


from typing import Mapping

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


_A							     =       logging.get_logger(__name__)

_A							     =       {
    """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 			lowerCamelCase_					(    _SCREAMING_SNAKE_CASE    ):
							_lowerCamelCase				: List[str]       =					"""longt5"""
							_lowerCamelCase				: Optional[int]       =					["""past_key_values"""]
							_lowerCamelCase				: Optional[Any]       =					{"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}





							def __init__(							self			,       _SCREAMING_SNAKE_CASE=3_2128			,       _SCREAMING_SNAKE_CASE=512			,       _SCREAMING_SNAKE_CASE=64			,       _SCREAMING_SNAKE_CASE=2048			,       _SCREAMING_SNAKE_CASE=6			,       _SCREAMING_SNAKE_CASE=None			,       _SCREAMING_SNAKE_CASE=8			,       _SCREAMING_SNAKE_CASE=127			,       _SCREAMING_SNAKE_CASE=16			,       _SCREAMING_SNAKE_CASE=32			,       _SCREAMING_SNAKE_CASE=128			,       _SCREAMING_SNAKE_CASE=0.1			,       _SCREAMING_SNAKE_CASE=1E-6			,       _SCREAMING_SNAKE_CASE=1.0			,       _SCREAMING_SNAKE_CASE="relu"			,       _SCREAMING_SNAKE_CASE=True			,       _SCREAMING_SNAKE_CASE="local"			,       _SCREAMING_SNAKE_CASE=True			,       _SCREAMING_SNAKE_CASE=0			,       _SCREAMING_SNAKE_CASE=1			,       **_SCREAMING_SNAKE_CASE			,       ):
								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 			lowerCamelCase_					(    _SCREAMING_SNAKE_CASE    ):





							@property
							def   __magic_name__      (							self    ):
								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   __magic_name__      (							self    ):
								return 13


"""simple docstring"""

import os





def 					__UpperCamelCase (     )					->      int:




					"""simple docstring"""



					__lowercase        =      os.path.dirname(os.path.realpath(a__      )      )
					__lowercase        =      os.path.join(a__			,						'''triangle.txt'''      )

					with open(a__      ) as f:
										__lowercase        =      f.readlines()

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

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


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


