




"""simple docstring"""




import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer






class 					__magic_name__							( UpperCAmelCase__       ):






				'''simple docstring'''
				__UpperCamelCase    						=		"Wav2Vec2FeatureExtractor"
				__UpperCamelCase    						=		"AutoTokenizer"





				def __init__( self	,				_a	,				_a				):





							"""simple docstring"""
							super().__init__(_a	,				_a				)
							lowerCamelCase       =      self.feature_extractor
							lowerCamelCase       =      False





				@classmethod
				def      _lowerCAmelCase       ( cls	,				_a	,				**_a				):





							"""simple docstring"""
							try:
										return super().from_pretrained(_a	,				**_a				)
							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: """	,				_a	,				)

										lowerCamelCase       =      WavaVecaFeatureExtractor.from_pretrained(_a	,				**_a				)
										lowerCamelCase       =      WavaVecaCTCTokenizer.from_pretrained(_a	,				**_a				)

										return cls(feature_extractor=_a	,				tokenizer=_a				)





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





							"""simple docstring"""
							# 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."""				)
										lowerCamelCase       =      kwargs.pop("""raw_speech"""				)
							else:
										lowerCamelCase       =      kwargs.pop("""audio"""	,				_a				)
							lowerCamelCase       =      kwargs.pop("""sampling_rate"""	,				_a				)
							lowerCamelCase       =      kwargs.pop("""text"""	,				_a				)
							if len(_a				) > 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(_a	,				*_a	,				sampling_rate=_a	,				**_a				)
							if text is not None:
										lowerCamelCase       =      self.tokenizer(_a	,				**_a				)

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





				def      _lowerCAmelCase       ( self	,				*_a	,				**_a				):





							"""simple docstring"""
							# For backward compatibility
							if self._in_target_context_manager:
										return self.current_processor.pad(*_a	,				**_a				)

							lowerCamelCase       =      kwargs.pop("""input_features"""	,				_a				)
							lowerCamelCase       =      kwargs.pop("""labels"""	,				_a				)
							if len(_a				) > 0:
										lowerCamelCase       =      args[0]
										lowerCamelCase       =      args[1:]

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

							if labels is None:
										return input_features
							elif input_features is None:
										return labels
							else:
										lowerCamelCase       =      labels["""input_ids"""]
										return input_features





				def      _lowerCAmelCase       ( self	,				*_a	,				**_a				):





							"""simple docstring"""
							return self.tokenizer.batch_decode(*_a	,				**_a				)





				def      _lowerCAmelCase       ( self	,				*_a	,				**_a				):





							"""simple docstring"""
							return self.tokenizer.decode(*_a	,				**_a				)





				@contextmanager
				def      _lowerCAmelCase       ( 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."""				)
							lowerCamelCase       =      True
							lowerCamelCase       =      self.tokenizer
							yield
							lowerCamelCase       =      self.feature_extractor
							lowerCamelCase       =      False









"""simple docstring"""




from collections.abc import Sequence



def 					a__				(       snake_case__ ,				snake_case__ = False       )		->      float:
			if not arr:
						return 0

			lowerCamelCase       =      0 if allow_empty_subarrays else float("""-inf"""       )
			lowerCamelCase       =      0.0
			for num in arr:
						lowerCamelCase       =      max(0 if allow_empty_subarrays else num ,				curr_sum + num       )
						lowerCamelCase       =      max(snake_case__ ,				snake_case__       )

			return max_sum


if __name__ == "__main__":
					from doctest import testmod

					testmod()

					lowerCAmelCase							:    Optional[int]  							=							[-2, 1, -3, 4, -1, 2, 1, -5, 4]
					print(F"""{max_subarray_sum(nums) = }""")



