






"""simple docstring"""
import numpy as np


class _lowerCAmelCase						:







						def __init__(    self							)		->							List[str]:
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    (0, 0)
										_SCREAMING_SNAKE_CASE   :		Dict       		=    None
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    0
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    0
										_SCREAMING_SNAKE_CASE   :		str       		=    0







						def __eq__(    self   ,				lowerCAmelCase_							)		->							Optional[int]:
										return self.position == cell.position







						def 			A      (    self							)		->							Any:
										print(self.position							)







class _lowerCAmelCase						:







						def __init__(    self   ,				lowerCAmelCase_=(5, 5)							)		->							Optional[Any]:
										_SCREAMING_SNAKE_CASE   :		int       		=    np.zeros(lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    world_size[0]
										_SCREAMING_SNAKE_CASE   :		int       		=    world_size[1]







						def 			A      (    self							)		->							str:
										print(self.w							)







						def 			A      (    self   ,				lowerCAmelCase_							)		->							Optional[Any]:
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    [
										    (-1, -1),
										    (-1, 0),
										    (-1, 1),
										    (0, -1),
										    (0, 1),
										    (1, -1),
										    (1, 0),
										    (1, 1),
										]
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    cell.position[0]
										_SCREAMING_SNAKE_CASE   :		int       		=    cell.position[1]
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    []
										for n in neughbour_cord:
														_SCREAMING_SNAKE_CASE   :		Tuple       		=    current_x + n[0]
														_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    current_y + n[1]
														if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
																		_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    Cell()
																		_SCREAMING_SNAKE_CASE   :		str       		=    (x, y)
																		_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    cell
																		neighbours.append(lowerCAmelCase_							)
										return neighbours







def 				lowercase__		(					lowerCamelCase,						lowerCamelCase,						lowerCamelCase       ):
				_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    []
				_SCREAMING_SNAKE_CASE   :		List[Any]       		=    []
				_open.append(lowerCamelCase       )

				while _open:
								_SCREAMING_SNAKE_CASE   :		List[str]       		=    np.argmin([n.f for n in _open]       )
								_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    _open[min_f]
								_closed.append(_open.pop(lowerCamelCase       )       )
								if current == goal:
												break
								for n in world.get_neigbours(lowerCamelCase       ):
												for c in _closed:
																if c == n:
																				continue
												_SCREAMING_SNAKE_CASE   :		Tuple       		=    current.g + 1
												_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		Tuple       		=    n.position
												_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		int       		=    goal.position
												_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    (ya - ya) ** 2 + (xa - xa) ** 2
												_SCREAMING_SNAKE_CASE   :		Dict       		=    n.h + n.g

												for c in _open:
																if c == n and c.f < n.f:
																				continue
												_open.append(lowerCamelCase       )
				_SCREAMING_SNAKE_CASE   :		Any       		=    []
				while current.parent is not None:
								path.append(current.position       )
								_SCREAMING_SNAKE_CASE   :		List[Any]       		=    current.parent
				path.append(current.position       )
				return path[::-1]


if __name__ == "__main__":
			lowerCAmelCase__    						=			Gridworld()
			# Start position and goal
			lowerCAmelCase__    						=			Cell()
			lowerCAmelCase__    						=			(0, 0)
			lowerCAmelCase__    						=			Cell()
			lowerCAmelCase__    						=			(4, 4)
			print(F'path from {start.position} to {goal.position}')
			lowerCAmelCase__    						=			astar(world, start, goal)
			#   Just for visual reasons.
			for i in s:
						lowerCAmelCase__    						=			1
			print(world.w)









"""simple docstring"""
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						(    __UpperCAmelCase       ):
						SCREAMING_SNAKE_CASE_:					Any 					=				'time_series_transformer'
						SCREAMING_SNAKE_CASE_:					List[str] 					=				{
						    'hidden_size': 'd_model',
						    'num_attention_heads': 'encoder_attention_heads',
						    'num_hidden_layers': 'encoder_layers',
						}







						def __init__(    self   ,				lowerCAmelCase_ = None   ,				lowerCAmelCase_ = None   ,				lowerCAmelCase_ = "student_t"   ,				lowerCAmelCase_ = "nll"   ,				lowerCAmelCase_ = 1   ,				lowerCAmelCase_ = [1, 2, 3, 4, 5, 6, 7]   ,				lowerCAmelCase_ = "mean"   ,				lowerCAmelCase_ = 0   ,				lowerCAmelCase_ = 0   ,				lowerCAmelCase_ = 0   ,				lowerCAmelCase_ = 0   ,				lowerCAmelCase_ = None   ,				lowerCAmelCase_ = None   ,				lowerCAmelCase_ = 3_2   ,				lowerCAmelCase_ = 3_2   ,				lowerCAmelCase_ = 2   ,				lowerCAmelCase_ = 2   ,				lowerCAmelCase_ = 2   ,				lowerCAmelCase_ = 2   ,				lowerCAmelCase_ = True   ,				lowerCAmelCase_ = "gelu"   ,				lowerCAmelCase_ = 6_4   ,				lowerCAmelCase_ = 0.1   ,				lowerCAmelCase_ = 0.1   ,				lowerCAmelCase_ = 0.1   ,				lowerCAmelCase_ = 0.1   ,				lowerCAmelCase_ = 0.1   ,				lowerCAmelCase_ = 1_0_0   ,				lowerCAmelCase_ = 0.02   ,				lowerCAmelCase_=True   ,				**lowerCAmelCase_   ,				)		->							str:
										# time series specific configuration
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    prediction_length
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    context_length or prediction_length
										_SCREAMING_SNAKE_CASE   :		Any       		=    distribution_output
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    loss
										_SCREAMING_SNAKE_CASE   :		Any       		=    input_size
										_SCREAMING_SNAKE_CASE   :		Any       		=    num_time_features
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    lags_sequence
										_SCREAMING_SNAKE_CASE   :		Dict       		=    scaling
										_SCREAMING_SNAKE_CASE   :		Any       		=    num_dynamic_real_features
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    num_static_real_features
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    num_static_categorical_features
										if cardinality and num_static_categorical_features > 0:
														if len(lowerCAmelCase_							) != num_static_categorical_features:
																		raise ValueError(
																		    'The cardinality should be a list of the same length as `num_static_categorical_features`'							)
														_SCREAMING_SNAKE_CASE   :		List[str]       		=    cardinality
										else:
														_SCREAMING_SNAKE_CASE   :		Dict       		=    [0]
										if embedding_dimension and num_static_categorical_features > 0:
														if len(lowerCAmelCase_							) != 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   :		int       		=    embedding_dimension
										else:
														_SCREAMING_SNAKE_CASE   :		List[str]       		=    [min(5_0   ,				(cat + 1) // 2							) for cat in self.cardinality]
										_SCREAMING_SNAKE_CASE   :		str       		=    num_parallel_samples

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

										_SCREAMING_SNAKE_CASE   :		Dict       		=    dropout
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    attention_dropout
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    activation_dropout
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    encoder_layerdrop
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    decoder_layerdrop

										_SCREAMING_SNAKE_CASE   :		Dict       		=    activation_function
										_SCREAMING_SNAKE_CASE   :		str       		=    init_std

										_SCREAMING_SNAKE_CASE   :		Tuple       		=    use_cache

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







						@property
						def 			A      (    self							)		->							int:
										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
										)

