


import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType


class 					lowerCAmelCase_ (       _a):
						lowerCamelCase_         =     ['image_processor', 'tokenizer']
						lowerCamelCase_         =     'ViltImageProcessor'
						lowerCamelCase_         =     ('BertTokenizer', 'BertTokenizerFast')




						def __init__(	self						:   Optional[Any]   ,     __A						:   List[Any]=None   ,     __A						:   List[str]=None   ,     **__A						:   int						)      ->Optional[Any]:






									"""simple docstring"""






									a__ :Optional[int]						  =    None
									if "feature_extractor" in kwargs:
												warnings.warn(
												    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
												    " instead."   ,     __A   ,     )
												a__ :List[Any]						  =    kwargs.pop("feature_extractor"						)

									a__ :Tuple						  =    image_processor if image_processor is not None else feature_extractor
									if image_processor is None:
												raise ValueError("You need to specify an `image_processor`."						)
									if tokenizer is None:
												raise ValueError("You need to specify a `tokenizer`."						)

									super().__init__(__A   ,     __A						)
									a__ :str						  =    self.image_processor




						def __call__(	self						:   int   ,     __A						:   Union[str, Any]   ,     __A						:   Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None   ,     __A						:   bool = True   ,     __A						:   Union[bool, str, PaddingStrategy] = False   ,     __A						:   Union[bool, str, TruncationStrategy] = None   ,     __A						:   Optional[int] = None   ,     __A						:   int = 0   ,     __A						:   Optional[int] = None   ,     __A						:   Optional[bool] = None   ,     __A						:   Optional[bool] = None   ,     __A						:   bool = False   ,     __A						:   bool = False   ,     __A						:   bool = False   ,     __A						:   bool = False   ,     __A						:   bool = True   ,     __A						:   Optional[Union[str, TensorType]] = None   ,     **__A						:   str   ,     )      ->BatchEncoding:






									"""simple docstring"""






									a__ :Optional[int]						  =    self.tokenizer(
									    text=__A   ,     add_special_tokens=__A   ,     padding=__A   ,     truncation=__A   ,     max_length=__A   ,     stride=__A   ,     pad_to_multiple_of=__A   ,     return_token_type_ids=__A   ,     return_attention_mask=__A   ,     return_overflowing_tokens=__A   ,     return_special_tokens_mask=__A   ,     return_offsets_mapping=__A   ,     return_length=__A   ,     verbose=__A   ,     return_tensors=__A   ,     **__A   ,     )
									# add pixel_values + pixel_mask
									a__ :Optional[int]						  =    self.image_processor(__A   ,     return_tensors=__A						)
									encoding.update(__A						)

									return encoding




						def  _snake_case					(	self						:   str   ,     *__A						:   List[str]   ,     **__A						:   str						)      ->Dict:






									"""simple docstring"""






									return self.tokenizer.batch_decode(*__A   ,     **__A						)




						def  _snake_case					(	self						:   Union[str, Any]   ,     *__A						:   Union[str, Any]   ,     **__A						:   Union[str, Any]						)      ->List[Any]:






									"""simple docstring"""






									return self.tokenizer.decode(*__A   ,     **__A						)




						@property
						def  _snake_case					(	self						:   List[Any]						)      ->Union[str, Any]:






									"""simple docstring"""






									a__ :Any						  =    self.tokenizer.model_input_names
									a__ :Optional[int]						  =    self.image_processor.model_input_names
									return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names						)						)




						@property
						def  _snake_case					(	self						:   str						)      ->Tuple:






									"""simple docstring"""






									warnings.warn(
									    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."   ,     __A   ,     )
									return self.image_processor_class






						@property
						def  _snake_case					(	self						:   List[str]						)      ->Optional[Any]:






									"""simple docstring"""






									warnings.warn(
									    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."   ,     __A   ,     )
									return self.image_processor






class 					lowerCAmelCase_ :




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






									"""simple docstring"""






									a__ :Tuple						  =    {}




						def  _snake_case					(	self						:   Tuple						)      ->None:






									"""simple docstring"""






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




						def  _snake_case					(	self						:   Optional[int]   ,     __A						:   int   ,     __A						:   int						)      ->None:






									"""simple docstring"""






									if from_vertex in self.vertex:
												self.vertex[from_vertex].append(__A						)
									else:
												# else make a new vertex
												a__ :Any						  =    [to_vertex]




						def  _snake_case					(	self						:   Optional[int]						)      ->None:






									"""simple docstring"""






									a__ :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(__A   ,     __A						)






						def  _snake_case					(	self						:   Union[str, Any]   ,     __A						:   int   ,     __A						:   list						)      ->None:






									"""simple docstring"""






									a__ :Dict						  =    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__":
		snake_case__			 =						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


