


import os
import warnings
from pathlib import Path
from typing import Optional, Union

from .dynamic_module_utils import custom_object_save
from .tokenization_utils_base import PreTrainedTokenizerBase
from .utils import PushToHubMixin, copy_func, direct_transformers_import, logging


__magic_name__   :   int  							=logging.get_logger(__name__)

# Dynamically import the Transformers module to grab the attribute classes of the processor form their names.
__magic_name__   :   Dict  							=direct_transformers_import(Path(__file__).parent)


__magic_name__   :   Any  							={
    'AutoTokenizer': 'PreTrainedTokenizerBase',
    'AutoFeatureExtractor': 'FeatureExtractionMixin',
    'AutoImageProcessor': 'ImageProcessingMixin',
}






class 		UpperCamelCase_		(					a_   ):





						"""simple docstring"""




						UpperCAmelCase__	:       Tuple         =     ['feature_extractor', 'tokenizer']
						# Names need to be attr_class for attr in attributes
						UpperCAmelCase__	:       List[str]         =     None
						UpperCAmelCase__	:       Union[str, Any]         =     None
						UpperCAmelCase__	:       Optional[Any]         =     None
						def __init__( self				:					Dict	,				*_lowerCamelCase				:					Union[str, Any]	,				**_lowerCamelCase				:					Union[str, Any]  )							->					List[Any]:
										# Sanitize args and kwargs
										for key in kwargs:
														if key not in self.attributes:
																		raise TypeError(f'Unexpected keyword argument {key}.'  )
										for arg, attribute_name in zip(_lowerCamelCase	,				self.attributes  ):
														if attribute_name in kwargs:
																		raise TypeError(f'Got multiple values for argument {attribute_name}.'  )
														else:
																		__magic_name__	  = arg

										if len(_lowerCamelCase  ) != len(self.attributes  ):
														raise ValueError(
														    f'This processor requires {len(self.attributes  )} arguments: {", ".join(self.attributes  )}. Got '
														    f'{len(_lowerCamelCase  )} arguments instead.'  )

										# Check each arg is of the proper class (this will also catch a user initializing in the wrong order)
										for attribute_name, arg in kwargs.items():
														__magic_name__	  = getattr(self	,				f'{attribute_name}_class'  )
														# Nothing is ever going to be an instance of "AutoXxx", in that case we check the base class.
														__magic_name__	  = AUTO_TO_BASE_CLASS_MAPPING.get(_lowerCamelCase	,				_lowerCamelCase  )
														if isinstance(_lowerCamelCase	,				_lowerCamelCase  ):
																		__magic_name__	  = tuple(getattr(_lowerCamelCase	,				_lowerCamelCase  ) for n in class_name if n is not None  )
														else:
																		__magic_name__	  = getattr(_lowerCamelCase	,				_lowerCamelCase  )

														if not isinstance(_lowerCamelCase	,				_lowerCamelCase  ):
																		raise ValueError(
																		    f'Received a {type(_lowerCamelCase  ).__name__} for argument {attribute_name}, but a {class_name} was expected.'  )

														setattr(self	,				_lowerCamelCase	,				_lowerCamelCase  )
						def __repr__( self				:					Union[str, Any]  )							->					str:
										__magic_name__	  = [f'- {name}: {repr(getattr(self	,				_lowerCamelCase  )  )}' for name in self.attributes]
										__magic_name__	  = '\n'.join(_lowerCamelCase  )
										return f'{self.__class__.__name__}:\n{attributes_repr}'
						def 					__A					( self				:					str	,				_lowerCamelCase				:					Dict	,				_lowerCamelCase				:					bool = False	,				**_lowerCamelCase				:					str  )							->					Union[str, Any]:
										os.makedirs(_lowerCamelCase	,				exist_ok=_lowerCamelCase  )

										if push_to_hub:
														__magic_name__	  = kwargs.pop("commit_message"	,				_lowerCamelCase  )
														__magic_name__	  = kwargs.pop("repo_id"	,				save_directory.split(os.path.sep  )[-1]  )
														__magic_name__	  = self._create_repo(_lowerCamelCase	,				**_lowerCamelCase  )
														__magic_name__	  = self._get_files_timestamps(_lowerCamelCase  )
										# If we have a custom config, we copy the file defining it in the folder and set the attributes so it can be
										# loaded from the Hub.
										if self._auto_class is not None:
														__magic_name__	  = [getattr(self	,				_lowerCamelCase  ) for attribute_name in self.attributes]
														__magic_name__	  = [(a.init_kwargs if isinstance(_lowerCamelCase	,				_lowerCamelCase  ) else a) for a in attrs]
														custom_object_save(self	,				_lowerCamelCase	,				config=_lowerCamelCase  )

										for attribute_name in self.attributes:
														__magic_name__	  = getattr(self	,				_lowerCamelCase  )
														# Include the processor class in the attribute config so this processor can then be reloaded with the
														# `AutoProcessor` API.
														if hasattr(_lowerCamelCase	,				"_set_processor_class"  ):
																		attribute._set_processor_class(self.__class__.__name__  )
														attribute.save_pretrained(_lowerCamelCase  )

