
from typing import List, Optional, Union

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


__lowerCAmelCase					   =       logging.get_logger(__name__)

__lowerCAmelCase					   =       {
    'huggingface/time-series-transformer-tourism-monthly': (
        'https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json'
    ),
    # See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer
}





class     lowerCamelCase      (       __lowerCamelCase					):
	UpperCamelCase_  :					Union[str, Any]    		=	'time_series_transformer'
	UpperCamelCase_  :					List[str]    		=	{
	    'hidden_size': 'd_model',
	    'num_attention_heads': 'encoder_attention_heads',
	    'num_hidden_layers': 'encoder_layers',
	}







	def __init__(     self      :Dict   ,     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      :Optional[Union[str, bool]] = "mean"   ,     lowercase      :int = 0   ,     lowercase      :int = 0   ,     lowercase      :int = 0   ,     lowercase      :int = 0   ,     lowercase      :Optional[List[int]] = None   ,     lowercase      :Optional[List[int]] = None   ,     lowercase      :int = 3_2   ,     lowercase      :int = 3_2   ,     lowercase      :int = 2   ,     lowercase      :int = 2   ,     lowercase      :int = 2   ,     lowercase      :int = 2   ,     lowercase      :bool = True   ,     lowercase      :str = "gelu"   ,     lowercase      :int = 6_4   ,     lowercase      :float = 0.1   ,     lowercase      :float = 0.1   ,     lowercase      :float = 0.1   ,     lowercase      :float = 0.1   ,     lowercase      :float = 0.1   ,     lowercase      :int = 1_0_0   ,     lowercase      :float = 0.02   ,     lowercase      :List[str]=True   ,     **lowercase      :List[str]   ,     )		->      Union[str, Any]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= prediction_length
				SCREAMING_SNAKE_CASE     						= context_length or prediction_length
				SCREAMING_SNAKE_CASE     						= distribution_output
				SCREAMING_SNAKE_CASE     						= loss
				SCREAMING_SNAKE_CASE     						= input_size
				SCREAMING_SNAKE_CASE     						= num_time_features
				SCREAMING_SNAKE_CASE     						= lags_sequence
				SCREAMING_SNAKE_CASE     						= scaling
				SCREAMING_SNAKE_CASE     						= num_dynamic_real_features
				SCREAMING_SNAKE_CASE     						= num_static_real_features
				SCREAMING_SNAKE_CASE     						= num_static_categorical_features
				if cardinality 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`'''			)
							SCREAMING_SNAKE_CASE     						= cardinality
				else:
							SCREAMING_SNAKE_CASE     						= [0]
				if embedding_dimension 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`'''			)
							SCREAMING_SNAKE_CASE     						= embedding_dimension
				else:
							SCREAMING_SNAKE_CASE     						= [min(5_0   ,     (cat + 1) // 2			) for cat in self.cardinality]
				SCREAMING_SNAKE_CASE     						= num_parallel_samples

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

				SCREAMING_SNAKE_CASE     						= dropout
				SCREAMING_SNAKE_CASE     						= attention_dropout
				SCREAMING_SNAKE_CASE     						= activation_dropout
				SCREAMING_SNAKE_CASE     						= encoder_layerdrop
				SCREAMING_SNAKE_CASE     						= decoder_layerdrop

				SCREAMING_SNAKE_CASE     						= activation_function
				SCREAMING_SNAKE_CASE     						= init_std

				SCREAMING_SNAKE_CASE     						= use_cache

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







	@property
	def  snake_case__     (     self      :Tuple			)		->      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    a      ( a = "matrix.txt"	)     ->int:




			'''simple docstring'''


			with open(os.path.join(os.path.dirname(a	)		,						a	)	) as in_file:
						SCREAMING_SNAKE_CASE     						= in_file.read()

			SCREAMING_SNAKE_CASE     						= [[int(a	) for cell in row.split(''','''	)] for row in data.strip().splitlines()]
			SCREAMING_SNAKE_CASE     						= [[0 for cell in row] for row in grid]
			SCREAMING_SNAKE_CASE     						= len(grid[0]	)

			SCREAMING_SNAKE_CASE     						= [[0 for i in range(a	)] for j in range(a	)]
			SCREAMING_SNAKE_CASE     						= grid[0][0]
			for i in range(1		,						a	):
						SCREAMING_SNAKE_CASE     						= grid[0][i] + dp[0][i - 1]
			for i in range(1		,						a	):
						SCREAMING_SNAKE_CASE     						= grid[i][0] + dp[i - 1][0]

			for i in range(1		,						a	):
						for j in range(1		,						a	):
									SCREAMING_SNAKE_CASE     						= grid[i][j] + min(dp[i - 1][j]		,						dp[i][j - 1]	)

			return dp[-1][-1]


if __name__ == "__main__":
							print(F'''{solution() = }''')