



import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin

class 	__snake_case (				SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__			: List[str]             =   """AutoFeatureExtractor"""
							UpperCamelCase__			: Union[str, Any]             =   """Speech2Text2Tokenizer"""


							def __init__(							self					,					a_					,					a_						):
											super().__init__(a_					,					a_						)
											a__			 =						self.feature_extractor
											a__			 =						False


							def __call__(							self					,					*a_					,					**a_						):
											# For backward compatibility
											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."""						)
															a__			 =						kwargs.pop("""raw_speech"""						)
											else:
															a__			 =						kwargs.pop("""audio"""					,					a_						)
											a__			 =						kwargs.pop("""sampling_rate"""					,					a_						)
											a__			 =						kwargs.pop("""text"""					,					a_						)
											if len(a_						) > 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(a_					,					*a_					,					sampling_rate=a_					,					**a_						)
											if text is not None:
															a__			 =						self.tokenizer(a_					,					**a_						)

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


							def _a (							self					,					*a_					,					**a_						):
											return self.tokenizer.batch_decode(*a_					,					**a_						)


							def _a (							self					,					*a_					,					**a_						):
											return self.tokenizer.decode(*a_					,					**a_						)







							@contextmanager
							def _a (							self						):
											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






from __future__ import annotations

def      A_ (		__a       :			list[int | float]						):

				"""simple docstring"""
				if len(__a						) == 0:
								raise ValueError("""find_max() arg is an empty sequence"""						)
				a__			 =						nums[0]
				for x in nums:
								if x > max_num:
												a__			 =						x
				return max_num


if __name__ == "__main__":
	import doctest

	doctest.testmod(verbose=True)

