



"""simple docstring"""

from typing import TYPE_CHECKING

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


a_       =       {'''configuration_xlnet''': ['''XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLNetConfig''']}

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

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

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_       =       [
			    '''XLNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''XLNetForMultipleChoice''',
			    '''XLNetForQuestionAnswering''',
			    '''XLNetForQuestionAnsweringSimple''',
			    '''XLNetForSequenceClassification''',
			    '''XLNetForTokenClassification''',
			    '''XLNetLMHeadModel''',
			    '''XLNetModel''',
			    '''XLNetPreTrainedModel''',
			    '''load_tf_weights_in_xlnet''',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_       =       [
			    '''TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TFXLNetForMultipleChoice''',
			    '''TFXLNetForQuestionAnsweringSimple''',
			    '''TFXLNetForSequenceClassification''',
			    '''TFXLNetForTokenClassification''',
			    '''TFXLNetLMHeadModel''',
			    '''TFXLNetMainLayer''',
			    '''TFXLNetModel''',
			    '''TFXLNetPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_xlnet import XLNetTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_xlnet_fast import XLNetTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_xlnet import (
						    XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
						    XLNetForMultipleChoice,
						    XLNetForQuestionAnswering,
						    XLNetForQuestionAnsweringSimple,
						    XLNetForSequenceClassification,
						    XLNetForTokenClassification,
						    XLNetLMHeadModel,
						    XLNetModel,
						    XLNetPreTrainedModel,
						    load_tf_weights_in_xlnet,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_xlnet import (
						    TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFXLNetForMultipleChoice,
						    TFXLNetForQuestionAnsweringSimple,
						    TFXLNetForSequenceClassification,
						    TFXLNetForTokenClassification,
						    TFXLNetLMHeadModel,
						    TFXLNetMainLayer,
						    TFXLNetModel,
						    TFXLNetPreTrainedModel,
						)

else:
			import sys

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








'''simple docstring'''






from __future__ import annotations


def    _snake_case  (	A			)		->       bool:
							lowerCAmelCase__			  =      str(A			)
							return len(A			) == 9 and set(A			) == set('''123456789'''			)


def    _snake_case  (	)		->       int | None:
							for base_num in range(9999					,       4999					,       -1			):
														lowerCAmelCase__			  =      100002 * base_num
														if is_9_pandigital(A			):
																					return candidate

							for base_num in range(333					,       99					,       -1			):
														lowerCAmelCase__			  =      1002003 * base_num
														if is_9_pandigital(A			):
																					return candidate

							return None


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