



'''simple docstring'''






import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig


lowerCAmelCase:					Optional[Any]						       =						logging.get_logger(__name__)

lowerCAmelCase:					List[Any]						       =						{
    'Intel/dpt-large': 'https://huggingface.co/Intel/dpt-large/resolve/main/config.json',
    # See all DPT models at https://huggingface.co/models?filter=dpt
}

class 							a__(					snake_case__	):
    lowercase__ =    """dpt"""







    def __init__(      self :  Union[str, Any]						,			__snake_case :  Union[str, Any]=7_68						,			__snake_case :  Optional[int]=12						,			__snake_case :  int=12						,			__snake_case :  Dict=30_72						,			__snake_case :  List[Any]="gelu"						,			__snake_case :  List[str]=0.0						,			__snake_case :  Any=0.0						,			__snake_case :  Dict=0.02						,			__snake_case :  int=1e-1_2						,			__snake_case :  Any=3_84						,			__snake_case :  Optional[Any]=16						,			__snake_case :  Union[str, Any]=3						,			__snake_case :  Optional[int]=False						,			__snake_case :  List[Any]=True						,			__snake_case :  Any=[2, 5, 8, 11]						,			__snake_case :  Optional[int]="project"						,			__snake_case :  List[Any]=[4, 2, 1, 0.5]						,			__snake_case :  Any=[96, 1_92, 3_84, 7_68]						,			__snake_case :  Tuple=2_56						,			__snake_case :  Optional[Any]=-1						,			__snake_case :  Tuple=False						,			__snake_case :  List[str]=True						,			__snake_case :  Optional[Any]=0.4						,			__snake_case :  str=2_55						,			__snake_case :  int=0.1						,			__snake_case :  Any=[1, 10_24, 24, 24]						,			__snake_case :  str=[0, 1]						,			__snake_case :  Union[str, Any]=None						,			**__snake_case :  List[Any]						,			):
      super().__init__(**_A	)

      a						:					Tuple        =     hidden_size
      a						:					Any        =     is_hybrid

      if self.is_hybrid:
        if backbone_config is None:
          logger.info('Initializing the config with a `BiT` backbone.'	)
          a						:					str        =     {
              'global_padding': 'same',
              'layer_type': 'bottleneck',
              'depths': [3, 4, 9],
              'out_features': ['stage1', 'stage2', 'stage3'],
              'embedding_dynamic_padding': True,
          }
          a						:					Tuple        =     BitConfig(**_A	)
        elif isinstance(_A						,			_A	):
          logger.info('Initializing the config with a `BiT` backbone.'	)
          a						:					Tuple        =     BitConfig(**_A	)
        elif isinstance(_A						,			_A	):
          a						:					Optional[int]        =     backbone_config
        else:
          raise ValueError(
              F"""backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}."""	)

        a						:					Optional[Any]        =     backbone_featmap_shape
        a						:					List[str]        =     neck_ignore_stages

        if readout_type != "project":
          raise ValueError('Readout type must be \'project\' when using `DPT-hybrid` mode.'	)
      else:
        a						:					str        =     None
        a						:					str        =     None
        a						:					Tuple        =     []

      a						:					Tuple        =     num_hidden_layers
      a						:					List[Any]        =     num_attention_heads
      a						:					str        =     intermediate_size
      a						:					Optional[Any]        =     hidden_act
      a						:					Optional[Any]        =     hidden_dropout_prob
      a						:					Optional[Any]        =     attention_probs_dropout_prob
      a						:					List[str]        =     initializer_range
      a						:					List[Any]        =     layer_norm_eps
      a						:					Tuple        =     image_size
      a						:					int        =     patch_size
      a						:					Union[str, Any]        =     num_channels
      a						:					List[Any]        =     qkv_bias
      a						:					Optional[Any]        =     backbone_out_indices
      if readout_type not in ["ignore", "add", "project"]:
        raise ValueError('Readout_type must be one of [\'ignore\', \'add\', \'project\']'	)
      a						:					int        =     readout_type
      a						:					List[str]        =     reassemble_factors
      a						:					Optional[int]        =     neck_hidden_sizes
      a						:					Any        =     fusion_hidden_size
      a						:					str        =     head_in_index
      a						:					List[Any]        =     use_batch_norm_in_fusion_residual
      # auxiliary head attributes (semantic segmentation)
      a						:					Union[str, Any]        =     use_auxiliary_head
      a						:					Tuple        =     auxiliary_loss_weight
      a						:					Union[str, Any]        =     semantic_loss_ignore_index
      a						:					str        =     semantic_classifier_dropout







    def 			lowercase_       (      self :  int	):
      a						:					Optional[int]        =     copy.deepcopy(self.__dict__	)

      if output["backbone_config"] is not None:
        a						:					Union[str, Any]        =     self.backbone_config.to_dict()

      a						:					Optional[Any]        =     self.__class__.model_type
      return output





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


UpperCamelCase__    			=			logging.get_logger(__name__)

UpperCamelCase__    			=			{
    """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 			a__    (					snake_case__      ):
						_a							:    Optional[int]					=    """decision_transformer"""
						_a							:    Optional[int]					=    ["""past_key_values"""]
						_a							:    Dict					=    {
						    """max_position_embeddings""": """n_positions""",
						    """num_attention_heads""": """n_head""",
						    """num_hidden_layers""": """n_layer""",
						}






						def __init__(   self		,			_A=1_7		,			_A=4		,			_A=1_2_8		,			_A=4_0_9_6		,			_A=True		,			_A=1		,			_A=1_0_2_4		,			_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_0_2_5_6		,			_A=5_0_2_5_6		,			_A=False		,			_A=False		,			**_A		,			):




											"""simple docstring"""



											__lowerCAmelCase				   =       state_dim
											__lowerCAmelCase				   =       act_dim
											__lowerCAmelCase				   =       hidden_size
											__lowerCAmelCase				   =       max_ep_len
											__lowerCAmelCase				   =       action_tanh
											__lowerCAmelCase				   =       vocab_size
											__lowerCAmelCase				   =       n_positions
											__lowerCAmelCase				   =       n_layer
											__lowerCAmelCase				   =       n_head
											__lowerCAmelCase				   =       n_inner
											__lowerCAmelCase				   =       activation_function
											__lowerCAmelCase				   =       resid_pdrop
											__lowerCAmelCase				   =       embd_pdrop
											__lowerCAmelCase				   =       attn_pdrop
											__lowerCAmelCase				   =       layer_norm_epsilon
											__lowerCAmelCase				   =       initializer_range
											__lowerCAmelCase				   =       scale_attn_weights
											__lowerCAmelCase				   =       use_cache
											__lowerCAmelCase				   =       scale_attn_by_inverse_layer_idx
											__lowerCAmelCase				   =       reorder_and_upcast_attn

											__lowerCAmelCase				   =       bos_token_id
											__lowerCAmelCase				   =       eos_token_id

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




