


'''simple docstring'''




from ctypes import c_float, sizeof
from enum import Enum
from typing import TYPE_CHECKING, Optional, Union


if TYPE_CHECKING:
				from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore





class _snake_case (			a__ ):
							"""simple docstring"""





							__a				       =  c_float







							@property
							def 		__SCREAMING_SNAKE_CASE( self						)  ->int:



											"""simple docstring"""







											return sizeof(self.value						)





def 				a_							(    lowercase				,    lowercase				,    lowercase = 0     )      ->int:






				'''simple docstring'''

				if dimension <= 0:
								__lowerCamelCase		:   Optional[Any]           =       fixed_dimension

				dimension -= num_token_to_add
				return dimension





def 				a_							(    lowercase				,    lowercase     )      ->int:






				'''simple docstring'''

				return num_parameters * dtype.size





def 				a_							(    lowercase     )      ->Optional[Union["AutoTokenizer", "AutoFeatureExtractor", "AutoProcessor"]]:






				'''simple docstring'''

				from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

				try:
								return AutoProcessor.from_pretrained(lowercase     )
				except (ValueError, OSError, KeyError):
								__lowerCamelCase    ,       __lowerCamelCase		:   Dict           =       None, None
								try:
												__lowerCamelCase		:   Union[str, Any]           =       AutoTokenizer.from_pretrained(lowercase     )
								except (OSError, KeyError):
												pass
								try:
												__lowerCamelCase		:   Tuple           =       AutoFeatureExtractor.from_pretrained(lowercase     )
								except (OSError, KeyError):
												pass

								if tokenizer is not None and feature_extractor is not None:
												raise ValueError(
												    F'Couldn\'t auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor.'     )
								elif tokenizer is None and feature_extractor is None:
												return None
								elif tokenizer is not None:
												return tokenizer
								else:
												return feature_extractor









'''simple docstring'''




import os





def 				a_							(    )      ->int:






				'''simple docstring'''

				__lowerCamelCase		:   Dict           =       os.path.dirname(os.path.realpath(lowercase     )     )
				__lowerCamelCase		:   Any           =       os.path.join(lowercase				,    "triangle.txt"     )

				with open(lowercase     ) as in_file:
								__lowerCamelCase		:   List[str]           =       [[int(lowercase     ) for i in line.split()] for line in in_file]

				while len(lowercase     ) != 1:
								__lowerCamelCase		:   List[str]           =       triangle.pop()
								__lowerCamelCase		:   Union[str, Any]           =       triangle[-1]
								for j in range(len(lowercase     ) - 1     ):
												curr_row[j] += max(last_row[j]				,    last_row[j + 1]     )
				return triangle[0][0]


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





