
import warnings
from contextlib import contextmanager

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







class 					lowerCamelCase		(  A_  ):
     UpperCAmelCase__    :							int									=		"Wav2Vec2FeatureExtractor"
     UpperCAmelCase__    :							Union[str, Any]									=		"AutoTokenizer"





     def __init__(self				:   Optional[int]    ,   _A				:   Optional[int]    ,   _A				:   Any       )						->						Dict:
         super().__init__(_A    ,   _A       )
         snake_case								= self.feature_extractor
         snake_case								= False





     @classmethod
     def 		UpperCAmelCase(cls				:   List[Any]    ,   _A				:   List[Any]    ,   **_A				:   int       )						->						Union[str, Any]:
         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    ,   )

             snake_case								= WavaVecaFeatureExtractor.from_pretrained(_A    ,   **_A       )
             snake_case								= WavaVecaCTCTokenizer.from_pretrained(_A    ,   **_A       )

             return cls(feature_extractor=_A    ,   tokenizer=_A       )





     def __call__(self				:   int    ,   *_A				:   int    ,   **_A				:   Any       )						->						Tuple:
         # 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."       )
             snake_case								= kwargs.pop("raw_speech"       )
         else:
             snake_case								= kwargs.pop("audio"    ,   _A       )
         snake_case								= kwargs.pop("sampling_rate"    ,   _A       )
         snake_case								= kwargs.pop("text"    ,   _A       )
         if len(_A       ) > 0:
             snake_case								= args[0]
             snake_case								= 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								= self.feature_extractor(_A    ,   *_A    ,   sampling_rate=_A    ,   **_A       )
         if text is not None:
             snake_case								= self.tokenizer(_A    ,   **_A       )

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





     def 		UpperCAmelCase(self				:   Dict    ,   *_A				:   str    ,   **_A				:   List[str]       )						->						str:
         # For backward compatibility
         if self._in_target_context_manager:
             return self.current_processor.pad(*_A    ,   **_A       )

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

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

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





     def 		UpperCAmelCase(self				:   Union[str, Any]    ,   *_A				:   Any    ,   **_A				:   Any       )						->						Optional[Any]:
         return self.tokenizer.batch_decode(*_A    ,   **_A       )





     def 		UpperCAmelCase(self				:   Any    ,   *_A				:   Any    ,   **_A				:   Tuple       )						->						Dict:
         return self.tokenizer.decode(*_A    ,   **_A       )





     @contextmanager
     def 		UpperCAmelCase(self				:   Any       )						->						Dict:
         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								= True
         snake_case								= self.tokenizer
         yield
         snake_case								= self.feature_extractor
         snake_case								= False



class 					lowerCamelCase		:





     def __init__(self				:   List[Any]    ,   _A				:   str       )						->						Any:
         # we need a list not a string, so do something to change the type
         snake_case								= arr.split(","       )





     def 		UpperCAmelCase(self				:   str       )						->						str:
         snake_case								= [int(self.array[0]       )] * len(self.array       )
         snake_case								= [int(self.array[0]       )] * len(self.array       )
         for i in range(1    ,   len(self.array       )       ):
             snake_case								= max(
                 int(self.array[i]       ) + sum_value[i - 1]    ,   int(self.array[i]       )       )
             snake_case								= max(sum_value[i]    ,   rear[i - 1]       )
         return rear[len(self.array       ) - 1]


if __name__ == "__main__":
   _A   =			input("please input some numbers:")
   _A   =			SubArray(whole_array)
   _A   =			array.solve_sub_array()
   print(("the results is:", re))

