



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


a     :    Optional[int]      						=					logging.get_logger(__name__)

a     :    List[Any]      						=					{
    """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_    ):
	lowercase    =				'decision_transformer'
	lowercase    =				['past_key_values']
	lowercase    =				{
	    '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=0.0_2	,	A=True	,	A=True	,	A=50256	,	A=50256	,	A=False	,	A=False	,	**A	,	)    ->    List[str]:
								UpperCAmelCase  :	List[str]							  =	state_dim
								UpperCAmelCase  :	List[str]							  =	act_dim
								UpperCAmelCase  :	Union[str, Any]							  =	hidden_size
								UpperCAmelCase  :	List[str]							  =	max_ep_len
								UpperCAmelCase  :	List[str]							  =	action_tanh
								UpperCAmelCase  :	List[str]							  =	vocab_size
								UpperCAmelCase  :	List[Any]							  =	n_positions
								UpperCAmelCase  :	Union[str, Any]							  =	n_layer
								UpperCAmelCase  :	List[str]							  =	n_head
								UpperCAmelCase  :	Tuple							  =	n_inner
								UpperCAmelCase  :	str							  =	activation_function
								UpperCAmelCase  :	Dict							  =	resid_pdrop
								UpperCAmelCase  :	Any							  =	embd_pdrop
								UpperCAmelCase  :	Any							  =	attn_pdrop
								UpperCAmelCase  :	Union[str, Any]							  =	layer_norm_epsilon
								UpperCAmelCase  :	List[str]							  =	initializer_range
								UpperCAmelCase  :	List[Any]							  =	scale_attn_weights
								UpperCAmelCase  :	Optional[int]							  =	use_cache
								UpperCAmelCase  :	Union[str, Any]							  =	scale_attn_by_inverse_layer_idx
								UpperCAmelCase  :	Dict							  =	reorder_and_upcast_attn

								UpperCAmelCase  :	List[str]							  =	bos_token_id
								UpperCAmelCase  :	Optional[int]							  =	eos_token_id

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






'''simple docstring'''




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


a     :    str      						=					logging.get_logger(__name__)

a     :    Any      						=					{
    """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/config.json""",
    """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/config.json"""
    # See all FNet models at https://huggingface.co/models?filter=fnet
}

class     UpperCamelCase_				(							__magic_name__    ):
	lowercase    =				'fnet'





	def __init__(						self	,	A=32000	,	A=768	,	A=12	,	A=3072	,	A="gelu_new"	,	A=0.1	,	A=512	,	A=4	,	A=0.0_2	,	A=1e-12	,	A=False	,	A=512	,	A=3	,	A=1	,	A=2	,	**A	,	)    ->    int:
								super().__init__(pad_token_id=A	,	bos_token_id=A	,	eos_token_id=A	,	**A					)

								UpperCAmelCase  :	Optional[Any]							  =	vocab_size
								UpperCAmelCase  :	Dict							  =	max_position_embeddings
								UpperCAmelCase  :	List[str]							  =	hidden_size
								UpperCAmelCase  :	int							  =	num_hidden_layers
								UpperCAmelCase  :	Any							  =	intermediate_size
								UpperCAmelCase  :	List[str]							  =	hidden_act
								UpperCAmelCase  :	Tuple							  =	hidden_dropout_prob
								UpperCAmelCase  :	List[str]							  =	initializer_range
								UpperCAmelCase  :	List[Any]							  =	type_vocab_size
								UpperCAmelCase  :	int							  =	layer_norm_eps
								UpperCAmelCase  :	Optional[Any]							  =	use_tpu_fourier_optimizations
								UpperCAmelCase  :	List[Any]							  =	tpu_short_seq_length

