



'''simple docstring'''





import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin






class 						__a (     SCREAMING_SNAKE_CASE				):
	SCREAMING_SNAKE_CASE       =       "Speech2TextFeatureExtractor"
	SCREAMING_SNAKE_CASE       =       "Speech2TextTokenizer"



	def __init__(						self							:    Dict				,	snake_case_							:    Any				,	snake_case_							:    Union[str, Any])->							Any:
						super().__init__(_lowerCAmelCase				,	_lowerCAmelCase)
						__lowerCAmelCase	   =self.feature_extractor
						__lowerCAmelCase	   =False



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

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



	def 		UpperCamelCase							(						self							:    Any				,	*snake_case_							:    List[str]				,	**snake_case_							:    Dict)->							Optional[int]:
						return self.tokenizer.batch_decode(*_lowerCAmelCase				,	**_lowerCAmelCase)



	def 		UpperCamelCase							(						self							:    List[Any]				,	*snake_case_							:    Tuple				,	**snake_case_							:    Dict)->							List[str]:
						return self.tokenizer.decode(*_lowerCAmelCase				,	**_lowerCAmelCase)



	@contextmanager
	def 		UpperCamelCase							(						self							:    Dict)->							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	   =True
						__lowerCAmelCase	   =self.tokenizer
						yield
						__lowerCAmelCase	   =self.feature_extractor
						__lowerCAmelCase	   =False









def 					__lowerCAmelCase		(  __lowerCamelCase	:    int = 3				,   __lowerCamelCase	:    int = 7				,   __lowerCamelCase	:    int = 1000000	)  ->						int:
					__lowerCAmelCase	   =0
					__lowerCAmelCase	   =1

					for current_denominator in range(1				,   limit + 1	):
										__lowerCAmelCase	   =current_denominator * numerator // denominator
										if current_denominator % denominator == 0:
															current_numerator -= 1
										if current_numerator * max_denominator > current_denominator * max_numerator:
															__lowerCAmelCase	   =current_numerator
															__lowerCAmelCase	   =current_denominator
					return max_numerator


if __name__ == "__main__":
				print(solution(numerator=3, denominator=7, limit=1_00_00_00))




