
"""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"""

def 		UpperCAmelCase (		a_      ):





		'''simple docstring'''


		return str(a_      ) == str(a_      )[::-1]
def 		UpperCAmelCase (		a_      ):





		'''simple docstring'''


		return int(a_      ) + int(str(a_      )[::-1]      )
def 		UpperCAmelCase (		a_ = 1_0000      ):





		'''simple docstring'''


		lowerCamelCase					:      Optional[Any]          =   []
		for num in range(1,						a_      ):
				lowerCamelCase					:      List[str]          =   0
				lowerCamelCase					:      Union[str, Any]          =   num
				while iterations < 50:
						lowerCamelCase					:      Optional[int]          =   sum_reverse(a_      )
						iterations += 1
						if is_palindrome(a_      ):
								break
				else:
						lychrel_nums.append(a_      )
		return len(a_      )


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






