


"""simple docstring"""







from typing import List, Optional, Union

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


a__				:							Tuple						=     logging.get_logger(__name__)

a__				:							Tuple						=     {
    '''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  UpperCamelCase_	( UpperCamelCase):

	"""simple docstring"""





	snake_case__				:  Optional[int]    					=			"time_series_transformer"
	snake_case__				:  Optional[int]    					=			{
	    "hidden_size": "d_model",
	    "num_attention_heads": "encoder_attention_heads",
	    "num_hidden_layers": "encoder_layers",
	}




	def __init__( self			:				Union[str, Any]			,			UpperCAmelCase__			:				Optional[int] = None			,			UpperCAmelCase__			:				Optional[int] = None			,			UpperCAmelCase__			:				str = "student_t"			,			UpperCAmelCase__			:				str = "nll"			,			UpperCAmelCase__			:				int = 1			,			UpperCAmelCase__			:				List[int] = [1, 2, 3, 4, 5, 6, 7]			,			UpperCAmelCase__			:				Optional[Union[str, bool]] = "mean"			,			UpperCAmelCase__			:				int = 0			,			UpperCAmelCase__			:				int = 0			,			UpperCAmelCase__			:				int = 0			,			UpperCAmelCase__			:				int = 0			,			UpperCAmelCase__			:				Optional[List[int]] = None			,			UpperCAmelCase__			:				Optional[List[int]] = None			,			UpperCAmelCase__			:				int = 3_2			,			UpperCAmelCase__			:				int = 3_2			,			UpperCAmelCase__			:				int = 2			,			UpperCAmelCase__			:				int = 2			,			UpperCAmelCase__			:				int = 2			,			UpperCAmelCase__			:				int = 2			,			UpperCAmelCase__			:				bool = True			,			UpperCAmelCase__			:				str = "gelu"			,			UpperCAmelCase__			:				int = 6_4			,			UpperCAmelCase__			:				float = 0.1			,			UpperCAmelCase__			:				float = 0.1			,			UpperCAmelCase__			:				float = 0.1			,			UpperCAmelCase__			:				float = 0.1			,			UpperCAmelCase__			:				float = 0.1			,			UpperCAmelCase__			:				int = 1_0_0			,			UpperCAmelCase__			:				float = 0.02			,			UpperCAmelCase__			:				Optional[int]=True			,			**UpperCAmelCase__			:				Tuple			,			)       ->	Dict:
						# time series specific configuration
						__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(UpperCAmelCase__   ) != 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(UpperCAmelCase__   ) != 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(UpperCAmelCase__   ) + 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=UpperCAmelCase__			,			**UpperCAmelCase__   )




	@property
	def   UpperCAmelCase_      ( self			:				Any   )       ->	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
						)


from collections import deque





def 			lowerCamelCase_ (				lowerCamelCase__    ):
				lowerCamelCase_						=			len(lowerCamelCase__    )
				lowerCamelCase_						=			deque()
				lowerCamelCase_						=			[False for _ in range(lowerCamelCase__    )]
				lowerCamelCase_						=			[-1 for _ in range(lowerCamelCase__    )]
				lowerCamelCase_						=			index_of[:]

				def strong_connect(lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    ):
								lowerCamelCase_						=			index  # the number when this node is seen
								lowerCamelCase_						=			index  # lowest rank node reachable from here
								index += 1
								stack.append(lowerCamelCase__    )
								lowerCamelCase_						=			True

								for w in g[v]:
												if index_of[w] == -1:
																lowerCamelCase_						=			strong_connect(lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    )
																lowerCamelCase_						=			(
																    lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
																)
												elif on_stack[w]:
																lowerCamelCase_						=			(
																    lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
																)

								if lowlink_of[v] == index_of[v]:
												lowerCamelCase_						=			[]
												lowerCamelCase_						=			stack.pop()
												lowerCamelCase_						=			False
												component.append(lowerCamelCase__    )
												while w != v:
																lowerCamelCase_						=			stack.pop()
																lowerCamelCase_						=			False
																component.append(lowerCamelCase__    )
												components.append(lowerCamelCase__    )
								return index

				lowerCamelCase_						=			[]
				for v in range(lowerCamelCase__    ):
								if index_of[v] == -1:
												strong_connect(lowerCamelCase__	, 0	, lowerCamelCase__    )

				return components





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__    ):
				lowerCamelCase_						=			[[] for _ in range(lowerCamelCase__    )]
				for u, v in edges:
								g[u].append(lowerCamelCase__    )
				return g


if __name__ == "__main__":
			# Test
			__A				=7
			__A				=[0, 0, 1, 2, 3, 3, 4, 4, 6]
			__A				=[1, 3, 2, 0, 1, 4, 5, 6, 5]
			__A				=[(u, v) for u, v in zip(source, target)]
			__A				=create_graph(n_vertices, edges)

			assert [[5], [6], [4], [3, 2, 1, 0]] == tarjan(g)


