

import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin






class    __A				(							lowerCAmelCase ):
    lowerCAmelCase_							:		Optional[int]						=							"AutoFeatureExtractor"
    lowerCAmelCase_							:		int						=							"Speech2Text2Tokenizer"






    def __init__(	self			:    Optional[int]    ,				UpperCAmelCase_			:    int    ,				UpperCAmelCase_			:    Optional[int]    ):
        super().__init__(UpperCAmelCase_    ,				UpperCAmelCase_    )
        lowerCAmelCase				:			Union[str, Any]    			=      self.feature_extractor
        lowerCAmelCase				:			int    			=      False






    def __call__(	self			:    Optional[int]    ,				*UpperCAmelCase_			:    Union[str, Any]    ,				**UpperCAmelCase_			:    Tuple    ):
        # For backward compatibility
        if self._in_target_context_manager:
            return self.current_processor(*UpperCAmelCase_    ,				**UpperCAmelCase_    )

        if "raw_speech" in kwargs:
            warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'    )
            lowerCAmelCase				:			List[Any]    			=      kwargs.pop('raw_speech'    )
        else:
            lowerCAmelCase				:			Tuple    			=      kwargs.pop('audio'    ,				UpperCAmelCase_    )
        lowerCAmelCase				:			Any    			=      kwargs.pop('sampling_rate'    ,				UpperCAmelCase_    )
        lowerCAmelCase				:			Any    			=      kwargs.pop('text'    ,				UpperCAmelCase_    )
        if len(UpperCAmelCase_    ) > 0:
            lowerCAmelCase				:			Union[str, Any]    			=      args[0]
            lowerCAmelCase				:			List[str]    			=      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				:			Optional[int]    			=      self.feature_extractor(UpperCAmelCase_    ,				*UpperCAmelCase_    ,				sampling_rate=UpperCAmelCase_    ,				**UpperCAmelCase_    )
        if text is not None:
            lowerCAmelCase				:			str    			=      self.tokenizer(UpperCAmelCase_    ,				**UpperCAmelCase_    )

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






    def 	lowercase__ (	self			:    Optional[int]    ,				*UpperCAmelCase_			:    str    ,				**UpperCAmelCase_			:    Any    ):
        return self.tokenizer.batch_decode(*UpperCAmelCase_    ,				**UpperCAmelCase_    )






    def 	lowercase__ (	self			:    int    ,				*UpperCAmelCase_			:    int    ,				**UpperCAmelCase_			:    str    ):
        return self.tokenizer.decode(*UpperCAmelCase_    ,				**UpperCAmelCase_    )






    @contextmanager
    def 	lowercase__ (	self			:    int    ):
        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				:			List[str]    			=      True
        lowerCAmelCase				:			Dict    			=      self.tokenizer
        yield
        lowerCAmelCase				:			Optional[int]    			=      self.feature_extractor
        lowerCAmelCase				:			Dict    			=      False








from __future__ import annotations
def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase				)					-> set[int]:



    '''simple docstring'''






    lowerCAmelCase				:			List[str]    			=      {1}
    lowerCAmelCase				:			int    			=      [1]
    for _ in range(2,      depth + 1				):
        lowerCAmelCase				:			str    			=      [*previous_coefficients, 0]
        lowerCAmelCase				:			Optional[int]    			=      [0, *previous_coefficients]
        lowerCAmelCase				:			Tuple    			=      []
        for x, y in zip(_UpperCAmelCase,      _UpperCAmelCase				):
            coefficients.add(x + y				)
            previous_coefficients.append(x + y				)
    return coefficients
def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase				)					-> set[int]:



    '''simple docstring'''






    lowerCAmelCase				:			str    			=      set()
    for number in unique_coefficients:
        lowerCAmelCase				:			Dict    			=      2
        lowerCAmelCase				:			Tuple    			=      number
        while divisor**2 <= copy_number:
            lowerCAmelCase				:			List[str]    			=      0
            while copy_number % divisor == 0:
                copy_number //= divisor
                multiplicity += 1
            if multiplicity >= 2:
                non_squarefrees.add(_UpperCAmelCase				)
                break
            divisor += 1

    return unique_coefficients.difference(_UpperCAmelCase				)
def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase = 51				)					-> int:



    '''simple docstring'''






    lowerCAmelCase				:			int    			=      get_pascal_triangle_unique_coefficients(_UpperCAmelCase				)
    lowerCAmelCase				:			Optional[int]    			=      get_squarefrees(_UpperCAmelCase				)
    return sum(_UpperCAmelCase				)


if __name__ == "__main__":
 print(F'{solution() = }')





