
import warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin




class     _SCREAMING_SNAKE_CASE     (      snake_case_						):
	lowerCAmelCase__         = 'MCTCTFeatureExtractor'
	lowerCAmelCase__         = 'AutoTokenizer'



	def __init__(    self						,  lowercase						,  lowercase )				->       str:
					super().__init__(lowercase						,  lowercase )
					lowerCamelCase_						=			self.feature_extractor
					lowerCamelCase_						=			False



	def __call__(    self						,  *lowercase						,  **lowercase )				->       Dict:
					# For backward compatibility
					if self._in_target_context_manager:
									return self.current_processor(*lowercase						,  **lowercase )

					if "raw_speech" in kwargs:
									warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead." )
									lowerCamelCase_						=			kwargs.pop("raw_speech" )
					else:
									lowerCamelCase_						=			kwargs.pop("audio"						,  lowercase )
					lowerCamelCase_						=			kwargs.pop("sampling_rate"						,  lowercase )
					lowerCamelCase_						=			kwargs.pop("text"						,  lowercase )
					if len(lowercase ) > 0:
									lowerCamelCase_						=			args[0]
									lowerCamelCase_						=			args[1:]

					if audio is None and text is None:
									raise ValueError("You need to specify either an `audio` or `text` input to process." )

					if audio is not None:
									lowerCamelCase_						=			self.feature_extractor(lowercase						,  *lowercase						,  sampling_rate=lowercase						,  **lowercase )
					if text is not None:
									lowerCamelCase_						=			self.tokenizer(lowercase						,  **lowercase )

					if text is None:
									return inputs
					elif audio is None:
									return encodings
					else:
									lowerCamelCase_						=			encodings["input_ids"]
									return inputs



	def     SCREAMING_SNAKE_CASE_(    self						,  *lowercase						,  **lowercase )				->       List[Any]:
					return self.tokenizer.batch_decode(*lowercase						,  **lowercase )



	def     SCREAMING_SNAKE_CASE_(    self						,  *lowercase						,  **lowercase )				->       List[str]:
					# For backward compatibility
					if self._in_target_context_manager:
									return self.current_processor.pad(*lowercase						,  **lowercase )

					lowerCamelCase_						=			kwargs.pop("input_features"						,  lowercase )
					lowerCamelCase_						=			kwargs.pop("labels"						,  lowercase )
					if len(lowercase ) > 0:
									lowerCamelCase_						=			args[0]
									lowerCamelCase_						=			args[1:]

					if input_features is not None:
									lowerCamelCase_						=			self.feature_extractor.pad(lowercase						,  *lowercase						,  **lowercase )
					if labels is not None:
									lowerCamelCase_						=			self.tokenizer.pad(lowercase						,  **lowercase )

					if labels is None:
									return input_features
					elif input_features is None:
									return labels
					else:
									lowerCamelCase_						=			labels["input_ids"]
									return input_features



	def     SCREAMING_SNAKE_CASE_(    self						,  *lowercase						,  **lowercase )				->       int:
					return self.tokenizer.decode(*lowercase						,  **lowercase )



	@contextmanager
	def     SCREAMING_SNAKE_CASE_(    self )				->       str:
					warnings.warn(
					    "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
					    "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
					    "your audio inputs, or in a separate call." )
					lowerCamelCase_						=			True
					lowerCamelCase_						=			self.tokenizer
					yield
					lowerCamelCase_						=			self.feature_extractor
					lowerCamelCase_						=			False








class        lowerCamelCase_							:


 '''simple docstring'''







 def __init__(							self  :		List[Any]						):
       SCREAMING_SNAKE_CASE_							=			{}



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



 def       lowerCAmelCase_  (							self  :		List[Any]				,				_lowerCAmelCase  :		int				,				_lowerCAmelCase  :		int						):
       # check if vertex is already present,
       if from_vertex in self.vertex:
             self.vertex[from_vertex].append(_lowerCAmelCase						)
       else:
             # else make a new vertex
             SCREAMING_SNAKE_CASE_							=			[to_vertex]



 def       lowerCAmelCase_  (							self  :		Dict						):
       # visited array for storing already visited nodes
       SCREAMING_SNAKE_CASE_							=			[False] * len(self.vertex						)

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




 def       lowerCAmelCase_  (							self  :		Union[str, Any]				,				_lowerCAmelCase  :		int				,				_lowerCAmelCase  :		list						):
       # mark start vertex as visited
       SCREAMING_SNAKE_CASE_							=			True

       print(_lowerCAmelCase				,				end=' '						)

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


if __name__ == "__main__":
   lowerCamelCase__ :   List[Any]					=							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