



"""simple docstring"""
class       SCREAMING_SNAKE_CASE__       :

    def __init__( self	: int       ,     lowerCAmelCase_	: Any):







      """simple docstring"""







      lowercase_	    =       n
      lowercase_	    =       [None] * self.n
      lowercase_	    =       0  # index of the first element
      lowercase_	    =       0
      lowercase_	    =       0

    def __len__( self	: Optional[Any]):







      """simple docstring"""







      return self.size

    def     _UpperCAmelCase		( self	: Any):







      """simple docstring"""







      return self.size == 0

    def     _UpperCAmelCase		( self	: Any):







      """simple docstring"""







      return False if self.is_empty() else self.array[self.front]

    def     _UpperCAmelCase		( self	: List[Any]       ,     lowerCAmelCase_	: int):







      """simple docstring"""







      if self.size >= self.n:
        raise Exception("""QUEUE IS FULL""")

      lowercase_	    =       data
      lowercase_	    =       (self.rear + 1) % self.n
      self.size += 1
      return self




    def     _UpperCAmelCase		( self	: Tuple):







      """simple docstring"""







      if self.size == 0:
        raise Exception("""UNDERFLOW""")

      lowercase_	    =       self.array[self.front]
      lowercase_	    =       None
      lowercase_	    =       (self.front + 1) % self.n
      self.size -= 1
      return temp








import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin






class 					__A    (    lowerCAmelCase ):

			'''simple docstring'''







			lowerCAmelCase_							     =						"""Speech2TextFeatureExtractor"""
			lowerCAmelCase_							     =						"""Speech2TextTokenizer"""





			def __init__(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase							):



				'''simple docstring'''







				super().__init__(__lowerCAmelCase       ,  __lowerCAmelCase							)
				lowerCamelCase__								=		self.feature_extractor
				lowerCamelCase__								=		False





			def __call__(    self       ,  *__lowerCAmelCase       ,  **__lowerCAmelCase							):



				'''simple docstring'''







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

				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'''       ,  __lowerCAmelCase							)
				lowerCamelCase__								=		kwargs.pop('''sampling_rate'''       ,  __lowerCAmelCase							)
				lowerCamelCase__								=		kwargs.pop('''text'''       ,  __lowerCAmelCase							)
				if len(__lowerCAmelCase							) > 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(__lowerCAmelCase       ,  *__lowerCAmelCase       ,  sampling_rate=__lowerCAmelCase       ,  **__lowerCAmelCase							)
				if text is not None:
					lowerCamelCase__								=		self.tokenizer(__lowerCAmelCase       ,  **__lowerCAmelCase							)

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





			def 		__lowerCamelCase				(    self       ,  *__lowerCAmelCase       ,  **__lowerCAmelCase							):



				'''simple docstring'''







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





			def 		__lowerCamelCase				(    self       ,  *__lowerCAmelCase       ,  **__lowerCAmelCase							):



				'''simple docstring'''







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





			@contextmanager
			def 		__lowerCamelCase				(    self							):



				'''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



