





"""simple docstring"""


from typing import TYPE_CHECKING

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


A_     :      int								={
    """configuration_imagegpt""": ["""IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ImageGPTConfig""", """ImageGPTOnnxConfig"""]
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A_     :      List[str]								=["""ImageGPTFeatureExtractor"""]
      A_     :      Tuple								=["""ImageGPTImageProcessor"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A_     :      List[str]								=[
          """IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """ImageGPTForCausalImageModeling""",
          """ImageGPTForImageClassification""",
          """ImageGPTModel""",
          """ImageGPTPreTrainedModel""",
          """load_tf_weights_in_imagegpt""",
      ]


if TYPE_CHECKING:
      from .configuration_imagegpt import IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, ImageGPTConfig, ImageGPTOnnxConfig

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_imagegpt import ImageGPTFeatureExtractor
            from .image_processing_imagegpt import ImageGPTImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_imagegpt import (
                IMAGEGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
                ImageGPTForCausalImageModeling,
                ImageGPTForImageClassification,
                ImageGPTModel,
                ImageGPTPreTrainedModel,
                load_tf_weights_in_imagegpt,
            )

else:
      import sys

      A_     :      Any								=_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)













"""simple docstring"""


A_     :      Dict								=1_0**8





def SCREAMING_SNAKE_CASE_   (       snake_case   :					int		)->		bool:
       if n % 10 == 0:
              return False
       _lowerCamelCase    	=					str(snake_case		)
       return s == s[::-1]





def SCREAMING_SNAKE_CASE_   (       )->		int:
       _lowerCamelCase    	=					set()
       _lowerCamelCase    	=					1
       _lowerCamelCase    	=					5
       while sum_squares < LIMIT:
              _lowerCamelCase    	=					first_square + 1
              while sum_squares < LIMIT:
                     if is_palindrome(snake_case		):
                            answer.add(snake_case		)
                     last_square += 1
                     sum_squares += last_square**2
              first_square += 1
              _lowerCamelCase    	=					first_square**2 + (first_square + 1) ** 2

       return sum(snake_case		)


if __name__ == "__main__":
      print(solution())






