





'''simple docstring'''

import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin






class    UpperCAmelCase	(       UpperCamelCase__				):
 __lowercase   		=				"""Speech2TextFeatureExtractor"""
 __lowercase   		=				"""Speech2TextTokenizer"""
 def __init__(				self      :Union[str, Any]						,       lowercase_      :Tuple						,       lowercase_      :str							)->							List[Any]:
     super().__init__(lowercase_						,       lowercase_							)
     A__				=			self.feature_extractor
     A__				=			False
 def __call__(				self      :Dict						,       *lowercase_      :Any						,       **lowercase_      :List[str]							)->							List[Any]:
     # For backward compatibility
     if self._in_target_context_manager:
         return self.current_processor(*lowercase_						,       **lowercase_							)

     if "raw_speech" in kwargs:
         warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."							)
         A__				=			kwargs.pop("raw_speech"							)
     else:
         A__				=			kwargs.pop("audio"						,       lowercase_							)
     A__				=			kwargs.pop("sampling_rate"						,       lowercase_							)
     A__				=			kwargs.pop("text"						,       lowercase_							)
     if len(lowercase_							) > 0:
         A__				=			args[0]
         A__				=			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__				=			self.feature_extractor(lowercase_						,       *lowercase_						,       sampling_rate=lowercase_						,       **lowercase_							)
     if text is not None:
         A__				=			self.tokenizer(lowercase_						,       **lowercase_							)

     if text is None:
         return inputs
     elif audio is None:
         return encodings
     else:
         A__				=			encodings["input_ids"]
         return inputs
 def      UpperCAmelCase_							(				self      :int						,       *lowercase_      :str						,       **lowercase_      :Optional[int]							)->							Union[str, Any]:
     return self.tokenizer.batch_decode(*lowercase_						,       **lowercase_							)
 def      UpperCAmelCase_							(				self      :int						,       *lowercase_      :Union[str, Any]						,       **lowercase_      :Union[str, Any]							)->							Dict:
     return self.tokenizer.decode(*lowercase_						,       **lowercase_							)



 @contextmanager
 def      UpperCAmelCase_							(				self      :Dict							)->							Dict:
     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__				=			True
     A__				=			self.tokenizer
     yield
     A__				=			self.feature_extractor
     A__				=			False










'''simple docstring'''

from __future__ import annotations





def 						UpperCamelCase				(							_lowerCamelCase					:					list[int | str]       ):
    create_state_space_tree(_lowerCamelCase						,       []						,       0						,       [0 for i in range(len(_lowerCamelCase       )       )]       )





def 						UpperCamelCase				(							_lowerCamelCase					:					list[int | str]						,       _lowerCamelCase					:					list[int | str]						,       _lowerCamelCase					:					int						,       _lowerCamelCase					:					list[int]						,       ):

    if index == len(_lowerCamelCase       ):
        print(_lowerCamelCase       )
        return

    for i in range(len(_lowerCamelCase       )       ):
        if not index_used[i]:
            current_sequence.append(sequence[i]       )
            A__				=			True
            create_state_space_tree(_lowerCamelCase						,       _lowerCamelCase						,       index + 1						,       _lowerCamelCase       )
            current_sequence.pop()
            A__				=			False
__lowerCAmelCase							:	list[int | str]			=[3, 1, 2, 4]
generate_all_permutations(sequence)

__lowerCAmelCase							:	list[int | str]			=["A", "B", "C"]



generate_all_permutations(sequence_a)



