




"""simple docstring"""

from __future__ import annotations

# Define the Gravitational Constant G and the function
__A 		=			6.6_7_4_3e-1_1  # unit of G : m^3 * kg^-1 * s^-2



def   UpperCamelCase__     (		lowercase__  :     float       ,     lowercase__  :     float       ,     lowercase__  :     float       ,     lowercase__  :     float   ):
		snake_case		:						Any								=				mass_a * mass_a

		if (force, mass_a, mass_a, distance).count(0   ) != 1:
				raise ValueError("One and only one argument must be 0"   )
		if force < 0:
				raise ValueError("Gravitational force can not be negative"   )
		if distance < 0:
				raise ValueError("Distance can not be negative"   )
		if mass_a < 0 or mass_a < 0:
				raise ValueError("Mass can not be negative"   )
		if force == 0:
				snake_case		:						Tuple								=				GRAVITATIONAL_CONSTANT * product_of_mass / (distance**2)
				return {"force": force}
		elif mass_a == 0:
				snake_case		:						str								=				(force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
				return {"mass_1": mass_a}
		elif mass_a == 0:
				snake_case		:						Any								=				(force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
				return {"mass_2": mass_a}
		elif distance == 0:
				snake_case		:						List[str]								=				(GRAVITATIONAL_CONSTANT * product_of_mass / (force)) ** 0.5
				return {"distance": distance}
		raise ValueError("One and only one argument must be 0"   )


# Run doctest
if __name__ == "__main__":
							import doctest

							doctest.testmod()







"""simple docstring"""

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class 			lowerCamelCase__    (  lowerCamelCase_			):
			a__ :      Tuple								=     ["""image_processor""", """tokenizer"""]
			a__ :      List[Any]								=     """EfficientNetImageProcessor"""
			a__ :      str								=     ("""BertTokenizer""", """BertTokenizerFast""")


			def __init__(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					super().__init__(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							)


			def __call__(     self				, SCREAMING_SNAKE_CASE=None				, SCREAMING_SNAKE_CASE=None				, SCREAMING_SNAKE_CASE="max_length"				, SCREAMING_SNAKE_CASE=64				, SCREAMING_SNAKE_CASE=None				, **SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					if text is None and images is None:
							raise ValueError("You have to specify either text or images. Both cannot be none."							)

					if text is not None:
							snake_case		:						Dict								=				self.tokenizer(
							    SCREAMING_SNAKE_CASE				, padding=SCREAMING_SNAKE_CASE				, max_length=SCREAMING_SNAKE_CASE				, return_tensors=SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							)

					if images is not None:
							snake_case		:						Tuple								=				self.image_processor(SCREAMING_SNAKE_CASE				, return_tensors=SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							)

					if text is not None and images is not None:
							snake_case		:						Dict								=				image_features.pixel_values
							return encoding
					elif text is not None:
							return encoding
					else:
							return BatchEncoding(data=dict(**SCREAMING_SNAKE_CASE							)				, tensor_type=SCREAMING_SNAKE_CASE							)


			def 							lowerCamelCase_				(     self				, *SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							)


			def 							lowerCamelCase_				(     self				, *SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					return self.tokenizer.decode(*SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							)



			@property
			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						Optional[Any]								=				self.tokenizer.model_input_names
					snake_case		:						Any								=				self.image_processor.model_input_names
					return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)

