



'''simple docstring'''

import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer







class 	SCREAMING_SNAKE_CASE	(      a__  ):
       snake_case__    :     Dict   		=			"Wav2Vec2FeatureExtractor"
       snake_case__    :     Optional[Any]   		=			"AutoTokenizer"




       def __init__(					self      : Optional[int]	,    A__      : Optional[int]	,    A__      : Optional[Any]					):


            """simple docstring"""







            super().__init__(lowerCamelCase_	,    lowerCamelCase_					)
            __lowerCamelCase						:       int				   =				self.feature_extractor
            __lowerCamelCase						:       Optional[int]				   =				False




       @classmethod
       def 					a_					(					cls      : Union[str, Any]	,    A__      : Tuple	,    **A__      : Dict					):


            """simple docstring"""







            try:
                 return super().from_pretrained(lowerCamelCase_	,    **lowerCamelCase_					)
            except OSError:
                 warnings.warn(
                     f"Loading a tokenizer inside {cls.__name__} from a config that does not"
                     """ include a `tokenizer_class` attribute is deprecated and will be """
                     """removed in v5. Please add `\'tokenizer_class\': \'Wav2Vec2CTCTokenizer\'`"""
                     """ attribute to either your `config.json` or `tokenizer_config.json` """
                     """file to suppress this warning: """	,    lowerCamelCase_	,    )

                 __lowerCamelCase						:       Tuple				   =				WavaVecaFeatureExtractor.from_pretrained(lowerCamelCase_	,    **lowerCamelCase_					)
                 __lowerCamelCase						:       str				   =				WavaVecaCTCTokenizer.from_pretrained(lowerCamelCase_	,    **lowerCamelCase_					)

                 return cls(feature_extractor=lowerCamelCase_	,    tokenizer=lowerCamelCase_					)




       def __call__(					self      : List[str]	,    *A__      : str	,    **A__      : List[str]					):


            """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						:       Optional[int]				   =				kwargs.pop("""raw_speech"""					)
            else:
                 __lowerCamelCase						:       int				   =				kwargs.pop("""audio"""	,    lowerCamelCase_					)
            __lowerCamelCase						:       int				   =				kwargs.pop("""sampling_rate"""	,    lowerCamelCase_					)
            __lowerCamelCase						:       Optional[Any]				   =				kwargs.pop("""text"""	,    lowerCamelCase_					)
            if len(lowerCamelCase_					) > 0:
                 __lowerCamelCase						:       Union[str, Any]				   =				args[0]
                 __lowerCamelCase						:       Optional[Any]				   =				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						:       Any				   =				self.feature_extractor(lowerCamelCase_	,    *lowerCamelCase_	,    sampling_rate=lowerCamelCase_	,    **lowerCamelCase_					)
            if text is not None:
                 __lowerCamelCase						:       str				   =				self.tokenizer(lowerCamelCase_	,    **lowerCamelCase_					)

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




       def 					a_					(					self      : Dict	,    *A__      : Tuple	,    **A__      : Tuple					):


            """simple docstring"""







            if self._in_target_context_manager:
                 return self.current_processor.pad(*lowerCamelCase_	,    **lowerCamelCase_					)

            __lowerCamelCase						:       Tuple				   =				kwargs.pop("""input_features"""	,    lowerCamelCase_					)
            __lowerCamelCase						:       List[str]				   =				kwargs.pop("""labels"""	,    lowerCamelCase_					)
            if len(lowerCamelCase_					) > 0:
                 __lowerCamelCase						:       Optional[Any]				   =				args[0]
                 __lowerCamelCase						:       List[Any]				   =				args[1:]

            if input_features is not None:
                 __lowerCamelCase						:       Optional[Any]				   =				self.feature_extractor.pad(lowerCamelCase_	,    *lowerCamelCase_	,    **lowerCamelCase_					)
            if labels is not None:
                 __lowerCamelCase						:       Tuple				   =				self.tokenizer.pad(lowerCamelCase_	,    **lowerCamelCase_					)

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




       def 					a_					(					self      : Optional[Any]	,    *A__      : str	,    **A__      : int					):


            """simple docstring"""







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




       def 					a_					(					self      : str	,    *A__      : int	,    **A__      : List[Any]					):


            """simple docstring"""







