def 					UpperCamelCase_						( snake_case_	:	int						,		snake_case_	:	int				) ->					int:






 '''simple docstring'''

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







def 					UpperCamelCase_						( snake_case_	:	int				) ->					int:






 '''simple docstring'''

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







def 					UpperCamelCase_						( snake_case_	:	int				) ->					int:






 '''simple docstring'''

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







def 					UpperCamelCase_						( snake_case_	:	int = 1_00				) ->					int:






 '''simple docstring'''

 return non_bouncy_upto(snake_case_				)


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







'''simple docstring'''

import argparse

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()







def 					UpperCamelCase_						( snake_case_	:	List[str]						,		snake_case_	:	Optional[Any]				) ->					Tuple:






 '''simple docstring'''

 __lowerCAmelCase    							=						CanineConfig()
 __lowerCAmelCase    							=						CanineModel(snake_case_				)
 model.eval()

 print(f"""Building PyTorch model from configuration: {config}"""				)

 # Load weights from tf checkpoint
 load_tf_weights_in_canine(snake_case_						,		snake_case_						,		snake_case_				)

 # Save pytorch-model (weights and configuration)
 print(f"""Save PyTorch model to {pytorch_dump_path}"""				)
 model.save_pretrained(snake_case_				)

 # Save tokenizer files
 __lowerCAmelCase    							=						CanineTokenizer()
 print(f"""Save tokenizer files to {pytorch_dump_path}"""				)
 tokenizer.save_pretrained(snake_case_				)


if __name__ == "__main__":
 _A      :				int 				=    argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--tf_checkpoint_path''',
     default=None,
     type=str,
     required=True,
     help='''Path to the TensorFlow checkpoint. Should end with model.ckpt''',
 )
 parser.add_argument(
     '''--pytorch_dump_path''',
     default=None,
     type=str,
     required=True,
     help='''Path to a folder where the PyTorch model will be placed.''',
 )
 _A      :				Dict 				=    parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)






