'''simple docstring'''


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


snake_case__	:     List[str]      =    logging.get_logger(__name__)

snake_case__	:     Any      =    {
    '''mnaylor/mega-base-wikitext''': '''https://huggingface.co/mnaylor/mega-base-wikitext/resolve/main/config.json''',
}






class 	__SCREAMING_SNAKE_CASE		(			lowerCamelCase_			):







  '''simple docstring'''






  lowerCamelCase_						:List[Any]		       =						'''mega'''


  def __init__(					self ,						snake_case_=3_0_5_2_2 ,						snake_case_=1_2_8 ,						snake_case_=4 ,						snake_case_=2_5_6 ,						snake_case_=1_6 ,						snake_case_=True ,						snake_case_=6_4 ,						snake_case_=False ,						snake_case_=-1 ,						snake_case_=None ,						snake_case_=True ,						snake_case_="scalenorm" ,						snake_case_=True ,						snake_case_="silu" ,						snake_case_="softmax" ,						snake_case_=0.1 ,						snake_case_=0.1 ,						snake_case_=0.1 ,						snake_case_=False ,						snake_case_=True ,						snake_case_=2_5_6 ,						snake_case_=True ,						snake_case_=0.1 ,						snake_case_=2_0_4_8 ,						snake_case_=False ,						snake_case_=2 ,						snake_case_=0.02 ,						snake_case_=0.2 ,						snake_case_=0.02 ,						snake_case_=1.0 ,						snake_case_=1 ,						snake_case_=0 ,						snake_case_=2 ,						snake_case_="rotary" ,						snake_case_=None ,						snake_case_=True ,						snake_case_=True ,						**snake_case_ ,						):




       '''simple docstring'''

       super().__init__(pad_token_id=snake_case_ ,						bos_token_id=snake_case_ ,						eos_token_id=snake_case_ ,						**snake_case_		)

       UpperCAmelCase_				:  Optional[Any]  = vocab_size
       UpperCAmelCase_				:  str  = hidden_size
       UpperCAmelCase_				:  List[str]  = num_hidden_layers
       UpperCAmelCase_				:  Tuple  = activation
       UpperCAmelCase_				:  List[Any]  = attention_activation
       UpperCAmelCase_				:  Dict  = intermediate_size
       UpperCAmelCase_				:  List[Any]  = ema_projection_size
       UpperCAmelCase_				:  int  = bidirectional
       UpperCAmelCase_				:  int  = shared_representation_size
       UpperCAmelCase_				:  Tuple  = use_chunking
       UpperCAmelCase_				:  int  = chunk_size
       UpperCAmelCase_				:  str  = truncation
       UpperCAmelCase_				:  Optional[int]  = normalize_before_mega
       UpperCAmelCase_				:  Union[str, Any]  = normalization_type
       UpperCAmelCase_				:  Dict  = norm_affine
       UpperCAmelCase_				:  Dict  = dropout_prob
       UpperCAmelCase_				:  Optional[int]  = hidden_dropout_prob
       UpperCAmelCase_				:  Tuple  = attention_probs_dropout_prob
       UpperCAmelCase_				:  Union[str, Any]  = use_feature_dropout
       UpperCAmelCase_				:  int  = use_normalized_ffn
       UpperCAmelCase_				:  Dict  = nffn_hidden_size
       UpperCAmelCase_				:  Optional[Any]  = normalize_before_ffn
       UpperCAmelCase_				:  Union[str, Any]  = nffn_activation_dropout_prob
       UpperCAmelCase_				:  Dict  = max_positions
       UpperCAmelCase_				:  Any  = add_token_type_embeddings
       UpperCAmelCase_				:  Dict  = type_vocab_size
       UpperCAmelCase_				:  Dict  = initializer_range
       UpperCAmelCase_				:  Dict  = ema_delta_alpha_range
       UpperCAmelCase_				:  str  = ema_beta_range
       UpperCAmelCase_				:  Optional[Any]  = ema_gamma_omega_range
       UpperCAmelCase_				:  str  = relative_positional_bias
       UpperCAmelCase_				:  str  = use_cache
       UpperCAmelCase_				:  List[str]  = classifier_dropout
       UpperCAmelCase_				:  List[Any]  = add_lm_hidden_dense_layer
       UpperCAmelCase_				:  Any  = 1  # not used but required by Hugging Face







class 	__SCREAMING_SNAKE_CASE		(			lowerCamelCase_			):







  '''simple docstring'''






  @property
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       if self.task == "multiple-choice":
            UpperCAmelCase_				:  int  = {0: 'batch', 1: 'choice', 2: 'sequence'}
       else:
            UpperCAmelCase_				:  List[Any]  = {0: 'batch', 1: 'sequence'}
       return OrderedDict(
           [
               ('input_ids', dynamic_axis),
               ('attention_mask', dynamic_axis),
           ]		)









import os







def      snake_case__							(  ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE 		=os.path.dirname(os.path.realpath(lowerCAmelCase_     )     )
     SCREAMING_SNAKE_CASE 		=os.path.join(lowerCAmelCase_,       'triangle.txt'     )

     with open(lowerCAmelCase_     ) as f:
          SCREAMING_SNAKE_CASE 		=f.readlines()

     SCREAMING_SNAKE_CASE 		=[[int(lowerCAmelCase_     ) for y in x.rstrip('\r\n'     ).split(' '     )] for x in triangle]

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


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

