




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


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

lowerCamelCase	:	List[str]				=   {
    '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 						__lowercase		(UpperCamelCase__							):




      """simple docstring"""





      _snake_case												=						"""trajectory_transformer"""
      _snake_case												=						["""past_key_values"""]
      _snake_case												=						{
          """hidden_size""": """n_embd""",
          """num_attention_heads""": """n_head""",
          """num_hidden_layers""": """n_layer""",
      }

      def __init__(				self    ,				A=1_0_0    ,				A=5    ,				A=1    ,				A=1    ,				A=2_4_9    ,				A=6    ,				A=1_7    ,				A=2_5    ,				A=4    ,				A=4    ,				A=1_2_8    ,				A=0.1    ,				A=0.1    ,				A=0.1    ,				A=0.00_06    ,				A=5_1_2    ,				A=0.02    ,				A=1e-1_2    ,				A=1    ,				A=True    ,				A=1    ,				A=5_0_2_5_6    ,				A=5_0_2_5_6    ,				**A    ,				)		->    Optional[int]:
         snake_case				:    Optional[int]           =	vocab_size
         snake_case				:    str           =	action_weight
         snake_case				:    str           =	reward_weight
         snake_case				:    List[str]           =	value_weight
         snake_case				:    List[Any]           =	max_position_embeddings
         snake_case				:    Any           =	block_size
         snake_case				:    List[Any]           =	action_dim
         snake_case				:    Tuple           =	observation_dim
         snake_case				:    Dict           =	transition_dim
         snake_case				:    Optional[int]           =	learning_rate
         snake_case				:    List[Any]           =	n_layer
         snake_case				:    Union[str, Any]           =	n_head
         snake_case				:    Optional[int]           =	n_embd
         snake_case				:    Any           =	embd_pdrop
         snake_case				:    int           =	attn_pdrop
         snake_case				:    Union[str, Any]           =	resid_pdrop
         snake_case				:    List[Any]           =	initializer_range
         snake_case				:    List[Any]           =	layer_norm_eps
         snake_case				:    Optional[int]           =	kaiming_initializer_range
         snake_case				:    Union[str, Any]           =	use_cache
         super().__init__(pad_token_id=A    ,				bos_token_id=A    ,				eos_token_id=A    ,				**A     )











def 						SCREAMING_SNAKE_CASE__ (   )  ->       Dict:
   snake_case				:    Optional[int]           =	[]
   snake_case				:    Tuple           =	1

   while len(lowercase					) < 1E6:
      constant.append(str(lowercase					)					)
      i += 1

   snake_case				:    int           =	"""""".join(lowercase					)

   return (
       int(constant[0]					)
       * int(constant[9]					)
       * int(constant[99]					)
       * int(constant[999]					)
       * int(constant[9999]					)
       * int(constant[99999]					)
       * int(constant[999999]					)
   )


if __name__ == "__main__":
       print(solution())





