

import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin

class  a     (		UpperCAmelCase ):
						_lowercase			  =	"AutoFeatureExtractor"
						_lowercase			  =	"Speech2Text2Tokenizer"


						def __init__(				self  ,    A_  ,    A_		):







										'''simple docstring'''


										super().__init__(A_  ,    A_		)
										_UpperCAmelCase				:						Optional[Any]       						=			self.feature_extractor
										_UpperCAmelCase				:						Optional[int]       						=			False


						def __call__(				self  ,    *A_  ,    **A_		):







										'''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."		)
														_UpperCAmelCase				:						Optional[Any]       						=			kwargs.pop("raw_speech"		)
										else:
														_UpperCAmelCase				:						str       						=			kwargs.pop("audio"  ,    A_		)
										_UpperCAmelCase				:						int       						=			kwargs.pop("sampling_rate"  ,    A_		)
										_UpperCAmelCase				:						Union[str, Any]       						=			kwargs.pop("text"  ,    A_		)
										if len(A_		) > 0:
														_UpperCAmelCase				:						Optional[int]       						=			args[0]
														_UpperCAmelCase				:						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:
														_UpperCAmelCase				:						Any       						=			self.feature_extractor(A_  ,    *A_  ,    sampling_rate=A_  ,    **A_		)
										if text is not None:
														_UpperCAmelCase				:						Union[str, Any]       						=			self.tokenizer(A_  ,    **A_		)

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


						def 				_UpperCAmelCase (				self  ,    *A_  ,    **A_		):







										'''simple docstring'''


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


						def 				_UpperCAmelCase (				self  ,    *A_  ,    **A_		):







										'''simple docstring'''


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





						@contextmanager
						def 				_UpperCAmelCase (				self		):







										'''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."		)
										_UpperCAmelCase				:						int       						=			True
										_UpperCAmelCase				:						List[str]       						=			self.tokenizer
										yield
										_UpperCAmelCase				:						str       						=			self.feature_extractor
										_UpperCAmelCase				:						Union[str, Any]       						=			False





from __future__ import annotations

SCREAMING_SNAKE_CASE_						=					'Alexander Joslin'

def 							__SCREAMING_SNAKE_CASE							( lowerCAmelCase:     int       )      ->				int:

				if number <= 0:
								_UpperCAmelCase				:						Optional[Any]       						=			F'n must be greater than 0. Got n = {number}'
								raise ValueError(lowerCAmelCase       )

				_UpperCAmelCase				:						Dict       						=			[number + 1] * (number + 1)

				# starting position
				_UpperCAmelCase				:						Dict       						=			0
				for i in range(1     ,					lowerCAmelCase       ):
								_UpperCAmelCase				:						int       						=			min(table[i + 1]     ,					table[i] + 1       )
								# check if out of bounds
								if i * 2 <= number:
												_UpperCAmelCase				:						int       						=			min(table[i * 2]     ,					table[i] + 1       )
								# check if out of bounds
								if i * 3 <= number:
												_UpperCAmelCase				:						int       						=			min(table[i * 3]     ,					table[i] + 1       )
				return table[number]


if __name__ == "__main__":
					import doctest

					doctest.testmod()


