





from typing import TYPE_CHECKING

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


UpperCAmelCase						:   List[Any]			  =     {
    '''configuration_roberta_prelayernorm''': [
        '''ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''RobertaPreLayerNormConfig''',
        '''RobertaPreLayerNormOnnxConfig''',
    ],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase						:   List[Any]			  =     [
				    '''ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''RobertaPreLayerNormForCausalLM''',
				    '''RobertaPreLayerNormForMaskedLM''',
				    '''RobertaPreLayerNormForMultipleChoice''',
				    '''RobertaPreLayerNormForQuestionAnswering''',
				    '''RobertaPreLayerNormForSequenceClassification''',
				    '''RobertaPreLayerNormForTokenClassification''',
				    '''RobertaPreLayerNormModel''',
				    '''RobertaPreLayerNormPreTrainedModel''',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase						:   Dict			  =     [
				    '''TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''TFRobertaPreLayerNormForCausalLM''',
				    '''TFRobertaPreLayerNormForMaskedLM''',
				    '''TFRobertaPreLayerNormForMultipleChoice''',
				    '''TFRobertaPreLayerNormForQuestionAnswering''',
				    '''TFRobertaPreLayerNormForSequenceClassification''',
				    '''TFRobertaPreLayerNormForTokenClassification''',
				    '''TFRobertaPreLayerNormMainLayer''',
				    '''TFRobertaPreLayerNormModel''',
				    '''TFRobertaPreLayerNormPreTrainedModel''',
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase						:   Dict			  =     [
				    '''FlaxRobertaPreLayerNormForCausalLM''',
				    '''FlaxRobertaPreLayerNormForMaskedLM''',
				    '''FlaxRobertaPreLayerNormForMultipleChoice''',
				    '''FlaxRobertaPreLayerNormForQuestionAnswering''',
				    '''FlaxRobertaPreLayerNormForSequenceClassification''',
				    '''FlaxRobertaPreLayerNormForTokenClassification''',
				    '''FlaxRobertaPreLayerNormModel''',
				    '''FlaxRobertaPreLayerNormPreTrainedModel''',
				]


if TYPE_CHECKING:
				from .configuration_roberta_prelayernorm import (
				    ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    RobertaPreLayerNormConfig,
				    RobertaPreLayerNormOnnxConfig,
				)

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_roberta_prelayernorm import (
								    ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
								    RobertaPreLayerNormForCausalLM,
								    RobertaPreLayerNormForMaskedLM,
								    RobertaPreLayerNormForMultipleChoice,
								    RobertaPreLayerNormForQuestionAnswering,
								    RobertaPreLayerNormForSequenceClassification,
								    RobertaPreLayerNormForTokenClassification,
								    RobertaPreLayerNormModel,
								    RobertaPreLayerNormPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_roberta_prelayernorm import (
								    TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFRobertaPreLayerNormForCausalLM,
								    TFRobertaPreLayerNormForMaskedLM,
								    TFRobertaPreLayerNormForMultipleChoice,
								    TFRobertaPreLayerNormForQuestionAnswering,
								    TFRobertaPreLayerNormForSequenceClassification,
								    TFRobertaPreLayerNormForTokenClassification,
								    TFRobertaPreLayerNormMainLayer,
								    TFRobertaPreLayerNormModel,
								    TFRobertaPreLayerNormPreTrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_roberta_prelayernorm import (
								    FlaxRobertaPreLayerNormForCausalLM,
								    FlaxRobertaPreLayerNormForMaskedLM,
								    FlaxRobertaPreLayerNormForMultipleChoice,
								    FlaxRobertaPreLayerNormForQuestionAnswering,
								    FlaxRobertaPreLayerNormForSequenceClassification,
								    FlaxRobertaPreLayerNormForTokenClassification,
								    FlaxRobertaPreLayerNormModel,
								    FlaxRobertaPreLayerNormPreTrainedModel,
								)

else:
				import sys

				UpperCAmelCase						:   Optional[Any]			  =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)











def  _SCREAMING_SNAKE_CASE				(   a  )      ->      bool:
					return str(a  ) == str(a  )[::-1]







def  _SCREAMING_SNAKE_CASE				(   a  )      ->      int:
					return int(a  ) + int(str(a  )[::-1]  )







def  _SCREAMING_SNAKE_CASE				(   a = 1_00_00  )      ->      int:
					__A	:						int       =				[]
					for num in range(1					,						a  ):
										__A	:						List[str]       =				0
										__A	:						List[Any]       =				num
										while iterations < 50:
															__A	:						str       =				sum_reverse(a  )
															iterations += 1
															if is_palindrome(a  ):
																				break
										else:
															lychrel_nums.append(a  )
					return len(a  )


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




