



'''simple docstring'''




from typing import List, Optional, Union

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


lowerCamelCase							:List[str]       = logging.get_logger(__name__)

lowerCamelCase							:Any       = {
    '''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      _lowerCAmelCase							(      __SCREAMING_SNAKE_CASE			):
	__SCREAMING_SNAKE_CASE       :		Tuple		      = 'time_series_transformer'
	__SCREAMING_SNAKE_CASE       :		Tuple		      = {
	    '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 = "mean"	,				lowercase = 0	,				lowercase = 0	,				lowercase = 0	,				lowercase = 0	,				lowercase = None	,				lowercase = None	,				lowercase = 32	,				lowercase = 32	,				lowercase = 2	,				lowercase = 2	,				lowercase = 2	,				lowercase = 2	,				lowercase = True	,				lowercase = "gelu"	,				lowercase = 64	,				lowercase = 0.1	,				lowercase = 0.1	,				lowercase = 0.1	,				lowercase = 0.1	,				lowercase = 0.1	,				lowercase = 100	,				lowercase = 0.02	,				lowercase=True	,				**lowercase	,				):
				# time series specific configuration
				A_     :						Any     =					prediction_length
				A_     :						Union[str, Any]     =					context_length or prediction_length
				A_     :						List[str]     =					distribution_output
				A_     :						int     =					loss
				A_     :						Union[str, Any]     =					input_size
				A_     :						Optional[int]     =					num_time_features
				A_     :						Optional[int]     =					lags_sequence
				A_     :						Tuple     =					scaling
				A_     :						int     =					num_dynamic_real_features
				A_     :						Union[str, Any]     =					num_static_real_features
				A_     :						Optional[int]     =					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_     :						str     =					cardinality
				else:
							A_     :						Any     =					[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_     :						Optional[int]     =					embedding_dimension
				else:
							A_     :						List[Any]     =					[min(50	,				(cat + 1) // 2							) for cat in self.cardinality]
				A_     :						List[Any]     =					num_parallel_samples

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

				A_     :						Union[str, Any]     =					dropout
				A_     :						Tuple     =					attention_dropout
				A_     :						Any     =					activation_dropout
				A_     :						Union[str, Any]     =					encoder_layerdrop
				A_     :						Tuple     =					decoder_layerdrop

				A_     :						List[Any]     =					activation_function
				A_     :						List[Any]     =					init_std

				A_     :						Dict     =					use_cache

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


	@property
	def    _a     (self							):
				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'''




def      a      (			lowerCamelCase__   ):
			'''simple docstring'''





			return " ".join(input_str.split()[::-1]   )


if __name__ == "__main__":
						import doctest

						doctest.testmod()