


'''simple docstring'''






import warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin
class 						__UpperCamelCase	(						lowercase__							):
							lowercase			:		Dict   		=  'MCTCTFeatureExtractor'
							lowercase			:		Any   		=  'AutoTokenizer'





							def __init__(  self		:int  ,_UpperCamelCase		:Any  ,_UpperCamelCase		:int			):
									super().__init__(_UpperCamelCase  ,_UpperCamelCase			)
									snake_case_		:							Any			       =						self.feature_extractor
									snake_case_		:							List[str]			       =						False





							def __call__(  self		:Optional[Any]  ,*_UpperCamelCase		:Optional[Any]  ,**_UpperCamelCase		:Union[str, Any]			):
									# 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."""			)
											snake_case_		:							Tuple			       =						kwargs.pop("""raw_speech"""			)
									else:
											snake_case_		:							Optional[Any]			       =						kwargs.pop("""audio"""  ,_UpperCamelCase			)
									snake_case_		:							str			       =						kwargs.pop("""sampling_rate"""  ,_UpperCamelCase			)
									snake_case_		:							int			       =						kwargs.pop("""text"""  ,_UpperCamelCase			)
									if len(_UpperCamelCase			) > 0:
											snake_case_		:							List[Any]			       =						args[0]
											snake_case_		:							int			       =						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:
											snake_case_		:							Union[str, Any]			       =						self.feature_extractor(_UpperCamelCase  ,*_UpperCamelCase  ,sampling_rate=_UpperCamelCase  ,**_UpperCamelCase			)
									if text is not None:
											snake_case_		:							str			       =						self.tokenizer(_UpperCamelCase  ,**_UpperCamelCase			)

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





							def 		a__   (  self		:int  ,*_UpperCamelCase		:Dict  ,**_UpperCamelCase		:Union[str, Any]			):
									return self.tokenizer.batch_decode(*_UpperCamelCase  ,**_UpperCamelCase			)





							def 		a__   (  self		:Union[str, Any]  ,*_UpperCamelCase		:List[str]  ,**_UpperCamelCase		:int			):
									# For backward compatibility
									if self._in_target_context_manager:
											return self.current_processor.pad(*_UpperCamelCase  ,**_UpperCamelCase			)

									snake_case_		:							Tuple			       =						kwargs.pop("""input_features"""  ,_UpperCamelCase			)
									snake_case_		:							Optional[int]			       =						kwargs.pop("""labels"""  ,_UpperCamelCase			)
									if len(_UpperCamelCase			) > 0:
											snake_case_		:							str			       =						args[0]
											snake_case_		:							Dict			       =						args[1:]

									if input_features is not None:
											snake_case_		:							Tuple			       =						self.feature_extractor.pad(_UpperCamelCase  ,*_UpperCamelCase  ,**_UpperCamelCase			)
									if labels is not None:
											snake_case_		:							Optional[int]			       =						self.tokenizer.pad(_UpperCamelCase  ,**_UpperCamelCase			)

									if labels is None:
											return input_features
									elif input_features is None:
											return labels
									else:
											snake_case_		:							List[Any]			       =						labels["""input_ids"""]
											return input_features





							def 		a__   (  self		:Union[str, Any]  ,*_UpperCamelCase		:List[str]  ,**_UpperCamelCase		:Dict			):
									return self.tokenizer.decode(*_UpperCamelCase  ,**_UpperCamelCase			)





							@contextmanager
							def 		a__   (  self		:List[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."""			)
									snake_case_		:							str			       =						True
									snake_case_		:							str			       =						self.tokenizer
									yield
									snake_case_		:							Tuple			       =						self.feature_extractor
									snake_case_		:							Dict			       =						False



'''simple docstring'''






import math





def      UpperCAmelCase      (		lowerCamelCase_  :list				,      lowerCamelCase_  :int ):







		'''simple docstring'''
		snake_case_		:							Union[str, Any]			       =						len(lowerCamelCase_ )
		snake_case_		:							List[Any]			       =						int(math.floor(math.sqrt(lowerCamelCase_ ) ) )
		snake_case_		:							str			       =						0
		while arr[min(lowerCamelCase_				,      lowerCamelCase_ ) - 1] < x:
				snake_case_		:							Any			       =						step
				step += int(math.floor(math.sqrt(lowerCamelCase_ ) ) )
				if prev >= n:
						return -1

		while arr[prev] < x:
				snake_case_		:							Optional[Any]			       =						prev + 1
				if prev == min(lowerCamelCase_				,      lowerCamelCase_ ):
						return -1
		if arr[prev] == x:
				return prev
		return -1


if __name__ == "__main__":
							__A	:	Any     =     input('Enter numbers separated by a comma:\n').strip()
							__A	:	Union[str, Any]     =     [int(item) for item in user_input.split(',')]
							__A	:	Optional[int]     =     int(input('Enter the number to be searched:\n'))
							__A	:	int     =     jump_search(arr, x)
							if res == -1:
														print('Number not found!')
							else:
														print(F'Number {x} is at index {res}')