		Union[str, Any] 					=					num - 1
					snake_case_					:		List[str] 					=					0

					while s % 2 == 0:
										snake_case_					:		str 					=					s // 2
										t += 1

					for _ in range(5			):
										snake_case_					:		List[Any] 					=					random.randrange(2				,    num - 1			)
										snake_case_					:		Dict 					=					pow(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)
										if v != 1:
															snake_case_					:		int 					=					0
															while v != (num - 1):
																				if i == t - 1:
																									return False
																				else:
																									snake_case_					:		str 					=					i + 1
																									snake_case_					:		int 					=					(v**2) % num
					return True







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  int			):



					"""simple docstring"""





					if num < 2:
										return False

					snake_case_					:		Dict 					=					[
					    2,
					    3,
					    5,
					    7,
					    1_1,
					    1_3,
					    1_7,
					    1_9,
					    2_3,
					    2_9,
					    3_1,
					    3_7,
					    4_1,
					    4_3,
					    4_7,
					    5_3,
					    5_9,
					    6_1,
					    6_7,
					    7_1,
					    7_3,
					    7_9,
					    8_3,
					    8_9,
					    9_7,
					    1_0_1,
					    1_0_3,
					    1_0_7,
					    1_0_9,
					    1_1_3,
					    1_2_7,
					    1_3_1,
					    1_3_7,
					    1_3_9,
					    1_4_9,
					    1_5_1,
					    1_5_7,
					    1_6_3,
					    1_6_7,
					    1_7_3,
					    1_7_9,
					    1_8_1,
					    1_9_1,
					    1_9_3,
					    1_9_7,
					    1_9_9,
					    2_1_1,
					    2_2_3,
					    2_2_7,
					    2_2_9,
					    2_3_3,
					    2_3_9,
					    2_4_1,
					    2_5_1,
					    2_5_7,
					    2_6_3,
					    2_6_9,
					    2_7_1,
					    2_7_7,
					    2_8_1,
					    2_8_3,
					    2_9_3,
					    3_0_7,
					    3_1_1,
					    3_1_3,
					    3_1_7,
					    3_3_1,
					    3_3_7,
					    3_4_7,
					    3_4_9,
					    3_5_3,
					    3_5_9,
					    3_6_7,
					    3_7_3,
					    3_7_9,
					    3_8_3,
					    3_8_9,
					    3_9_7,
					    4_0_1,
					    4_0_9,
					    4_1_9,
					    4_2_1,
					    4_3_1,
					    4_3_3,
					    4_3_9,
					    4_4_3,
					    4_4_9,
					    4_5_7,
					    4_6_1,
					    4_6_3,
					    4_6_7,
					    4_7_9,
					    4_8_7,
					    4_9_1,
					    4_9_9,
					    5_0_3,
					    5_0_9,
					    5_2_1,
					    5_2_3,
					    5_4_1,
					    5_4_7,
					    5_5_7,
					    5_6_3,
					    5_6_9,
					    5_7_1,
					    5_7_7,
					    5_8_7,
					    5_9_3,
					    5_9_9,
					    6_0_1,
					    6_0_7,
					    6_1_3,
					    6_1_7,
					    6_1_9,
					    6_3_1,
					    6_4_1,
					    6_4_3,
					    6_4_7,
					    6_5_3,
					    6_5_9,
					    6_6_1,
					    6_7_3,
					    6_7_7,
					    6_8_3,
					    6_9_1,
					    7_0_1,
					    7_0_9,
					    7_1_9,
					    7_2_7,
					    7_3_3,
					    7_3_9,
					    7_4_3,
					    7_5_1,
					    7_5_7,
					    7_6_1,
					    7_6_9,
					    7_7_3,
					    7_8_7,
					    7_9_7,
					    8_0_9,
					    8_1_1,
					    8_2_1,
					    8_2_3,
					    8_2_7,
					    8_2_9,
					    8_3_9,
					    8_5_3,
					    8_5_7,
					    8_5_9,
					    8_6_3,
					    8_7_7,
					    8_8_1,
					    8_8_3,
					    8_8_7,
					    9_0_7,
					    9_1_1,
					    9_1_9,
					    9_2_9,
					    9_3_7,
					    9_4_1,
					    9_4_7,
					    9_5_3,
					    9_6_7,
					    9_7_1,
					    9_7_7,
					    9_8_3,
					    9_9_1,
					    9_9_7,
					]

					if num in low_primes:
										return True

					for prime in low_primes:
										if (num % prime) == 0:
															return False

					return rabin_miller(SCREAMING_SNAKE_CASE__			)







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  int = 1_0_2_4			):



					"""simple docstring"""





					while True:
										snake_case_					:		Tuple 					=					random.randrange(2 ** (keysize - 1)				,    2 ** (keysize)			)
										if is_prime_low_num(SCREAMING_SNAKE_CASE__			):
															return num


if __name__ == "__main__":
			a_			   =					generate_large_prime()
			print(('''Prime number:''', num))
			print(('''is_prime_low_num:''', is_prime_low_num(num)))