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




       @contextmanager
       def 					a_					(					self      : Optional[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						:       Optional[int]				   =				True
            __lowerCamelCase						:       Any				   =				self.tokenizer
            yield
            __lowerCamelCase						:       Union[str, Any]				   =				self.feature_extractor
            __lowerCamelCase						:       Dict				   =				False











'''simple docstring'''

from __future__ import annotations

from random import random







class 	SCREAMING_SNAKE_CASE	:




       def __init__(					self      : Tuple	,    A__      : int | None = None					):


            """simple docstring"""







            __lowerCamelCase						:       int				   =				value
            __lowerCamelCase						:       Dict				   =				random()
            __lowerCamelCase						:       Node | None				   =				None
            __lowerCamelCase						:       Node | None				   =				None




       def __repr__(					self      : Any					):


            """simple docstring"""







            from pprint import pformat

            if self.left is None and self.right is None:
                 return f"'{self.value}: {self.prior:.5}'"
            else:
                 return pformat(
                     {f"{self.value}: {self.prior:.5}": (self.left, self.right)}	,    indent=1					)




       def __str__(					self      : List[Any]					):


            """simple docstring"""







            __lowerCamelCase						:       List[str]				   =				str(self.value					) + """ """
            __lowerCamelCase						:       Any				   =				str(self.left or """"""					)
            __lowerCamelCase						:       List[str]				   =				str(self.right or """"""					)
            return value + left + right

def     __lowercase  (_lowercase,						_lowercase     )   ->     tuple[Node | None, Node | None]:


     """simple docstring"""
     if root is None:  # None tree is split into 2 Nones
          return None, None
     elif root.value is None:
          return None, None
     else:
          if value < root.value:
               __lowerCamelCase   ,      __lowerCamelCase						:       int				   =				split(root.left,						_lowercase     )
               return left, root
          else:
               __lowerCamelCase   ,      __lowerCamelCase						:       Union[str, Any]				   =				split(root.right,						_lowercase     )
               return root, right
def     __lowercase  (_lowercase,						_lowercase     )   ->     Node | None:


     """simple docstring"""
     if (not left) or (not right):  # If one node is None, return the other
          return left or right
     elif left.prior < right.prior:
          __lowerCamelCase						:       Tuple				   =				merge(left.right,						_lowercase     )
          return left
     else:
          __lowerCamelCase						:       int				   =				merge(_lowercase,						right.left     )
          return right
def     __lowercase  (_lowercase,						_lowercase     )   ->     Node | None:


     """simple docstring"""
     __lowerCamelCase						:       List[str]				   =				Node(_lowercase     )
     __lowerCamelCase   ,      __lowerCamelCase						:       List[Any]				   =				split(_lowercase,						_lowercase     )
     return merge(merge(_lowercase,						_lowercase     ),						_lowercase     )
def     __lowercase  (_lowercase,						_lowercase     )   ->     Node | None:


     """simple docstring"""
     __lowerCamelCase   ,      __lowerCamelCase						:       int				   =				split(_lowercase,						value - 1     )
     __lowerCamelCase   ,      __lowerCamelCase						:       List[str]				   =				split(_lowercase,						_lowercase     )
     return merge(_lowercase,						_lowercase     )
def     __lowercase  (_lowercase     )   ->     None:


     """simple docstring"""
     if not root:  # None
          return
     else:
          inorder(root.left     )
          print(root.value,						end=""","""     )
          inorder(root.right     )
def     __lowercase  (_lowercase,						_lowercase     )   ->     Node | None:


     """simple docstring"""
     for arg in args.split():
          if arg[0] == "+":
               __lowerCamelCase						:       Dict				   =				insert(_lowercase,						int(arg[1:]     )     )

          elif arg[0] == "-":
               __lowerCamelCase						:       List[str]				   =				erase(_lowercase,						int(arg[1:]     )     )

          else:
               print("""Unknown command"""     )

     return root
def     __lowercase  ()   ->     None:


     """simple docstring"""
     __lowerCamelCase						:       str				   =				None
     print(
         """enter numbers to create a tree, + value to add value into treap, """
         """- value to erase all nodes with value. 'q' to quit. """     )

     __lowerCamelCase						:       int				   =				input()
     while args != "q":
          __lowerCamelCase						:       List[Any]				   =				interact_treap(_lowercase,						_lowercase     )
          print(_lowercase     )
          __lowerCamelCase						:       List[Any]				   =				input()

     print("""good by!"""     )


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   main()



