

'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


__lowerCamelCase		       =    {
    'configuration_gpt_neo': ['GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTNeoConfig', 'GPTNeoOnnxConfig'],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCamelCase		       =    [
						    'GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'GPTNeoForCausalLM',
						    'GPTNeoForQuestionAnswering',
						    'GPTNeoForSequenceClassification',
						    'GPTNeoForTokenClassification',
						    'GPTNeoModel',
						    'GPTNeoPreTrainedModel',
						    'load_tf_weights_in_gpt_neo',
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCamelCase		       =    [
						    'FlaxGPTNeoForCausalLM',
						    'FlaxGPTNeoModel',
						    'FlaxGPTNeoPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_gpt_neo import (
												    GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
												    GPTNeoForCausalLM,
												    GPTNeoForQuestionAnswering,
												    GPTNeoForSequenceClassification,
												    GPTNeoForTokenClassification,
												    GPTNeoModel,
												    GPTNeoPreTrainedModel,
												    load_tf_weights_in_gpt_neo,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
						import sys

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



'''simple docstring'''







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


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