



"""simple docstring"""






import warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin



class     A__     (						__a							):




 '''simple docstring'''





 SCREAMING_SNAKE_CASE					   =			'MCTCTFeatureExtractor'
 SCREAMING_SNAKE_CASE					   =			'AutoTokenizer'

 def __init__(  self:	List[str] ,    _SCREAMING_SNAKE_CASE:	Optional[int] ,    _SCREAMING_SNAKE_CASE:	int)       -> Union[str, Any]:

    """simple docstring"""

    super().__init__(A__ ,    A__)
    __lowerCAmelCase   :						Optional[int]				= self.feature_extractor
    __lowerCAmelCase   :						Optional[int]				= False

 def __call__(  self:	Union[str, Any] ,    *_SCREAMING_SNAKE_CASE:	Optional[Any] ,    **_SCREAMING_SNAKE_CASE:	Dict)       -> Tuple:

    """simple docstring"""

    if self._in_target_context_manager:
       return self.current_processor(*A__ ,    **A__)

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

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

 def 							_SCREAMING_SNAKE_CASE     (  self:	Union[str, Any] ,    *_SCREAMING_SNAKE_CASE:	Optional[int] ,    **_SCREAMING_SNAKE_CASE:	Optional[int])       -> List[str]:

    """simple docstring"""

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

 def 							_SCREAMING_SNAKE_CASE     (  self:	Tuple ,    *_SCREAMING_SNAKE_CASE:	Tuple ,    **_SCREAMING_SNAKE_CASE:	Any)       -> Dict:

    """simple docstring"""

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

    __lowerCAmelCase   :						List[Any]				= kwargs.pop("input_features" ,    A__)
    __lowerCAmelCase   :						List[Any]				= kwargs.pop("labels" ,    A__)
    if len(A__) > 0:
       __lowerCAmelCase   :						Tuple				= args[0]
       __lowerCAmelCase   :						Dict				= args[1:]

    if input_features is not None:
       __lowerCAmelCase   :						Dict				= self.feature_extractor.pad(A__ ,    *A__ ,    **A__)
    if labels is not None:
       __lowerCAmelCase   :						List[str]				= self.tokenizer.pad(A__ ,    **A__)

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

 def 							_SCREAMING_SNAKE_CASE     (  self:	Any ,    *_SCREAMING_SNAKE_CASE:	List[Any] ,    **_SCREAMING_SNAKE_CASE:	int)       -> Dict:

    """simple docstring"""

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







 @contextmanager
 def 							_SCREAMING_SNAKE_CASE     (  self:	List[Any])       -> 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   :						List[str]				= True
    __lowerCAmelCase   :						Union[str, Any]				= self.tokenizer
    yield
    __lowerCAmelCase   :						List[str]				= self.feature_extractor
    __lowerCAmelCase   :						str				= False




"""simple docstring"""






def     _lowercase       (						__snake_case = 3      ,__snake_case = 7      ,__snake_case = 1_000_000			)		->     int:
			__lowerCAmelCase   :						Optional[Any]				= 0
			__lowerCAmelCase   :						List[str]				= 1

			for current_denominator in range(1      ,limit + 1			):
						__lowerCAmelCase   :						Optional[int]				= current_denominator * numerator // denominator
						if current_denominator % denominator == 0:
									current_numerator -= 1
						if current_numerator * max_denominator > current_denominator * max_numerator:
									__lowerCAmelCase   :						Union[str, Any]				= current_numerator
									__lowerCAmelCase   :						int				= current_denominator
			return max_numerator


if __name__ == "__main__":
	print(solution(numerator=3, denominator=7, limit=1_000_000))