


from copy import deepcopy



class        a					:





     def __init__(    self					:				Tuple    ,	SCREAMING_SNAKE_CASE_					:				list[int] | None = None    ,	SCREAMING_SNAKE_CASE_					:				int | None = None							):

        if arr is None and size is not None:
           __lowerCamelCase:				Dict     =	size
           __lowerCamelCase:				Union[str, Any]     =	[0] * size
        elif arr is not None:
           self.init(SCREAMING_SNAKE_CASE_							)
        else:
           raise ValueError("""Either arr or size must be specified"""							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Any    ,	SCREAMING_SNAKE_CASE_					:				list[int]							):
        __lowerCamelCase:				str     =	len(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Tuple     =	deepcopy(SCREAMING_SNAKE_CASE_							)
        for i in range(1    ,	self.size							):
           __lowerCamelCase:				int     =	self.next_(SCREAMING_SNAKE_CASE_							)
           if j < self.size:
              self.tree[j] += self.tree[i]





     def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple							):
        __lowerCamelCase:				Any     =	self.tree[:]
        for i in range(self.size - 1    ,	0    ,	-1							):
           __lowerCamelCase:				List[str]     =	self.next_(SCREAMING_SNAKE_CASE_							)
           if j < self.size:
              arr[j] -= arr[i]
        return arr





     @staticmethod
     def  SCREAMING_SNAKE_CASE__       (    SCREAMING_SNAKE_CASE_					:				int							):
        return index + (index & (-index))





     @staticmethod
     def  SCREAMING_SNAKE_CASE__       (    SCREAMING_SNAKE_CASE_					:				int							):
        return index - (index & (-index))





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]    ,	SCREAMING_SNAKE_CASE_					:				int    ,	SCREAMING_SNAKE_CASE_					:				int							):
        if index == 0:
           self.tree[0] += value
           return
        while index < self.size:
           self.tree[index] += value
           __lowerCamelCase:				Union[str, Any]     =	self.next_(SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[Any]    ,	SCREAMING_SNAKE_CASE_					:				int    ,	SCREAMING_SNAKE_CASE_					:				int							):
        self.add(SCREAMING_SNAKE_CASE_    ,	value - self.get(SCREAMING_SNAKE_CASE_							)							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[str]    ,	SCREAMING_SNAKE_CASE_					:				int							):
        if right == 0:
           return 0
        __lowerCamelCase:				int     =	self.tree[0]
        right -= 1  # make right inclusive
        while right > 0:
           result += self.tree[right]
           __lowerCamelCase:				List[str]     =	self.prev(SCREAMING_SNAKE_CASE_							)
        return result





     def  SCREAMING_SNAKE_CASE__       (    self					:				str    ,	SCREAMING_SNAKE_CASE_					:				int    ,	SCREAMING_SNAKE_CASE_					:				int							):
        return self.prefix(SCREAMING_SNAKE_CASE_							) - self.prefix(SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]    ,	SCREAMING_SNAKE_CASE_					:				int							):
        return self.query(SCREAMING_SNAKE_CASE_    ,	index + 1							)







     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[Any]    ,	SCREAMING_SNAKE_CASE_					:				int							):
        value -= self.tree[0]
        if value < 0:
           return -1

        __lowerCamelCase:				Optional[int]     =	1  # Largest power of 2 <= size
        while j * 2 < self.size:
           j *= 2

        __lowerCamelCase:				Dict     =	0

        while j > 0:
           if i + j < self.size and self.tree[i + j] <= value:
              value -= self.tree[i + j]
              i += j
           j //= 2
        return i


if __name__ == "__main__":
   import doctest

   doctest.testmod()






import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin



class        a					(			_UpperCAmelCase   ):
     UpperCAmelCase__ :  Dict						      =	"Speech2TextFeatureExtractor"
     UpperCAmelCase__ :  str						      =	"Speech2TextTokenizer"





     def __init__(    self					:				Optional[Any]    ,	SCREAMING_SNAKE_CASE_					:				List[str]    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]							):
        super().__init__(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Any     =	self.feature_extractor
        __lowerCamelCase:				List[Any]     =	False





     def __call__(    self					:				List[str]    ,	*SCREAMING_SNAKE_CASE_					:				Union[str, Any]    ,	**SCREAMING_SNAKE_CASE_					:				List[Any]							):
        # For backward compatibility
        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:				Dict     =	kwargs.pop("""raw_speech"""							)
        else:
           __lowerCamelCase:				int     =	kwargs.pop("""audio"""    ,	SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Dict     =	kwargs.pop("""sampling_rate"""    ,	SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				int     =	kwargs.pop("""text"""    ,	SCREAMING_SNAKE_CASE_							)
        if len(SCREAMING_SNAKE_CASE_							) > 0:
           __lowerCamelCase:				Dict     =	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:				List[str]     =	self.feature_extractor(SCREAMING_SNAKE_CASE_    ,	*SCREAMING_SNAKE_CASE_    ,	sampling_rate=SCREAMING_SNAKE_CASE_    ,	**SCREAMING_SNAKE_CASE_							)
        if text is not None:
           __lowerCamelCase:				Any     =	self.tokenizer(SCREAMING_SNAKE_CASE_    ,	**SCREAMING_SNAKE_CASE_							)

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





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[str]    ,	*SCREAMING_SNAKE_CASE_					:				Optional[Any]    ,	**SCREAMING_SNAKE_CASE_					:				str							):
        return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_    ,	**SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple    ,	*SCREAMING_SNAKE_CASE_					:				Any    ,	**SCREAMING_SNAKE_CASE_					:				List[Any]							):
        return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_    ,	**SCREAMING_SNAKE_CASE_							)







     @contextmanager
     def  SCREAMING_SNAKE_CASE__       (    self					:				Any							):
        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:				Any     =	True
        __lowerCamelCase:				Any     =	self.tokenizer
        yield
        __lowerCamelCase:				Any     =	self.feature_extractor
        __lowerCamelCase:				Optional[Any]     =	False


