





class        snake_case_    :






  def __init__(							self			:					Optional[Any]      )->Dict:






   '''simple docstring'''

   __lowerCAmelCase					:	Optional[Any]      =       {}






  def 				UpperCAmelCase__						(							self			:					List[Any]      )->None:






   '''simple docstring'''

   print(self.vertex      )
   for i in self.vertex:
    print(_snake_case       ,  """ -> """       ,  """ -> """.join([str(_snake_case      ) for j in self.vertex[i]]      )      )






  def 				UpperCAmelCase__						(							self			:					List[str]       ,  _snake_case			:					int       ,  _snake_case			:					int      )->None:






   '''simple docstring'''

   if from_vertex in self.vertex:
    self.vertex[from_vertex].append(_snake_case      )
   else:
    # else make a new vertex
    __lowerCAmelCase					:	List[str]      =       [to_vertex]






  def 				UpperCAmelCase__						(							self			:					Union[str, Any]      )->None:






   '''simple docstring'''

   __lowerCAmelCase					:	List[Any]      =       [False] * len(self.vertex      )

   # call the recursive helper function
   for i in range(len(self.vertex      )      ):
    if not visited[i]:
     self.dfs_recursive(_snake_case       ,  _snake_case      )






  def 				UpperCAmelCase__						(							self			:					Any       ,  _snake_case			:					int       ,  _snake_case			:					list      )->None:






   '''simple docstring'''

   __lowerCAmelCase					:	Dict      =       True

   print(_snake_case       ,  end=""" """      )

   # Recur for all the vertices that are adjacent to this node
   for i in self.vertex:
    if not visited[i]:
     self.dfs_recursive(_snake_case       ,  _snake_case      )


if __name__ == "__main__":
      _UpperCAmelCase             =							Graph()
      g.add_edge(0, 1)
      g.add_edge(0, 2)
      g.add_edge(1, 2)
      g.add_edge(2, 0)
      g.add_edge(2, 3)
      g.add_edge(3, 3)

      g.print_graph()
      print('DFS:')
      g.dfs()

      # OUTPUT:
      # 0  ->  1 -> 2
      # 1  ->  2
      # 2  ->  0 -> 3
      # 3  ->  3
      # DFS:
      #  0 1 2 3












from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


_UpperCAmelCase             =							logging.get_logger(__name__)

_UpperCAmelCase             =							{
    'EleutherAI/gpt-j-6B': 'https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json',
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}






class        snake_case_    (		__lowercase						):
  A_      							=   'gptj'
  A_      							=   {
      'max_position_embeddings': 'n_positions',
      'hidden_size': 'n_embd',
      'num_attention_heads': 'n_head',
      'num_hidden_layers': 'n_layer',
  }






  def __init__(							self			:					Tuple       ,  _snake_case			:					Optional[int]=50400       ,  _snake_case			:					Union[str, Any]=2048       ,  _snake_case			:					Tuple=4096       ,  _snake_case			:					Optional[int]=28       ,  _snake_case			:					Tuple=16       ,  _snake_case			:					Optional[Any]=64       ,  _snake_case			:					Optional[int]=None       ,  _snake_case			:					str="gelu_new"       ,  _snake_case			:					str=0.0       ,  _snake_case			:					Optional[int]=0.0       ,  _snake_case			:					List[str]=0.0       ,  _snake_case			:					Tuple=1E-5       ,  _snake_case			:					List[str]=0.02       ,  _snake_case			:					Optional[int]=True       ,  _snake_case			:					Optional[Any]=50256       ,  _snake_case			:					List[str]=50256       ,  _snake_case			:					str=False       ,  **_snake_case			:					Tuple       ,  )->List[Any]:






   '''simple docstring'''

   __lowerCAmelCase					:	str      =       vocab_size
   __lowerCAmelCase					:	Any      =       n_positions
   __lowerCAmelCase					:	Optional[Any]      =       n_embd
   __lowerCAmelCase					:	Optional[int]      =       n_layer
   __lowerCAmelCase					:	Optional[int]      =       n_head
   __lowerCAmelCase					:	List[Any]      =       n_inner
   __lowerCAmelCase					:	List[str]      =       rotary_dim
   __lowerCAmelCase					:	int      =       activation_function
   __lowerCAmelCase					:	str      =       resid_pdrop
   __lowerCAmelCase					:	Union[str, Any]      =       embd_pdrop
   __lowerCAmelCase					:	Dict      =       attn_pdrop
   __lowerCAmelCase					:	Optional[Any]      =       layer_norm_epsilon
   __lowerCAmelCase					:	Optional[Any]      =       initializer_range
   __lowerCAmelCase					:	Dict      =       use_cache

