



"""simple docstring"""






from typing import List, Optional

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


_lowercase				:				Optional[Any]   		=		logging.get_logger(__name__)

_lowercase				:				Tuple   		=		{
    "huggingface/autoformer-tourism-monthly": "https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json",
}

class  __SCREAMING_SNAKE_CASE			(   lowerCAmelCase_     ):


					'''simple docstring'''





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



					def __init__( self							:						str,						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							:						Optional[Any]=True,						lowerCamelCase							:						int = 10,						lowerCamelCase							:						int = 25,						lowerCamelCase							:						int = 3,						**lowerCamelCase							:						Optional[int],						)->      List[Any]:
								# time series specific configuration
								lowerCamelCase__	:	Union[str, Any]				=prediction_length
								lowerCamelCase__	:	List[Any]				=context_length if context_length is not None else prediction_length
								lowerCamelCase__	:	Dict				=distribution_output
								lowerCamelCase__	:	int				=loss
								lowerCamelCase__	:	Any				=input_size
								lowerCamelCase__	:	Optional[Any]				=num_time_features
								lowerCamelCase__	:	Tuple				=lags_sequence
								lowerCamelCase__	:	List[str]				=scaling
								lowerCamelCase__	:	Any				=num_dynamic_real_features
								lowerCamelCase__	:	Any				=num_static_real_features
								lowerCamelCase__	:	Dict				=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__	:	Tuple				=cardinality
								else:
											lowerCamelCase__	:	Tuple				=[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__	:	List[str]				=embedding_dimension
								else:
											lowerCamelCase__	:	List[str]				=[min(50,						(cat + 1) // 2  ) for cat in self.cardinality]
								lowerCamelCase__	:	Tuple				=num_parallel_samples

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

								lowerCamelCase__	:	List[str]				=dropout
								lowerCamelCase__	:	str				=attention_dropout
								lowerCamelCase__	:	Optional[int]				=activation_dropout
								lowerCamelCase__	:	Optional[Any]				=encoder_layerdrop
								lowerCamelCase__	:	Optional[int]				=decoder_layerdrop

								lowerCamelCase__	:	int				=activation_function
								lowerCamelCase__	:	Union[str, Any]				=init_std

								lowerCamelCase__	:	List[str]				=use_cache

								# Autoformer
								lowerCamelCase__	:	List[Any]				=label_length
								lowerCamelCase__	:	List[Any]				=moving_average
								lowerCamelCase__	:	Optional[int]				=autocorrelation_factor

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




					@property
					def 	snake_case ( self							:						Tuple  )->      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
								)









"""simple docstring"""






_lowercase				:				str   		=		0  # The first color of the flag.
_lowercase				:				Dict   		=		1  # The second color of the flag.
_lowercase				:				Tuple   		=		2  # The third color of the flag.
_lowercase				:				Optional[int]   		=		(red, white, blue)






def   snake_case__     (	__lowerCamelCase       :					list			):
			"""simple docstring"""
			if not sequence:
						return []
			if len(__lowerCamelCase			) == 1:
						return list(__lowerCamelCase			)
			lowerCamelCase__	:	List[Any]				=0
			lowerCamelCase__	:	Dict				=len(__lowerCamelCase			) - 1
			lowerCamelCase__	:	Tuple				=0
			while mid <= high:
						if sequence[mid] == colors[0]:
									lowerCamelCase__			,     lowerCamelCase__	:	Optional[Any]				=sequence[mid], sequence[low]
									low += 1
									mid += 1
						elif sequence[mid] == colors[1]:
									mid += 1
						elif sequence[mid] == colors[2]:
									lowerCamelCase__			,     lowerCamelCase__	:	Union[str, Any]				=sequence[high], sequence[mid]
									high -= 1
						else:
									lowerCamelCase__	:	Dict				=f'''The elements inside the sequence must contains only {colors} values'''
									raise ValueError(__lowerCamelCase			)
			return sequence


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		_lowercase				:				Optional[Any]   		=		input("Enter numbers separated by commas:\n").strip()
		_lowercase				:				int   		=		[int(item.strip()) for item in user_input.split(",")]
		print(f'{dutch_national_flag_sort(unsorted)}')




