






import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


__UpperCamelCase    :						Dict		     =  logging.get_logger(__name__)


class      __SCREAMING_SNAKE_CASE:
							def __init__(   self:					Optional[int]				,		UpperCamelCase:					Tuple				,		UpperCamelCase:					str  )						->						str:
											snake_case__													=   question_encoder
											snake_case__													=   generator
											snake_case__													=   self.question_encoder
							def lowerCAmelCase_       (   self:					int				,		UpperCamelCase:					Optional[int]  )						->						str:






											if os.path.isfile(_a  ):
															raise ValueError(F'''Provided path ({save_directory}) should be a directory, not a file'''  )
											os.makedirs(_a				,		exist_ok=_a  )
											snake_case__													=   os.path.join(_a				,		'question_encoder_tokenizer'  )
											snake_case__													=   os.path.join(_a				,		'generator_tokenizer'  )
											self.question_encoder.save_pretrained(_a  )
											self.generator.save_pretrained(_a  )
							@classmethod
							def lowerCAmelCase_       (   cls:					Optional[Any]				,		UpperCamelCase:					List[str]				,		**UpperCamelCase:					Optional[int]  )						->						int:
											from ..auto.tokenization_auto import AutoTokenizer

											snake_case__													=   kwargs.pop('config'				,		_a  )

											if config is None:
															snake_case__													=   RagConfig.from_pretrained(_a  )

											snake_case__													=   AutoTokenizer.from_pretrained(
											    _a				,		config=config.question_encoder				,		subfolder='question_encoder_tokenizer'  )
											snake_case__													=   AutoTokenizer.from_pretrained(
											    _a				,		config=config.generator				,		subfolder='generator_tokenizer'  )
											return cls(question_encoder=_a				,		generator=_a  )
							def __call__(   self:					int				,		*UpperCamelCase:					List[Any]				,		**UpperCamelCase:					List[str]  )						->						Union[str, Any]:
											return self.current_tokenizer(*_a				,		**_a  )
							def lowerCAmelCase_       (   self:					int				,		*UpperCamelCase:					Optional[int]				,		**UpperCamelCase:					Union[str, Any]  )						->						Union[str, Any]:
											return self.generator.batch_decode(*_a				,		**_a  )
							def lowerCAmelCase_       (   self:					Union[str, Any]				,		*UpperCamelCase:					int				,		**UpperCamelCase:					Optional[int]  )						->						int:
											return self.generator.decode(*_a				,		**_a  )
							def lowerCAmelCase_       (   self:					List[str]  )						->						Optional[Any]:
											snake_case__													=   self.question_encoder
							def lowerCAmelCase_       (   self:					Optional[Any]  )						->						Optional[int]:
											snake_case__													=   self.generator


							def lowerCAmelCase_       (   self:					Any				,		UpperCamelCase:					List[str]				,		UpperCamelCase:					Optional[List[str]] = None				,		UpperCamelCase:					Optional[int] = None				,		UpperCamelCase:					Optional[int] = None				,		UpperCamelCase:					str = "longest"				,		UpperCamelCase:					str = None				,		UpperCamelCase:					bool = True				,		**UpperCamelCase:					Any				,		)						->						Tuple:
											warnings.warn(
											    '`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the '
											    'regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` '
											    'context manager to prepare your targets. See the documentation of your specific tokenizer for more '
											    'details'				,		_a				,		)
											if max_length is None:
															snake_case__													=   self.current_tokenizer.model_max_length
											snake_case__													=   self(
											    _a				,		add_special_tokens=_a				,		return_tensors=_a				,		max_length=_a				,		padding=_a				,		truncation=_a				,		**_a				,		)
											if tgt_texts is None:
															return model_inputs
											# Process tgt_texts
											if max_target_length is None:
															snake_case__													=   self.current_tokenizer.model_max_length
											snake_case__													=   self(
											    text_target=_a				,		add_special_tokens=_a				,		return_tensors=_a				,		padding=_a				,		max_length=_a				,		truncation=_a				,		**_a				,		)
											snake_case__													=   labels['input_ids']
											return model_inputs






'''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



