


"""simple docstring"""




def 						__a      (  a,		a					):





       """simple docstring"""







       _a          =				(boundary[1] - boundary[0]) / steps
       _a          =				boundary[0]
       _a          =				boundary[1]
       _a          =				make_points(a,		a,		a					)
       _a          =				0.0
       y += (h / 3.0) * f(a					)
       _a          =				2
       for i in x_i:
              y += (h / 3) * (4 - 2 * (cnt % 2)) * f(a					)
              cnt += 1
       y += (h / 3.0) * f(a					)
       return y



def 						__a      (  a,		a,		a					):





       """simple docstring"""







       _a          =				a + h
       while x < (b - h):
              yield x
              _a          =				x + h



def 						__a      (  a					):  # enter your function here





       """simple docstring"""







       _a          =				(x - 0) * (x - 0)
       return y



def 						__a      (  ):





       """simple docstring"""







       _a          =				0.0  # Lower bound of integration
       _a          =				1.0  # Upper bound of integration
       _a          =				10.0  # define number of steps or resolution
       _a          =				[a, b]  # define boundary of integration
       _a          =				method_a(a,		a					)
       print(F'y = {y}'					)


if __name__ == "__main__":
   main()




"""simple docstring"""




import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin





class __snake_case		(					_SCREAMING_SNAKE_CASE      ):







   """simple docstring"""




   lowerCAmelCase_				:   str     	=			'AutoFeatureExtractor'
   lowerCAmelCase_				:   List[Any]     	=			'Speech2Text2Tokenizer'




   def __init__(		self							:int				,  UpperCamelCase__							:List[str]				,  UpperCamelCase__							:Union[str, Any]       ):
          super().__init__(UpperCamelCase__				,  UpperCamelCase__       )
          _a          =				self.feature_extractor
          _a          =				False




   def __call__(		self							:List[str]				,  *UpperCamelCase__							:List[str]				,  **UpperCamelCase__							:Dict       ):
          # For backward compatibility
          if self._in_target_context_manager:
                 return self.current_processor(*UpperCamelCase__				,  **UpperCamelCase__       )

          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"				,  UpperCamelCase__       )
          _a          =				kwargs.pop("sampling_rate"				,  UpperCamelCase__       )
          _a          =				kwargs.pop("text"				,  UpperCamelCase__       )
          if len(UpperCamelCase__       ) > 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(UpperCamelCase__				,  *UpperCamelCase__				,  sampling_rate=UpperCamelCase__				,  **UpperCamelCase__       )
          if text is not None:
                 _a          =				self.tokenizer(UpperCamelCase__				,  **UpperCamelCase__       )

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




   def SCREAMING_SNAKE_CASE_      (		self							:Union[str, Any]				,  *UpperCamelCase__							:int				,  **UpperCamelCase__							:Optional[Any]       ):
          return self.tokenizer.batch_decode(*UpperCamelCase__				,  **UpperCamelCase__       )




   def SCREAMING_SNAKE_CASE_      (		self							:Optional[Any]				,  *UpperCamelCase__							:List[Any]				,  **UpperCamelCase__							:List[Any]       ):
          return self.tokenizer.decode(*UpperCamelCase__				,  **UpperCamelCase__       )




   @contextmanager
   def SCREAMING_SNAKE_CASE_      (		self							: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."       )
          _a          =				True
          _a          =				self.tokenizer
          yield
          _a          =				self.feature_extractor
          _a          =				False
