

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


lowercase_             = logging.get_logger(__name__)

lowercase_             = {
    'CarlCochet/trajectory-transformer-halfcheetah-medium-v2': (
        'https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-medium-v2/resolve/main/config.json'
    ),
    # See all TrajectoryTransformer models at https://huggingface.co/models?filter=trajectory_transformer
}





class 				__lowerCAmelCase  (      snake_case__							):
  _a		    =						"""trajectory_transformer"""
  _a		    =						["""past_key_values"""]
  _a		    =						{
      """hidden_size""": """n_embd""",
      """num_attention_heads""": """n_head""",
      """num_hidden_layers""": """n_layer""",
  }




  def __init__(       self							,     lowerCAmelCase=100							,     lowerCAmelCase=5							,     lowerCAmelCase=1							,     lowerCAmelCase=1							,     lowerCAmelCase=249							,     lowerCAmelCase=6							,     lowerCAmelCase=17							,     lowerCAmelCase=25							,     lowerCAmelCase=4							,     lowerCAmelCase=4							,     lowerCAmelCase=128							,     lowerCAmelCase=0.1							,     lowerCAmelCase=0.1							,     lowerCAmelCase=0.1							,     lowerCAmelCase=0.0006							,     lowerCAmelCase=512							,     lowerCAmelCase=0.02							,     lowerCAmelCase=1e-12							,     lowerCAmelCase=1							,     lowerCAmelCase=True							,     lowerCAmelCase=1							,     lowerCAmelCase=50_256							,     lowerCAmelCase=50_256							,     **lowerCAmelCase							,     )						->				Optional[Any]:
        '''simple docstring'''






        _lowercase			 =vocab_size
        _lowercase			 =action_weight
        _lowercase			 =reward_weight
        _lowercase			 =value_weight
        _lowercase			 =max_position_embeddings
        _lowercase			 =block_size
        _lowercase			 =action_dim
        _lowercase			 =observation_dim
        _lowercase			 =transition_dim
        _lowercase			 =learning_rate
        _lowercase			 =n_layer
        _lowercase			 =n_head
        _lowercase			 =n_embd
        _lowercase			 =embd_pdrop
        _lowercase			 =attn_pdrop
        _lowercase			 =resid_pdrop
        _lowercase			 =initializer_range
        _lowercase			 =layer_norm_eps
        _lowercase			 =kaiming_initializer_range
        _lowercase			 =use_cache
        super().__init__(pad_token_id=lowercase_							,     bos_token_id=lowercase_							,     eos_token_id=lowercase_							,     **lowercase_      )





def   a   (    A__			:     Optional[int]  )					->   Tuple:





      """simple docstring"""



      _lowercase			 =[0] * len(A__  )
      _lowercase			 =[]
      _lowercase			 =[]
      _lowercase			 =0

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

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

      while queue:
            _lowercase			 =queue.pop(0  )
            cnt += 1
            topo.append(A__  )
            for x in graph[vertex]:
                  indegree[x] -= 1
                  if indegree[x] == 0:
                        queue.append(A__  )

      if cnt != len(A__  ):
            print('Cycle exists'  )
      else:
            print(A__  )


# Adjacency List of Graph
lowercase_             =     {0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}







topological_sort(graph)


