




"""simple docstring"""







from __future__ import annotations

from typing import Any






class       SCREAMING_SNAKE_CASE__					:


						def __init__(							self				:			Optional[Any]						,						lowerCAmelCase				:			int					):
										lowerCAmelCase  =				num_of_nodes
										lowerCAmelCase  =				[]
										lowerCAmelCase  =				{}


						def   __lowercase (							self				:			Union[str, Any]						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			int					):
										self.m_edges.append([u_node, v_node, weight]					)


						def   __lowercase (							self				:			List[Any]						,						lowerCAmelCase				:			int					):

										if self.m_component[u_node] == u_node:
														return u_node
										return self.find_component(self.m_component[u_node]					)


						def   __lowercase (							self				:			List[str]						,						lowerCAmelCase				:			int					):

										if self.m_component[u_node] != u_node:
														for k in self.m_component:
																		lowerCAmelCase  =				self.find_component(lowerCAmelCase					)


						def   __lowercase (							self				:			str						,						lowerCAmelCase				:			list[int]						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			int					):

										if component_size[u_node] <= component_size[v_node]:
														lowerCAmelCase  =				v_node
														component_size[v_node] += component_size[u_node]
														self.set_component(lowerCAmelCase					)

										elif component_size[u_node] >= component_size[v_node]:
														lowerCAmelCase  =				self.find_component(lowerCAmelCase					)
														component_size[u_node] += component_size[v_node]
														self.set_component(lowerCAmelCase					)




						def   __lowercase (							self				:			Union[str, Any]					):
										lowerCAmelCase  =				[]
										lowerCAmelCase  =				0

										lowerCAmelCase  =				[-1] * self.m_num_of_nodes

										# A list of components (initialized to all of the nodes)
										for node in range(self.m_num_of_nodes					):
														self.m_component.update({node: node}					)
														component_size.append(1					)

										lowerCAmelCase  =				self.m_num_of_nodes

										while num_of_components > 1:
														for edge in self.m_edges:
																		lowerCAmelCase ,						lowerCAmelCase ,						lowerCAmelCase  =				edge

																		lowerCAmelCase  =				self.m_component[u]
																		lowerCAmelCase  =				self.m_component[v]

																		if u_component != v_component:

																						for component in (u_component, v_component):
																										if (
																										    minimum_weight_edge[component] == -1
																										    or minimum_weight_edge[component][2] > w
																										):
																														lowerCAmelCase  =				[u, v, w]

														for edge in minimum_weight_edge:
																		if isinstance(lowerCAmelCase						,						lowerCAmelCase					):
																						lowerCAmelCase ,						lowerCAmelCase ,						lowerCAmelCase  =				edge

																						lowerCAmelCase  =				self.m_component[u]
																						lowerCAmelCase  =				self.m_component[v]

																						if u_component != v_component:
																										mst_weight += w
																										self.union(lowerCAmelCase						,						lowerCAmelCase						,						lowerCAmelCase					)
																										print(f'''Added edge [{u} - {v}]\nAdded weight: {w}\n'''					)
																										num_of_components -= 1

														lowerCAmelCase  =				[-1] * self.m_num_of_nodes
										print(f'''The total weight of the minimal spanning tree is: {mst_weight}'''					)





def 							lowercase					()     ->  None:


				'''simple docstring'''








if __name__ == "__main__":
		import doctest

		doctest.testmod()











"""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       SCREAMING_SNAKE_CASE__					(					_a					):
						_a    						=						'autoformer'
						_a    						=						{
						    'hidden_size': 'd_model',
						    'num_attention_heads': 'encoder_attention_heads',
						    'num_hidden_layers': 'encoder_layers',
						}


						def __init__(							self				:			Dict						,						lowerCAmelCase				:			Optional[int] = None						,						lowerCAmelCase				:			Optional[int] = None						,						lowerCAmelCase				:			str = "student_t"						,						lowerCAmelCase				:			str = "nll"						,						lowerCAmelCase				:			int = 1						,						lowerCAmelCase				:			List[int] = [1, 2, 3, 4, 5, 6, 7]						,						lowerCAmelCase				:			bool = True						,						lowerCAmelCase				:			int = 0						,						lowerCAmelCase				:			int = 0						,						lowerCAmelCase				:			int = 0						,						lowerCAmelCase				:			int = 0						,						lowerCAmelCase				:			Optional[List[int]] = None						,						lowerCAmelCase				:			Optional[List[int]] = None						,						lowerCAmelCase				:			int = 64						,						lowerCAmelCase				:			int = 2						,						lowerCAmelCase				:			int = 2						,						lowerCAmelCase				:			int = 2						,						lowerCAmelCase				:			int = 2						,						lowerCAmelCase				:			int = 32						,						lowerCAmelCase				:			int = 32						,						lowerCAmelCase				:			str = "gelu"						,						lowerCAmelCase				:			float = 0.1						,						lowerCAmelCase				:			float = 0.1						,						lowerCAmelCase				:			float = 0.1						,						lowerCAmelCase				:			float = 0.1						,						lowerCAmelCase				:			float = 0.1						,						lowerCAmelCase				:			int = 100						,						lowerCAmelCase				:			float = 0.02						,						lowerCAmelCase				:			bool = True						,						lowerCAmelCase				:			Tuple=True						,						lowerCAmelCase				:			int = 10						,						lowerCAmelCase				:			int = 25						,						lowerCAmelCase				:			int = 3						,						**lowerCAmelCase				:			Tuple						,						):
										# time series specific configuration
										lowerCAmelCase  =				prediction_length
										lowerCAmelCase  =				context_length if context_length is not None else prediction_length
										lowerCAmelCase  =				distribution_output
										lowerCAmelCase  =				loss
										lowerCAmelCase  =				input_size
										lowerCAmelCase  =				num_time_features
										lowerCAmelCase  =				lags_sequence
										lowerCAmelCase  =				scaling
										lowerCAmelCase  =				num_dynamic_real_features
										lowerCAmelCase  =				num_static_real_features
										lowerCAmelCase  =				num_static_categorical_features
										if cardinality is not None 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`"""					)
														lowerCAmelCase  =				cardinality
										else:
														lowerCAmelCase  =				[0]
										if embedding_dimension is not None 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`"""					)
														lowerCAmelCase  =				embedding_dimension
										else:
														lowerCAmelCase  =				[min(50						,						(cat + 1) // 2					) for cat in self.cardinality]
										lowerCAmelCase  =				num_parallel_samples

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

										lowerCAmelCase  =				dropout
										lowerCAmelCase  =				attention_dropout
										lowerCAmelCase  =				activation_dropout
										lowerCAmelCase  =				encoder_layerdrop
										lowerCAmelCase  =				decoder_layerdrop

										lowerCAmelCase  =				activation_function
										lowerCAmelCase  =				init_std

										lowerCAmelCase  =				use_cache

										# Autoformer
										lowerCAmelCase  =				label_length
										lowerCAmelCase  =				moving_average
										lowerCAmelCase  =				autocorrelation_factor

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




						@property
						def   __lowercase (							self				:			Tuple					):
										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
										)





