





"""simple docstring"""






from __future__ import annotations

A 			=  [
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]

def     __A      (			a_						:list[list[int]]   ,       a_						:list[int]   ,       a_						:list[int]   ,       a_						:int   ,       a_						:list[list[int]]   ,       )		->		tuple[list[list[int]], list[list[int]]]:
				__a :						Any			       =  [
				    [0 for col in range(len(grid[0]))] for row in range(len(a_))
				]  # the reference grid
				__a :						Optional[Any]			       =  1
				__a :						Dict			       =  [
				    [0 for col in range(len(grid[0]))] for row in range(len(a_))
				]  # the action grid

				__a :						Any			       =  init[0]
				__a :						Any			       =  init[1]
				__a :						List[Any]			       =  0
				__a :						Union[str, Any]			       =  g + heuristic[x][y]  # cost from starting cell to destination cell
				__a :						int			       =  [[f, g, x, y]]

				__a :						Optional[Any]			       =  False  # flag that is set when search is complete
				__a :						List[Any]			       =  False  # flag set if we can't find expand

				while not found and not resign:
								if len(a_) == 0:
												raise ValueError('''Algorithm is unable to find solution''')
								else:  # to choose the least costliest action so as to move closer to the goal
												cell.sort()
												cell.reverse()
												__a :						List[str]			       =  cell.pop()
												__a :						Optional[int]			       =  next_cell[2]
												__a :						int			       =  next_cell[3]
												__a :						Union[str, Any]			       =  next_cell[1]

												if x == goal[0] and y == goal[1]:
																__a :						Optional[int]			       =  True
												else:
																for i in range(len(a_)):  # to try out different valid actions
																				__a :						List[Any]			       =  x + DIRECTIONS[i][0]
																				__a :						str			       =  y + DIRECTIONS[i][1]
																				if xa >= 0 and xa < len(a_) and ya >= 0 and ya < len(grid[0]):
																								if closed[xa][ya] == 0 and grid[xa][ya] == 0:
																												__a :						List[Any]			       =  g + cost
																												__a :						Optional[Any]			       =  ga + heuristic[xa][ya]
																												cell.append([fa, ga, xa, ya])
																												__a :						Any			       =  1
																												__a :						int			       =  i
				__a :						Tuple			       =  []
				__a :						int			       =  goal[0]
				__a :						Union[str, Any]			       =  goal[1]
				invpath.append([x, y])  # we get the reverse path from here
				while x != init[0] or y != init[1]:
								__a :						str			       =  x - DIRECTIONS[action[x][y]][0]
								__a :						str			       =  y - DIRECTIONS[action[x][y]][1]
								__a :						List[Any]			       =  xa
								__a :						Union[str, Any]			       =  ya
								invpath.append([x, y])

				__a :						Optional[Any]			       =  []
				for i in range(len(a_)):
								path.append(invpath[len(a_) - 1 - i])
				return path, action


if __name__ == "__main__":
		A 			=  [
		    [0, 1, 0, 0, 0, 0],
		    [0, 1, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
		    [0, 1, 0, 0, 0, 0],
		    [0, 1, 0, 0, 1, 0],
		    [0, 0, 0, 0, 1, 0],
		]

		A 			=  [0, 0]
		# all coordinates are given in format [y,x]
		A 			=  [len(grid) - 1, len(grid[0]) - 1]
		A 			=  1

		# the cost map which pushes the path closer to the goal
		A 			=  [[0 for row in range(len(grid[0]))] for col in range(len(grid))]
		for i in range(len(grid)):
				for j in range(len(grid[0])):
						A 			=  abs(i - goal[0]) + abs(j - goal[1])
						if grid[i][j] == 1:
								# added extra penalty in the heuristic map
								A 			=  99

		A			,   A 			=  search(grid, init, goal, cost, heuristic)

		print('''ACTION MAP''')
		for i in range(len(action)):
				print(action[i])

		for i in range(len(path)):
				print(path[i])






"""simple docstring"""






from typing import List, Optional

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


A 			=  logging.get_logger(__name__)

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




class 				__lowercase			(			_UpperCamelCase							):







					'''simple docstring'''




