



"""simple docstring"""





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


__lowerCAmelCase       :					int      					=					logging.get_logger(__name__)

__lowerCAmelCase       :					Union[str, 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    _lowerCAmelCase    (				__lowercase		):





  """simple docstring"""






  _lowerCamelCase			  =       '''decision_transformer'''
  _lowerCamelCase			  =       ['''past_key_values''']
  _lowerCamelCase			  =       {
      '''max_position_embeddings''': '''n_positions''',
      '''num_attention_heads''': '''n_head''',
      '''num_hidden_layers''': '''n_layer''',
  }





  def __init__(					self							,    _lowercase=1_7							,    _lowercase=4							,    _lowercase=1_2_8							,    _lowercase=4_0_9_6							,    _lowercase=True							,    _lowercase=1							,    _lowercase=1_0_2_4							,    _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_0_2_5_6							,    _lowercase=5_0_2_5_6							,    _lowercase=False							,    _lowercase=False							,    **_lowercase							,    ) ->						List[Any]:







        '''simple docstring'''

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

        snake_case_   : Optional[Any]						=			bos_token_id
        snake_case_   : Union[str, Any]						=			eos_token_id

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






"""simple docstring"""





def 	__lowerCAmelCase						(		__UpperCamelCase      :			Union[str, Any]    ):


						'''simple docstring'''






						snake_case_   : int						=			[0] * len(__UpperCamelCase    )
						snake_case_   : List[str]						=			[]
						snake_case_   : Any						=			[1] * len(__UpperCamelCase    )

						for values in graph.values():
												for i in values:
																		indegree[i] += 1

						for i in range(len(__UpperCamelCase    )    ):
												if indegree[i] == 0:
																		queue.append(__UpperCamelCase    )

						while queue:
												snake_case_   : Optional[int]						=			queue.pop(0    )
												for x in graph[vertex]:
																		indegree[x] -= 1

																		if long_dist[vertex] + 1 > long_dist[x]:
																								snake_case_   : Union[str, Any]						=			long_dist[vertex] + 1

																		if indegree[x] == 0:
																								queue.append(__UpperCamelCase    )

						print(max(__UpperCamelCase    )    )


# Adjacency list of Graph
__lowerCAmelCase       :					str      					=					{0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}






longest_distance(graph)

