
"""simple docstring"""


import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING


_lowercase							      = logging.get_logger(__name__)







class  lowerCAmelCase_ (					_lowercase			):



						'''simple docstring'''


						_lowerCamelCase:			List[str]					=			"upernet"


						def __init__(       self   :					Any     ,A_   :					Optional[int]=None     ,A_   :					int=512     ,A_   :					Optional[Any]=0.02     ,A_   :					Any=[1, 2, 3, 6]     ,A_   :					Optional[int]=True     ,A_   :					List[Any]=0.4     ,A_   :					int=384     ,A_   :					Tuple=256     ,A_   :					Tuple=1     ,A_   :					Any=False     ,A_   :					Tuple=255     ,**A_   :					Optional[Any]     ,)   ->      int:
							super().__init__(**__A					)

							if backbone_config is None:
								logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'					)
								A        =      CONFIG_MAPPING["resnet"](out_features=['stage1', 'stage2', 'stage3', 'stage4']					)
							elif isinstance(__A     ,__A					):
								A        =      backbone_config.get('model_type'					)
								A        =      CONFIG_MAPPING[backbone_model_type]
								A        =      config_class.from_dict(__A					)

							A        =      backbone_config
							A        =      hidden_size
							A        =      initializer_range
							A        =      pool_scales
							A        =      use_auxiliary_head
							A        =      auxiliary_loss_weight
							A        =      auxiliary_in_channels
							A        =      auxiliary_channels
							A        =      auxiliary_num_convs
							A        =      auxiliary_concat_input
							A        =      loss_ignore_index




						def   _SCREAMING_SNAKE_CASE					(       self   :					Optional[int]					)   ->      str:
							A        =      copy.deepcopy(self.__dict__					)
							A        =      self.backbone_config.to_dict()
							A        =      self.__class__.model_type
							return output



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


_snake_case             =     logging.get_logger(__name__)

_snake_case             =     {
    '''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 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   __A :      str     		=		"decision_transformer"
   __A :      Union[str, Any]     		=		["past_key_values"]
   __A :      Optional[int]     		=		{
       "max_position_embeddings": "n_positions",
       "num_attention_heads": "n_head",
       "num_hidden_layers": "n_layer",
   }






   def __init__(		self						,				__A=17						,				__A=4						,				__A=128						,				__A=4096						,				__A=True						,				__A=1						,				__A=1024						,				__A=3						,				__A=1						,				__A=None						,				__A="relu"						,				__A=0.1						,				__A=0.1						,				__A=0.1						,				__A=1e-5						,				__A=0.02						,				__A=True						,				__A=True						,				__A=5_0256						,				__A=5_0256						,				__A=False						,				__A=False						,				**__A						,				):






     """simple docstring"""





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

     lowerCamelCase :					Dict    =				bos_token_id
     lowerCamelCase :					Any    =				eos_token_id

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



