


class       lowercase_					:





				def __init__(				self       ,  lowercase_       ,  lowercase_)  -> str:
											a__      =name
											a__      =val





				def __str__(				self)  -> Tuple:
											return F"""{self.__class__.__name__}({self.name}, {self.val})"""





				def __lt__(				self       ,  lowercase_)  -> Any:
											return self.val < other.val







class       lowercase_					:





				def __init__(				self       ,  lowercase_)  -> Any:
											a__      ={}
											a__      ={}
											a__      =self.build_heap(lowercase_)





				def __getitem__(				self       ,  lowercase_)  -> List[str]:
											return self.get_value(lowercase_)





				def 						__UpperCamelCase    (				self       ,  lowercase_)  -> Optional[int]:
											return (idx - 1) // 2





				def 						__UpperCamelCase    (				self       ,  lowercase_)  -> int:
											return idx * 2 + 1





				def 						__UpperCamelCase    (				self       ,  lowercase_)  -> List[Any]:
											return idx * 2 + 2





				def 						__UpperCamelCase    (				self       ,  lowercase_)  -> Any:
											return self.heap_dict[key]





				def 						__UpperCamelCase    (				self       ,  lowercase_)  -> str:
											a__      =len(lowercase_) - 1
											a__      =self.get_parent_idx(lowercase_)

											for idx, i in enumerate(lowercase_):
																		a__      =idx
																		a__      =i.val

											for i in range(lowercase_       ,  -1       ,  -1):
																		self.sift_down(lowercase_       ,  lowercase_)
											return array





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_)  -> List[str]:
											while True:
																		a__      =self.get_left_child_idx(lowercase_)  # noqa: E741
																		a__      =self.get_right_child_idx(lowercase_)

																		a__      =idx
																		if l < len(lowercase_) and array[l] < array[idx]:
																									a__      =l
																		if r < len(lowercase_) and array[r] < array[smallest]:
																									a__      =r

																		if smallest != idx:
																									a__    ,     a__      =array[smallest], array[idx]
																									(
																									    (
																									    a__
																									)    ,     (
																									    a__
																									)    ,     
																									)      =(
																									    self.idx_of_element[array[smallest]],
																									    self.idx_of_element[array[idx]],
																									)
																									a__      =smallest
																		else:
																									break





				def 						__UpperCamelCase    (				self       ,  lowercase_)  -> Dict:
											a__      =self.get_parent_idx(lowercase_)
											while p >= 0 and self.heap[p] > self.heap[idx]:
																		a__    ,     a__      =self.heap[idx], self.heap[p]
																		a__    ,     a__      =(
																		    self.idx_of_element[self.heap[idx]],
																		    self.idx_of_element[self.heap[p]],
																		)
																		a__      =p
																		a__      =self.get_parent_idx(lowercase_)





				def 						__UpperCamelCase    (				self)  -> List[str]:
											return self.heap[0]





				def 						__UpperCamelCase    (				self)  -> Optional[int]:
											a__    ,     a__      =self.heap[-1], self.heap[0]
											a__    ,     a__      =(
											    self.idx_of_element[self.heap[-1]],
											    self.idx_of_element[self.heap[0]],
											)

											a__      =self.heap.pop()
											del self.idx_of_element[x]
											self.sift_down(0       ,  self.heap)
											return x





				def 						__UpperCamelCase    (				self       ,  lowercase_)  -> Tuple:
											self.heap.append(lowercase_)
											a__      =len(self.heap) - 1
											a__      =node.val
											self.sift_up(len(self.heap) - 1)





				def 						__UpperCamelCase    (				self)  -> Union[str, Any]:
											return len(self.heap) == 0





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_)  -> int:
											assert (
											    self.heap[self.idx_of_element[node]].val > new_value
											), "newValue must be less that current value"
											a__      =new_value
											a__      =new_value
											self.sift_up(self.idx_of_element[node])







_lowerCAmelCase:						Tuple   				=       Node('R', -1)
_lowerCAmelCase:						Optional[int]   				=       Node('B', 6)
_lowerCAmelCase:						Tuple   				=       Node('A', 3)
_lowerCAmelCase:						int   				=       Node('X', 1)
_lowerCAmelCase:						List[str]   				=       Node('E', 4)

# Use one of these two ways to generate Min-Heap

# Generating Min-Heap from array
_lowerCAmelCase:						int   				=       MinHeap([r, b, a, x, e])

# Generating Min-Heap by Insert method
# myMinHeap.insert(a)
# myMinHeap.insert(b)
# myMinHeap.insert(x)
# myMinHeap.insert(r)
# myMinHeap.insert(e)

# Before
print('Min Heap - before decrease key')
for i in my_min_heap.heap:
	print(i)

print('Min Heap - After decrease key of node [B -> -17]')
my_min_heap.decrease_key(b, -17)

# After
for i in my_min_heap.heap:
	print(i)

if __name__ == "__main__":
	import doctest

	doctest.testmod()





from typing import List, Optional

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


_lowerCAmelCase:						List[Any]   				=       logging.get_logger(__name__)

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




class       lowercase_					(lowercase__   ):
				snake_case	    ='autoformer'
				snake_case	    ={
				    'hidden_size': 'd_model',
				    'num_attention_heads': 'encoder_attention_heads',
				    'num_hidden_layers': 'encoder_layers',
				}





				def __init__(				self       ,  lowercase_ = None       ,  lowercase_ = None       ,  lowercase_ = "student_t"       ,  lowercase_ = "nll"       ,  lowercase_ = 1       ,  lowercase_ = [1, 2, 3, 4, 5, 6, 7]       ,  lowercase_ = True       ,  lowercase_ = 0       ,  lowercase_ = 0       ,  lowercase_ = 0       ,  lowercase_ = 0       ,  lowercase_ = None       ,  lowercase_ = None       ,  lowercase_ = 64       ,  lowercase_ = 2       ,  lowercase_ = 2       ,  lowercase_ = 2       ,  lowercase_ = 2       ,  lowercase_ = 32       ,  lowercase_ = 32       ,  lowercase_ = "gelu"       ,  lowercase_ = 0.1       ,  lowercase_ = 0.1       ,  lowercase_ = 0.1       ,  lowercase_ = 0.1       ,  lowercase_ = 0.1       ,  lowercase_ = 100       ,  lowercase_ = 0.02       ,  lowercase_ = True       ,  lowercase_=True       ,  lowercase_ = 10       ,  lowercase_ = 25       ,  lowercase_ = 3       ,  **lowercase_       ,  )  -> Union[str, Any]:
											# 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:
											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
											)