   __lowerCAmelCase					:	List[str]      =       bos_token_id
   __lowerCAmelCase					:	List[Any]      =       eos_token_id

   super().__init__(
       bos_token_id=_snake_case       ,  eos_token_id=_snake_case       ,  tie_word_embeddings=_snake_case       ,  **_snake_case      )






class        snake_case_    (		__lowercase						):






  def __init__(							self			:					Union[str, Any]       ,  _snake_case			:					PretrainedConfig       ,  _snake_case			:					str = "default"       ,  _snake_case			:					List[PatchingSpec] = None       ,  _snake_case			:					bool = False       ,  )->str:






   '''simple docstring'''

   super().__init__(_snake_case       ,  task=_snake_case       ,  patching_specs=_snake_case       ,  use_past=_snake_case      )
   if not getattr(self._config       ,  """pad_token_id"""       ,  _snake_case      ):
    # TODO: how to do that better?
    __lowerCAmelCase					:	Dict      =       0






  @property
  def 				UpperCAmelCase__						(							self			:					Tuple      )->Mapping[str, Mapping[int, str]]:






   '''simple docstring'''

   __lowerCAmelCase					:	Any      =       OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}      )
   if self.use_past:
    self.fill_with_past_key_values_(_snake_case       ,  direction="""inputs"""      )
    __lowerCAmelCase					:	Dict      =       {0: """batch""", 1: """past_sequence + sequence"""}
   else:
    __lowerCAmelCase					:	Optional[Any]      =       {0: """batch""", 1: """sequence"""}

   return common_inputs






  @property
  def 				UpperCAmelCase__						(							self			:					Dict      )->int:






   '''simple docstring'''

   return self._config.n_layer






  @property
  def 				UpperCAmelCase__						(							self			:					str      )->int:






   '''simple docstring'''

   return self._config.n_head






  def 				UpperCAmelCase__						(							self			:					str       ,  _snake_case			:					PreTrainedTokenizer       ,  _snake_case			:					int = -1       ,  _snake_case			:					int = -1       ,  _snake_case			:					bool = False       ,  _snake_case			:					Optional[TensorType] = None       ,  )->Mapping[str, Any]:






   '''simple docstring'''

   __lowerCAmelCase					:	Optional[Any]      =       super(_snake_case       ,  self      ).generate_dummy_inputs(
       _snake_case       ,  batch_size=_snake_case       ,  seq_length=_snake_case       ,  is_pair=_snake_case       ,  framework=_snake_case      )

   # We need to order the input in the way they appears in the forward()
   __lowerCAmelCase					:	Union[str, Any]      =       OrderedDict({"""input_ids""": common_inputs["""input_ids"""]}      )

   # Need to add the past_keys
   if self.use_past:
    if not is_torch_available():
     raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""      )
    else:
     import torch

     __lowerCAmelCase ,					__lowerCAmelCase					:	List[Any]      =       common_inputs["""input_ids"""].shape
     # Not using the same length for past_key_values
     __lowerCAmelCase					:	Optional[Any]      =       seqlen + 2
     __lowerCAmelCase					:	Dict      =       (
         batch,
         self.num_attention_heads,
         past_key_values_length,
         self._config.hidden_size // self.num_attention_heads,
     )
     __lowerCAmelCase					:	Tuple      =       [
         (torch.zeros(_snake_case      ), torch.zeros(_snake_case      )) for _ in range(self.num_layers      )
     ]

   __lowerCAmelCase					:	int      =       common_inputs["""attention_mask"""]
   if self.use_past:
    __lowerCAmelCase					:	Union[str, Any]      =       ordered_inputs["""attention_mask"""].dtype
    __lowerCAmelCase					:	List[str]      =       torch.cat(
        [ordered_inputs["""attention_mask"""], torch.ones(_snake_case       ,  _snake_case       ,  dtype=_snake_case      )]       ,  dim=1      )

   return ordered_inputs






  @property
  def 				UpperCAmelCase__						(							self			:					str      )->int:






   '''simple docstring'''

   return 13