



from typing import List, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase__			= logging.get_logger(__name__)

lowerCamelCase__			= {
    """huggingface/autoformer-tourism-monthly""": """https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json""",
}






class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__					):
							__lowerCamelCase	:	str    ='autoformer'
							__lowerCamelCase	:	str    ={
							    'hidden_size': 'd_model',
							    'num_attention_heads': 'encoder_attention_heads',
							    'num_hidden_layers': 'encoder_layers',
							}





							def __init__(   self		:		Union[str, Any]      ,				__lowercase		:		Optional[int] = None      ,				__lowercase		:		Optional[int] = None      ,				__lowercase		:		str = "student_t"      ,				__lowercase		:		str = "nll"      ,				__lowercase		:		int = 1      ,				__lowercase		:		List[int] = [1, 2, 3, 4, 5, 6, 7]      ,				__lowercase		:		bool = True      ,				__lowercase		:		int = 0      ,				__lowercase		:		int = 0      ,				__lowercase		:		int = 0      ,				__lowercase		:		int = 0      ,				__lowercase		:		Optional[List[int]] = None      ,				__lowercase		:		Optional[List[int]] = None      ,				__lowercase		:		int = 64      ,				__lowercase		:		int = 2      ,				__lowercase		:		int = 2      ,				__lowercase		:		int = 2      ,				__lowercase		:		int = 2      ,				__lowercase		:		int = 32      ,				__lowercase		:		int = 32      ,				__lowercase		:		str = "gelu"      ,				__lowercase		:		float = 0.1      ,				__lowercase		:		float = 0.1      ,				__lowercase		:		float = 0.1      ,				__lowercase		:		float = 0.1      ,				__lowercase		:		float = 0.1      ,				__lowercase		:		int = 100      ,				__lowercase		:		float = 0.02      ,				__lowercase		:		bool = True      ,				__lowercase		:		Tuple=True      ,				__lowercase		:		int = 10      ,				__lowercase		:		int = 25      ,				__lowercase		:		int = 3      ,				**__lowercase		:		Any      ,				):

											'''simple docstring'''
											# time series specific configuration
											__a       =		prediction_length
											__a       =		context_length if context_length is not None else prediction_length
											__a       =		distribution_output
											__a       =		loss
											__a       =		input_size
											__a       =		num_time_features
											__a       =		lags_sequence
											__a       =		scaling
											__a       =		num_dynamic_real_features
											__a       =		num_static_real_features
											__a       =		num_static_categorical_features
											if cardinality is not None and num_static_categorical_features > 0:
															if len(__lowercase	) != num_static_categorical_features:
																			raise ValueError(
																			    """The cardinality should be a list of the same length as `num_static_categorical_features`"""	)
															__a       =		cardinality
											else:
															__a       =		[0]
											if embedding_dimension is not None and num_static_categorical_features > 0:
															if len(__lowercase	) != num_static_categorical_features:
																			raise ValueError(
																			    """The embedding dimension should be a list of the same length as `num_static_categorical_features`"""	)
															__a       =		embedding_dimension
											else:
															__a       =		[min(50      ,				(cat + 1) // 2	) for cat in self.cardinality]
											__a       =		num_parallel_samples

											# Transformer architecture configuration
											__a       =		input_size * len(self.lags_sequence	) + self._number_of_features
											__a       =		d_model
											__a       =		encoder_attention_heads
											__a       =		decoder_attention_heads
											__a       =		encoder_ffn_dim
											__a       =		decoder_ffn_dim
											__a       =		encoder_layers
											__a       =		decoder_layers

											__a       =		dropout
											__a       =		attention_dropout
											__a       =		activation_dropout
											__a       =		encoder_layerdrop
											__a       =		decoder_layerdrop

											__a       =		activation_function
											__a       =		init_std

											__a       =		use_cache

											# Autoformer
											__a       =		label_length
											__a       =		moving_average
											__a       =		autocorrelation_factor

											super().__init__(is_encoder_decoder=__lowercase      ,				**__lowercase	)







							@property
							def 						UpperCamelCase_					(   self		:		int	):

											'''simple docstring'''
											return (
											    sum(self.embedding_dimension	)
											    + self.num_dynamic_real_features
											    + self.num_time_features
											    + self.num_static_real_features
											    + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
											)





import os

def   lowerCAmelCase__						(							):
				"""simple docstring"""







				__a       =		os.path.dirname(os.path.realpath(_SCREAMING_SNAKE_CASE    )    )
				__a       =		os.path.join(_SCREAMING_SNAKE_CASE				,     """triangle.txt"""    )

				with open(_SCREAMING_SNAKE_CASE    ) as f:
								__a       =		f.readlines()

				__a       =		[]
				for line in triangle:
								__a       =		[]
								for number in line.strip().split(""" """    ):
												numbers_from_line.append(int(_SCREAMING_SNAKE_CASE    )    )
								a.append(_SCREAMING_SNAKE_CASE    )

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


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