





'''simple docstring'''






import sys

sys.set_int_max_str_digits(0)  # type: ignore





def      lowercase  (  lowerCAmelCase :						int):



				"""simple docstring"""


				_A :				List[Any]				=  [0] * 11
				_A :				Optional[Any]				=  [0] * 11

				# mark last 9 numbers
				for _ in range(9):
								_A :				Dict				=  1
								_A :				Optional[int]				=  number // 10
				# flag
				_A :				str				=  True

				# check last 9 numbers for pandigitality

				for x in range(9):
								if not check_last[x + 1]:
												_A :				Dict				=  False
				if not f:
								return f

				# mark first 9 numbers
				_A :				str				=  int(str(lowerCAmelCase)[:9])

				for _ in range(9):
								_A :				Tuple				=  1
								_A :				Union[str, Any]				=  number // 10

				# check first 9 numbers for pandigitality

				for x in range(9):
								if not check_front[x + 1]:
												_A :				Union[str, Any]				=  False
				return f





def      lowercase  (  lowerCAmelCase :						int):



				"""simple docstring"""


				_A :				Tuple				=  [0] * 11

				# mark last 9 numbers
				for _ in range(9):
								_A :				int				=  1
								_A :				Optional[int]				=  number // 10
				# flag
				_A :				List[str]				=  True

				# check last 9 numbers for pandigitality

				for x in range(9):
								if not check_last[x + 1]:
												_A :				Any				=  False
				return f





def      lowercase  (  ):



				"""simple docstring"""


				_A :				Union[str, Any]				=  1
				_A :				Tuple				=  1
				_A :				List[str]				=  2
				# temporary Fibonacci numbers

				_A :				List[str]				=  1
				_A :				str				=  1
				_A :				List[str]				=  2
				# temporary Fibonacci numbers mod 1e9

				# mod m=1e9, done for fast optimisation
				_A :				Any				=  [0] * 100_0000
				_A :				List[Any]				=  10_0000_0000

				for x in range(100_0000):
								_A :				Dict				=  (aa + ba) % m
								_A :				str				=  ba % m
								_A :				Union[str, Any]				=  ca % m
								if checka(lowerCAmelCase):
												_A :				List[str]				=  1

				for x in range(100_0000):
								_A :				str				=  a + b
								_A :				List[str]				=  b
								_A :				int				=  c
								# perform check only if in tocheck
								if tocheck[x + 3] and check(lowerCAmelCase):
												return x + 3  # first 2 already done
				return -1


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







'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__UpperCamelCase      :   List[str]								=     {'''configuration_gptj''': ['''GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTJConfig''', '''GPTJOnnxConfig''']}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__UpperCamelCase      :   List[Any]								=     [
	    '''GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''GPTJForCausalLM''',
	    '''GPTJForQuestionAnswering''',
	    '''GPTJForSequenceClassification''',
	    '''GPTJModel''',
	    '''GPTJPreTrainedModel''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__UpperCamelCase      :   Optional[int]								=     [
	    '''TFGPTJForCausalLM''',
	    '''TFGPTJForQuestionAnswering''',
	    '''TFGPTJForSequenceClassification''',
	    '''TFGPTJModel''',
	    '''TFGPTJPreTrainedModel''',
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__UpperCamelCase      :   Union[str, Any]								=     [
	    '''FlaxGPTJForCausalLM''',
	    '''FlaxGPTJModel''',
	    '''FlaxGPTJPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gptj import (
		    GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTJForCausalLM,
		    GPTJForQuestionAnswering,
		    GPTJForSequenceClassification,
		    GPTJModel,
		    GPTJPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_gptj import (
		    TFGPTJForCausalLM,
		    TFGPTJForQuestionAnswering,
		    TFGPTJForSequenceClassification,
		    TFGPTJModel,
		    TFGPTJPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
	import sys

	__UpperCamelCase      :   List[str]								=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
