





import warnings
from contextlib import contextmanager

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

class   A							(						__a    ):
      _snake_case				  ="""Wav2Vec2FeatureExtractor"""
      _snake_case				  ="""AutoTokenizer"""



      def __init__(						self:    str		,	_lowerCAmelCase:    Optional[int]		,	_lowerCAmelCase:    Union[str, Any]							)    ->     List[str]:
         '''simple docstring'''

         super().__init__(_lowerCamelCase		,	_lowerCamelCase							)
         UpperCAmelCase_           =self.feature_extractor
         UpperCAmelCase_           =False



      @classmethod
      def 						lowerCAmelCase__     (						cls:    Tuple		,	_lowerCAmelCase:    Any		,	**_lowerCAmelCase:    Optional[Any]							)    ->     Any:
         '''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		,	)

            UpperCAmelCase_           =WavaVecaFeatureExtractor.from_pretrained(_lowerCamelCase		,	**_lowerCamelCase							)
            UpperCAmelCase_           =WavaVecaCTCTokenizer.from_pretrained(_lowerCamelCase		,	**_lowerCamelCase							)

            return cls(feature_extractor=_lowerCamelCase		,	tokenizer=_lowerCamelCase							)



      def __call__(						self:    Optional[int]		,	*_lowerCAmelCase:    Optional[int]		,	**_lowerCAmelCase:    Any							)    ->     List[Any]:
         '''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."							)
            UpperCAmelCase_           =kwargs.pop("raw_speech"							)
         else:
            UpperCAmelCase_           =kwargs.pop("audio"		,	_lowerCamelCase							)
         UpperCAmelCase_           =kwargs.pop("sampling_rate"		,	_lowerCamelCase							)
         UpperCAmelCase_           =kwargs.pop("text"		,	_lowerCamelCase							)
         if len(_lowerCamelCase							) > 0:
            UpperCAmelCase_           =args[0]
            UpperCAmelCase_           =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:
            UpperCAmelCase_           =self.feature_extractor(_lowerCamelCase		,	*_lowerCamelCase		,	sampling_rate=_lowerCamelCase		,	**_lowerCamelCase							)
         if text is not None:
            UpperCAmelCase_           =self.tokenizer(_lowerCamelCase		,	**_lowerCamelCase							)

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



      def 						lowerCAmelCase__     (						self:    List[Any]		,	*_lowerCAmelCase:    Tuple		,	**_lowerCAmelCase:    List[Any]							)    ->     List[str]:
         '''simple docstring'''

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

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

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

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



      def 						lowerCAmelCase__     (						self:    List[str]		,	*_lowerCAmelCase:    Optional[Any]		,	**_lowerCAmelCase:    List[Any]							)    ->     Optional[int]:
         '''simple docstring'''

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



      def 						lowerCAmelCase__     (						self:    Dict		,	*_lowerCAmelCase:    Tuple		,	**_lowerCAmelCase:    int							)    ->     Dict:
         '''simple docstring'''

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






      @contextmanager
      def 						lowerCAmelCase__     (						self:    str							)    ->     str:
         '''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."							)
         UpperCAmelCase_           =True
         UpperCAmelCase_           =self.tokenizer
         yield
         UpperCAmelCase_           =self.feature_extractor
         UpperCAmelCase_           =False






"""simple docstring"""






from __future__ import annotations

from random import random

class 					__a     :



							'''simple docstring'''







							def __init__(     self  ,    _lowerCamelCase = None       )			->							str:




										'''simple docstring'''
										__lowercase       =       value
										__lowercase       =       random()
										__lowercase       =       None
										__lowercase       =       None
							def __repr__(     self       )			->							str:




										'''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       )			->							str:




										'''simple docstring'''
										__lowercase       =       str(self.value       ) + " "
										__lowercase       =       str(self.left or ""       )
										__lowercase       =       str(self.right or ""       )
										return value + left + right





def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__      )						->tuple[Node | None, Node | None]:
			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:
									__lowercase   ,					__lowercase       =       split(root.left			,   __magic_name__      )
									return left, root
						else:
									__lowercase   ,					__lowercase       =       split(root.right			,   __magic_name__      )
									return root, right



def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__      )						->Node | None:
			if (not left) or (not right):  # If one node is None, return the other
						return left or right
			elif left.prior < right.prior:
						__lowercase       =       merge(left.right			,   __magic_name__      )
						return left
			else:
						__lowercase       =       merge(__magic_name__			,   right.left      )
						return right



def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__      )						->Node | None:
			__lowercase       =       Node(__magic_name__      )
			__lowercase   ,					__lowercase       =       split(__magic_name__			,   __magic_name__      )
			return merge(merge(__magic_name__			,   __magic_name__      )			,   __magic_name__      )



def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__      )						->Node | None:
			__lowercase   ,					__lowercase       =       split(__magic_name__			,   value - 1      )
			__lowercase   ,					__lowercase       =       split(__magic_name__			,   __magic_name__      )
			return merge(__magic_name__			,   __magic_name__      )



def      lowerCAmelCase__			(		__magic_name__      )						->None:
			if not root:  # None
						return
			else:
						inorder(root.left      )
						print(root.value			,   end=","      )
						inorder(root.right      )



def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__      )						->Node | None:
			for arg in args.split():
						if arg[0] == "+":
									__lowercase       =       insert(__magic_name__			,   int(arg[1:]      )      )

						elif arg[0] == "-":
									__lowercase       =       erase(__magic_name__			,   int(arg[1:]      )      )

						else:
									print("Unknown command"      )

			return root



def      lowerCAmelCase__			(		)						->None:
			__lowercase       =       None
			print(
			    "enter numbers to create a tree, + value to add value into treap, "
			    "- value to erase all nodes with value. 'q' to quit. "      )

			__lowercase       =       input()
			while args != "q":
						__lowercase       =       interact_treap(__magic_name__			,   __magic_name__      )
						print(__magic_name__      )
						__lowercase       =       input()

			print("good by!"      )


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					main()





