




'''simple docstring'''





from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class 			_snake_case		(     lowercase_							):
			lowerCAmelCase_					:				int							=   "ClapFeatureExtractor"
			lowerCAmelCase_					:				str							=   ("RobertaTokenizer", "RobertaTokenizerFast")




			def __init__( self    ,							a__    ,							a__							)    ->    Optional[int]:

										'''simple docstring'''




										super().__init__(a__    ,							a__							)




			def __call__( self    ,							a__=None    ,							a__=None    ,							a__=None    ,							**a__							)    ->    Union[str, Any]:

										'''simple docstring'''




										snake_case_      =				kwargs.pop("sampling_rate"    ,							a__							)

										if text is None and audios is None:
																	raise ValueError("You have to specify either text or audios. Both cannot be none."							)

										if text is not None:
																	snake_case_      =				self.tokenizer(a__    ,							return_tensors=a__    ,							**a__							)

										if audios is not None:
																	snake_case_      =				self.feature_extractor(
																	    a__    ,							sampling_rate=a__    ,							return_tensors=a__    ,							**a__							)

										if text is not None and audios is not None:
																	snake_case_      =				audio_features.input_features
																	return encoding
										elif text is not None:
																	return encoding
										else:
																	return BatchEncoding(data=dict(**a__							)    ,							tensor_type=a__							)




			def        lowerCAmelCase__    ( self    ,							*a__    ,							**a__							)    ->    Dict:

										'''simple docstring'''




										return self.tokenizer.batch_decode(*a__    ,							**a__							)




			def        lowerCAmelCase__    ( self    ,							*a__    ,							**a__							)    ->    Optional[int]:

										'''simple docstring'''




										return self.tokenizer.decode(*a__    ,							**a__							)




			@property
			def        lowerCAmelCase__    ( self							)    ->    List[str]:

										'''simple docstring'''




										snake_case_      =				self.tokenizer.model_input_names
										snake_case_      =				self.feature_extractor.model_input_names
										return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names							)							)










'''simple docstring'''





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


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

_SCREAMING_SNAKE_CASE							:       Optional[int]						=			{
    "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_							):
			lowerCAmelCase_					:				Dict							=   "decision_transformer"
			lowerCAmelCase_					:				List[Any]							=   ["past_key_values"]
			lowerCAmelCase_					:				Tuple							=   {
			    "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__=4_096    ,							a__=True    ,							a__=1    ,							a__=1_024    ,							a__=3    ,							a__=1    ,							a__=None    ,							a__="relu"    ,							a__=0.1    ,							a__=0.1    ,							a__=0.1    ,							a__=1e-5    ,							a__=0.0_2    ,							a__=True    ,							a__=True    ,							a__=50_256    ,							a__=50_256    ,							a__=False    ,							a__=False    ,							**a__    ,							)    ->    Optional[int]:

										'''simple docstring'''




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

										snake_case_      =				bos_token_id
										snake_case_      =				eos_token_id

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




