





from typing import TYPE_CHECKING

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


_A :   Optional[Any]  			=  {
    'configuration_longformer': [
        'LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'LongformerConfig',
        'LongformerOnnxConfig',
    ],
    'tokenization_longformer': ['LongformerTokenizer'],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _A :   List[str]  			=  ['LongformerTokenizerFast']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _A :   Dict  			=  [
           'LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
           'LongformerForMaskedLM',
           'LongformerForMultipleChoice',
           'LongformerForQuestionAnswering',
           'LongformerForSequenceClassification',
           'LongformerForTokenClassification',
           'LongformerModel',
           'LongformerPreTrainedModel',
           'LongformerSelfAttention',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _A :   Union[str, Any]  			=  [
           'TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
           'TFLongformerForMaskedLM',
           'TFLongformerForMultipleChoice',
           'TFLongformerForQuestionAnswering',
           'TFLongformerForSequenceClassification',
           'TFLongformerForTokenClassification',
           'TFLongformerModel',
           'TFLongformerPreTrainedModel',
           'TFLongformerSelfAttention',
       ]


if TYPE_CHECKING:
       from .configuration_longformer import (
           LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
           LongformerConfig,
           LongformerOnnxConfig,
       )
       from .tokenization_longformer import LongformerTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_longformer_fast import LongformerTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_longformer import (
                  LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  LongformerForMaskedLM,
                  LongformerForMultipleChoice,
                  LongformerForQuestionAnswering,
                  LongformerForSequenceClassification,
                  LongformerForTokenClassification,
                  LongformerModel,
                  LongformerPreTrainedModel,
                  LongformerSelfAttention,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_longformer import (
                  TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFLongformerForMaskedLM,
                  TFLongformerForMultipleChoice,
                  TFLongformerForQuestionAnswering,
                  TFLongformerForSequenceClassification,
                  TFLongformerForTokenClassification,
                  TFLongformerModel,
                  TFLongformerPreTrainedModel,
                  TFLongformerSelfAttention,
              )

else:
       import sys

       _A :   int  			=  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)










def 	_a						(   UpperCAmelCase					)    ->    bool:







   """simple docstring"""
   return str(UpperCAmelCase					) == str(UpperCAmelCase					)[::-1]



def 	_a						(   UpperCAmelCase					)    ->    int:







   """simple docstring"""
   return int(UpperCAmelCase					) + int(str(UpperCAmelCase					)[::-1]					)



def 	_a						(   UpperCAmelCase = 10000					)    ->    int:







   """simple docstring"""
   lowerCamelCase__       :						Tuple        =							[]
   for num in range(1      ,  UpperCAmelCase					):
      lowerCamelCase__       :						List[str]        =							0
      lowerCamelCase__       :						Union[str, Any]        =							num
      while iterations < 50:
         lowerCamelCase__       :						Dict        =							sum_reverse(UpperCAmelCase					)
         iterations += 1
         if is_palindrome(UpperCAmelCase					):
            break
      else:
         lychrel_nums.append(UpperCAmelCase					)
   return len(UpperCAmelCase					)


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



