





"""simple docstring"""




from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean





def 	a_    (							_lowercase       ):

						# A local function to see if a dot lands in the circle.
						def is_in_circle(_lowercase	,						_lowercase       ) -> bool:
												_UpperCamelCase      : int         =   sqrt((x**2) + (y**2)       )
												# Our circle has a radius of 1, so a distance
												# greater than 1 would land outside the circle.
												return distance_from_centre <= 1

						# The proportion of guesses that landed in the circle
						_UpperCamelCase      : List[Any]         =   mean(
						    int(is_in_circle(uniform(-1.0	,						1.0       )	,						uniform(-1.0	,						1.0       )       )       )
						    for _ in range(SCREAMING_SNAKE_CASE_       )       )
						# The ratio of the area for circle to square is pi/4.
						_UpperCamelCase      : Optional[Any]         =   proportion * 4
						print(F"""The estimated value of pi is {pi_estimate}"""       )
						print(F"""The numpy value of pi is {pi}"""       )
						print(F"""The total error is {abs(pi - pi_estimate       )}"""       )





def 	a_    (							_lowercase	,						_lowercase	,						_lowercase = 0.0	,						_lowercase = 1.0	,						):
						return mean(
						    function_to_integrate(uniform(SCREAMING_SNAKE_CASE_	,						SCREAMING_SNAKE_CASE_       )       ) for _ in range(SCREAMING_SNAKE_CASE_       )       ) * (max_value - min_value)





def 	a_    (							_lowercase	,						_lowercase = 0.0	,						_lowercase = 1.0       ):

						def identity_function(_lowercase       ) -> float:
												return x

						_UpperCamelCase      : str         =   area_under_curve_estimator(
						    SCREAMING_SNAKE_CASE_	,						SCREAMING_SNAKE_CASE_	,						SCREAMING_SNAKE_CASE_	,						SCREAMING_SNAKE_CASE_       )
						_UpperCamelCase      : int         =   (max_value * max_value - min_value * min_value) / 2

						print('''******************'''       )
						print(F"""Estimating area under y=x where x varies from {min_value} to {max_value}"""       )
						print(F"""Estimated value is {estimated_value}"""       )
						print(F"""Expected value is {expected_value}"""       )
						print(F"""Total error is {abs(estimated_value - expected_value       )}"""       )
						print('''******************'''       )





def 	a_    (							_lowercase       ):

						def function_to_integrate(_lowercase       ) -> float:
												return sqrt(4.0 - x * x       )

						_UpperCamelCase      : Any         =   area_under_curve_estimator(
						    SCREAMING_SNAKE_CASE_	,						SCREAMING_SNAKE_CASE_	,						0.0	,						2.0       )

						print('''******************'''       )
						print('''Estimating pi using area_under_curve_estimator'''       )
						print(F"""Estimated value is {estimated_value}"""       )
						print(F"""Expected value is {pi}"""       )
						print(F"""Total error is {abs(estimated_value - pi       )}"""       )
						print('''******************'''       )


if __name__ == "__main__":
						import doctest

						doctest.testmod()










"""simple docstring"""




from ...processing_utils import ProcessorMixin




class 			_a						(				_lowerCAmelCase    ):
				UpperCamelCase								=   ['''image_processor''', '''feature_extractor''']
				UpperCamelCase								=   '''TvltImageProcessor'''
				UpperCamelCase								=   '''TvltFeatureExtractor'''






				def __init__(			self		:   Union[str, Any],			lowerCAmelCase__		:   str,			lowerCAmelCase__		:   List[str]							)     ->				Optional[Any]:




										'''simple docstring'''



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

										_UpperCamelCase      : List[str]         =   image_processor
										_UpperCamelCase      : int         =   feature_extractor






				def __call__(			self		:   List[str],			lowerCAmelCase__		:   Optional[int]=None,			lowerCAmelCase__		:   str=None,			lowerCAmelCase__		:   Dict=None,			lowerCAmelCase__		:   str=None,			lowerCAmelCase__		:   Optional[int]=False,			lowerCAmelCase__		:   str=False,			*lowerCAmelCase__		:   List[str],			**lowerCAmelCase__		:   Optional[int],			)     ->				Dict:




										'''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      : Optional[int]         =   None
										if images is not None:
																_UpperCamelCase      : Optional[int]         =   self.image_processor(lowerCAmelCase__,			mask_pixel=lowerCAmelCase__,			*lowerCAmelCase__,			**lowerCAmelCase__							)
										if images_mixed is not None:
																_UpperCamelCase      : str         =   self.image_processor(lowerCAmelCase__,			is_mixed=lowerCAmelCase__,			*lowerCAmelCase__,			**lowerCAmelCase__							)
										if audio is not None:
																_UpperCamelCase      : Union[str, Any]         =   self.feature_extractor(
																    lowerCAmelCase__,			*lowerCAmelCase__,			sampling_rate=lowerCAmelCase__,			mask_audio=lowerCAmelCase__,			**lowerCAmelCase__							)

										_UpperCamelCase      : str         =   {}
										if audio is not None:
																output_dict.update(lowerCAmelCase__							)
										if images is not None:
																output_dict.update(lowerCAmelCase__							)
										if images_mixed_dict is not None:
																output_dict.update(lowerCAmelCase__							)
										return output_dict






				@property
				def     snake_case     (			self		:   List[str]							)     ->				Tuple:




										'''simple docstring'''



										_UpperCamelCase      : List[str]         =   self.image_processor.model_input_names
										_UpperCamelCase      : List[Any]         =   self.feature_extractor.model_input_names
										return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names							)							)



