
"""simple docstring"""




from __future__ import annotations

import math
from itertools import permutations

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_     ):

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

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_ = 7     ):
					SCREAMING_SNAKE_CASE           =      ''.join(str(SCREAMING_SNAKE_CASE_     ) for i in range(1,	n + 1     )     )
					SCREAMING_SNAKE_CASE           =      [int(''.join(SCREAMING_SNAKE_CASE_     )     ) for i in permutations(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     )]
					SCREAMING_SNAKE_CASE           =      [num for num in perm_list if is_prime(SCREAMING_SNAKE_CASE_     )]
					return max(SCREAMING_SNAKE_CASE_     ) if pandigitals else 0


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


"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


snake_case					  =						{'configuration_qdqbert': ['QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'QDQBertConfig']}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case					  =						[
	    'QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'QDQBertForMaskedLM',
	    'QDQBertForMultipleChoice',
	    'QDQBertForNextSentencePrediction',
	    'QDQBertForQuestionAnswering',
	    'QDQBertForSequenceClassification',
	    'QDQBertForTokenClassification',
	    'QDQBertLayer',
	    'QDQBertLMHeadModel',
	    'QDQBertModel',
	    'QDQBertPreTrainedModel',
	    'load_tf_weights_in_qdqbert',
	]


if TYPE_CHECKING:
	from .configuration_qdqbert import QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, QDQBertConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_qdqbert import (
		    QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    QDQBertForMaskedLM,
		    QDQBertForMultipleChoice,
		    QDQBertForNextSentencePrediction,
		    QDQBertForQuestionAnswering,
		    QDQBertForSequenceClassification,
		    QDQBertForTokenClassification,
		    QDQBertLayer,
		    QDQBertLMHeadModel,
		    QDQBertModel,
		    QDQBertPreTrainedModel,
		    load_tf_weights_in_qdqbert,
		)


else:
	import sys

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