






"""simple docstring"""




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


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

UpperCAmelCase_       :							Dict             =				{
    '''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		(				snake_case_	):


	'''simple docstring'''





	lowercase		:	List[Any]							     =				"decision_transformer"
	lowercase		:	Optional[int]							     =				["past_key_values"]
	lowercase		:	Tuple							     =				{
	    "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'''

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

		_SCREAMING_SNAKE_CASE						=bos_token_id
		_SCREAMING_SNAKE_CASE						=eos_token_id

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












from __future__ import annotations





def 		__UpperCAmelCase     (	a_				,	a_				,	a_):
							snake_case_           =				list(range(len(a_)))
							snake_case_           =				[v / w for v, w in zip(a_				,	a_)]
							index.sort(key=lambda a_: ratio[i]				,	reverse=a_)

							snake_case_           =				0
							snake_case_           =				[0] * len(a_)
							for i in index:
														if weight[i] <= capacity:
																					snake_case_           =				1
																					max_value += value[i]
																					capacity -= weight[i]
														else:
																					snake_case_           =				capacity / weight[i]
																					max_value += value[i] * capacity / weight[i]
																					break

							return max_value, fractions


if __name__ == "__main__":
	import doctest

	doctest.testmod()





