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


SCREAMING_SNAKE_CASE__		:					Any						=					logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__		:					Union[str, Any]						=					{
    "edbeeching/decision-transformer-gym-hopper-medium": (
        "https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json"
    ),
    # See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}

class       snake_case					(      lowercase__						):
  lowercase_			=     'decision_transformer'
  lowercase_			=     ['past_key_values']
  lowercase_			=     {
      'max_position_embeddings': 'n_positions',
      'num_attention_heads': 'n_head',
      'num_hidden_layers': 'n_layer',
  }




  def __init__(      self     :	str     , a_     :	Optional[int]=17     , a_     :	Any=4     , a_     :	int=128     , a_     :	Tuple=4096     , a_     :	List[Any]=True     , a_     :	int=1     , a_     :	Any=1024     , a_     :	Optional[Any]=3     , a_     :	List[str]=1     , a_     :	str=None     , a_     :	Dict="relu"     , a_     :	int=0.1     , a_     :	Tuple=0.1     , a_     :	Optional[int]=0.1     , a_     :	Tuple=1e-5     , a_     :	str=0.02     , a_     :	Optional[int]=True     , a_     :	Optional[int]=True     , a_     :	Tuple=5_0256     , a_     :	List[Any]=5_0256     , a_     :	int=False     , a_     :	Optional[Any]=False     , **a_     :	List[Any]     , )->       Optional[int]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		state_dim
       SCREAMING_SNAKE_CASE__				:				Tuple			=		act_dim
       SCREAMING_SNAKE_CASE__				:				Dict			=		hidden_size
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		max_ep_len
       SCREAMING_SNAKE_CASE__				:				List[str]			=		action_tanh
       SCREAMING_SNAKE_CASE__				:				Any			=		vocab_size
       SCREAMING_SNAKE_CASE__				:				str			=		n_positions
       SCREAMING_SNAKE_CASE__				:				int			=		n_layer
       SCREAMING_SNAKE_CASE__				:				List[str]			=		n_head
       SCREAMING_SNAKE_CASE__				:				Dict			=		n_inner
       SCREAMING_SNAKE_CASE__				:				Tuple			=		activation_function
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		resid_pdrop
       SCREAMING_SNAKE_CASE__				:				Tuple			=		embd_pdrop
       SCREAMING_SNAKE_CASE__				:				Dict			=		attn_pdrop
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		layer_norm_epsilon
       SCREAMING_SNAKE_CASE__				:				Tuple			=		initializer_range
       SCREAMING_SNAKE_CASE__				:				List[str]			=		scale_attn_weights
       SCREAMING_SNAKE_CASE__				:				List[str]			=		use_cache
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		scale_attn_by_inverse_layer_idx
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		reorder_and_upcast_attn

       SCREAMING_SNAKE_CASE__				:				int			=		bos_token_id
       SCREAMING_SNAKE_CASE__				:				Any			=		eos_token_id

       super().__init__(bos_token_id=a_     , eos_token_id=a_     , **a_				)




from math import factorial, radians







def     _a				(      lowercase__  :			float     ,  lowercase__  :			int = 18     ,  lowercase__  :			int = 10 ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0)

     # Converting from degrees to radians
     SCREAMING_SNAKE_CASE__				:				int			=		radians(lowercase__ )

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

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

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

     return round(lowercase__     ,  lowercase__ )


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



