




"""simple docstring"""

class     lowercase_	:



 '''simple docstring'''

 def __init__(			self	:	Optional[int]		):
       _A        =       {}

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

 def       lowerCAmelCase_							(			self	:	str   ,							_UpperCAmelCase	:	int   ,							_UpperCAmelCase	:	int		):
       # check if vertex is already present,
       if from_vertex in self.vertex:
             self.vertex[from_vertex].append(_UpperCAmelCase		)
       else:
             # else make a new vertex
             _A        =       [to_vertex]

 def       lowerCAmelCase_							(			self	:	Union[str, Any]		):
       # visited array for storing already visited nodes
       _A        =       [False] * len(self.vertex		)

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



 def       lowerCAmelCase_							(			self	:	List[str]   ,							_UpperCAmelCase	:	int   ,							_UpperCAmelCase	:	list		):
       # mark start vertex as visited
       _A        =       True

       print(_UpperCAmelCase   ,							end=' '		)

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


if __name__ == "__main__":
       a         =      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







"""simple docstring"""

import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


a         =      logging.getLogger(__name__)





@dataclass
@add_start_docstrings(TrainingArguments.__doc__							)
class     lowercase_	(					__lowerCAmelCase							):



 '''simple docstring'''
 UpperCAmelCase   :					bool				  =     field(default=__lowerCAmelCase      ,     metadata={'''help''': '''Whether to use SortishSampler or not.'''}							)
 UpperCAmelCase   :					bool				  =     field(
     default=__lowerCAmelCase      ,     metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}							)
 UpperCAmelCase   :					Optional[int]				  =     field(
     default=__lowerCAmelCase      ,     metadata={
         '''help''': (
             '''The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default '''
             '''to the `max_length` value of the model configuration.'''
         )
     }      ,     )
 UpperCAmelCase   :					Optional[int]				  =     field(
     default=__lowerCAmelCase      ,     metadata={
         '''help''': (
             '''The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default '''
             '''to the `num_beams` value of the model configuration.'''
         )
     }      ,     )
 UpperCAmelCase   :					Optional[Union[str, Path, GenerationConfig]]				  =     field(
     default=__lowerCAmelCase      ,     metadata={
         '''help''': '''Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'''
     }      ,     )

 def       lowerCAmelCase_							(			self	:	int		):
       _A        =       super().to_dict()
       for k, v in d.items():
             if isinstance(_UpperCAmelCase   ,							_UpperCAmelCase		):
                   _A        =       v.to_dict()
       return d

