



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 		snake_case						(	SCREAMING_SNAKE_CASE_  ):



   '''simple docstring'''






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

   def __init__(					self							:			List[Any]				,		lowerCAmelCase							:			str=17				,		lowerCAmelCase							:			Union[str, Any]=4				,		lowerCAmelCase							:			List[Any]=128				,		lowerCAmelCase							:			Union[str, Any]=4096				,		lowerCAmelCase							:			Dict=True				,		lowerCAmelCase							:			Dict=1				,		lowerCAmelCase							:			Any=1024				,		lowerCAmelCase							:			Optional[Any]=3				,		lowerCAmelCase							:			Union[str, Any]=1				,		lowerCAmelCase							:			Dict=None				,		lowerCAmelCase							:			Any="relu"				,		lowerCAmelCase							:			List[str]=0.1				,		lowerCAmelCase							:			List[str]=0.1				,		lowerCAmelCase							:			List[Any]=0.1				,		lowerCAmelCase							:			Tuple=1E-5				,		lowerCAmelCase							:			Optional[int]=0.02				,		lowerCAmelCase							:			Optional[int]=True				,		lowerCAmelCase							:			Tuple=True				,		lowerCAmelCase							:			List[Any]=5_0256				,		lowerCAmelCase							:			Optional[Any]=5_0256				,		lowerCAmelCase							:			Union[str, Any]=False				,		lowerCAmelCase							:			int=False				,		**lowerCAmelCase							:			List[str]				,		)				->				Union[str, Any]:

    """simple docstring"""







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

    _snake_case    :  Optional[Any]					=					bos_token_id
    _snake_case    :  List[Any]					=					eos_token_id

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






import warnings

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







class 		snake_case						(	SCREAMING_SNAKE_CASE_  ):



   '''simple docstring'''






   snake_case_					: List[str]          =		["""image_processor""", """tokenizer"""]
   snake_case_					: str          =		"""ChineseCLIPImageProcessor"""
   snake_case_					: Tuple          =		("""BertTokenizer""", """BertTokenizerFast""")

   def __init__(					self							:			Any				,		lowerCAmelCase							:			Dict=None				,		lowerCAmelCase							:			List[Any]=None				,		**lowerCAmelCase							:			str)				->				Optional[Any]:

    """simple docstring"""







    _snake_case    :  List[str]					=					None
    if "feature_extractor" in kwargs:
     warnings.warn(
         """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
         """ instead."""				,		lowerCAmelCase				,		)
     _snake_case    :  Tuple					=					kwargs.pop("""feature_extractor""")

    _snake_case    :  Dict					=					image_processor if image_processor is not None else feature_extractor
    if image_processor is None:
     raise ValueError("""You need to specify an `image_processor`.""")
    if tokenizer is None:
     raise ValueError("""You need to specify a `tokenizer`.""")

    super().__init__(lowerCAmelCase				,		lowerCAmelCase)
    _snake_case    :  Optional[int]					=					self.image_processor

   def __call__(					self							:			List[Any]				,		lowerCAmelCase							:			str=None				,		lowerCAmelCase							:			Optional[int]=None				,		lowerCAmelCase							:			List[Any]=None				,		**lowerCAmelCase							:			Optional[int])				->				Optional[Any]:

    """simple docstring"""







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

    if text is not None:
     _snake_case    :  Dict					=					self.tokenizer(lowerCAmelCase				,		return_tensors=lowerCAmelCase				,		**lowerCAmelCase)

    if images is not None:
     _snake_case    :  Any					=					self.image_processor(lowerCAmelCase				,		return_tensors=lowerCAmelCase				,		**lowerCAmelCase)

    if text is not None and images is not None:
     _snake_case    :  Optional[Any]					=					image_features.pixel_values
     return encoding
    elif text is not None:
     return encoding
    else:
     return BatchEncoding(data=dict(**lowerCAmelCase)				,		tensor_type=lowerCAmelCase)

   def      UpperCamelCase_  (					self							:			Dict				,		*lowerCAmelCase							:			Tuple				,		**lowerCAmelCase							:			Any)				->				Optional[Any]:

    """simple docstring"""







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

   def      UpperCamelCase_  (					self							:			Tuple				,		*lowerCAmelCase							:			Tuple				,		**lowerCAmelCase							:			int)				->				str:

    """simple docstring"""







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

   @property
   def      UpperCamelCase_  (					self							:			Union[str, Any])				->				List[str]:

    """simple docstring"""







    _snake_case    :  Dict					=					self.tokenizer.model_input_names
    _snake_case    :  int					=					self.image_processor.model_input_names
    return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))


   @property
   def      UpperCamelCase_  (					self							:			str)				->				Tuple:

    """simple docstring"""







    warnings.warn(
        """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""				,		lowerCAmelCase				,		)
    return self.image_processor_class
