





"""simple docstring"""





from typing import TYPE_CHECKING

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


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

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__ =       ["ImageGPTFeatureExtractor"]
       SCREAMING_SNAKE_CASE__ =       ["ImageGPTImageProcessor"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__ =       [
           "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

       SCREAMING_SNAKE_CASE__ =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)













"""simple docstring"""





def       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						int							,						SCREAMING_SNAKE_CASE					:						int      ):







      '''simple docstring'''




      lowerCAmelCase  						=	1.0
      for i in range(1							,						r + 1      ):
            ret *= (n + 1 - i) / i
      return round(SCREAMING_SNAKE_CASE      )


def       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						int      ):







      '''simple docstring'''




      return choose(8 + n							,						SCREAMING_SNAKE_CASE      ) + choose(9 + n							,						SCREAMING_SNAKE_CASE      ) - 10


def       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						int      ):







      '''simple docstring'''




      return sum(non_bouncy_exact(SCREAMING_SNAKE_CASE      ) for i in range(1							,						n + 1      )      )


def       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						int = 1_00      ):







      '''simple docstring'''




      return non_bouncy_upto(SCREAMING_SNAKE_CASE      )


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






