





from ...processing_utils import ProcessorMixin

class 	A_       (a_			):
	UpperCAmelCase__					=			['''image_processor''', '''feature_extractor''']
	UpperCAmelCase__					=			'''TvltImageProcessor'''
	UpperCAmelCase__					=			'''TvltFeatureExtractor'''
	def __init__(    self							,					_A							,					_A		):






				'''simple docstring'''







				super().__init__(image_processor=_A							,					feature_extractor=_A		)

				UpperCAmelCase   		=				image_processor
				UpperCAmelCase   		=				feature_extractor
	def __call__(    self							,					_A=None							,					_A=None							,					_A=None							,					_A=None							,					_A=False							,					_A=False							,					*_A							,					**_A							,					):






				'''simple docstring'''







				if images is None and audio is None:
							raise ValueError('''You need to specify either an `images` or `audio` input to process.'''		)

				UpperCAmelCase   		=				None
				if images is not None:
							UpperCAmelCase   		=				self.image_processor(_A							,					mask_pixel=_A							,					*_A							,					**_A		)
				if images_mixed is not None:
							UpperCAmelCase   		=				self.image_processor(_A							,					is_mixed=_A							,					*_A							,					**_A		)
				if audio is not None:
							UpperCAmelCase   		=				self.feature_extractor(
							    _A							,					*_A							,					sampling_rate=_A							,					mask_audio=_A							,					**_A		)

				UpperCAmelCase   		=				{}
				if audio is not None:
							output_dict.update(_A		)
				if images is not None:
							output_dict.update(_A		)
				if images_mixed_dict is not None:
							output_dict.update(_A		)
				return output_dict







	@property
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.image_processor.model_input_names
				UpperCAmelCase   		=				self.feature_extractor.model_input_names
				return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names		)		)










from __future__ import annotations

from collections.abc import Callable





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__ = 100			,			)  ->				float:







			'''simple docstring'''

			UpperCAmelCase   		=				x_start
			UpperCAmelCase   		=				fnc(UpperCamelCase__ )
			UpperCAmelCase   		=				0.0
			for _ in range(UpperCamelCase__ ):
						# Approximates small segments of curve as linear and solve
						# for trapezoidal area
						UpperCAmelCase   		=				(x_end - x_start) / steps + xa
						UpperCAmelCase   		=				fnc(UpperCamelCase__ )
						area += abs(fxa + fxa ) * (xa - xa) / 2
						# Increment step
						UpperCAmelCase   		=				xa
						UpperCAmelCase   		=				fxa
			return area


if __name__ == "__main__":





				def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__ )  ->				str:







							'''simple docstring'''

							return x**3 + x**2

				print("f(x) = x^3 + x^2")
				print("The area between the curve, x = -5, x = 5 and the x axis is:")
				__A				:    List[Any]       =   10
				while i <= 100_000:
								print(F'with {i} steps: {trapezoidal_area(f, -5, 5, i)}')
								i *= 10



