


class 				__A    :






 """simple docstring"""




 def __init__(		self			,   __A						) ->					int:
     # we need a list not a string, so do something to change the type
     a					=arr.split(''','''						)





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


if __name__ == "__main__":
       lowerCamelCase_       :       Optional[int]      						=  input("""please input some numbers:""")
       lowerCamelCase_       :       Any      						=  SubArray(whole_array)
       lowerCamelCase_       :       Optional[int]      						=  array.solve_sub_array()
       print(("""the results is:""", re))



"""simple docstring"""







from ...processing_utils import ProcessorMixin






class 				__A    (	_SCREAMING_SNAKE_CASE      ):






 """simple docstring"""




 __lowerCAmelCase			 =  "WhisperFeatureExtractor"
 __lowerCAmelCase			 =  "WhisperTokenizer"


 def __init__(		self			,   __A			,   __A						) ->					Dict:
     super().__init__(__A			,   __A						)
     a					=self.feature_extractor
     a					=False


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A=None			,   __A=None			,   __A=True						) ->					int:
     return self.tokenizer.get_decoder_prompt_ids(task=__A			,   language=__A			,   no_timestamps=__A						)


 def __call__(		self			,   *__A			,   **__A						) ->					Tuple:
     # For backward compatibility
     if self._in_target_context_manager:
         return self.current_processor(*__A			,   **__A						)

     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 				SCREAMING_SNAKE_CASE							(		self			,   *__A			,   **__A						) ->					Optional[Any]:
     return self.tokenizer.batch_decode(*__A			,   **__A						)


 def 				SCREAMING_SNAKE_CASE							(		self			,   *__A			,   **__A						) ->					Union[str, Any]:
     return self.tokenizer.decode(*__A			,   **__A						)





 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A="np"						) ->					Optional[Any]:
     return self.tokenizer.get_prompt_ids(__A			,   return_tensors=__A						)