


"""simple docstring"""






import math


def __lowercase  (   lowerCamelCase_	:   int	):
				return sum(int(lowerCamelCase_	) for digit in str(lowerCamelCase_	)	)


def __lowercase  (   lowerCamelCase_	:   int = 30	):
				SCREAMING_SNAKE_CASE__  				=					[]
				for digit in range(2    ,       100	):
								for power in range(2    ,       100	):
												SCREAMING_SNAKE_CASE__  				=					int(math.pow(lowerCamelCase_    ,       lowerCamelCase_	)	)
												if digit == digit_sum(lowerCamelCase_	):
																digit_to_powers.append(lowerCamelCase_	)

				digit_to_powers.sort()
				return digit_to_powers[n - 1]


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




"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


_lowerCamelCase  =	{
    'configuration_open_llama': ['OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OpenLlamaConfig'],
}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase  =	['LlamaTokenizer']

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase  =	['LlamaTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase  =	[
	    'OpenLlamaForCausalLM',
	    'OpenLlamaModel',
	    'OpenLlamaPreTrainedModel',
	    'OpenLlamaForSequenceClassification',
	]


if TYPE_CHECKING:
	from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from transformers import LlamaTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from transformers import LlamaTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_open_llama import (
		    OpenLlamaForCausalLM,
		    OpenLlamaForSequenceClassification,
		    OpenLlamaModel,
		    OpenLlamaPreTrainedModel,
		)


else:
	import sys

	_lowerCamelCase  =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
