





from typing import Mapping

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


snake_case__   :				Any						=	logging.get_logger(__name__)

snake_case__   :				Dict						=	{
    'google/long-t5-local-base': 'https://huggingface.co/google/long-t5-local-base/blob/main/config.json',
    'google/long-t5-local-large': 'https://huggingface.co/google/long-t5-local-large/blob/main/config.json',
    'google/long-t5-tglobal-base': 'https://huggingface.co/google/long-t5-tglobal-base/blob/main/config.json',
    'google/long-t5-tglobal-large': 'https://huggingface.co/google/long-t5-tglobal-large/blob/main/config.json',
}





class A_   (     _lowerCamelCase  ):
   lowerCAmelCase__  	=					"""longt5"""
   lowerCAmelCase__  	=					["""past_key_values"""]
   lowerCAmelCase__  	=					{"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}




   def __init__(self      :Tuple       ,  _UpperCamelCase      :Union[str, Any]=3_2128       ,  _UpperCamelCase      :Union[str, Any]=512       ,  _UpperCamelCase      :List[str]=64       ,  _UpperCamelCase      :str=2048       ,  _UpperCamelCase      :int=6       ,  _UpperCamelCase      :Any=None       ,  _UpperCamelCase      :List[str]=8       ,  _UpperCamelCase      :int=127       ,  _UpperCamelCase      :List[Any]=16       ,  _UpperCamelCase      :Optional[int]=32       ,  _UpperCamelCase      :List[Any]=128       ,  _UpperCamelCase      :Tuple=0.1       ,  _UpperCamelCase      :Optional[Any]=1e-6       ,  _UpperCamelCase      :int=1.0       ,  _UpperCamelCase      :int="relu"       ,  _UpperCamelCase      :Optional[int]=True       ,  _UpperCamelCase      :Union[str, Any]="local"       ,  _UpperCamelCase      :Dict=True       ,  _UpperCamelCase      :Tuple=0       ,  _UpperCamelCase      :str=1       ,  **_UpperCamelCase      :Optional[int]       ,  )->   Tuple:
         __A			  =							vocab_size
         __A			  =							d_model
         __A			  =							d_kv
         __A			  =							d_ff
         __A			  =							num_layers
         # default = symmetry
         __A			  =							num_decoder_layers if num_decoder_layers is not None else self.num_layers
         __A			  =							num_heads
         __A			  =							local_radius
         __A			  =							global_block_size
         __A			  =							relative_attention_num_buckets
         __A			  =							relative_attention_max_distance
         __A			  =							dropout_rate
         __A			  =							layer_norm_epsilon
         __A			  =							initializer_factor
         __A			  =							feed_forward_proj
         __A			  =							encoder_attention_type
         __A			  =							use_cache

         __A			  =							self.feed_forward_proj.split('''-'''					)
         __A			  =							act_info[-1]
         __A			  =							act_info[0] == '''gated'''

         if len(_UpperCamelCase					) > 1 and act_info[0] != "gated" or len(_UpperCamelCase					) > 2:
               raise ValueError(
                   f"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
                   '''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
                   '''\'gated-gelu\' or \'relu\''''					)

         # for backwards compatibility
         if feed_forward_proj == "gated-gelu":
               __A			  =							'''gelu_new'''

         super().__init__(
             pad_token_id=_UpperCamelCase       ,  eos_token_id=_UpperCamelCase       ,  is_encoder_decoder=_UpperCamelCase       ,  **_UpperCamelCase       ,  )





class A_   (     _lowerCamelCase  ):




   @property
   def _lowerCAmelCase (self      :Any					)->   Mapping[str, Mapping[int, str]]:
         __A			  =							{
             '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
             '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
         }
         if self.use_past:
               __A			  =							'''past_encoder_sequence + sequence'''
               __A			  =							{0: '''batch'''}
               __A			  =							{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
         else:
               __A			  =							{0: '''batch''', 1: '''decoder_sequence'''}
               __A			  =							{0: '''batch''', 1: '''decoder_sequence'''}

         if self.use_past:
               self.fill_with_past_key_values_(_UpperCamelCase       ,  direction='''inputs'''					)

         return common_inputs






   @property
   def _lowerCAmelCase (self      :str					)->   int:
         return 13












import os



def _a							(      )      ->		int:







      '''simple docstring'''






      __A			  =							os.path.dirname(os.path.realpath(lowerCamelCase					)					)
      __A			  =							os.path.join(lowerCamelCase   ,							'''triangle.txt'''					)

      with open(lowerCamelCase					) as in_file:
            __A			  =							[[int(lowerCamelCase					) for i in line.split()] for line in in_file]

      while len(lowerCamelCase					) != 1:
            __A			  =							triangle.pop()
            __A			  =							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())





