
from typing import List, Optional, Union

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


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

SCREAMING_SNAKE_CASE							:str							 =				{
    '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      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_					    =     "time_series_transformer"
	snake_case_					    =     {
	    "hidden_size": "d_model",
	    "num_attention_heads": "encoder_attention_heads",
	    "num_hidden_layers": "encoder_layers",
	}






	def __init__(       self					: Tuple							,A					: Optional[int] = None							,A					: Optional[int] = None							,A					: str = "student_t"							,A					: str = "nll"							,A					: int = 1							,A					: List[int] = [1, 2, 3, 4, 5, 6, 7]							,A					: Optional[Union[str, bool]] = "mean"							,A					: int = 0							,A					: int = 0							,A					: int = 0							,A					: int = 0							,A					: Optional[List[int]] = None							,A					: Optional[List[int]] = None							,A					: int = 32							,A					: int = 32							,A					: int = 2							,A					: int = 2							,A					: int = 2							,A					: int = 2							,A					: bool = True							,A					: str = "gelu"							,A					: int = 64							,A					: float = 0.1							,A					: float = 0.1							,A					: float = 0.1							,A					: float = 0.1							,A					: float = 0.1							,A					: int = 1_00							,A					: float = 0.02							,A					: Union[str, Any]=True							,**A					: Optional[int]							,):
					# time series specific configuration
					__A   			= prediction_length
					__A   			= context_length or 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 and num_static_categorical_features > 0:
									if len(A ) != 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 and num_static_categorical_features > 0:
									if len(A ) != 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(A ) + 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

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







	@property
	def 				UpperCamelCase_  (       self					: Optional[Any] ):
					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
					)


def 						UpperCAmelCase			(	a_						)		->	list:







				"""simple docstring"""



				if len(a_						) <= 1:
								return [tuple(a_						)]

				__A   			= []

				def generate(a_		,   a_						):
								if k == 1:
												res.append(tuple(arr[:]						)						)
												return

								generate(k - 1		,   a_						)

								for i in range(k - 1						):
												if k % 2 == 0:  # k is even
																__A			,							__A   			= arr[k - 1], arr[i]
												else:  # k is odd
																__A			,							__A   			= arr[k - 1], arr[0]
												generate(k - 1		,   a_						)

				generate(len(a_						)		,   a_						)
				return res


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:int							 =				input('Enter numbers separated by a comma:\n').strip()
					SCREAMING_SNAKE_CASE							:Dict							 =				[int(item) for item in user_input.split(',')]
					print(heaps(arr))
