
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


a_											=						logging.get_logger(__name__)

a_											=						{
    "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__					):
 UpperCAmelCase_		    =						"decision_transformer"
 UpperCAmelCase_		    =						["past_key_values"]
 UpperCAmelCase_		    =						{
     "max_position_embeddings": "n_positions",
     "num_attention_heads": "n_head",
     "num_hidden_layers": "n_layer",
 }





 def __init__(    self ,       lowercase_=17 ,       lowercase_=4 ,       lowercase_=1_28 ,       lowercase_=40_96 ,       lowercase_=True ,       lowercase_=1 ,       lowercase_=10_24 ,       lowercase_=3 ,       lowercase_=1 ,       lowercase_=None ,       lowercase_="relu" ,       lowercase_=0.1 ,       lowercase_=0.1 ,       lowercase_=0.1 ,       lowercase_=1E-5 ,       lowercase_=0.02 ,       lowercase_=True ,       lowercase_=True ,       lowercase_=5_02_56 ,       lowercase_=5_02_56 ,       lowercase_=False ,       lowercase_=False ,       **lowercase_ ,       ):
     snake_case_ :    Optional[int]     			=						state_dim
     snake_case_ :    List[Any]     			=						act_dim
     snake_case_ :    Dict     			=						hidden_size
     snake_case_ :    Union[str, Any]     			=						max_ep_len
     snake_case_ :    Dict     			=						action_tanh
     snake_case_ :    List[str]     			=						vocab_size
     snake_case_ :    Any     			=						n_positions
     snake_case_ :    Tuple     			=						n_layer
     snake_case_ :    List[str]     			=						n_head
     snake_case_ :    str     			=						n_inner
     snake_case_ :    int     			=						activation_function
     snake_case_ :    Optional[int]     			=						resid_pdrop
     snake_case_ :    Any     			=						embd_pdrop
     snake_case_ :    Union[str, Any]     			=						attn_pdrop
     snake_case_ :    Optional[Any]     			=						layer_norm_epsilon
     snake_case_ :    Tuple     			=						initializer_range
     snake_case_ :    str     			=						scale_attn_weights
     snake_case_ :    Tuple     			=						use_cache
     snake_case_ :    str     			=						scale_attn_by_inverse_layer_idx
     snake_case_ :    Union[str, Any]     			=						reorder_and_upcast_attn

     snake_case_ :    List[Any]     			=						bos_token_id
     snake_case_ :    int     			=						eos_token_id

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




'''simple docstring'''
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class    UpperCAmelCase_				(	snake_case__					):
 UpperCAmelCase_		    =						"""ClapFeatureExtractor"""
 UpperCAmelCase_		    =						("""RobertaTokenizer""", """RobertaTokenizerFast""")





 def __init__(    self ,       lowercase_ ,       lowercase_):
     super().__init__(lowercase_ ,       lowercase_)





 def __call__(    self ,       lowercase_=None ,       lowercase_=None ,       lowercase_=None ,       **lowercase_):
     snake_case_ :    Any     			=						kwargs.pop("sampling_rate" ,       lowercase_)

     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_ :    Optional[int]     			=						self.tokenizer(lowercase_ ,       return_tensors=lowercase_ ,       **lowercase_)

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

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





 def        snake_case__		(    self ,       *lowercase_ ,       **lowercase_):
     return self.tokenizer.batch_decode(*lowercase_ ,       **lowercase_)





 def        snake_case__		(    self ,       *lowercase_ ,       **lowercase_):
     return self.tokenizer.decode(*lowercase_ ,       **lowercase_)





 @property
 def        snake_case__		(    self):
     snake_case_ :    Union[str, Any]     			=						self.tokenizer.model_input_names
     snake_case_ :    int     			=						self.feature_extractor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names))




