




from typing import TYPE_CHECKING

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


UpperCamelCase__  =		{'configuration_regnet': ['REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RegNetConfig']}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase__  =		[
							    'REGNET_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'RegNetForImageClassification',
							    'RegNetModel',
							    'RegNetPreTrainedModel',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase__  =		[
							    'TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'TFRegNetForImageClassification',
							    'TFRegNetModel',
							    'TFRegNetPreTrainedModel',
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase__  =		[
							    'FlaxRegNetForImageClassification',
							    'FlaxRegNetModel',
							    'FlaxRegNetPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_regnet import REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP, RegNetConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_regnet import (
														    REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
														    RegNetForImageClassification,
														    RegNetModel,
														    RegNetPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_regnet import (
														    TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFRegNetForImageClassification,
														    TFRegNetModel,
														    TFRegNetPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_regnet import (
														    FlaxRegNetForImageClassification,
														    FlaxRegNetModel,
														    FlaxRegNetPreTrainedModel,
														)


else:
							import sys

							UpperCamelCase__  =		_LazyModule(__name__, globals()['__file__'], _import_structure)






def       snake_case      (							A = 998001    )							->							int:




	'''simple docstring'''


	for number in range(n - 1		,     9999		,     -1    ):
		lowerCamelCase_							:	Tuple							     =							str(A    )

		# checks whether 'str_number' is a palindrome.
		if str_number == str_number[::-1]:
			lowerCamelCase_							:	Tuple							     =							999

			# if 'number' is a product of two 3-digit numbers
			# then number is the answer otherwise fetch next number.
			while divisor != 99:
				if (number % divisor == 0) and (len(str(number // divisor    )    ) == 3.0):
					return number
				divisor -= 1
	raise ValueError('That number is larger than our acceptable range.'    )


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