


'''simple docstring'''




import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import (
    TaBlock,
    TaConfig,
    TaLayerNorm,
)

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin





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





							@register_to_config
							def __init__( self      ,					__A      ,					__A      ,					__A      ,					__A      ,					__A      ,					__A      ,					__A      ,					__A      ,					__A      ,					__A = False      ,					)  ->Optional[int]:



											"""simple docstring"""







											super().__init__()

											__lowerCamelCase		:   Dict           =       nn.Linear(__A      ,					__A      ,					bias=__A						)

											__lowerCamelCase		:   str           =       nn.Embedding(__A      ,					__A						)
											__lowerCamelCase		:   Optional[int]           =       False

											__lowerCamelCase		:   Any           =       nn.Dropout(p=__A						)

											__lowerCamelCase		:   str           =       TaConfig(
											    d_model=__A      ,					num_heads=__A      ,					d_kv=__A      ,					d_ff=__A      ,					feed_forward_proj=__A      ,					dropout_rate=__A      ,					is_decoder=__A      ,					is_encoder_decoder=__A      ,					)
											__lowerCamelCase		:   int           =       nn.ModuleList()
											for lyr_num in range(__A						):
															__lowerCamelCase		:   Any           =       TaBlock(__A						)
															self.encoders.append(__A						)

											__lowerCamelCase		:   List[Any]           =       TaLayerNorm(__A						)
											__lowerCamelCase		:   int           =       nn.Dropout(p=__A						)







							def 		__SCREAMING_SNAKE_CASE( self      ,					__A      ,					__A						)  ->Any:



											"""simple docstring"""







											__lowerCamelCase		:   List[Any]           =       self.input_proj(__A						)

											# terminal relative positional encodings
											__lowerCamelCase		:   List[Any]           =       encoder_inputs.shape[1]
											__lowerCamelCase		:   List[Any]           =       torch.arange(__A      ,					device=encoder_inputs.device						)

											__lowerCamelCase		:   Optional[Any]           =       encoder_inputs_mask.sum(-1						)
											__lowerCamelCase		:   Optional[Any]           =       torch.roll(input_positions.unsqueeze(0						)      ,					tuple(seq_lens.tolist()						)      ,					dims=0						)
											x += self.position_encoding(__A						)

											__lowerCamelCase		:   Tuple           =       self.dropout_pre(__A						)

											# inverted the attention mask
											__lowerCamelCase		:   Tuple           =       encoder_inputs.size()
											__lowerCamelCase		:   str           =       self.get_extended_attention_mask(__A      ,					__A						)

											for lyr in self.encoders:
															__lowerCamelCase		:   Any           =       lyr(__A      ,					__A						)[0]
											__lowerCamelCase		:   int           =       self.layer_norm(__A						)

											return self.dropout_post(__A						), encoder_inputs_mask









'''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())





