

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


__lowercase										= logging.get_logger(__name__)

__lowercase										= {
    "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      lowerCamelCase_					(       lowercase_			):






    '''simple docstring'''






    a__     :						Dict      =				"decision_transformer"
    a__     :						List[Any]      =				["past_key_values"]
    a__     :						Tuple      =				{
        "max_position_embeddings": "n_positions",
        "num_attention_heads": "n_head",
        "num_hidden_layers": "n_layer",
    }







    def __init__(     self			,			__lowercase=17			,			__lowercase=4			,			__lowercase=128			,			__lowercase=4_096			,			__lowercase=True			,			__lowercase=1			,			__lowercase=1_024			,			__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=50_256			,			__lowercase=50_256			,			__lowercase=False			,			__lowercase=False			,			**__lowercase			,			)	->     Optional[int]:
        __UpperCamelCase					:int        =		state_dim
        __UpperCamelCase					:List[Any]        =		act_dim
        __UpperCamelCase					:List[str]        =		hidden_size
        __UpperCamelCase					:Tuple        =		max_ep_len
        __UpperCamelCase					:Optional[int]        =		action_tanh
        __UpperCamelCase					:Union[str, Any]        =		vocab_size
        __UpperCamelCase					:str        =		n_positions
        __UpperCamelCase					:List[str]        =		n_layer
        __UpperCamelCase					:List[str]        =		n_head
        __UpperCamelCase					:List[str]        =		n_inner
        __UpperCamelCase					:Optional[int]        =		activation_function
        __UpperCamelCase					:List[Any]        =		resid_pdrop
        __UpperCamelCase					:List[Any]        =		embd_pdrop
        __UpperCamelCase					:str        =		attn_pdrop
        __UpperCamelCase					:Optional[Any]        =		layer_norm_epsilon
        __UpperCamelCase					:List[str]        =		initializer_range
        __UpperCamelCase					:str        =		scale_attn_weights
        __UpperCamelCase					:Dict        =		use_cache
        __UpperCamelCase					:Any        =		scale_attn_by_inverse_layer_idx
        __UpperCamelCase					:List[str]        =		reorder_and_upcast_attn

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

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






def lowerCamelCase  (       SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    if not grid or not grid[0]:
        raise TypeError('''The grid does not contain the appropriate information'''     )

    for cell_n in range(1  ,      len(grid[0]     )     ):
        grid[0][cell_n] += grid[0][cell_n - 1]
    __UpperCamelCase					:Union[str, Any]        =		grid[0]

    for row_n in range(1  ,      len(SCREAMING_SNAKE_CASE     )     ):
        __UpperCamelCase					:Optional[int]        =		grid[row_n]
        __UpperCamelCase					:Dict        =		fill_row(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )
        __UpperCamelCase					:Any        =		grid[row_n]

    return grid[-1][-1]



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    current_row[0] += row_above[0]
    for cell_n in range(1  ,      len(SCREAMING_SNAKE_CASE     )     ):
        current_row[cell_n] += min(current_row[cell_n - 1]  ,      row_above[cell_n]     )

    return current_row


if __name__ == "__main__":
    import doctest

    doctest.testmod()