										if self._auto_class is not None:
														# We added an attribute to the init_kwargs of the tokenizers, which needs to be cleaned up.
														for attribute_name in self.attributes:
																		__magic_name__	  = getattr(self	,				_lowerCamelCase  )
																		if isinstance(_lowerCamelCase	,				_lowerCamelCase  ):
																						del attribute.init_kwargs["auto_map"]

										if push_to_hub:
														self._upload_modified_files(
														    _lowerCamelCase	,				_lowerCamelCase	,				_lowerCamelCase	,				commit_message=_lowerCamelCase	,				token=kwargs.get("use_auth_token"  )	,				)
						@classmethod
						def 					__A					( cls				:					Union[str, Any]	,				_lowerCamelCase				:					Union[str, os.PathLike]	,				_lowerCamelCase				:					Optional[Union[str, os.PathLike]] = None	,				_lowerCamelCase				:					bool = False	,				_lowerCamelCase				:					bool = False	,				_lowerCamelCase				:					Optional[Union[str, bool]] = None	,				_lowerCamelCase				:					str = "main"	,				**_lowerCamelCase				:					List[Any]	,				)							->					Any:
										__magic_name__	  = cache_dir
										__magic_name__	  = force_download
										__magic_name__	  = local_files_only
										__magic_name__	  = revision

										__magic_name__	  = kwargs.pop("use_auth_token"	,				_lowerCamelCase  )
										if use_auth_token is not None:
														warnings.warn(
														    "The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers."	,				_lowerCamelCase  )
														if token is not None:
																		raise ValueError(
																		    "`token` and `use_auth_token` are both specified. Please set only the argument `token`."  )
														__magic_name__	  = use_auth_token

										if token is not None:
														# change to `token` in a follow-up PR
														__magic_name__	  = token

										__magic_name__	  = cls._get_arguments_from_pretrained(_lowerCamelCase	,				**_lowerCamelCase  )
										return cls(*_lowerCamelCase  )
						@classmethod
						def 					__A					( cls				:					Optional[Any]	,				_lowerCamelCase				:					Union[str, Any]="AutoProcessor"  )							->					List[str]:
										if not isinstance(_lowerCamelCase	,				_lowerCamelCase  ):
														__magic_name__	  = auto_class.__name__

										import transformers.models.auto as auto_module

										if not hasattr(_lowerCamelCase	,				_lowerCamelCase  ):
														raise ValueError(f'{auto_class} is not a valid auto class.'  )

										__magic_name__	  = auto_class
						@classmethod
						def 					__A					( cls				:					Dict	,				_lowerCamelCase				:					List[str]	,				**_lowerCamelCase				:					List[Any]  )							->					Dict:
										__magic_name__	  = []
										for attribute_name in cls.attributes:
														__magic_name__	  = getattr(cls	,				f'{attribute_name}_class'  )
														if isinstance(_lowerCamelCase	,				_lowerCamelCase  ):
																		__magic_name__	  = tuple(getattr(_lowerCamelCase	,				_lowerCamelCase  ) if n is not None else None for n in class_name  )
																		__magic_name__	  = kwargs.get("use_fast"	,				_lowerCamelCase  )
																		if use_fast and classes[1] is not None:
																						__magic_name__	  = classes[1]
																		else:
																						__magic_name__	  = classes[0]
														else:
																		__magic_name__	  = getattr(_lowerCamelCase	,				_lowerCamelCase  )

														args.append(attribute_class.from_pretrained(_lowerCamelCase	,				**_lowerCamelCase  )  )
										return args

						@property
						def 					__A					( self				:					List[Any]  )							->					int:
										__magic_name__	  = getattr(self	,				self.attributes[0]  )
										return getattr(_lowerCamelCase	,				"model_input_names"	,				_lowerCamelCase  )





__magic_name__   :   str  							=copy_func(ProcessorMixin.push_to_hub)
if ProcessorMixin.push_to_hub.__doc__ is not None:
						__magic_name__   :   Tuple  							=ProcessorMixin.push_to_hub.__doc__.format(
						    object='processor', object_class='AutoProcessor', object_files='processor files'
						)







'''simple docstring'''
def      __snake_case       (       lowerCamelCase_ :				int = 100						):
				'''simple docstring'''




				__magic_name__	  = [[0 for _ in range(lowerCamelCase_						)] for _ in range(m + 1						)]
				for i in range(m + 1						):
								__magic_name__	  = 1

				for n in range(m + 1						):
								for k in range(1	,  lowerCamelCase_						):
												memo[n][k] += memo[n][k - 1]
												if n > k:
																memo[n][k] += memo[n - k - 1][k]

				return memo[m][m - 1] - 1


if __name__ == "__main__":
						print(solution(int(input('Enter a number: ').strip())))



