




from typing import List, Optional, Union

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


lowerCamelCase__ :   Optional[int]					=							logging.get_logger(__name__)

lowerCamelCase__ :   List[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        lowerCamelCase_							(      __snake_case					):


 '''simple docstring'''







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



 def __init__(							self  :		str				,				_lowerCAmelCase  :		int = None				,				_lowerCAmelCase  :		Dict = None				,				_lowerCAmelCase  :		str = "student_t"				,				_lowerCAmelCase  :		int = "nll"				,				_lowerCAmelCase  :		List[str] = 1				,				_lowerCAmelCase  :		List[Any] = [1, 2, 3, 4, 5, 6, 7]				,				_lowerCAmelCase  :		Optional[int] = "mean"				,				_lowerCAmelCase  :		Optional[int] = 0				,				_lowerCAmelCase  :		Optional[int] = 0				,				_lowerCAmelCase  :		Any = 0				,				_lowerCAmelCase  :		Any = 0				,				_lowerCAmelCase  :		str = None				,				_lowerCAmelCase  :		Optional[int] = None				,				_lowerCAmelCase  :		List[str] = 32				,				_lowerCAmelCase  :		List[Any] = 32				,				_lowerCAmelCase  :		List[Any] = 2				,				_lowerCAmelCase  :		Dict = 2				,				_lowerCAmelCase  :		List[Any] = 2				,				_lowerCAmelCase  :		Dict = 2				,				_lowerCAmelCase  :		List[Any] = True				,				_lowerCAmelCase  :		int = "gelu"				,				_lowerCAmelCase  :		str = 64				,				_lowerCAmelCase  :		Optional[Any] = 0.1				,				_lowerCAmelCase  :		Optional[int] = 0.1				,				_lowerCAmelCase  :		Union[str, Any] = 0.1				,				_lowerCAmelCase  :		int = 0.1				,				_lowerCAmelCase  :		Optional[Any] = 0.1				,				_lowerCAmelCase  :		str = 100				,				_lowerCAmelCase  :		Tuple = 0.02				,				_lowerCAmelCase  :		Tuple=True				,				**_lowerCAmelCase  :		List[Any]				,				):
       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(a_						) != 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(a_						) != 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(50				,				(cat + 1) // 2						) for cat in self.cardinality]
       SCREAMING_SNAKE_CASE_							=			num_parallel_samples

       # Transformer architecture configuration
       SCREAMING_SNAKE_CASE_							=			input_size * len(a_						) + 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=a_				,				**a_						)




 @property
 def       lowerCAmelCase_  (							self  :		List[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
       )



"""simple docstring"""
from math import factorial, radians






def 		lowercase							(     _snake_case						:      float     ,   _snake_case						:      int = 18     ,   _snake_case						:      int = 10     )     ->float:
	"""simple docstring"""





	__snake_case	:						Any						=				angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0)

	# Converting from degrees to radians
	__snake_case	:						int						=				radians(_snake_case     )

	__snake_case	:						str						=				angle_in_radians
	__snake_case	:						Optional[int]						=				3
	__snake_case	:						List[Any]						=				-1

	for _ in range(_snake_case     ):
		result += (b * (angle_in_radians**a)) / factorial(_snake_case     )

		__snake_case	:						int						=				-b  # One positive term and the next will be negative and so on...
		a += 2  # Increased by 2 for every term.

	return round(_snake_case     ,   _snake_case     )


if __name__ == "__main__":
			__import__("""doctest""").testmod()





