





from __future__ import annotations

from typing import Generic, TypeVar

_UpperCAmelCase										=					TypeVar('T')



class 				_UpperCamelCase       (						Generic[T]  ):

       def __init__(		self:			Tuple       , _SCREAMING_SNAKE_CASE:			T		)							->			Any:




              """simple docstring"""


              UpperCamelCase_ 							=	data  # Assign data
              UpperCamelCase_ 							=	None  # Initialize next as null
              UpperCamelCase_ 							=	None  # Initialize prev as null



class 				_UpperCamelCase       (						Generic[T]  ):

       def __init__(		self:			int		)							->			None:




              """simple docstring"""


              UpperCamelCase_ 							=	None

       def lowercase			(		self:			List[Any]       , _SCREAMING_SNAKE_CASE:			T		)							->			None:




              """simple docstring"""


              if self.head is None:
                     UpperCamelCase_ 							=	Node(_SCREAMING_SNAKE_CASE		)
              else:
                     UpperCamelCase_ 							=	Node(_SCREAMING_SNAKE_CASE		)
                     UpperCamelCase_ 							=	new_node
                     UpperCamelCase_ 							=	self.head
                     UpperCamelCase_ 							=	None
                     UpperCamelCase_ 							=	new_node

       def lowercase			(		self:			int		)							->			T | None:




              """simple docstring"""


              if self.head is None:
                     return None
              else:
                     assert self.head is not None
                     UpperCamelCase_ 							=	self.head.data
                     UpperCamelCase_ 							=	self.head.next
                     if self.head is not None:
                            UpperCamelCase_ 							=	None
                     return temp

       def lowercase			(		self:			Tuple		)							->			T | None:




              """simple docstring"""


              return self.head.data if self.head is not None else None

       def __len__(		self:			List[Any]		)							->			int:




              """simple docstring"""


              UpperCamelCase_ 							=	self.head
              UpperCamelCase_ 							=	0
              while temp is not None:
                     count += 1
                     UpperCamelCase_ 							=	temp.next
              return count

       def lowercase			(		self:			Tuple		)							->			bool:




              """simple docstring"""


              return self.head is None



       def lowercase			(		self:			Optional[Any]		)							->			None:




              """simple docstring"""


              print("stack elements are:"		)
              UpperCamelCase_ 							=	self.head
              while temp is not None:
                     print(temp.data       , end="->"		)
                     UpperCamelCase_ 							=	temp.next


# Code execution starts here
if __name__ == "__main__":
     # Start with the empty stack
     _UpperCAmelCase										=					Stack()

     # Insert 4 at the beginning. So stack becomes 4->None
     print('Stack operations using Doubly LinkedList')
     stack.push(4)

     # Insert 5 at the beginning. So stack becomes 4->5->None
     stack.push(5)

     # Insert 6 at the beginning. So stack becomes 4->5->6->None
     stack.push(6)

     # Insert 7 at the beginning. So stack becomes 4->5->6->7->None
     stack.push(7)

     # Print the stack
     stack.print_stack()

     # Print the top element
     print('\nTop element is ', stack.top())

     # Print the stack size
     print('Size of the stack is ', len(stack))

     # pop the top element
     stack.pop()

     # pop the top element
     stack.pop()

     # two elements have now been popped off
     stack.print_stack()

     # Print True if the stack is empty else False
     print('\nstack is empty:', stack.is_empty())







'''simple docstring'''


import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin






class 						lowerCAmelCase_	(       UpperCAmelCase_							):







  '''simple docstring'''

  UpperCamelCase_			:      Optional[int]										=				"""AutoFeatureExtractor"""
  UpperCamelCase_			:      List[Any]										=				"""Speech2Text2Tokenizer"""



  def __init__( self				:							str					,  SCREAMING_SNAKE_CASE_				:							str					,  SCREAMING_SNAKE_CASE_				:							int	)			->		Any:







       '''simple docstring'''



       super().__init__(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_	)
       A:		Union[str, Any]          =		self.feature_extractor
       A:		Dict          =		False



  def __call__( self				:							Any					,  *SCREAMING_SNAKE_CASE_				:							Any					,  **SCREAMING_SNAKE_CASE_				:							Optional[int]	)			->		Optional[Any]:







       '''simple docstring'''



       if self._in_target_context_manager:
            return self.current_processor(*SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_	)

       if "raw_speech" in kwargs:
            warnings.warn('''Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'''	)
            A:		List[str]          =		kwargs.pop('''raw_speech'''	)
       else:
            A:		Any          =		kwargs.pop('''audio'''					,  SCREAMING_SNAKE_CASE_	)
       A:		List[Any]          =		kwargs.pop('''sampling_rate'''					,  SCREAMING_SNAKE_CASE_	)
       A:		Optional[int]          =		kwargs.pop('''text'''					,  SCREAMING_SNAKE_CASE_	)
       if len(SCREAMING_SNAKE_CASE_	) > 0:
            A:		str          =		args[0]
            A:		Tuple          =		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:
            A:		Tuple          =		self.feature_extractor(SCREAMING_SNAKE_CASE_					,  *SCREAMING_SNAKE_CASE_					,  sampling_rate=SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_	)
       if text is not None:
            A:		List[Any]          =		self.tokenizer(SCREAMING_SNAKE_CASE_					,  **SCREAMING_SNAKE_CASE_	)

       if text is None:
            return inputs
       elif audio is None:
            return encodings
       else:
            A:		str          =		encodings['''input_ids''']
            return inputs



  def 					_snake_case     ( self				:							Tuple					,  *SCREAMING_SNAKE_CASE_				:							int					,  **SCREAMING_SNAKE_CASE_				:							Union[str, Any]	)			->		str:







       '''simple docstring'''



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



  def 					_snake_case     ( self				:							Optional[int]					,  *SCREAMING_SNAKE_CASE_				:							str					,  **SCREAMING_SNAKE_CASE_				:							int	)			->		Union[str, Any]:







       '''simple docstring'''



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




  @contextmanager
  def 					_snake_case     ( self				:							str	)			->		int:







       '''simple docstring'''



       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.'''	)
       A:		List[Any]          =		True
       A:		List[str]          =		self.tokenizer
       yield
       A:		List[Any]          =		self.feature_extractor
       A:		int          =		False






