






import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer
class      UpperCamelCase (				__lowerCAmelCase ):







		"""simple docstring"""



		snake_case    =    "Wav2Vec2FeatureExtractor"
		snake_case    =    "AutoTokenizer"
		def __init__(  self		:       Union[str, Any]						,_SCREAMING_SNAKE_CASE		:       Optional[Any]						,_SCREAMING_SNAKE_CASE		:       Tuple		)    ->   int:


						'''simple docstring'''
						super().__init__(lowerCamelCase__						,lowerCamelCase__		)
						A	 =      self.feature_extractor
						A	 =      False
		@classmethod
		def       A(  cls		:       Optional[Any]						,_SCREAMING_SNAKE_CASE		:       int						,**_SCREAMING_SNAKE_CASE		:       Union[str, Any]		)    ->   Union[str, Any]:


						'''simple docstring'''






						try:
										return super().from_pretrained(lowerCamelCase__						,**lowerCamelCase__		)
						except OSError:
										warnings.warn(
										    f'Loading a tokenizer inside {cls.__name__} from a config that does not'
										    ' include a `tokenizer_class` attribute is deprecated and will be '
										    'removed in v5. Please add `\'tokenizer_class\': \'Wav2Vec2CTCTokenizer\'`'
										    ' attribute to either your `config.json` or `tokenizer_config.json` '
										    'file to suppress this warning: '						,lowerCamelCase__						,)

										A	 =      WavaVecaFeatureExtractor.from_pretrained(lowerCamelCase__						,**lowerCamelCase__		)
										A	 =      WavaVecaCTCTokenizer.from_pretrained(lowerCamelCase__						,**lowerCamelCase__		)

										return cls(feature_extractor=lowerCamelCase__						,tokenizer=lowerCamelCase__		)
		def __call__(  self		:       Any						,*_SCREAMING_SNAKE_CASE		:       Optional[int]						,**_SCREAMING_SNAKE_CASE		:       Dict		)    ->   Any:


						'''simple docstring'''






						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.'		)
										A	 =      kwargs.pop('raw_speech'		)
						else:
										A	 =      kwargs.pop('audio'						,lowerCamelCase__		)
						A	 =      kwargs.pop('sampling_rate'						,lowerCamelCase__		)
						A	 =      kwargs.pop('text'						,lowerCamelCase__		)
						if len(lowerCamelCase__		) > 0:
										A	 =      args[0]
										A	 =      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:
										A	 =      self.feature_extractor(lowerCamelCase__						,*lowerCamelCase__						,sampling_rate=lowerCamelCase__						,**lowerCamelCase__		)
						if text is not None:
										A	 =      self.tokenizer(lowerCamelCase__						,**lowerCamelCase__		)

						if text is None:
										return inputs
						elif audio is None:
										return encodings
						else:
										A	 =      encodings['''input_ids''']
										return inputs
		def       A(  self		:       Optional[Any]						,*_SCREAMING_SNAKE_CASE		:       Any						,**_SCREAMING_SNAKE_CASE		:       Tuple		)    ->   Union[str, Any]:


						'''simple docstring'''






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

						A	 =      kwargs.pop('input_features'						,lowerCamelCase__		)
						A	 =      kwargs.pop('labels'						,lowerCamelCase__		)
						if len(lowerCamelCase__		) > 0:
										A	 =      args[0]
										A	 =      args[1:]

						if input_features is not None:
										A	 =      self.feature_extractor.pad(lowerCamelCase__						,*lowerCamelCase__						,**lowerCamelCase__		)
						if labels is not None:
										A	 =      self.tokenizer.pad(lowerCamelCase__						,**lowerCamelCase__		)

						if labels is None:
										return input_features
						elif input_features is None:
										return labels
						else:
										A	 =      labels['''input_ids''']
										return input_features
		def       A(  self		:       Optional[Any]						,*_SCREAMING_SNAKE_CASE		:       Tuple						,**_SCREAMING_SNAKE_CASE		:       int		)    ->   List[str]:


						'''simple docstring'''
						return self.tokenizer.batch_decode(*lowerCamelCase__						,**lowerCamelCase__		)
		def       A(  self		:       List[Any]						,*_SCREAMING_SNAKE_CASE		:       List[str]						,**_SCREAMING_SNAKE_CASE		:       Optional[Any]		)    ->   int:


						'''simple docstring'''
						return self.tokenizer.decode(*lowerCamelCase__						,**lowerCamelCase__		)







		@contextmanager
		def       A(  self		:       Optional[int]		)    ->   Optional[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.'		)
						A	 =      True
						A	 =      self.tokenizer
						yield
						A	 =      self.feature_extractor
						A	 =      False













import math







def      snake_case		(							UpperCAmelCase	:       list,						UpperCAmelCase	:       int   ):
				A	 =      len(UpperCAmelCase   )
				A	 =      int(math.floor(math.sqrt(UpperCAmelCase   )   )   )
				A	 =      0
				while arr[min(UpperCAmelCase,						UpperCAmelCase   ) - 1] < x:
								A	 =      step
								step += int(math.floor(math.sqrt(UpperCAmelCase   )   )   )
								if prev >= n:
												return -1

				while arr[prev] < x:
								A	 =      prev + 1
								if prev == min(UpperCAmelCase,						UpperCAmelCase   ):
												return -1
				if arr[prev] == x:
								return prev
				return -1


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





