






"""simple docstring"""
from collections import OrderedDict
from typing import Mapping

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


A_			: Tuple				=			logging.get_logger(__name__)

A_			: int				=			{
    "google/rembert": "https://huggingface.co/google/rembert/resolve/main/config.json",
    # See all RemBERT models at https://huggingface.co/models?filter=rembert
}



class 		a_      (	snake_case_ ):







      '''simple docstring'''

      lowerCamelCase__       :				Optional[Any] =	'rembert'




      def __init__(self,     lowerCamelCase_=2_5_0_3_0_0,     lowerCamelCase_=1_1_5_2,     lowerCamelCase_=3_2,     lowerCamelCase_=1_8,     lowerCamelCase_=2_5_6,     lowerCamelCase_=1_6_6_4,     lowerCamelCase_=4_6_0_8,     lowerCamelCase_="gelu",     lowerCamelCase_=0.0,     lowerCamelCase_=0.0,     lowerCamelCase_=0.1,     lowerCamelCase_=5_1_2,     lowerCamelCase_=2,     lowerCamelCase_=0.02,     lowerCamelCase_=1e-12,     lowerCamelCase_=True,     lowerCamelCase_=0,     lowerCamelCase_=3_1_2,     lowerCamelCase_=3_1_3,     **lowerCamelCase_,     ):

       '''simple docstring'''



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

       lowerCamelCase__     :			Dict     				=						vocab_size
       lowerCamelCase__     :			List[Any]     				=						input_embedding_size
       lowerCamelCase__     :			Any     				=						output_embedding_size
       lowerCamelCase__     :			Union[str, Any]     				=						max_position_embeddings
       lowerCamelCase__     :			Optional[int]     				=						hidden_size
       lowerCamelCase__     :			Union[str, Any]     				=						num_hidden_layers
       lowerCamelCase__     :			int     				=						num_attention_heads
       lowerCamelCase__     :			str     				=						intermediate_size
       lowerCamelCase__     :			Dict     				=						hidden_act
       lowerCamelCase__     :			Dict     				=						hidden_dropout_prob
       lowerCamelCase__     :			Any     				=						attention_probs_dropout_prob
       lowerCamelCase__     :			Union[str, Any]     				=						classifier_dropout_prob
       lowerCamelCase__     :			List[Any]     				=						initializer_range
       lowerCamelCase__     :			Union[str, Any]     				=						type_vocab_size
       lowerCamelCase__     :			List[Any]     				=						layer_norm_eps
       lowerCamelCase__     :			Dict     				=						use_cache
       lowerCamelCase__     :			Any     				=						False



class 		a_      (	snake_case_ ):







      '''simple docstring'''

      @property
      def 			a__							(self					):

       '''simple docstring'''



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





      @property
      def 			a__							(self					):

       '''simple docstring'''



       return 1e-4









"""simple docstring"""
import os






def        lowerCamelCase_  (						):
 lowerCamelCase__     :			List[Any]     				=						os.path.dirname(os.path.realpath(_lowerCamelCase				)				)
 lowerCamelCase__     :			Any     				=						os.path.join(_lowerCamelCase			,   'triangle.txt'				)

 with open(_lowerCamelCase				) as in_file:
  lowerCamelCase__     :			Dict     				=						[[int(_lowerCamelCase				) for i in line.split()] for line in in_file]

 while len(_lowerCamelCase				) != 1:
  lowerCamelCase__     :			Dict     				=						triangle.pop()
  lowerCamelCase__     :			Optional[Any]     				=						triangle[-1]
  for j in range(len(_lowerCamelCase				) - 1				):
   curr_row[j] += max(last_row[j]			,   last_row[j + 1]				)
 return triangle[0][0]


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