					__lowerCAmelCase  =  '''autoformer'''
					__lowerCAmelCase  =  {
					    '''hidden_size''': '''d_model''',
					    '''num_attention_heads''': '''encoder_attention_heads''',
					    '''num_hidden_layers''': '''encoder_layers''',
					}







					def __init__(						self	,			_UpperCAmelCase = None	,			_UpperCAmelCase = None	,			_UpperCAmelCase = "student_t"	,			_UpperCAmelCase = "nll"	,			_UpperCAmelCase = 1	,			_UpperCAmelCase = [1, 2, 3, 4, 5, 6, 7]	,			_UpperCAmelCase = True	,			_UpperCAmelCase = 0	,			_UpperCAmelCase = 0	,			_UpperCAmelCase = 0	,			_UpperCAmelCase = 0	,			_UpperCAmelCase = None	,			_UpperCAmelCase = None	,			_UpperCAmelCase = 64	,			_UpperCAmelCase = 2	,			_UpperCAmelCase = 2	,			_UpperCAmelCase = 2	,			_UpperCAmelCase = 2	,			_UpperCAmelCase = 32	,			_UpperCAmelCase = 32	,			_UpperCAmelCase = "gelu"	,			_UpperCAmelCase = 0.1	,			_UpperCAmelCase = 0.1	,			_UpperCAmelCase = 0.1	,			_UpperCAmelCase = 0.1	,			_UpperCAmelCase = 0.1	,			_UpperCAmelCase = 100	,			_UpperCAmelCase = 0.0_2	,			_UpperCAmelCase = True	,			_UpperCAmelCase=True	,			_UpperCAmelCase = 10	,			_UpperCAmelCase = 25	,			_UpperCAmelCase = 3	,			**_UpperCAmelCase	,			):
									# time series specific configuration
									__a :						Optional[int]			       =  prediction_length
									__a :						Optional[int]			       =  context_length if context_length is not None else prediction_length
									__a :						Union[str, Any]			       =  distribution_output
									__a :						str			       =  loss
									__a :						Optional[Any]			       =  input_size
									__a :						str			       =  num_time_features
									__a :						Optional[Any]			       =  lags_sequence
									__a :						Any			       =  scaling
									__a :						Optional[Any]			       =  num_dynamic_real_features
									__a :						str			       =  num_static_real_features
									__a :						Any			       =  num_static_categorical_features
									if cardinality is not None and num_static_categorical_features > 0:
													if len(_UpperCAmelCase     ) != num_static_categorical_features:
																	raise ValueError(
																	    '''The cardinality should be a list of the same length as `num_static_categorical_features`'''     )
													__a :						List[str]			       =  cardinality
									else:
													__a :						Any			       =  [0]
									if embedding_dimension is not None and num_static_categorical_features > 0:
													if len(_UpperCAmelCase     ) != num_static_categorical_features:
																	raise ValueError(
																	    '''The embedding dimension should be a list of the same length as `num_static_categorical_features`'''     )
													__a :						Any			       =  embedding_dimension
									else:
													__a :						Tuple			       =  [min(50	,			(cat + 1) // 2     ) for cat in self.cardinality]
									__a :						Union[str, Any]			       =  num_parallel_samples

									# Transformer architecture configuration
									__a :						Union[str, Any]			       =  input_size * len(self.lags_sequence     ) + self._number_of_features
									__a :						List[Any]			       =  d_model
									__a :						List[str]			       =  encoder_attention_heads
									__a :						Union[str, Any]			       =  decoder_attention_heads
									__a :						Optional[int]			       =  encoder_ffn_dim
									__a :						List[str]			       =  decoder_ffn_dim
									__a :						List[str]			       =  encoder_layers
									__a :						Optional[int]			       =  decoder_layers

									__a :						Union[str, Any]			       =  dropout
									__a :						Union[str, Any]			       =  attention_dropout
									__a :						Optional[Any]			       =  activation_dropout
									__a :						List[str]			       =  encoder_layerdrop
									__a :						Any			       =  decoder_layerdrop

									__a :						Union[str, Any]			       =  activation_function
									__a :						Union[str, Any]			       =  init_std

									__a :						List[str]			       =  use_cache

									# Autoformer
									__a :						int			       =  label_length
									__a :						List[Any]			       =  moving_average
									__a :						str			       =  autocorrelation_factor

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







					@property
					def  _lowerCamelCase		(						self     ):
									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
									)