


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


lowerCamelCase		   =			logging.get_logger(__name__)

lowerCamelCase		   =			{
    'CarlCochet/trajectory-transformer-halfcheetah-medium-v2': (
        'https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-medium-v2/resolve/main/config.json'
    ),
    # See all TrajectoryTransformer models at https://huggingface.co/models?filter=trajectory_transformer
}




class A    (  UpperCamelCase_	):
      UpperCamelCase__				:    int ='trajectory_transformer'
      UpperCamelCase__				:    Union[str, Any] =['past_key_values']
      UpperCamelCase__				:    str ={
          'hidden_size': 'n_embd',
          'num_attention_heads': 'n_head',
          'num_hidden_layers': 'n_layer',
      }






      def __init__(   self		:	Optional[Any]     ,  lowercase_		:	int=100     ,  lowercase_		:	int=5     ,  lowercase_		:	List[Any]=1     ,  lowercase_		:	Optional[Any]=1     ,  lowercase_		:	Any=249     ,  lowercase_		:	Dict=6     ,  lowercase_		:	Optional[int]=17     ,  lowercase_		:	List[Any]=25     ,  lowercase_		:	List[str]=4     ,  lowercase_		:	Any=4     ,  lowercase_		:	int=128     ,  lowercase_		:	str=0.1     ,  lowercase_		:	int=0.1     ,  lowercase_		:	Tuple=0.1     ,  lowercase_		:	List[str]=0.0006     ,  lowercase_		:	Tuple=512     ,  lowercase_		:	Any=0.02     ,  lowercase_		:	List[str]=1E-12     ,  lowercase_		:	List[Any]=1     ,  lowercase_		:	List[Any]=True     ,  lowercase_		:	Union[str, Any]=1     ,  lowercase_		:	Union[str, Any]=5_0256     ,  lowercase_		:	Optional[int]=5_0256     ,  **lowercase_		:	List[Any]     ,  )   ->		List[Any]:
             """simple docstring"""




             _lowerCamelCase			:						List[Any]            =vocab_size
             _lowerCamelCase			:						List[Any]            =action_weight
             _lowerCamelCase			:						Dict            =reward_weight
             _lowerCamelCase			:						Optional[int]            =value_weight
             _lowerCamelCase			:						int            =max_position_embeddings
             _lowerCamelCase			:						Dict            =block_size
             _lowerCamelCase			:						Any            =action_dim
             _lowerCamelCase			:						Optional[Any]            =observation_dim
             _lowerCamelCase			:						Union[str, Any]            =transition_dim
             _lowerCamelCase			:						int            =learning_rate
             _lowerCamelCase			:						Tuple            =n_layer
             _lowerCamelCase			:						Dict            =n_head
             _lowerCamelCase			:						List[str]            =n_embd
             _lowerCamelCase			:						int            =embd_pdrop
             _lowerCamelCase			:						Dict            =attn_pdrop
             _lowerCamelCase			:						Dict            =resid_pdrop
             _lowerCamelCase			:						Any            =initializer_range
             _lowerCamelCase			:						List[str]            =layer_norm_eps
             _lowerCamelCase			:						Optional[int]            =kaiming_initializer_range
             _lowerCamelCase			:						Union[str, Any]            =use_cache
             super().__init__(pad_token_id=lowercase_     ,  bos_token_id=lowercase_     ,  eos_token_id=lowercase_     ,  **lowercase_					)






import numpy as np




def       a_ (       SCREAMING_SNAKE_CASE__   : np.ndarray						,     SCREAMING_SNAKE_CASE__   : float	):
       '''simple docstring'''





       return np.where(vector > 0						,     SCREAMING_SNAKE_CASE__						,     (alpha * (np.exp(SCREAMING_SNAKE_CASE__	) - 1))	)


if __name__ == "__main__":
 import doctest

 doctest.testmod()


