
'''simple docstring'''







from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput







@dataclass
class UpperCAmelCase__	( lowercase__	):





  """simple docstring"""

  __UpperCAmelCase				:				Optional[torch.FloatTensor]   =					None
  __UpperCAmelCase				:				torch.FloatTensor   =					None
  __UpperCAmelCase				:				Optional[Tuple[torch.FloatTensor]]   =					None
  __UpperCAmelCase				:				Optional[Tuple[torch.FloatTensor]]   =					None







class UpperCAmelCase__	( lowercase__	):





  """simple docstring"""

  def __init__(		self      :     List[str]							,_a      :     Union[str, Any]=1							,_a      :     List[Any]=0							,_a      :     List[Any]=2							,_a      :     str=512							,_a      :     Optional[int]="cls"							,_a      :     Union[str, Any]=False							,_a      :     Optional[int]=True							,**_a      :     List[Any]							,):


     '''simple docstring'''






     super().__init__(pad_token_id=_a							,bos_token_id=_a							,eos_token_id=_a							,**_a       )
     _a				:	Dict			=   project_dim
     _a				:	int			=   pooler_fn
     _a				:	Tuple			=   learn_encoder
     _a				:	Optional[int]			=   use_attention_mask







class UpperCAmelCase__	( lowercase__	):





  """simple docstring"""

  __UpperCAmelCase				:				List[Any]   =					[R'''pooler''', R'''logit_scale''']
  __UpperCAmelCase				:				int   =					[R'''position_ids''', R'''predictions.decoder.bias''']
  __UpperCAmelCase				:				Dict   =					'''roberta'''
  __UpperCAmelCase				:				str   =					RobertaSeriesConfig


  def __init__(		self      :     Tuple							,_a      :     List[Any]       ):


     '''simple docstring'''






     super().__init__(_a       )
     _a				:	Union[str, Any]			=   XLMRobertaModel(_a       )
     _a				:	Tuple			=   nn.Linear(config.hidden_size							,config.project_dim       )
     _a				:	str			=   getattr(_a							,'has_pre_transformation'							,_a       )
     if self.has_pre_transformation:
        _a				:	int			=   nn.Linear(config.hidden_size							,config.project_dim       )
        _a				:	int			=   nn.LayerNorm(config.hidden_size							,eps=config.layer_norm_eps       )
     self.post_init()


  def   __lowercase		(		self      :     Union[str, Any]							,_a      :     Optional[torch.Tensor] = None							,_a      :     Optional[torch.Tensor] = None							,_a      :     Optional[torch.Tensor] = None							,_a      :     Optional[torch.Tensor] = None							,_a      :     Optional[torch.Tensor] = None							,_a      :     Optional[torch.Tensor] = None							,_a      :     Optional[torch.Tensor] = None							,_a      :     Optional[torch.Tensor] = None							,_a      :     Optional[bool] = None							,_a      :     Optional[bool] = None							,_a      :     Optional[bool] = None							,):


     '''simple docstring'''






     _a				:	Optional[int]			=   return_dict if return_dict is not None else self.config.use_return_dict

     _a				:	str			=   self.base_model(
         input_ids=_a							,attention_mask=_a							,token_type_ids=_a							,position_ids=_a							,head_mask=_a							,inputs_embeds=_a							,encoder_hidden_states=_a							,encoder_attention_mask=_a							,output_attentions=_a							,output_hidden_states=True if self.has_pre_transformation else output_hidden_states							,return_dict=_a							,)

     if self.has_pre_transformation:
        _a				:	Optional[Any]			=   outputs['hidden_states'][-2]
        _a				:	str			=   self.pre_LN(_a       )
        _a				:	Dict			=   self.transformation_pre(_a       )

        return TransformationModelOutput(
            projection_state=_a							,last_hidden_state=outputs.last_hidden_state							,hidden_states=outputs.hidden_states							,attentions=outputs.attentions							,)
     else:
        _a				:	List[str]			=   self.transformation(outputs.last_hidden_state       )
        return TransformationModelOutput(
            projection_state=_a							,last_hidden_state=outputs.last_hidden_state							,hidden_states=outputs.hidden_states							,attentions=outputs.attentions							,)





'''simple docstring'''







class UpperCAmelCase__	:





  """simple docstring"""

  def __init__(		self      :     Dict       ):


     '''simple docstring'''






     _a				:	Dict			=   {}


  def   __lowercase		(		self      :     Union[str, Any]       ):


     '''simple docstring'''






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


  def   __lowercase		(		self      :     Dict							,_a      :     int							,_a      :     int       ):


     '''simple docstring'''






     if from_vertex in self.vertex:
        self.vertex[from_vertex].append(_a       )
     else:
        # else make a new vertex
        _a				:	int			=   [to_vertex]


  def   __lowercase		(		self      :     Optional[int]       ):


     '''simple docstring'''






     _a				:	Tuple			=   [False] * len(self.vertex       )

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


  def   __lowercase		(		self      :     Union[str, Any]							,_a      :     int							,_a      :     list       ):


     '''simple docstring'''






     _a				:	List[Any]			=   True

     print(_a							,end=' '       )

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


if __name__ == "__main__":
  __lowerCAmelCase         =    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



