
'''simple docstring'''

from typing import List, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCAmelCase   :      Optional[int] 						=  logging.get_logger(__name__)

_UpperCAmelCase   :      Optional[Any] 						=  {
    """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/config.json""",
    # See all LED models at https://huggingface.co/models?filter=led
}

class 			UpperCAmelCase				(					a_ ):





		"""simple docstring"""





		A__  :   List[str]          =    'led'
		A__  :   Union[str, Any]          =    {
		    'num_attention_heads': 'encoder_attention_heads',
		    'hidden_size': 'd_model',
		    'attention_probs_dropout_prob': 'attention_dropout',
		    'initializer_range': 'init_std',
		}
		def __init__(    self  ,					_snake_case=50265  ,					_snake_case=16384  ,					_snake_case=1024  ,					_snake_case=12  ,					_snake_case=4096  ,					_snake_case=16  ,					_snake_case=12  ,					_snake_case=4096  ,					_snake_case=16  ,					_snake_case=0.0  ,					_snake_case=0.0  ,					_snake_case=True  ,					_snake_case=True  ,					_snake_case="gelu"  ,					_snake_case=1024  ,					_snake_case=0.1  ,					_snake_case=0.0  ,					_snake_case=0.0  ,					_snake_case=0.02  ,					_snake_case=2  ,					_snake_case=0.0  ,					_snake_case=1  ,					_snake_case=0  ,					_snake_case=2  ,					_snake_case = 512  ,					**_snake_case  ,					)				->    int:
				_UpperCamelCase       : Dict    =      vocab_size
				_UpperCamelCase       : List[str]    =      max_encoder_position_embeddings
				_UpperCamelCase       : Dict    =      max_decoder_position_embeddings
				_UpperCamelCase       : Any    =      d_model
				_UpperCamelCase       : Dict    =      encoder_ffn_dim
				_UpperCamelCase       : Union[str, Any]    =      encoder_layers
				_UpperCamelCase       : Optional[Any]    =      encoder_attention_heads
				_UpperCamelCase       : Optional[Any]    =      decoder_ffn_dim
				_UpperCamelCase       : Union[str, Any]    =      decoder_layers
				_UpperCamelCase       : int    =      decoder_attention_heads
				_UpperCamelCase       : Any    =      dropout
				_UpperCamelCase       : int    =      attention_dropout
				_UpperCamelCase       : Optional[int]    =      activation_dropout
				_UpperCamelCase       : int    =      activation_function
				_UpperCamelCase       : Union[str, Any]    =      init_std
				_UpperCamelCase       : Optional[Any]    =      encoder_layerdrop
				_UpperCamelCase       : Tuple    =      decoder_layerdrop
				_UpperCamelCase       : str    =      classifier_dropout
				_UpperCamelCase       : Any    =      use_cache
				_UpperCamelCase       : Any    =      encoder_layers
				_UpperCamelCase       : Union[str, Any]    =      attention_window

				super().__init__(
				    pad_token_id=_snake_case  ,					bos_token_id=_snake_case  ,					eos_token_id=_snake_case  ,					is_encoder_decoder=_snake_case  ,					decoder_start_token_id=_snake_case  ,					**_snake_case  ,					)







'''simple docstring'''

import os







def 						snake_case__	(      ) ->      Tuple:
		_UpperCamelCase       : Dict    =      os.path.dirname(os.path.realpath(UpperCamelCase	)	)
		_UpperCamelCase       : str    =      os.path.join(UpperCamelCase			,'''triangle.txt'''	)

		with open(UpperCamelCase	) as f:
				_UpperCamelCase       : Optional[Any]    =      f.readlines()

		_UpperCamelCase       : List[str]    =      [[int(UpperCamelCase	) for y in x.rstrip('''\r\n'''	).split(''' '''	)] for x in triangle]

		for i in range(1			,len(UpperCamelCase	)	):
				for j in range(len(a[i]	)	):
						_UpperCamelCase       : str    =      a[i - 1][j] if j != len(a[i - 1]	) else 0
						_UpperCamelCase       : Any    =      a[i - 1][j - 1] if j > 0 else 0
						a[i][j] += max(UpperCamelCase			,UpperCamelCase	)
		return max(a[-1]	)


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





