
"""simple docstring"""






from __future__ import annotations

from typing import Generic, TypeVar

UpperCAmelCase          =							TypeVar("""T""")



class  lowercase				(   lowercase__		):
			pass





class  lowercase				(   lowercase__		):
			pass





class  lowercase				(   Generic[T]		):






			def __init__(self     :  Any			,SCREAMING_SNAKE_CASE_     :  int = 10 )					->  Any:





				"""simple docstring"""


				lowerCAmelCase				       =     []
				lowerCAmelCase				       =     limit






			def __bool__(self     :  List[Any] )					->  bool:





				"""simple docstring"""


				return bool(self.stack )






			def __str__(self     :  Any )					->  str:





				"""simple docstring"""


				return str(self.stack )






			def 				UpperCAmelCase       (self     :  Optional[Any]			,SCREAMING_SNAKE_CASE_     :  T )					->  None:





				"""simple docstring"""


				if len(self.stack ) >= self.limit:
					raise StackOverflowError
				self.stack.append(SCREAMING_SNAKE_CASE_ )






			def 				UpperCAmelCase       (self     :  Tuple )					->  T:





				"""simple docstring"""


				if not self.stack:
					raise StackUnderflowError
				return self.stack.pop()






			def 				UpperCAmelCase       (self     :  int )					->  T:





				"""simple docstring"""


				if not self.stack:
					raise StackUnderflowError
				return self.stack[-1]






			def 				UpperCAmelCase       (self     :  Any )					->  bool:





				"""simple docstring"""


				return not bool(self.stack )






			def 				UpperCAmelCase       (self     :  Optional[Any] )					->  bool:





				"""simple docstring"""


				return self.size() == self.limit






			def 				UpperCAmelCase       (self     :  Tuple )					->  int:





				"""simple docstring"""


				return len(self.stack )






			def __contains__(self     :  List[str]			,SCREAMING_SNAKE_CASE_     :  T )					->  bool:





				"""simple docstring"""


				return item in self.stack





def __magic_name__  (							)				->	None:

	'''simple docstring'''




	lowerCAmelCase				       =     Stack(10 )
	assert bool(_lowerCamelCase ) is False
	assert stack.is_empty() is True
	assert stack.is_full() is False
	assert str(_lowerCamelCase ) == "[]"

	try:
		lowerCAmelCase				       =     stack.pop()
		raise AssertionError  # This should not happen
	except StackUnderflowError:
		assert True  # This should happen

	try:
		lowerCAmelCase				       =     stack.peek()
		raise AssertionError  # This should not happen
	except StackUnderflowError:
		assert True  # This should happen

	for i in range(10 ):
		assert stack.size() == i
		stack.push(_lowerCamelCase )

	assert bool(_lowerCamelCase )
	assert not stack.is_empty()
	assert stack.is_full()
	assert str(_lowerCamelCase ) == str(list(range(10 ) ) )
	assert stack.pop() == 9
	assert stack.peek() == 8

	stack.push(100 )
	assert str(_lowerCamelCase ) == str([0, 1, 2, 3, 4, 5, 6, 7, 8, 100] )

	try:
		stack.push(200 )
		raise AssertionError  # This should not happen
	except StackOverflowError:
		assert True  # This should happen

	assert not stack.is_empty()
	assert stack.size() == 10

	assert 5 in stack
	assert 55 not in stack


if __name__ == "__main__":
					test_stack()




"""simple docstring"""






import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin



class  lowercase				(   lowercase__		):
			lowercase			     =    '''AutoFeatureExtractor'''
			lowercase			     =    '''Speech2Text2Tokenizer'''






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





				"""simple docstring"""


				super().__init__(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_ )
				lowerCAmelCase				       =     self.feature_extractor
				lowerCAmelCase				       =     False






			def __call__(self     :  Tuple			,*SCREAMING_SNAKE_CASE_     :  Optional[Any]			,**SCREAMING_SNAKE_CASE_     :  Dict )					->  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.''' )
					lowerCAmelCase				       =     kwargs.pop('''raw_speech''' )
				else:
					lowerCAmelCase				       =     kwargs.pop('''audio'''			,SCREAMING_SNAKE_CASE_ )
				lowerCAmelCase				       =     kwargs.pop('''sampling_rate'''			,SCREAMING_SNAKE_CASE_ )
				lowerCAmelCase				       =     kwargs.pop('''text'''			,SCREAMING_SNAKE_CASE_ )
				if len(SCREAMING_SNAKE_CASE_ ) > 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(SCREAMING_SNAKE_CASE_			,*SCREAMING_SNAKE_CASE_			,sampling_rate=SCREAMING_SNAKE_CASE_			,**SCREAMING_SNAKE_CASE_ )
				if text is not None:
					lowerCAmelCase				       =     self.tokenizer(SCREAMING_SNAKE_CASE_			,**SCREAMING_SNAKE_CASE_ )

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






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





				"""simple docstring"""


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






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





				"""simple docstring"""


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






			@contextmanager
			def 				UpperCAmelCase       (self     :  Optional[int] )					->  Union[str, Any]:





				"""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.''' )
				lowerCAmelCase				       =     True
				lowerCAmelCase				       =     self.tokenizer
				yield
				lowerCAmelCase				       =     self.feature_extractor
				lowerCAmelCase				       =     False


