
'''simple docstring'''
from __future__ import annotations

from collections import Counter







def 						A_(			A		:						int):
							UpperCamelCase					       =			[True] * n
							UpperCamelCase					       =			False
							UpperCamelCase					       =			False
							UpperCamelCase					       =			True

							for i in range(3						,			int(n**0.5 + 1)						,			2):
														UpperCamelCase					       =			i * 2
														while index < n:
																					UpperCamelCase					       =			False
																					UpperCamelCase					       =			index + i

							UpperCamelCase					       =			[2]

							for i in range(3						,			A						,			2):
														if is_prime[i]:
																					primes.append(A)

							return primes







def 						A_(			A		:						int):
							UpperCamelCase					       =			str(A)
							UpperCamelCase					       =			[]
							UpperCamelCase					       =			['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']

							for duplicate in Counter(A) - Counter(set(A)):
														UpperCamelCase					       =			[int(number_str.replace(A						,			A)) for digit in digits]
														replacements.append(A)

							return replacements







def 						A_(			A		:						int = 8):
							UpperCamelCase					       =			set()

							# Filter primes with less than 3 replaceable digits
							UpperCamelCase					       =			{
							    x for x in set(prime_sieve(100_0000)) if len(str(A)) - len(set(str(A))) >= 3
							}

							for prime in primes:
														if prime in numbers_checked:
																					continue

														UpperCamelCase					       =			digit_replacements(A)

														for family in replacements:
																					numbers_checked.update(A)
																					UpperCamelCase					       =			primes.intersection(A)

																					if len(A) != family_length:
																												continue

																					return min(A)

							return -1


if __name__ == "__main__":
						print(solution())






'''simple docstring'''
import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


lowerCAmelCase :  Union[str, Any]      =      logging.get_logger(__name__)

lowerCAmelCase :  List[Any]      =      {
    'facebook/musicgen-small': 'https://huggingface.co/facebook/musicgen-small/resolve/main/config.json',
    # See all Musicgen models at https://huggingface.co/models?filter=musicgen
}


class        SCREAMING_SNAKE_CASE__	(			snake_case_):
				lowerCAmelCase_			       =				"""musicgen_decoder"""
				lowerCAmelCase_			       =				["""past_key_values"""]







				def __init__(    self		,							A_=2048		,							A_=2048		,							A_=24		,							A_=4096		,							A_=16		,							A_=0.0		,							A_=True		,							A_="gelu"		,							A_=1024		,							A_=0.1		,							A_=0.0		,							A_=0.0		,							A_=0.02		,							A_=False		,							A_=4		,							A_=2048		,							A_=2048		,							A_=None		,							A_=False		,							**A_		,							)->						Any:







											'''simple docstring'''






											UpperCamelCase					       =			vocab_size
											UpperCamelCase					       =			max_position_embeddings
											UpperCamelCase					       =			hidden_size
											UpperCamelCase					       =			ffn_dim
											UpperCamelCase					       =			num_hidden_layers
											UpperCamelCase					       =			num_attention_heads
											UpperCamelCase					       =			dropout
											UpperCamelCase					       =			attention_dropout
											UpperCamelCase					       =			activation_dropout
											UpperCamelCase					       =			activation_function
											UpperCamelCase					       =			initializer_factor
											UpperCamelCase					       =			layerdrop
											UpperCamelCase					       =			use_cache
											UpperCamelCase					       =			scale_embedding  # scale factor will be sqrt(d_model) if True
											UpperCamelCase					       =			num_codebooks
											super().__init__(
											    pad_token_id=A_		,							bos_token_id=A_		,							eos_token_id=A_		,							tie_word_embeddings=A_		,							**A_		,							)


class        SCREAMING_SNAKE_CASE__	(			snake_case_):
				lowerCAmelCase_			       =				"""musicgen"""
				lowerCAmelCase_			       =				True







				def __init__(    self		,							**A_			)->						Union[str, Any]:







											'''simple docstring'''






											super().__init__(**A_			)
											if "text_encoder" not in kwargs or "audio_encoder" not in kwargs or "decoder" not in kwargs:
																		raise ValueError('Config has to be initialized with text_encoder, audio_encoder and decoder config'			)

											UpperCamelCase					       =			kwargs.pop('text_encoder'			)
											UpperCamelCase					       =			text_encoder_config.pop('model_type'			)

											UpperCamelCase					       =			kwargs.pop('audio_encoder'			)
											UpperCamelCase					       =			audio_encoder_config.pop('model_type'			)

											UpperCamelCase					       =			kwargs.pop('decoder'			)

											UpperCamelCase					       =			AutoConfig.for_model(A_		,							**A_			)
											UpperCamelCase					       =			AutoConfig.for_model(A_		,							**A_			)
											UpperCamelCase					       =			MusicgenDecoderConfig(**A_			)
											UpperCamelCase					       =			True







				@classmethod
				def 		UpperCAmelCase_  (    cls		,							A_		,							A_		,							A_		,							**A_		,							)->						Tuple:







											'''simple docstring'''






											return cls(
											    text_encoder=text_encoder_config.to_dict()		,							audio_encoder=audio_encoder_config.to_dict()		,							decoder=decoder_config.to_dict()		,							**A_		,							)







				def 		UpperCAmelCase_  (    self			)->						Optional[Any]:







											'''simple docstring'''






											UpperCamelCase					       =			copy.deepcopy(self.__dict__			)
											UpperCamelCase					       =			self.text_encoder.to_dict()
											UpperCamelCase					       =			self.audio_encoder.to_dict()
											UpperCamelCase					       =			self.decoder.to_dict()
											UpperCamelCase					       =			self.__class__.model_type
											return output




